Texture in Food: Solid Foods v. 2

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Author: David Kilcast

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Texture in food Volume 2: Solid foods

Related titles from Woodhead’s food science, technology and nutrition list: Texture in food Volume 1: Semi-solid foods (ISBN 1 85573 673 X) Understanding and controlling the texture of semi-solid foods such as yoghurt and ice cream is a complex process. With a distinguished international team of contributors, this important collection summarises some of the most significant research in this area. The first part of the book looks at the behaviour of gels and emulsions, how they can be measured and their textural properties improved. The second part of the collection discusses the control of texture in particular foods such as yoghurt, ice cream, spreads and sauces. Understanding and measuring the shelf-life of food (ISBN 1 85573 732 9) The shelf-life of a product is critical in determining both its quality and profitability. This important collection reviews the key factors in determining shelf-life and how they can be measured. Taints and off-flavours in foods (ISBN 1 85573 449 4) Taints and off-flavours are a major problem for the food industry. The first part of this important collection reviews the major causes of taints and off-flavours, from oxidative rancidity and microbiologically-derived off-flavours, to packaging materials as a source of taints. The second part of the book discusses the range of techniques for detecting taints and off-flavours, from sensory analysis to instrumental techniques, including the development of new rapid, on-line sensors. Details of these books and a complete list of Woodhead’s food science, technology and nutrition titles can be obtained by: • visiting our web site at www.woodhead-publishing.com • contacting Customer services (e-mail: [email protected]; fax: +44 (0) 1223 893694; tel.: +44 (0) 1223 891358 ext.30; address: Woodhead Publishing Ltd, Abington Hall, Abington, Cambridge CB1 6AH, England) Selected food science and technology titles are also available in electronic form. Visit our web site (www.woodhead-publishing.com) to find out more. If you would like to receive information on forthcoming titles in this area, please send your address details to: Francis Dodds (address, tel. and fax as above; e-mail: [email protected]). Please confirm which subject areas you are interested in.

Texture in food Volume 2: Solid foods Edited by David Kilcast

CRC Press Boca Raton Boston New York Washington, DC

WOODHEAD

PUBLISHING LIMITED Cambridge England

Published by Woodhead Publishing Limited, Abington Hall, Abington Cambridge CB1 6AH, England www.woodhead-publishing.com Published in North America by CRC Press LLC, 2000 Corporate Blvd, NW Boca Raton FL 33431, USA First published 2004, Woodhead Publishing Ltd and CRC Press LLC © 2004, Woodhead Publishing Ltd The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from the publishers. The consent of Woodhead Publishing and CRC Press does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing or CRC Press for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN 1 85573 724 8 (book) 1 85573 836 8 (e-book) CRC Press ISBN 0-8493-2537-4 CRC Press order number: WP2537 The publisher’s policy is to use permanent paper from mills that operate a sustainable forestry policy, and which have been manufactured from pulp which is processed using acid-free and elementary chlorine-free practices. Furthermore, the publisher ensures that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by Replika Press Pvt Ltd, India. Printed by TJ International Ltd, Padstow, Cornwall, England.

Contents

Contributor contact details ................................................................... List of abbreviations ..............................................................................

xiii xix

Consumers, texture and food quality ..................................

1

Part I

1 Measuring consumer perceptions of texture: an overview .......................................................................................... D. Kilcast, Leatherhead Food International, UK 1.1 Introduction: texture and food quality ................................. 1.2 Perception and sensory assessment of food texture .................................................................................... 1.3 Tests and test procedures ...................................................... 1.4 Instrumental measurement of texture .................................. 1.5 In vivo texture measurement ................................................ 1.6 Future developments ............................................................. 1.7 Conclusions ........................................................................... 1.8 References ............................................................................. 2 Consumers and texture: understanding their perceptions and preferences .............................................................................. J-F. Meullenet, University of Arkansas, USA 2.1 Introduction: problems with consumer descriptions of texture .................................................................................... 2.2 Investigating consumer descriptions of texture ................... 2.3 Tests and test procedures ...................................................... 2.4 Understanding consumer preferences .................................. 2.5 Challenges to understanding consumer preferences ............................................................................ 2.6 Future trends ......................................................................... 2.7 Conclusions ........................................................................... 2.8 References .............................................................................

3 3 6 8 13 20 23 26 28

33

33 34 36 39 44 48 50 51

vi

Contents

3 Texture and mastication ............................................................... A. C. Smith, Institute of Food Research, UK 3.1 Introduction ........................................................................... 3.2 The mastication process ....................................................... 3.3 Measuring mastication .......................................................... 3.4 Chewing, swallowing, salivation and bolus formation ....... 3.5 Future trends ......................................................................... 3.6 Mastication and particular foods .......................................... 3.7 Reviews ................................................................................. 3.8 Acknowledgement ................................................................ 3.9 References .............................................................................

53 53 55 56 66 71 75 76 76 77

4 Understanding and measuring consumer perceptions of crispness ..................................................................................... P. Mallikarjunan, Virginia Polytechnic Institute and State University, USA 4.1 Introduction ........................................................................... 4.2 Characterization and determination of crispness ................ 4.3 Methods of data correlation, evaluation and analysis ......... 4.4 Case-study: breaded chicken nuggets .................................. 4.5 Future trends ......................................................................... 4.6 References .............................................................................

82 85 91 94 103 103

Instrumental techniques for analysing texture ................

107

5 Force/deformation techniques for measuring texture .............. R. Lu and J. A. Abbott, USDA Agricultural Research Service, USA 5.1 Introduction ........................................................................... 5.2 Mechanical characterization of solid foods ......................... 5.3 Destructive measurements .................................................... 5.4 Non-destructive measurements ............................................ 5.5 Conclusions ........................................................................... 5.6 References .............................................................................

109

6 Sound input techniques for measuring texture ......................... L. M. Duizer, Massey University, New Zealand 6.1 Introduction ........................................................................... 6.2 Sound and its detection: what is sound? ............................. 6.3 Destructive testing ................................................................ 6.4 Non-destructive testing ......................................................... 6.5 Application of sound measurement techniques ................... 6.6 Future trends ......................................................................... 6.7 Sources of further information and advice .......................... 6.8 References .............................................................................

146

Part II

82

109 110 118 128 138 139

146 147 148 155 158 162 162 163

Contents vii

7 Near infrared (NIR) diffuse reflectance in texture measurement .................................................................................. S. Millar, Campden and Chorleywood Food Research Association, UK 7.1 Introduction ........................................................................... 7.2 Application of NIR to cereals and their products ............... 7.3 Application of NIR to fruit and vegetables ......................... 7.4 Application of NIR to meat ................................................. 7.5 Application of NIR to other foods ....................................... 7.6 Conclusions and future trends .............................................. 7.7 Sources of further information ............................................. 7.8 References ............................................................................. 8 Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) in texture measurement .................. A. K. Thybo, A. H. Karlsson, H. C. Bertram and H. J. Andersen, Danish Institute of Agricultural Sciences, P. M. Szczypinski, Technical University of Lodz, Poland and S. Donstrup, Aarhus University Hospital, Denmark 8.1 Introduction ........................................................................... 8.2 Methods and analysis ........................................................... 8.3 Application of NMR: texture determination of solid foods ...................................................................................... 8.4 Application of MRI: texture determination of solid foods ...................................................................................... 8.5 Future trends ......................................................................... 8.6 References ............................................................................. 9 Modelling food texture ................................................................. L. M. M. Tijskens and H. Luyten, Wageningen University and Research Centre, The Netherlands 9.1 Introduction ........................................................................... 9.2 Factors affecting texture ....................................................... 9.3 Effects of enzymes on texture .............................................. 9.4 Applying models to predict texture ..................................... 9.5 Future trends ......................................................................... 9.6 Notation ................................................................................. 9.7 References ............................................................................. Part III Understanding and improving the texture of particular foods .................................................................................... 10 Plant structure and fruit and vegetable texture ........................ K. W. Waldron, Institute of Food Research, UK 10.1 Introduction ...........................................................................

167

167 170 173 176 178 179 180 180

184

184 187 189 195 199 200 205

205 211 218 222 233 234 235

239 241 241

viii Contents

10.2 Measurement of texture ........................................................ 10.3 Plant structure ....................................................................... 10.4 Cellular basis of crispness, juiciness and mealiness in fruit tissue ............................................................................. 10.5 Cellular stability during processing ..................................... 10.6 Improving cell adhesion ....................................................... 10.7 Future trends ......................................................................... 10.8 Acknowledgements ............................................................... 10.9 References ............................................................................. 11 Plant compounds and fruit texture: the case of pear .............. T. Kojima, S. Fujita and M. Tanaka, Saga University, Japan and P. Sirisomboon, King Mongkut’s Institute of Technology Ladkrabang, Thailand 11.1 Introduction: variations in pear texture ............................... 11.2 Measuring and modelling fruit firmness ............................. 11.3 Chemical compounds affecting firmness: the example of Japanese pear .................................................................... 11.4 The effect of constituents on fruit texture .................................................................................... 11.5 Use of near infrared spectroscopy (NIR) to evaluate textural properties ................................................................. 11.6 Future trends ......................................................................... 11.7 Sources of further information and advice .......................... 11.8 Acknowledgement ................................................................ 11.9 References ............................................................................. 12 Controlling the texture of fruit and vegetables: the role of oxidising enzymes ..................................................................... H. J. Wichers and C. Boeriu, Agrotechnology and Food Innovations, The Netherlands 12.1 Introduction: distribution of polyphenoloxidases (PPOs) and peroxidases (PODs) in plants and plant cells .............................................................................. 12.2 Biochemical and physiological role of PPOs and PODs ..................................................................................... 12.3 PPOs and PODs: structure and mechanisms of action ..................................................................................... 12.4 PPOs, PODs and texture development ................................ 12.5 Controlling PPO and POD activity ...................................... 12.6 PPOs and PODs: implications for food texture .................. 12.7 Future trends ......................................................................... 12.8 Sources of further information ............................................. 12.9 References .............................................................................

242 244 244 249 251 254 255 255 259

259 262 270 275 279 283 288 289 290

295

295 296 300 304 307 311 311 312 312

Contents

13 Improving fruit and vegetable texture by genetic transformation ............................................................................... G. Tucker, University of Nottingham, UK 13.1 Introduction ........................................................................... 13.2 Tools of genetic modification .............................................. 13.3 Approaches to the manipulation of texture: the tomato .................................................................................... 13.4 Other approaches to the manipulation of texture ................ 13.5 Future trends ......................................................................... 13.6 References ............................................................................. 14 Raw materials quality and the texture of processed vegetables ........................................................................................ J. B. Adams, formerly of CCFRA, UK 14.1 Introduction ........................................................................... 14.2 Vegetable texture determined by starch ............................... 14.3 Vegetable texture determined by cell wall polysaccharides ..................................................................... 14.4 Vegetable texture affected by phenolic reactions ................ 14.5 Future trends ......................................................................... 14.6 Sources of further information and advice .......................... 14.7 References ............................................................................. 15 Improving the texture of processed vegetables by vacuum infusion ............................................................................ R. Saurel, University of Lyon, France 15.1 Introduction ........................................................................... 15.2 Vacuum infusion technology ................................................ 15.3 Applications to improve texture ........................................... 15.4 Future trends ......................................................................... 15.5 Sources of further information and advice .......................... 15.6 References ............................................................................. 16 Improving the texture of frozen fruit: the case of berries ............................................................................................. M. Suutarinen and K. Autio, VTT Biotechnology, Finland 16.1 Introduction: the effects of freezing and thawing on berry texture .................................................................... 16.2 Maintaining texture: conventional pre-freezing treatments .............................................................................. 16.3 Maintaining texture: alternative pre-freezing treatments .............................................................................. 16.4 Application: frozen berries and jams ................................... 16.5 Future trends ......................................................................... 16.6 References .............................................................................

ix

321 321 323 327 333 335 336

342 342 342 349 354 358 359 360

364 364 365 373 383 383 384

388

388 390 393 400 405 405

x

Contents

17 Improving the texture of processed fruit: the case of olives ........................................................................................... I. Mafra, University of Porto and M. A. Coimbra, University of Aveiro, Portugal 17.1 Introduction: the texture of table olives............................... 17.2 Factors affecting the texture quality of raw olives .............. 17.3 Influence of processing on table olives ............................... 17.4 Improving texture .................................................................. 17.5 Future trends ......................................................................... 17.6 Sources of further information and advice .......................... 17.7 References .............................................................................

410

410 412 418 425 426 428 430

18 Improving the texture of bread ................................................... S. P. Cauvain, CCFRA, UK 18.1 Introduction ........................................................................... 18.2 Textural characteristics of bread and other cereal-based foods ................................................................ 18.3 Definitions of texture ........................................................... 18.4 Measuring texture ................................................................. 18.5 Influence of processing and storage .................................... 18.6 Improving texture .................................................................. 18.7 Future trends ......................................................................... 18.8 Sources of further information and advice .......................... 18.9 References .............................................................................

432

19 Analysing and improving the texture of cooked rice ............... S. K. Kim, Dankook University and C. O. Rhee, Chonnam National University, Korea 19.1 Introduction ........................................................................... 19.2 Criteria for evaluating rice quality ....................................... 19.3 Hydration of rice ................................................................... 19.4 Factors affecting cooking quality ......................................... 19.5 Testing texture quality .......................................................... 19.6 Problems and challenges ...................................................... 19.7 Sources of further information and advice .......................... 19.8 References .............................................................................

451

20 Improving the texture of pasta .................................................... B. A. Marchylo and J. E. Dexter, Canadian Grain Commission and L. J. Malcolmson, Canadian International Grains Institute 20.1 Introduction ........................................................................... 20.2 Measuring the texture of cooked pasta ................................ 20.3 Influence of raw materials .................................................... 20.4 Influence of processing ........................................................ 20.5 Trends in consumer preference ............................................ 20.6 References .............................................................................

475

432 434 436 437 442 445 447 448 449

451 452 455 459 464 469 470 470

475 478 484 490 492 494

Contents

xi

21 Improving the texture of fried food ............................................ C-J. Shieh and C-Y. Chang, Da-Yeh University and C-S. Chen, Chao-Yang University of Technology, Taiwan 21.1 Introduction ........................................................................... 21.2 Measuring texture ................................................................. 21.3 Factors influencing texture ................................................... 21.4 The use of response surface methodology (RSM) .............. 21.5 A case study: fried gluten balls ........................................... 21.6 Conclusions ........................................................................... 21.7 References .............................................................................

501

Index ................................................................................................

525

501 501 504 508 514 521 522

Contributor contact details

(* = main point of contact)

Chapter 1

Chapter 3

Dr D. Kilcast Leatherhead Food International Randalls Road Leatherhead Surrey KT22 7RY UK

Dr A. C. Smith Food Quality & Materials Science Division Institute of Food Research Norwich Research Park, Colney Norwich NR4 7UA UK

Tel: +44 (0) 1372 822321 Fax: +44 (0) 1372 386228 E-mail: [email protected]


Chapter 2

Chapter 4

Professor J-F. Meullenet Department of Food Science University of Arkansas 2650 N. Young Avenue Fayetteville, AR 72704 USA

Dr P. Mallikarjunan Biological Systems Engineering Department 312 Seitz Hall Virginia Polytechnic Institute and State University Blacksburg VA 24060 USA



xiv


Chapter 5 Dr Renfu Lu* USDA ARS Sugarbeet and Bean Research Unit 224 Farrall Hall Michigan State University East Lansing, MI 48824 USA Tel: +1 (517) 432-8062 Fax: +1 (517) 337-6782 E-mail: [email protected] Dr Judith A. Abbot USDA ARS Produce Quality & Safety Laboratory Building 002, BARC-W 10300 Baltimore Avenue Beltsville, MD 20705-2350 USA Tel: +1 (301) 504 6128 Fax: +1 (301) 504 5107 E-mail: [email protected]

Chapter 6 Dr L. M. Duizer Institute of Food, Nutrition and Human Health Massey University Private Bag 102 904 NSMC Albany, Auckland New Zealand Tel: +64 09 443 9753 Fax: +64 09 443 9640 E-mail: [email protected]

Chapter 7 Dr S. Millar

Baking and Cereals Processing Department Campden & Chorleywood Food Research Association Chipping Campden Gloucestershire GL55 6LD UK Tel: +44 (0) 1386 842157 Fax: +44 (0) 1386 842150 E-mail: [email protected]

Chapter 8 Dr A. K. Thybo* Department of Food Science Danish Institute of Agricultural Sciences DK-5792 Aarslev Denmark Tel: +045 63 90 43 05 Fax: +045 63 90 43 95 E-mail: [email protected] Dr A. H. Karlsson, Dr H. C. Bertram and Dr H. J. Andersen Department of Food Science Danish Institute of Agricultural Sciences DK-8830 Tjele Denmark Tel: +045 89 99 19 00 Fax: +045 89 99 15 64 E-mail: [email protected] Dr S. Donstrup Aarhus University Hospital Department of Biomedical Engineering DK-8200 Aarhus N Denmark


Dr P. M. Szczypinski Institute of Electronics Technical University of Lodz 90-924 Lodz Poland Tel: +48 42 636 2238 Fax: +48 42 631 2638 E-mail: [email protected]

Chapter 9 L. M. M. Tijskens* and Dr H. Luyten Wageningen University and Research Centre Agrotechnology & Food Innovations PO Box 17 6700 AA Wageningen The Netherlands Tel: +31 317 475 303 Fax: +31 317 475 347 E-mail: [email protected] [email protected]

Chapter 10 Dr K. W. Waldron Institute of Food Research Norwich Research Park Colney Norwich NR4 7UA UK Tel: +44 1603 255385 Fax: +44 1603 507723 E-mail: [email protected]

xv

Chapter 11 Dr T. Kojima, Dr S. Fujita and Dr M. Tanaka Saga University Japan 840-8502 Tel: +81 952 288 750 Fax: +81 952 288 768 E-mail: [email protected] Dr P. Sirisomboon* Department of Agricultural Engineering Faculty of Engineering King Mongkut’s Institute of Technology Ladkrabang Bangkok 10520 Thailand Tel: +66 2737 300 ext 5120 Fax: +66 2326 4178 E-mail: [email protected]

Chapter 12 Dr H. J. Wichers and Dr C. Boeriu Agrotechnology & Food Innovations Bornsesteeg 59 6708 PD Wageningen The Netherlands Tel: +31 317 475228 Fax: +31 317 475347 E-mail: [email protected] [email protected]

Chapter 13 Professor G. Tucker University of Nottingham School of Biosciences

xvi Contributor contact details

Sutton Bonington Campus Loughborough Leicestershire LE12 5RD UK Tel: +44 (0) 1159 516 126 Fax: +44 (0) 1159 516 122 E-mail: [email protected]

Tel: +358 9 4565175 Fax: +358 9 4552103 E-mail: [email protected]

Chapter 17 Professor M. A. Coimbra* Department of Chemistry University of Aveiro PT 3810-193 Aveiro Portugal

Chapter 14 Dr J. B. Adams 5 Orchard View Draycott Moreton-in-Marsh Gloucestershire GL56 9LW UK Tel: +44 (01386) 700374 E-mail: [email protected]

Tel: +1 351 234 370 706 Fax: +1 351 234 370 084 E-mail: [email protected] Dr I. Mafra REQUIMTE Laboratory of Bromatology Faculty of Pharmacy University of Porto R Anibal Cunha, 164 PT 4050-047 Porto Portugal

Chapter 15 Dr R. Saurel University of Lyon Rue Henri de Boissieu 01 060 Bourg-en-Bresse Cedex 09 France

Tel: +351 22 2078902 Fax: +351 22 2003977 E-mail: [email protected]

Tel: +33 (0)4 74 45 52 52 Fax: +33 (0)4 74 45 52 53 E-mail:[email protected]

Chapter 16

Dr S. P. Cauvain Campden & Chorleywood Food Research Association Chipping Campden Gloucestershire GL55 6LD UK

Dr M. Suutarinen and Dr K. Autio* VTT Biotechnology PO Box 1500, 02044 VTT Finland

Tel: +44 (0) 1386 842 000 Fax: +44 (0) 1386 842 150 E-mail: [email protected]

Chapter 18


Chapter 19 Dr S. K. Kim* Dankook University Korea Tel: +82 2 709 2426 Fax: +82 2 790 2447 E-mail: [email protected] Dr C. O. Rhee Chonnam National University Korea

xvii

Dr L. J. Malcolmson Canadian International Grains Institute 1000-303 Main Strect Winnipeg MB R3C 3G7 Canada Tel: +1 (204) 983 8584 Fax: +1 (204) 983 2642 E-mail: [email protected]

Chapter 21

Tel: +82 62 530 2142 Fax: +82 530 2149 E-mail: [email protected]

Dr C-J. Shieh and Dr C-Y. Chang Department of Food Engineering Da-Yeh University Cheng-Hwa Taiwan 515

Chapter 20

E-mail: [email protected] [email protected]

Dr B. A. Marchylo* and Dr J. E. Dexter Grain Research Laboratory Canadian Grain Commission 1404-303 Main Street Winnipeg MB R3C 3G8 Canada Tel: +1 (204) 983 3320 Fax: +1 (204) 983 0724 E-mail: [email protected] [email protected]

Dr C-S. Chen* Department of Applied Chemistry Chao-Yang University of Technology 168 Gifeng E. Rd, Wufeng Taichung County Taiwan Fax: +886 4 2374 2341 E-mail: [email protected]

Abbreviations

AACC ADC AF AFC AIS AMG AOAC APX ASAE

American Association of Cereal Chemists Analogue to digital converter aqueous freezant alternative forced choice alcohol insoluble solids acoustic myography Association of Analytical Communities ascorbate peroxidase American Society of Agricultural Engineers

bpm

beats per minute

CA CCFRA CcP CDTA CVA CWC

controlled atmosphere Campden and Chorleywood Food Research Assn cytochrome C peroxidase 1,2-cyclohexanediaminetetraacetic acid canonical variate analysis Chinese water chestnut

DAC DATI DFD DHF DIS DMTA DOPA DP DSP

dynamic axial compression dual-attribute time-intensity dark, firm, dry dihydroxyfumaric acid dewatering impregnation soaking dynamic mechanical thermal analysis dihydroxyphenylananine degree of polymerisation deformation at skin puncture

E EMC

enzyme equilibrium moisture content

xx Abbreviations

EMG Endo-PG EPG Eth

electromyography endo-polygalacturonase electropalatography ethylene

FA F/D FES FFT FITC FMBRA FT-NIR FW

ferulic acid force/deformation functional electrical stimulation fast Fourier transform fluorescein isothiocyanate Flour Milling and Baking Research Association Fourier transform NIR fresh weight

GF GPIB GRL GS

general foods general purpose interface board Grain Research Laboratory glutein subunits

HDM HM HPLC HRP HT HTC HVK

hydrodynamic mechanism high methylated high performance liquid chromatography horseradish peroxidase high temperature hard-to-cook hard vitreous kernel

IAA ICF IDT IFR IQF ISO

indole-3-acetic acid immersion chilling and freezing iso-dityrosine impact force response individually quick frozen International Standards Organisation

KI

kinesiograph

LAM LiP LM LMW LT LVDT

labelled affective magnitude lignin peroxidase low methylated low molecular weight low temperature linear voltage displacement transducer

MA MARS

modified atmosphere multivariate adaptive regression splines

Abbreviations

MassL, MassR MLR MRI MT

masseter left, right multiple linear regression magnetic resonance imaging Magness-Taylor

NIR NIRS NMR NSP

near-infrared near-infrared reflectance spectroscopy nuclear magnetic resonance non-soluble pectin

OD OLS ORF OSP

osmotic dehydration ordinary least squares open reading frame oxalate soluble pectin

P PCA PEL PG PIHMI PLS PME POD POM PPO PSE PSP PVOD

product principal component analysis pectate lyase polygalacturonas paired increasing-height multiple-impacting partial least squares pectin methylesterase peroxidase proportional odds modelling polyphenoloxidase pale, soft, exudative polysterene pack pulsed vacuum osmotic dehydration

QC QDA QTL

quality control quantitatived descriptive analysis quantitative trait loci

Re RH RMS RMSEP RSM RSREG RVA

respiration relative humidity root-mean-square root mean square error of prediction response surface methodology response surface regression rapid visco analyser

S SAS SDP SE

substrate statistical analysis system symmetrised dot pattern softness equivalent

xxi

xxii

Abbreviations

SEM SEP SS

scanning electron microscope standard error of prediction soluble solids

TEM TempL, TempR T-I TOF TOM TP TPA

transmission electron microscopy temporalis left, temporalis right time-intensity time-of-flight total organic matter total pectin texture profile analysis

UHT UV

ultra high temperature ultraviolet

VI VMG VOD

vacuum infusion vibromyography vacuum osmotic dehydration

WB WSP WVP

Warner-Bratzler water-soluble pectin water vapour permeability

XET

xyloglucan endotransglycosylase

Part I Consumers, texture and food quality

1 Measuring consumer perceptions of texture: an overview D. Kilcast, Leatherhead Food International, UK

1.1

Introduction: texture and food quality

In prosperous societies, we have available an enormous and ever-increasing range of foods, and manufacturers find themselves in an intensely competitive situation. In less well-developed societies, hunger will be the constant driving force, and our diet will be determined by availability of any food that satisfies our basic nutritional needs. It is increasingly clear that if we are to understand what drives consumers’ choice of food, no single factor can be considered in isolation from others. For some years, psychology researchers have been developing models to understand consumer behaviour (e.g. Shepherd and Sparks, 1994). Although there are many possible circumstances under which non-sensory factors such as price and nutritional image can have dominant effects, the sensory characteristics of foods are central to their continued purchase. The importance of a holistic approach is also becoming more clear when the components of sensory perception are considered. During the sequences of actions that constitute food consumption, we perceive a whole range of different characteristics relating to the appearance, flavour and texture of the food. Numerous tools are available for investigating the sensory properties of foods, and the information required must be carefully defined if appropriate tools are to be selected. Systematic development of new products will inevitably depend on the use of different tools at different stages of the development cycle. 1.1.1 The human senses It is generally accepted that human beings have five senses in operation, namely sight, smell, taste, touch and hearing, although warmth, cold, movement

4

Texture in food

and pain may also be considered as senses of importance in a food context (Fig. 1.1). Foods are complex mixtures of chemical compounds, arranged into structural units. The perception of the sensory characteristics of foods results from the stimulation of all our senses to some extent by the physicochemical properties of the foods. The sensory characteristics of food are generally grouped into three categories, namely appearance, flavour and texture. These categories are, however, not independent of one another. For example, colour, which is obviously an important appearance characteristic, can be shown to have an influence on flavour perception; consumers will assign higher scores for flavour intensity to darker foods than to lighter foods. The interaction between appearance and flavour is referred to as ‘visual flavour’. Similarly, textural characteristics such as viscosity can influence the perception of flavour, and some flavour characteristics, e.g. acidity, can affect textural characteristics. One means of defining flavour, texture and appearance is by taking into account the fact that each can be attributed to the stimulation of one or possibly two of the senses. On this basis the International Standards Organisation (ISO, 1992) has proposed working definitions for flavour, texture and appearance, as given below. • Appearance: sensory characteristics of foods perceived largely by way of the visual sense. Input from other senses, especially smell, may contribute. • Flavour: the combination of taste and odour. Pain, heat, cold, tactile and visual sensations may also contribute. • Texture: sensory characteristics perceived largely by way of the senses of movement and touch. Input from other senses, especially vision and taste, may sometimes contribute. The above definitions give little information on how the senses are used in the perception of quality attributes. Appearance is sometimes, mistakenly, equated only with colour, and yet many other visual aspects of form, shape, translucency, etc., may influence our use of the visual senses. Taste (gustation)

Vision

Gustation

Olfaction

Trigeminal

Taste

Odour

Irritant

Touch

Hearing

FLAVOUR SOUND

APPEARANCE

TEXTURE

Fig. 1.1 Schematic diagram of the human senses and their operation in the perception of food quality.

Measuring consumer perceptions of texture: an overview

5

is strictly defined as the response by the tongue to soluble, involatile materials. These have classically been defined as four primary basic taste sensations: salt, sweet, sour and bitter, although umami, the sensation associated with monosodium glutamate, is now widely recognised as a basic taste. This list is frequently extended further to include sensations such as metallic and astringency. The taste receptors are organised groups of cells, known as taste buds, located within specialised structures called papillae. These are located mainly on the tip, sides and rear upper surface of the tongue. Taste stimuli are characterised by the relatively narrow range between the weakest and the strongest stimulants (ca 104), and are strongly influenced by factors such as temperature and pH (Meilgaard et al., 1999). The odour response is much more complex, and odours are detected as volatiles entering the nasal passage, either directly via the nose or indirectly through the retronasal path via the mouth. The odorants are sensed by the olfactory epithelium, which is located in the roof of the nasal cavity. Some 150–200 odour qualities have been recognised, and there is a very wide range (ca 1012) between the weakest and the strongest stimulants (Meilgaard et al., 1999). The odour receptors are easily saturated, and specific anosmia (blindness to specific odours) is common. It is thought that the wide range of possible odour responses contributes to variety in flavour perception. Both taste and odour stimuli can be detected only if they are released effectively from the food matrix during the course of mastication. The chemical sense corresponds to a pain response through stimulation of the trigeminal nerve. This is produced by chemical irritants such as ginger and capsaicin (from chilli), both of which give a heat response, and chemicals such as menthol and sorbitol, which give a cooling response. With the exception of capsaicin, these stimulants are characterised by high thresholds. The combined effect of the taste, odour and chemical responses gives rise to the sensation generally perceived as flavour, although these terms are often used loosely. Texture is perceived by the sense of touch, and comprises two components: somesthesis, a tactile, surface response from skin, and kinesthesis (or proprioception), which is a deep response from muscles and tendons. For many foods, visual stimuli will generate an expectation of textural properties. The touch stimuli themselves can arise from tactile manipulation of the food with the hands and fingers, either directly or through the intermediary of utensils such as a knife or spoon. Oral contact with food can occur through the lips, tongue, palate and teeth, all of which provide textural information.

1.1.2 Texture and food enjoyment Most studies which have investigated the importance of different sensory modalities on consumer acceptability conclude that flavour is the most important modality, followed by texture and then appearance (e.g. Moskowitz and Krieger, 1995). Such conclusions do not reflect the enormous efforts

6

Texture in food

that the food industry devotes to designing appealing textural characteristics, and to maintaining those characteristics to long-term production. Research with consumers in the USA carried out by Szczesniak and Kahn (1971) showed that awareness of texture lies at a subconscious level, and that textural properties are taken for granted. If the expectations of texture are violated, however, awareness of textural defects is accentuated, and texture becomes a focal point for criticism and rejection of the food. Expectations are being increasingly recognised as important factors in food choice by consumers (e.g. Vickers, 1991; Cardello, 1994).

1.1.3 The interactive role of texture In addition to its direct contribution to consumer acceptance, texture has a vitally important secondary effect, through modulation of flavour release. If flavour components are to be perceived, they must be released from the food matrix in order to reach the appropriate receptors. This release of flavour is intimately related to the way in which the food structure breaks down in the mouth, and consequently to both the initial texture of the food and the change in texture throughout mastication (Section 1.2). In addition, the structural factors that deliver a specific texture can also influence appearance characteristics, for example the glossy surface of chocolate confectionery.

1.1.4 Texture and product design Texture and food structure are inextricably linked; the micro- and macrostructural composition of foods will determine the sensory perception, and any change in structure carries the risk of changing perceived texture and violating consumer expectations. Industry therefore needs to take great care to ensure that products with key textural characteristics, such as snack foods and confectionery products, are manufactured to consistently high quality. This can present an enormous challenge for foods relying on primary components such as meat and vegetables that are naturally subject to high variability, and for any processed food manufactured on high-volume production lines. Product modifications, for example to produce low-fat variants, can introduce structural changes that can generate substantial textural modifications. Industry therefore needs methods to measure textural characteristics. However, designing suitable measurement systems requires an understanding of the mechanisms by which texture is perceived.

1.2 Perception and sensory assessment of food texture 1.2.1 Oral breakdown processes The importance of the interaction between the texture of foods and their perceived flavour can be clearly seen if the sequence of events during food


7

consumption is considered. A strong expectation of the flavour and texture characteristics can be generated before the food is introduced into the mouth. As food enters the mouth, and is either bitten or manipulated between tongue and palate, catastrophic changes occur to the structure of the food that strongly influence the way in which tastants and odorants are released from it. Of particular importance are temperature increase (cold foods) or decrease (hot foods) and dilution by saliva. Salivary introduction also serves to lubricate the food bolus. The factors that influence such release are under active study (e.g. Overbosch et al., 1991), and include: • • • • •

rate and mode of production of new surface rate of production of saliva dissolution and dispersion of the food release of involatile tastants and volatile odorants transport of volatiles to the nasal cavity

1.2.2 Oral food management Even the complex picture of food breakdown described previously is an over-simplification of actual oral processes (Heath and Prinz, 1999), which show substantial differences for different foods and between individuals. The first bite by the incisors is an important stage which generates an early textural response that can influence subsequent chewing actions. The food is then transported between the cheek teeth, the jaw closes and main-sequence mastication starts. Hard foods are comminuted into particles, which are then formed into a soft bolus with saliva. Before this bolus is swallowed, the tongue is used to clear any remaining particles. Hutchings and Lillford (1988) have described two thresholds that need to be satisfied before swallowing is initiated: a food particle size threshold and a lubrication threshold. Finally, debris can be left in the mouth after swallowing, and further clearance and swallows may be necessary. 1.2.3 Mechanisms of texture perception Either of the two mechanisms described in Section 1.1.1 (proprioception and somesthesis) can operate during the mastication process, depending on the nature and texture of the food. The texture of solid foods is perceived primarily through proprioception, as the food is chopped by the incisors and ground by the molars. As the physical state of the food changes dramatically during mastication, both mechanisms can be operative. In particular, during the mastication of solid foods somesthesis becomes important as the bolus is formed and manipulated. However, even at the early stages of mastication (and prior to mastication, through use of fingers and lips), somesthesis can give important textural sensations. The texture of semi-solid and liquid foods is perceived primarily through somesthesis, from the action of the tongue and the soft palate, and is usually expressed by the term mouthfeel.

8

Texture in food

1.2.4 Sensory assessment of texture A sensory stimulus to a human subject produces a set of physiological sensations that are interpreted by the brain as perceptions. The sensations will vary considerably between subjects, reflecting the natural physiological differences in the population, and the interpretation by the brain will be modified by psychological differences. Perceptions are recorded as actions by the subject, or as verbal responses. This may be the selection of a certain sample from a group (as in a difference test), words to describe the nature of a perception, and numbers to measure the size of a perception. To ensure that the subjects’ responses relate as closely to their perception as possible, it is necessary to use carefully controlled environmental conditions and test procedures to carry out experiments. In particular, it is important to minimise the many sources of psychological bias that can produce unwanted influences on responses, and it is frequently necessary to minimise the spread of physiological responses characteristic of biological systems through careful panel selection and training procedures.

1.3 Tests and test procedures 1.3.1 Procedures A basic classification of the main sensory test procedures is shown in Fig. 1.2. The primary classification is into analytical tests and hedonic tests. Analytical tests use trained panels as a form of analytical instrument to generate information on the sensory properties of the food, whereas hedonic tests measure the response of untrained consumers to the sensory properties in terms of liking or acceptability. Different psychological procedures form

Sensory test methods

Analytical Difference tests

Descriptive tests

Paired comparison

Qualitative

Duo-trio

Profile

Hedonic Preference tests Paired preference

Acceptability tests Acceptability scaling

Triangle

Instrumental measures of perceived quality

Response of consumers to perceived quality

Fig. 1.2 Classification of the main sensory testing procedures.


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the basis for this test distinction, and the information produced is distinct but complementary. Discrimination tests are commonly used to establish, for example, if a formulation modification has changed the sensory quality. Although the tests can be a sensitive measure of change, and more recently have been generalised from their traditional use as difference tests to permit testing for similarity (Meilgaard et al., 1999), they generate relatively little information. The most informative analytical procedures identify the sensory properties that are characteristic of that food, and quantify the individual characteristics; these are termed sensory profile methods. Numerous sensory profile methods have been developed for foods, but the most important in practical use are the Texture Profile Method, Quantitative Descriptive Analysis and the Spectrum Method (reviewed in Kilcast, 1999).

1.3.2 Difference tests Paired comparison test In the most common form of the test, two coded samples are presented either sequentially or simultaneously in a balanced presentation order (i.e. AB and BA). There are two variations on the test. In the directional difference variant, the panellists are asked to choose the sample with the greater or lesser amount of a specified characteristic. The panellists are usually instructed to make a choice (forced-choice procedure), even if they have to guess, or they may be allowed to record a ‘no-difference’ response. Duo-trio test In the most common variant of the duo-trio test, the panellists are presented with a sample that is identified as a reference, followed by two coded samples, one of which is the same as the reference and the other different. These coded samples are presented in a balanced presentation order, i.e. A (reference) A (reference)

A B

B A

The panellists are asked to identify which sample is the same as the reference. The duo-trio test is particularly useful when testing foods that are difficult to prepare in identical portions. Testing such heterogeneous foods using the triangle test, which relies on identical portions, can give rise to practical difficulties, but in the duo-trio test there are no major difficulties in asking the question: Which sample is most similar to the reference? Triangle test Three coded samples are presented to the panellists, two of which are identical, using all possible sample permutations, i.e.

10 Texture in food

ABB BAB BBA

AAB ABA BAA

The panellists are asked to select the odd sample in a fixed-choice procedure. The increased number of samples compared with a paired comparison test can result in problems with flavour carry-over when using strongly flavoured samples, making identification of the odd sample more difficult. Difficulties can also be encountered in ensuring presentation of identical samples of some foods (see above). 3-AFC (Alternative Forced Choice) test This less common procedure uses one-half of the same sample permutations from the triangle test in a triad format, but either the difference of interest between the samples is revealed to the panellists in advance, or the panellists identify the nature of any difference in advance. In the test itself, the panellists are then asked to identify the sample (or samples) with the specified characteristic. For example, a typical instruction might be: One of these samples is more bitter than the others; please identify this sample. O’Mahony (1995) has identified the reasons why this test can be more sensitive than the triangle test, but the test suffers from the need to identify the nature of the difference positively in advance. R-index test This short-cut signal-detection method (O’Mahony, 1979; 1986) is less well used but has found applications in industry. The test samples are compared against a previously presented standard, and rated in one of four categories. For difference testing, these categories are standard, perhaps standard, perhaps not standard and not standard. The test can also be carried out as a recognition test, in which case the categories are standard recognised, perhaps standard recognised, perhaps standard not recognised and standard not recognised. The results are expressed in terms of R-indices, which represent probability values of correct discrimination or correct identification. The method is claimed to give some quantification of magnitude of difference, but its use has not been widely reported in the literature. One important limitation is that a relatively high number of judgements is needed in this form of test, leading to the risk of severe panellist fatigue. Difference from control test The test is of particular value when a control is available; the panellists are presented with an identified control and a range of test samples. They are asked to rate the samples on suitable scales anchored by the points ‘not different from control’ to ‘very different from control’. The test results are usually analysed as scaled data.


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1.3.3 Quantitative descriptive tests The Texture Profile Method The Texture Profile Method was developed by the General Foods Company specifically to define and measure the textural parameters of foods. Panellists are selected on the basis of ability to discriminate known textural differences in the specific product application for which the panel is to be trained (solid foods, beverages, semi-solid foods, skin care products, fabrics and paper goods). Panellists selected for training are exposed to a wide range of products from the category under investigation, to provide a wide frame of references. The characteristics of the product, the order of appearance and the degree to which each is present are determined. Attributes are usually evaluated in the following order: 1 2 3 4

surface characteristics (can be visual); initial compression (perceived on first bite); masticatory phase (perceived during chewing); residual phase (changes made during mastication and often perceived after swallowing).

In addition to the mechanical (e.g. firmness, adhesiveness, viscosity, springiness, cohesiveness) and geometrical (e.g. flakiness, grittiness, beady, crystalline) characteristics evaluated during the initial compression and masticatory stages, auditory characteristics such as crunchiness, crackliness or crispness might be evaluated. The panel verdicts are derived by group consensus or by statistical analysis of the data. Results are displayed in tabular or graphic form. Quantitative Descriptive Analysis (QDA) QDA is a total system covering sample selection, panellist screening, vocabulary development, testing and data analysis (Meilgaard et al., 1999). Variants of the original QDA procedures are probably used more than any other profiling procedure. The QDA technique uses small numbers of highly trained panellists. Typically, 6 to 12 people are screened for sensory acuity and trained to perform the descriptive task to evaluate the product. Three major steps are required: development of a standardised vocabulary, quantification of selected sensory characteristics and analysis of the results using parametric statistics. Development of the vocabulary is a group process for creating a complete list of descriptors for the products under study. Panellists freely describe the flavour, appearance, odour, mouthfeel, texture and aftertaste characteristics of different samples. No hedonic (good or balanced ), general (full or typical ) or intensity-based (strong or weak) terms are permitted. Terminology should be consistent from product to product and tied to reference materials. The references decrease panellist variability, reduce the amount of time necessary to train sensory panellists, and allow calibration of the panel in the use of intensity scales. References should be simple, reproducible and clear to the

12 Texture in food

panellists, and illustrate only a single sensory descriptor. They can be single chemical substances or finished products, and are made available during both the training and the testing phase, at various concentrations or intensity. The attributes are collected and compiled into a master list. This individual preliminary evaluation of the samples may be revised during an open discussion to eliminate any redundant or synonymous descriptors. New terms might be added and physical references proposed. The panel leader condenses and formats the information into a proposal for standardised vocabulary. This vocabulary is then modified and improved in several interactive sessions. Multivariate statistical methods (e.g. factor analysis) are sometimes used to reduce the number of descriptors. Finally, definitions for the attributes are agreed. When the panellists have agreed a vocabulary, further training is performed. The number of training sessions is dependent on the subject’s performance, product and attribute difficulties and the time allowed for QDA testing. Panel training increases panellist sensitivity and memory and helps panellists to make valid, reliable judgements independent of personal preferences. Once the training sessions have established satisfactory panel performance, and after removal of ambiguities and misunderstandings, the test samples can be evaluated. This is usually carried out in replicated (commonly three) sessions, using experimental designs that minimise biases. In each session, the mean is calculated for group and individual judgements of each attribute. The results are then subjected to univariate statistics (e.g. analysis of variance) or multivariate statistics (e.g. principal component analysis). Test results may also be visualised via bar charts or line graphs. The Spectrum Method This more recent method provides a tool with which to design a descriptive procedure for a given product category. The method resembles QDA in many respects; for example, the panel must be trained to fully define all product sensory attributes, to rate the intensity of each and to include other relevant characterising aspects such as change over time, difference in the order of appearance of attributes, and integrated total aroma and/or flavour impact. Panellists develop their lists of descriptors by first evaluating a broad array of products that define the product category. The process includes using references to determine the best choice of term and to best define that term so that it is understood in the same way by all panellists. Words such as vanilla, chocolate or orange must describe an authentic vanilla, chocolate and orange character, for which clear references are supplied. All terms from all panellists are then compiled into a list that is comprehensive but not overlapping. The Spectrum Method is based on an extensive use of reference points. The choice of scaling technique may depend on the available facilities for


13

computer manipulation of data and on the need for sophisticated data analysis. Whatever the scale chosen, it must have at least two, preferably three or five, reference points distributed across the range.

1.3.4 Time-intensity methods Sensory attributes are not perceived instantaneously and can change in intensity with time in the mouth. Time-intensity methods are used to measure intensity of a specific attribute as a function of time in the mouth. They have been used to investigate the temporal behaviour of tastants, such as sweet and bitter molecules, and to investigate the release of volatile flavour materials from foods (Overbosch et al., 1991; Shamil et al., 1992) during mastication. Such studies are particularly important in the reformulation of foods that results in structural modifications, and in changes that can occur on storage. These structural modifications are often accompanied by textural changes, and these often result in complex perceptual phenomena that are direct consequences of the changes in texture with time producing different flavour release phenomena. The use of time-intensity for flavour measurement is well established, and there have also been studies to measure textural changes using the method (Burger, 1992; Duizer et al., 1993). A major limitation of time-intensity methods is that only a single attribute can be tracked with time, and, if a number of important attributes are thought to be time-dependent, separate sessions are needed for each attribute. Difficulties encountered in time-intensity profiling prompted the development of a hybrid technique, progressive profiling (Jack et al., 1994). In this technique, assessors carried out a profile on a set of texture descriptors at each chew stroke over the mastication period. Such a method has a number of potential advantages: several attributes can be assessed in one session; scaling is reduced to a unidimensional process; and the most important aspects of the shape of a time-intensity curve are retained.

1.4 Instrumental measurement of texture Sensory methods are, for the foreseeable future, the primary means of measuring the range of textural characteristics of food that are important to consumer acceptance. The highly labour-intensive nature of sensory analysis has inevitably led to the development of instrumental methods designed to measure food properties that relate to relevant sensory characteristics. These methods have been classified in various ways, according to the type of measurement and the type of food.

14 Texture in food

1.4.1 Empirical, imitative and fundamental measurement Instrumental methods have been classified into three main categories: empirical, imitative and fundamental. Empirical methods Empirical tests often measure ill-defined variables that are indicated by practical experience to be related to some aspect of textural quality. Devices have been developed within different sectors of the industry that are appropriate to specific product types. Even for the same product type, different food manufacturers have developed their own in-house devices. Fuller details of the devices described in this section are given in Bourne (2002). • Puncture or penetration devices measure either the force needed to push a probe into the food to a specified depth or the penetration distance achieved by application of a specified force. Examples include MagnessTaylor testers (for fruit), the Bloom Gelometer and the FIRA Jelly Tester (for gels), the cone penetrometer (for fats) and the Christel Texture Meter (for peas). • Shearing devices measure the force needed for one or more blades to shear through the food. The maximum force is often assumed to measure toughness, firmness or fibrousness. Instruments include the Warner-Bratzler Shear (for meat), the Kramer Shear Cell (general-purpose) and the FMC Pea Tenderometer. • Compression devices measure the force needed to achieve a given compression or the compression achieved at a given force. Examples include the Baker Compressimeter (for bread) and the ball indenter (for fats). In extrusion tests, the food is forced through one or more orifices and the maximum force, average force or work done over a specified period is measured. The measured values are assumed to relate to firmness, toughness, consistency or spreadability. Examples include the FIRA-NIRD Extruder (for fats) and various cells used in conjunction with generalpurpose instruments. • Cutting devices use wires or blades (sometimes rotating) to cut through the food and measure the maximum force developed or the time needed to cut through a standard size of sample. Measurements are assumed to relate to fibrousness, firmness or hardness. The FMBRA Biscuit Texture Meter is a rotating blade device used to measure biscuit hardness. • Flow and mixing devices are used to give a measure of viscosity or consistency of liquid and semi-liquid foods. They often measure the extent to which samples flow or spread under specific geometric conditions, e.g. the Bostwick Consistometer and the Lyons Gel Flow Meter. Although such empirical devices are often simple, inexpensive and portable, precision and reproducibility are generally poor, and the measured parameters are poor measures of perceived texture. Extensive use is still made of them in industry, however, mainly for quality control purposes.


15

Imitative methods Imitative methods of measurement mimic the conditions to which the material is subjected in practice during eating. The Volodkevich bite tenderometer attempted to mimic the action of teeth on food. It recorded the force of biting on a piece of food as a function of the deformation incurred. Two wedges with rounded points were substituted for teeth, the lower being fixed to a frame. The upper wedge moved with a linear motion through the arc of a circle by a lever, squeezing a sample between the wedges. A device using human dentures served as the prototype for the General Foods (GF) Texturometer (Friedman et al., 1963), in which the dentures are replaced by a plunger. The location of the sensing element was moved from the articular arm to the sample area to eliminate gravity forces, and the oscilloscope was replaced by a chart recorder, enabling easy and permanent recording of any chosen number of consecutive chews. In this device, the driving mechanism no longer imparts a combined lateral and forward motion to the lower jaw, although it still drives the plunger through the arc of a circle. Although the GF Texturometer remains in use to a small extent in North America and in Japan, the general-purpose testing machines designed for use with foods, exemplified by those made by Stable Micro Systems, Stevens, Lloyd and Instron, are commonly used in the food industry in most countries. The instruments differ in their mechanical construction and in their data acquisition and data analysis capabilities, but they have a number of important features in common. All have a crosshead containing a load cell, which is driven vertically at a range of constant speeds, and which can cycle over a fixed distance or load range. Probes can be attached to the crosshead for penetrating, shearing or crushing food, which can be held in a variety of cells. The load is recorded relative to time or to penetration/deformation distance, and displayed on a suitable recorder. Computer control of the instrument and sophisticated and rapid computer analysis of the data are increasingly common. A major advantage of such instruments is that flexibility of design allows them to be used for a wide range of foods. This is particularly useful for companies that are handling or manufacturing a varied product range. Load cells can be changed to give a high level of accuracy for relatively soft foods through to very hard foods. Probes and sample holders can easily be changed to accommodate measurements on different product types. An additional advantage is that such instruments can often be adapted for fundamental texture measurement. Fundamental methods Fundamental methods involve measuring well-defined physical properties of food, which, if measured properly, are independent of the method of measurement. The most common fundamental parameters are Young’s modulus, shear modulus, bulk modulus and Poisson’s ratio (for solids) and viscosity

16 Texture in food

(for liquids). Fundamental parameters for solids can be measured on generalpurpose testing machines, but such measurements require a carefully designed experimental set-up and are consequently slow. In addition, foods are generally heterogeneous and do not exhibit simple elastic behaviour. Fundamental parameters can be measured on liquids using suitable instrumentation, for example the Weissenberg Rheogoniometer and the Carri-Med Rheometer. Again, however, liquid foods rarely exhibit simple viscosity behaviour. Such fundamental measurements are valuable in investigating the physical properties of food, but are too complex for routine use and, in common with other instrumental measurements, rarely correlate well with perceived texture. Some reasons for this can be identified on examining some of the physiological factors associated with chewing.

1.4.2 Application to solid foods Development of measurement methods For most solid foods, key sensory attributes can be defined that are known to be highly important in defining consumer acceptability, for example crispness in salad vegetables and snack foods, tenderness in meat, and snap in chocolate. The evolution of measurement methods has followed the need both to control these attributes in routine production and to understand how they can be designed into new products. The computerised modern instruments that utilise force-deformation principles are used almost universally in research functions and, with particular success, in quality control (QC) functions. A good example of this is the use by the French company Isigny Sainte-Mère of Stable Micro Systems TAXT2 Texture Analysers for on-line measurement of the firmness of Camembert cheeses to sort for different maturing conditions (Toursel, 1996). The reason for the successful applications in QC is easy to see. In such applications, it is more important to be able to detect changes in measurable parameters than to measure precisely specific textural parameters. A change in any measured parameter outside set control limits can act as a signal that some aspect of the food production cycle has drifted. Of course, this introduces the risk that actions could be taken on the basis of changes in parameters that have little importance to consumer acceptability, but the measurements would normally trigger sensory tests that would minimise any production holds. A more serious risk is that the measurement system would not identify textural changes that were unmeasurable by that system, and, unless routine sensory tests were also carried out, defective material could be supplied to consumers. For example, a standard texture instrument capable of measuring the characteristic snap of chocolate might not detect the dryness that characterises stale chocolate. The deficiencies of texture measurement instruments become particularly apparent when they are used for R&D purposes. One practical problem is that no single instrument is likely to be able to measure the food properties


17

that are detected by both the tactile and the deep senses. One route to address this problem is to investigate more closely those physical processes that give rise to the texture sensations. In a study of stickiness in confectionery products, Kilcast and Roberts (1998) explained the need to understand both the meaning of the word ‘stickiness’ by consumers, and in what context the phenomenon is perceived, as well as the physical processes that contribute to the various phenomena commonly described as stickiness. They showed that the perception of stickiness can occur through combinations of both adhesive and cohesive failure (Fig. 1.3). Most industrial problems are associated with cohesive failure, which leaves unwanted material behind on surfaces. Both product rheology and the surface energy of the surfaces can contribute to the observed sticking phenomena, and under critical conditions it is possible to minimise sticking by either changing the surfaces involved or changing product composition or operating conditions. The research led to a test procedure adapted from that developed by Chen and Hoseney (1995) for measuring the stickiness of dough that can be used to study the stickiness of caramels over a wide range of conditions. Figure 1.4 shows the cell, which consists of a Probe movement

Probe movement

Adhesive failure

Cohesive failure a2

a1

start

c1

c2

Fig. 1.3 Adhesive and cohesive failure mechanisms during the force–deformation testing of food. The sequence to the left (start, a1, a2) illustrates adhesive failure; the sequence to the right (start, c1, c2) illustrates cohesive failure. Grid

Perforated cap

Sample reservoir

Water jacket

Piston

Fig. 1.4 Modified Chen-Hoseney cell for measuring the stickiness of caramel.

18 Texture in food

water-jacketed cylinder holding the caramel sample at the required temperature. The screw piston is used to extrude the caramel through a perforated stainless steel cap to give a fresh surface. The instrumental measurements are carried out by lowering a cylindrical probe onto the caramel surface, and then withdrawing it and measuring the force-deformation characteristics. For solid foods giving the proprioceptive (deep) response, the principles of fracture mechanics have been applied widely, and are outlined in the following section. Fracture mechanics in food texture measurements The science of fracture mechanics was originally developed to explain the fracture of brittle materials such as glass. The breakage of a material is influenced by the relationship between the applied force and the bond holding the material together. Bonds will break more readily under the high local stress concentration around the tip of a sharp blade, for example. The basic premise underlying fracture mechanics is that all solids contain inhomogeneities, which exist in the form of flaws, or cracks. The magnitude and distribution of these defects govern the strength of the material. Fracture occurs when these defects grow and traverse the solid, creating new fracture surfaces. Early studies showed the potential of wedge fracture testing to brittle and semi-brittle foods such as apples and cheeses (Vincent et al., 1991). One difficulty in applying the principles of fracture mechanics to food lies in the great complexity of food structure, and in the complex viscoelastic behaviour of most foods, although recent research has demonstrated the feasibility of these techniques (Dolores Alvarez et al., 2000). However, deformation tests applied to carefully prepared food samples have been shown to give improved correlations between instrumentally measured properties and sensory measures. The principles of fracture mechanics have been extended to the measurement of fracture toughness of complex foods, such as the pastry casing of spring rolls (Sim et al., 1993). Lillford (2001) has reviewed the work relating to fracture mechanisms in food, and Lucas et al., (2002) have proposed physiological models relating to the fragmentation and swallowing of food particles.

1.4.3 Analysis and validation of instrumental measurements Statistical methods A physical measurement of textural characteristics can be of practical value only if it is shown to relate to some relevant sensory texture measure. The relationship should take the form of a statistic that represents the fit between the instrumental measurement and the sensory attribute, or an equation that relates the instrumental measure (or set of measures) to the required sensory attribute. Two basic procedures are used.


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• Pearson product moment correlation coefficients (r), where a perfect positive correlation gives r = +1, a perfect negative correlation gives r = –1, and no correlation gives r = 0. The square of the correlation coefficient (r2) gives a measure of the data variance accounted for by the linear correlation, and is a measure of the value of the correlation. • Multiple linear regression (MLR), in which the variable of interest (e.g. sensory attribute) is expressed as a linear combination of other variables (e.g. instrumental parameters). The variable combination is usually found using stepwise selection procedures. The method is of greatest value when the number of data points exceeds the number of attributes of interest. The complexity of both instrumental and sensory data, however, increasingly demands the use of multivariate statistical procedures. Many techniques are available, but, when examining for structure and relationships in data sets, the most common technique is principal component analysis (PCA). PCA is a data reduction technique that replaces a large number of original variables by a smaller number of linear combinations, whilst still explaining a substantial proportion of the original variation in the data. Essentially, PCA projects an n-dimensional space onto a 2-dimensional plot. Other multivariate analyses, such as partial least squares (PLS) analysis are increasingly being used for combined data sets. For example, in a study of the sensory and instrumental characteristics of Reggiano grating cheeses, PLS was used to show that sensory texture correlated best with strain at breaking point (Hough et al., 1996). Novel methods for analysing force–deformation data Analytical software included in the modern force-deformation test systems is capable of parameterising the curve shapes generated by many foods. The most extreme deviation from the idealised force–deformation curves is commonly found in testing brittle foods, which are characterised by very jagged curves. In a series of papers (e.g. Barrett et al., 1992), Peleg and coworkers have described different mathematical approaches to analysing these highly irregular curve shapes, which can give rise to difficulties in parameterisation. One approach has been to carry out a Fast Fourier Transform on the force–deformation curve, giving a power spectrum of underlying frequencies. This procedure gives a qualitative representation of the jaggedness of the original curve, but cannot give a quantitative representation. Such quantification can be carried out through fractal analysis. The fractal concept is based on the geometry of self-similar objects expressed in terms of noninteger dimensions. The fractal dimension is determined from the slope, in logarithmic co-ordinates between the length of the force–deformation contour and the corresponding measurement scale. The latter measurement was found to be convenient in giving a measure of overall jaggedness in terms of a single number, but the power spectrum gave more information on the location of the fracture, and its shape could be related more directly to structure and

20 Texture in food

texture. The use of the power spectrum was subsequently described in relation to the measurement of crunchiness in friable baked products (Rohde et al., 1993). A further innovative attempt to analyse jagged force–deformation curves has been to use a technique used in speech analysis and medical diagnosis, symmetrised dot-pattern (SDP) displays (Peleg and Normand, 1992). This concept is based on the premise that, whereas humans find it difficult to analyse the visual appearance of highly irregular shapes, they are highly sensitive to changes in symmetric patterns. In the method, the recorded data are transformed into several symmetrically arranged sets of points, each reflected by a mirror plane, in an analogous way to the production of symmetric visual images by a kaleidoscope. The technique was used in the hope that the displays could be used to identify crunchiness in the same way that they can be used to identify vowels, but, in practice, the SDP displays were so sensitive to minor details that every signature appeared unique (Peleg, 1998).

1.5 In vivo texture measurement The limitations in trying to mimic the events occurring during mastication using relatively simple instruments have long been appreciated. An alternative approach that has gained credence in recent years has been to attempt to record signals generated by or within the human subject that may relate to the texture of the food being masticated. 1.5.1 Electromyography (EMG) and associated techniques EMG involves the use of a polygraph to measure electrical signals generated in muscles that are active during mastication. For certain muscles that lie close to the surface of the skin, for example the masseter muscle, which is active during the chewing of solid foods, this activity can be related to a specific muscle. Other oral activity, for example tongue movement, is controlled by groups of muscles that are deeper-lying. Monitoring of signals from this latter type of musculature ideally requires implanted electrodes, whereas signals from the masseter muscle can be readily recorded using surface electrodes. Early attempts to use EMG in the study of food texture were limited by difficulty in interpreting the complex data patterns produced. In the absence of suitable computerised acquisition and analysis equipment, visual inspection of the raw data was carried out. For example, motor pauses (or silent periods) were more frequent with hard foods than with soft foods (Boyar and Kilcast, 1986). The development of more sophisticated EMG equipment and computer systems, however, has permitted much deeper analysis of EMG data and their relationship to food texture. An added potential advantage of this technique is studying changes in food texture in the mouth throughout the whole chewing


21

cycle. Research projects carried out at the Leatherhead Food Research Association (e.g. Eves, 1990; Kilcast and Eves, 1991) were carried out to investigate the potential use for EMG as a means of characterising food texture, and the way in which texture changes during mastication. Following this work, a number of papers have appeared in the literature in which EMG has been used to investigate food texture. Further studies on the use of EMG in the confectionery sector were reported by Smalls (1992), and indicated good correlations between EMG and Instron measurements. Applications to cheese texture were described by Jack et al. (1993). In this work, EMG was used in conjunction with sensory and instrumental measurements of the texture of a range of Cheddar cheeses, but inconsistency between subjects resulted in difficulties in correlating EMG and Instron measurements. In a review of texture measurements for use in product development, Jack et al. (1995) described the use of EMG in conjunction with other methods. Duizer et al. (1996) used a combination of EMG, timeintensity measurement and instrumental measurement to investigate beef tenderness. The results indicated that the effects of early mastication should be compared with the effects of late mastication. More fundamental aspects of the use of EMG in understanding the oral breakdown process have been reported by Brown and co-workers (e.g. Brown et al., 1994; Brown, 1995; Brown et al., 1998). These studies focused primarily on understanding the chewing behaviour of consumers rather than on texture measurement. EMG was used either as the sole technique, or in combination with synchronous measurement of jaw movement (kinesthesiology) by a set of transducers mounted on a head-frame to track the movement of a small magnet attached to the lower front incisors. Several oral techniques, including EMG, have also been used by Mioche and co-workers to study the mastication process (e.g. Mathevon et al., 1995; Peyron et al., 1996; Mioche and Martin, 1998). A more recent study within the EU HealthSense project has seen the use of EMG to investigate differences in chewing patterns between young and elderly populations (Kohyama et al., 2002). An unusual related technique for studying mastication behaviour has been reported by Jack and Gibbon (1995). The technique, electropalatography (EPG), is used to measure tongue movement during eating and swallowing, and comprises an artificial plate, moulded to the individual’s hard palate, embedded in which are 62 electrodes covering the entire palate surface. Tongue contact with the electrodes generates a signal that can be used to monitor the movement of the tongue. Experiments were carried out with liquid, semi-solid and gelled foods. The authors concluded that the technique could be used for liquid and semi-solid foods, but that bulky or sticky foods prevented the tongue making contact with the palate. 1.5.2 Sound emission Early studies on food-crushing sounds (Drake, 1963) showed that sounds from crisp foods differed from those of non-crisp foods, primarily in terms

22 Texture in food

of amplitude. Frequency and duration played a less important role. A subsequent paper (Vickers and Bourne, 1976) presented studies of the acoustical properties of tape-recorded biting sounds of wet and dry crisp foods. Amplitude–time plots indicated that both sound amplitude and the number of sounds produced in a given bite distance discriminated between different levels of crispness. A series of papers on crispness appeared subsequently (reviewed in Vickers, 1988). In one of this series (Vickers, 1985), it was shown that pitch was a useful parameter in distinguishing between crispness and crunchiness; crisp sounds tended to be higher in pitch than crunchy sounds. These extensive studies gave considerable improvements to the understanding of crispness, and associated work has continued in other laboratories. Dacremont et al. (1992) investigated the contribution of both air-transmitted and bone-conducted sound to the perception of crispness, crackliness and crunchiness in foods. In a further paper, Dacremont and Colas (1993) investigated whether a person’s perception of duration, loudness and pitch of biting sounds might be biased by the sight of food samples. The results suggest that the visual perception of foods has no influence on the loudness or pitch judgement; however, it does influence the judgement of sound duration during eating. This paper pointed out that sound propagation in air is different between an incisor bite, with lips open, and a molar chew, with lips closed. The contact area between food and teeth is different, and the amount of vibration through the bones is different. Although the bite sounds are different for the three textural types, the differences are not sufficient to distinguish them clearly. Roudaut et al. (1998) used acoustic emission in conjunction with sensory analysis, compression tests and Dynamic Mechanical Thermal Analysis (DMTA) to investigate the effect of water on the texture of crispy breads. One difficulty in appraising the practical relevance of the research into sound emission and the texture of foods lies in the difficulty in the use of descriptive terminology. The words crispy and crunchy are, in particular, commonly used without agreed definitions. Work carried out at Leatherhead Food International on the measurement of the texture of fruit and vegetables has taken a consumer-led approach, and has investigated the understanding of textural terms by consumers. Repertory Grid interviewing methods were used to elicit textural descriptions of fruit and vegetables by consumers, and to relate them to attributes developed by trained sensory panels (Kilcast and Fillion, 2001). The trained panel developed a sensory profile that included different attributes associated with the sounds emitted during mastication. Table 1.1 shows a comparison of term usage between consumers and panellists. Of the ten consumers interviewed, nine used the term ‘crunchy’ to describe the texture of the products, but only five mentioned the term ‘crispy’, while one did not mention ‘crispy’ or ‘crunchy’ at all. Those who mentioned both ‘crispy’ and ‘crunchy’ were always using the two terms with different meanings and usually found it easier to describe one against the other. There was generally a good agreement on the meaning of the term ‘crunchy’, with


23

Table 1.1 Definitions of crispy and crunchy attributes from consumer and panellist interviews

Number of subjects mentioning the attribute Assessment on front teeth only Assessment on back teeth only Number of times a descriptor was given: Hard, dense Sound Loud Low pitch High pitch Repetition of sound Brittle Snap, clean break Light Fresh Moist

Consumers*

Panellists**

Crunchy

Crispy

Crunchy

Crispy

9

5 1

11

10 6 2

1 8 8 2

6 3

8 10 4 8

9 4 8

1 3 2 3 1

5 3 8 6 1

* n = 11 ** n = 10

most definitions mentioning hardness and sound. Definitions for the ‘crispy’ attributes were more diverse, and were a combination of a snap clean break, a light texture and a sound, and were also associated with freshness, moistness and brittleness for some consumers. These studies have assisted in developing improved methods for texture measurement. Measurement of sound can be used to show the close relationship between the release of sound energy and failure events that result in detectable changes in force. Figure 1.5 shows a comparison of force–deformation and emitted sounds for celery subjected to an incisor probe test in a Stable Micro Systems TA-HD instrument (Kilcast, 2001). The upper curve shows the sound emitted during the test, and the lower curve shows the differential of the force curve, measured from the signal taken directly from the load cell; the close correspondence is clear. However, substantial practical difficulties remain in the measurement of sound during the test, primarily from interferences by the noise emitted from the motor drive of the test machine, and noise from extraneous sources.

1.6 Future developments Although it is increasingly being recognised that only sensory methods can give a complete picture of food texture, many needs can be identified for the

24 Texture in food

Sound level

Differential of the force curve

Fig. 1.5 Comparison of the emitted sound level with the differential of the force changes from cutting celery using an incisor probe.

instrumental measurement of key textural characteristics, and considerable research is being carried out to find new methods. There is particular interest in finding and validating non-destructive methods that can be used for quality control purposes, and for portable instruments that can be used directly in the field.

1.6.1 Selection procedures for sensory panellists Methods for the selection of panellists to ensure that they are capable of carrying out the required sensory tests are described in an ISO Standard (ISO, 1993). This recommends tests for acuity of visual perception, taste and odour perception, but not tests for texture perception ability, other than the generation of verbal descriptions. As part of a European Union-funded project on Healthy Eating for the Elderly, Fillion and Kilcast (2001) have developed tests designed to investigate texture perception and oral dexterity in the elderly. These tests are potentially of great value in screening panellists for use in food texture assessment, and are currently being adapted for this purpose.

1.6.2 Dynamic force/deformation methods The most common material parameters linked to firmness are the elastic properties of the product: hard materials have high values of the elastic modulus, while soft materials have lower values (Pitts et al., 1994). Abbott and Massie (1995) believed that approximate values of the elastic properties of produce were adequate for sorting, and that a dynamic force/deformation (F/D) test might be relatively simple to implement on a kiwifruit packing line. They compared various parameters from a low-frequency F/D test with


25

data from the Magness-Taylor puncture test, and found that a relatively simple firmness tester could be devised on the basis of 60-Hz vibrations. 1.6.3 Sound input methods Sonic resonance testing is a potential method for non-destructive measurement of firmness. Resonant frequencies of intact fruits and vegetables decrease with ripening and are directly related to their rigidity, firmness and ripeness (Abbott et al., 1968). Abbott et al. (1995) showed that sonic stiffness coefficients were satisfactory predictors of the firmness of Golden Delicious apples, when compared with the maximum force in compression or puncture tests. Mealiness in apples has been studied using acoustic impulse methods (Tu and de Baerdemaeker, 1996). Chen et al. (1996) and Armstrong et al. (1997) also used the resonant frequency of the vibrational modes to evaluate firmness of melons and peaches, respectively. Muramatsu et al. (1997) used pulsed sound transmission to measure the firmness of kiwifruit during ripening, and found good correlations with penetration testing. Low-frequency ultrasonics have been used (Nielsen and Martens, 1997) to evaluate the texture of carrots during cooking. Ultrasonic methods have also been used in the study of the texture of beef (Park et al., 1994), wafers (Juodeikiene et al., 1990) and crackers (Juodeikiene et al., 1994). 1.6.4 Spectroscopic and related methods A number of both direct and indirect methods for assessing texture have been reported. Near-infrared spectroscopy was investigated as a method for the quality control of asparagus (Garrido Varo et al., 2000). Laser scattering properties of apples were investigated as a means of measuring firmness (Cho and Han, 1999). Scatter parameters showed good correlations with compression testing, but strong varietal differences were found. Laser Doppler techniques have been used to measure firmness of fruit and to detect fruit disorders (Muramatsu et al., 1999). The Doppler method gave good correlations with conventional instrumental testing on peaches, pears and citrus fruit, and is claimed to have potential as a remote-sensing device. Textural investigations using nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) have also been reported. Schmidt (1999) has reviewed general uses of NMR methods, and Kim et al. (1998) have studied possible uses of both NMR and MRI for textural investigations. 1.6.5 Image analysis Image analysis techniques have been reported for measuring different aspects of food texture (Affeldt et al., 1994). The paper reviews methods for the measurement of surface texture, dimension and shape and surface cracks. Coles and Wang (1997), described the use of a Bread Quality Imaging System to measure the fineness of bread texture by detecting bubbles above a minimum

26 Texture in food

size. Measurement of the texture of corn puffs by image analysis has been described (Gao et al., 1999). Visually determined sensory scores were effectively predicted by the intensity band image features. A further aspect of visualisation was demonstrated by Kilcast et al. (1984) and Boyar et al. (1984), in which the photoelastic properties of gelatin were used to visualise stress distributions in gelatin during the course of penetration testing. Figure 1.6 shows an example of the interference fringes produced on testing a gelatin gel with a hemispherically ended probe viewed under sodium light between crossed polarisers. Whilst such imaging is restricted to a small number of transparent gels, computation techniques are now available to calculate stress distributions in such systems, but they do not appear to have been used in this context.

1.7 Conclusions Given the complex nature of food texture, developments in measurement techniques are likely to be evolutionary rather than revolutionary. Can the ideal instrumental test, that incorporates the essential elements of fundamental tests, imitative tests and empirical tests, be devised? Table 1.2 summarises

Fig. 1.6 Interference fringes produced on testing a gelatin gel with a hemispherically-ended probe, and viewed under sodium light between crossed polarisers.

Mainly solids

Mainly solids

Physiological5

Spectroscopic High

High/moderate

Moderate/high

Moderate/high

High

Moderate

Low

High

Initial costs

Moderate/high

Moderate

Low

Low/moderate

Low

Low

Low

Moderate/high

Running costs

2

Trained profile panels e.g. hand-held penetrometers 3 e.g. instrumental test rigs, General Foods Texturometer 4 Force–deformation devices under strictly defined operational conditions 5 Including EMG 6 Consumer relevance depends on ability to correlate test with subjective textural responses

1

Brittle solids

Solids

Sound emission

Sound input

Solids

All

All

Empirical2

Fundamental4

All

Sensory1

Imitative3

Food types

Class

Table 1.2 Summary of the main classes of texture measurement

Laboratory

Laboratory

Laboratory

Laboratory

Laboratory

Laboratory/QC

QC/production

Laboratory/QC/ production

Operating environment

Continuing

Continuing

Continuing

Continuing

Mature

Continuing

Mature/ continuing

Mature/ continuing

Development status

Moderate

Moderate/high

Low/moderate

Moderate/high

Low

Low/moderate

Low

High

Consumer relevance6

Measuring consumer perceptions of texture: an overview 27

28 Texture in food

the status of texture measurement methods at the time of writing. In recent years, an increasing number of novel techniques have been investigated, and more are being used in production/QC environments. A number of factors have contributed to this situation. Firstly, conventional force–deformation instruments have undergone considerable enhancement through the extensive use of computerised data acquisition and analysis software, and through the availability of a wider range of test rigs for specific purposes. Secondly, the increased interest in the mechanics of mastication has enhanced our understanding of oral processes and of their relationship to texture perception. Thirdly, the importance of texture as an important attribute driving consumer acceptance has stimulated further research, particularly in the search for smaller but reliable measuring devices that can be used outside the QC laboratory. However, in recognition of the importance of texture to the consumer, and in keeping with a food characteristic that is defined by the human senses, texture assessment is increasingly being carried out using trained sensory panels alongside the assessment of other organoleptic characteristics such as appearance, odour and taste.

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and KRIEGER B (1995) The contributions of sensory liking to overall liking: An analysis of 6 food categories, Food Quality and Preference, 6(2), 83–90. MURAMATSU N, SAKURAI N, YAMAMOTO D, NEVINS D J, TAKAHARA T and OGATA T (1997) Comparison of a non-destructive acoustic method with an intrusive method for firmness measurement of kiwi fruit, Postharvest Biology and Technology, 12(3), 221–8. MURAMATSU N, SAKURAI N, WADA N, YAMAMOTO D, TAKAHARA T and OGATA T (1999) Evaluation of fruit tissue texture and internal disorders by laser Doppler detection, Postharvest Biology and Technology, 15(1), 83–8. NIELSON M and MARTENS H J (1997) Low-frequency ultrasonics for texture measurement in cooked carrots (Daucus carota L), Journal of Food Science, 62(6), 1167–70+1175. O’MAHONY M (1979) Short-cut signal detection measures for sensory analysis, Journal of Food Science, 44(1), 302–3. O’MAHONY M (1986) Sensory Evaluation of Food. Statistical Methods and Procedures, New York, Marcel Dekker Inc. O’MAHONY M (1995) Who told you the triangle test was simple? Food Quality and Preference, 6(4), 227–38. OVERBOSCH P, AFTEROF W G M and HARING P G M (1991) Flavour release in the mouth, Food Reviews International, 7(2), 137–84. PARK B, WHITTAKER A D, MILLER R K and HALE D S (1994) Ultrasonic spectral analysis for beef sensory attributes, Journal of Food Science, 59(4), 697–701+724. PELEG M (1998) Extracting useful information from irregular and irreproducible mechanical and other signatures. In New Techniques in the Analysis of Foods. Ed. M H Tunick, New York, Kluwer, 37–52. PELEG M and NORMAND M D (1992) Symmetrized dot-patterns (SDP) of irregular compressive stress-strain relationships, Journal of Texture Studies, 23(4), 427–38. PEYRON M-A, MIOCHE L, RENON P and ABOUELKARAM S (1996) Masticatory jaw movement recordings: a new method to investigate food texture, Food Quality and Preference, 7(3-4), 229–37. PITTS M J, ABBOTT J A, ARMSTRONG P R, BROWN G K, BRUSEWITZ G H, DAVIS D C, DELWICHE M J, GALILI N, GAN-MOR S, HAUGH C G, MASSIE D R, MIZRACH A, NAHIR D, PELEG K, ROHRBACH R P, SARIG Y, SCHAARE P N, SCHNILOVITCH Z, SHMULEVICH I, STONE M L, STROSHINE R L and YOUNCE F L (1994) Sensing fruit and vegetable firmness. In Nondestructive Technologies for Quality Evaluation of Fruits and Vegetables: Proceedings of the International Workshop, Spokane, Washington, June 1993. Michigan, ASAE, 31–43. ROHDE F, nORMAND M D and PELEG M (1993) Characterization of the power spectrum of force-deformation relationships of crunchy foods, Journal of Texture Studies, 24(1), 45–62. ROUDAUT G, DACREMONT C and LE MESTE M (1998) Influence of water on the crispness of cereal-based foods: acoustic, mechanical and sensory studies, Journal of Texture Studies, 29(2), 199–213. SCHMIDT S J (1999). Probing the physical and sensory properties of food systems using NMR spectroscopy. In Advances in Magnetic Resonance in Food Science: Proceedings of the Fourth International Conference on Applications of Magnetic Resonance in Food Science, Norwich, September 1998. Ed. P S Belton, Cambridge, RSC, 79–94. SHAMIL S H, WYETH L J and KILCAST D (1992) Flavour release and perception in reduced-fat foods, Food Quality and Preference, 3(1), 51–60. SHEPHERD R and SPARKS P (1994) Modelling food choice. In Measurement of Food Preferences. Eds H J H MacFie and D M H Thomson, London, Blackie 202–26. SIM B J, LUCAS P W, PEREIRA B P and OATES C G (1993) Mechanical and sensory assessment of the texture of refrigerator-stored spring roll pastry, Journal of Texture Studies, 24(1), 27–44. SMALLS I (1992) Electromyography versus Instron texture measurement of confectionery products, Proceedings of the 46th Annual Production Conference, Hershey, April 1992. Medford, Pennsylvania Manufacturing Confectioners Association, 56–61. MOSKOWITZ H R

32 Texture in food and KAHN E L (1971) Consumer attitudes to and awareness of food texture 1: adults, Journal of Texture Studies, 2, 280–95. TOURSEL P (1996) Equipment for texture analysis, Process, 1122, 48–50. TU K and DE BAERDEMAEKER J (1996) Investigation of apple quality using instrumental methods, Agri-food Quality: an Interdisciplinary Approach; Proceedings of a Conference, Norwich, June 1995, Cambridge, RSC, 204–7. VICKERS Z M (1985) The relationships of pitch, loudness and eating technique to judgements of the crispness and crunchiness of food sounds, Journal of Texture Studies, 16(1), 85– 95. VICKERS Z M (1988) Evaluation of crispness In Food Structure – its Creation and Evaluation. Eds J M V Blanshard and J R Mitchell, Oxford, Butterworths, 433–48. VICKERS Z M (1991) Sound perceptions and food quality, Journal of Food Quality, 14(1), 87–96. VICKERS Z M and BOURNE M C (1976) A psychoacoustical theory of crispness, Journal of Food Science, 41(5), 1158–64. VINCENT J F V, JERONIMIDIS G, KHAN A A and LUYTEN H (1991) The wedge fracture test. A new method for the measurement of food texture, Journal of Texture Studies, 22(1), 45–57. SZCZESNIAK A S

2 Consumers and texture: understanding their perceptions and preferences J-F. Meullenet, University of Arkansas, USA

2.1 Introduction: problems with consumer descriptions of texture The British Standards Institution defined texture as an attribute of a substance resulting from a combination of physical properties and perceived by the senses of touch, sight and hearing (Brennan, 1980). From a consumer point of view, this definition probably makes little sense. In fact, the obscureness of the various definitions proposed and their disconnection with what consumers define as texture is a major challenge for product developers in the food industry today. This will be the focus of this chapter. We shall start with a simple example. In the recent past, our lab was involved in a project dealing with a processor of poultry meat. The project had to do with meat tenderness and how long you have to leave the breast meat on the carcass to achieve maximum tenderness. Various treatments were evaluated using a trained descriptive panel and the resulting profiles presented to the client. On the profiles were obscure attributes such as cohesiveness of mass, toothpack and toothpull in addition to more commonly known attributes such as hardness. The client pondered for a few minutes and finally asked: “So, where are the results for tenderness?” I had to explain that tenderness is an integrated term and that a trained panel uses several attributes that, all put together, contribute to a consumer’s perception of tenderness. This illustrates the fact that descriptive analysis is sometimes not a very actionable tool for food processors, if the relationship between the sensory attributes defined by a descriptive panel and the consumer’s perception of the texture in the product is not well understood. This is a common problem in sensory evaluation, and stems from the fact that consumers use

34 Texture in food

loosely defined integrated terms that sometimes do not have the same meaning to the whole population. This seems, at first, like an insurmountable problem. However, several techniques are available to elucidate the complex nature of the acceptance of food from a hedonic standpoint by consumers. This chapter will discuss the issue of bridging the gap between science (rheology or sensory) and the perception and acceptance of texture by consumers.

2.2 Investigating consumer descriptions of texture 2.2.1 Establishing the importance of texture to consumers The effects of appearance and flavor on consumer acceptance of foods have been extensively studied. However, there are fewer studies dealing with the effect of texture attributes on food acceptability. In 1971, Szczesniak assessed the apparent relative and a priori importance of texture and flavour on the acceptability of foods via a survey of 150 consumers. Surprisingly, the texture/ flavour index, a measure of the relative importance of texture in comparison to flavour was 0.89 for the group. This demonstrated that texture was an important attribute of food acceptance and that more attention should be given to it. Texture has also been reported to be especially important in foods that are bland in flavour, such as rice, or those that are crispy or crunchy, such as puffed cereals (Szczesniak,1990). One of the major issues with attempting to understand consumer responses is the fact that the language used by consumers is very different from words used by experts, such as sensory scientists, practising descriptive analysis to describe the texture of food products. This is well illustrated by the classification of texture characteristics proposed by Szczesniak (1963; see Table 2.1). As seen in Table 2.1, consumers (popular terms) use different words to refer to increasing intensities on a specific continuum. For example, hardness, which is the term used in descriptive analysis, is referred to by consumers as soft, firm or hard, depending on the intensity of the stimulus. The main problem is that those terms are mutually exclusive of each other and that consumers will associate specific foods with specific descriptors. For example, soft and crumbly is often more appropriate to consumers for describing the texture of foods (for example, some cheeses such as Feta) of very low hardness and cohesiveness (i.e. by the sensory and not engineering definition) while hard and brittle are more applicable to describing confectionary products such as candies. Although these examples are trivial, it implies that those involved with consumer testing have to carefully design questionnaires in order to fit the language of consumers. 2.2.2 One model fits all? There is today a body of knowledge developed on how specific texture attributes are governing the acceptability of particular food products. Textural

Consumers and texture

35

Table 2.1 Relations between textural parameters and popular nomenclature (Szczesniak, 1963) Mechanical characteristics Primary parameters Hardness Cohesiveness

Secondary parameters Brittleness Chewiness Gumminess

Viscosity Elasticity Adhesiveness

Popular terms Soft → Firm → Hard Crumbly → Crunchy → Brittle Tender → Chewy → Tough Short → Mealy → Pasty → Gummy Thin → Viscous Plastic → Elastic Sticky → Tacky → Gooey

Geometrical characteristics Class Particle size and shape Particle shape and orientation

Examples Gritty, Grainy, Coarse, etc. Fibrous, Cellular, Crystalline, etc.

Other characteristics Primary parameters Moisture content Fat content

Secondary parameters Oiliness Greasiness

Popular terms Dry → Moist → Wet → Watery Oily Greasy

attributes have been found to have both positive and negative impacts on consumer acceptance of foods (Szczesniak and Kahn, 1971). Universally liked texture attributes include crispness, crunchiness, juiciness and tenderness, while attributes such as toughness or sogginess are for the most part disliked. However, although these general rules provide useful information, they do not address specific products or various consumer groups. A simple example of this is cooked rice. Rice is a staple food in a large portion of the world and Asian cultures are very discriminating about the rice they eat. It is known that texture is a very important attribute of rice. Its two main characteristics are hardness/firmness and stickiness. Okabe (1979) studied the acceptability of rice by Japanese consumers. He reported that there were narrow regions of acceptability for cooked Japanese rice for both hardness and stickiness, and that consumers favoured sticky and tender rice. However, this observation needs to be confined to Japan as other Asian cultures have different expectations. For example, a study by Meullenet et al. (2000) reported on the acceptance of various rices by consumers primarily from South-east Asia and concluded that rice texture was not a very important driver of overall acceptance when compared to rice appearance and aroma. However, the same general rules (i.e. preference for tender and sticky rice) were confirmed. These results were further confirmed by work published by Suwansri et al. (2002) on the acceptability of Jasmine rice by US Asian consumers.

36 Texture in food

This shows that, unfortunately, the importance of texture attributes to the acceptance of food is both product and consumer group dependent. The conclusion is that texture should be considered in every product development process.

2.3 Tests and test procedures Before considering methods designed to develop an understanding of consumers’ perception and acceptance of texture, we should briefly consider the methodologies best suited for this type of endeavour. Consumer testing methods described in the literature vary greatly. However, one common denominator of all methods is the use of some type of rating scale, at least when the hedonic response to a product is assessed. The primary goal in designing scales for use with consumers has usually been to keep them easy to use and easily understandable by all. The type of information gathered from consumers is either related to the acceptance/preference for a food product or to the perception of sensory attribute intensity or appropriateness. The main three types of scales are category, line and magnitude estimation scales. Category scales are probably overall most broadly used followed by line and magnitude estimation scales, respectively. Category scales are those using a fixed number of possible responses. For use with consumers, verbal anchors are associated with some or all of the categories. The most common examples of these scales are the nine-point hedonic scale and the relative to ideal or just right scales. Line scales have been used as hedonic scales (Pangborn et al., 1989) or as just right scales (Shepherd et al., 1988), but they can also be used to assess the intensity perceived by consumers for a specific attribute. Line scales described in the literature have varied between 100 and 150 mm, and it is not clear whether the scale length has true impacts on the results.

2.3.1 Hedonic scales The most commonly used hedonic scale, at least in English speaking countries, is the nine-point hedonic scale (Jones et al. 1955). The main characteristics of the scale are that each category is associated with a verbal descriptor from “dislike extremely” to “like extremely” and that the scale has a neutral category “neither like nor dislike”. Although it has been widely used for almost 50 years, the scale has been equally criticised. The nine-point hedonic scale has been popular because of its simplicity, accuracy and precision while being criticised mostly for end effects (i.e. avoidance of extreme categories) and the lack of equal hedonic intervals between categories. The hedonic scale has been accepted by sensory professionals to infer consumer acceptance from “liking”, despite its flaws, because it provides internal validity (accurate and precise results of consumer liking) at the expense of external validity (relevance to the marketplace) as described by van Trijp and Schifferstein (1995). In


37

addition, it has been shown not to translate well into Spanish (Curia et al., 2001) and several Asian languages (Yeh et al., 1998). The most reported complaint is the different meaning of the word “extreme” in these languages. Some illustration of results obtained from hedonic ratings is given in Fig. 2.1, which is a frequency plot of responses given in each category for various products. These results point out several of the challenges associated not only with the nine-point hedonic scale but also with most other hedonic scales. First is the type of distribution yielded by hedonic scales. With scales featuring a neutral category, it is often seen that the “neither like nor dislike” category is grossly under-represented. An obvious observation is that the distributions do not closely follow a normal distribution, rendering the use of analysis of variance questionable. Figure 2.1 also points out that product C was not perceived uniformly by the consumer group studied. Under these conditions, the mean hedonic scores for products B and C would be very similar although the distributions of the scores are quite dissimilar, leading to potentially erroneous conclusions. These results point out the need, when dealing with consumer data, to assess the distribution of the responses and to ensure that the distributions are unimodal (i.e. that a relative consensus exists among the group about the hedonic status of the products).

2.3.2 Just-about-right scales Another type of scale used for the optimisation of attribute levels in food products is the just-about-right (JAR) scale. Individual samples can be rated 35

Number of responses

30

A B C

25 20 15 10 5 0 Dislike Dislike very Dislike Dislike extremely much moderately slightly

Neither Like Like Like very dislike nor slightly moderately much like

Like extremely

9-point hedonic scale

Fig. 2.1 Distribution of consumer acceptance scores for texture of three candy bars. The question to consumers was worded as follows: Considering only the texture of this product, which of these statements best describes your impression?

38 Texture in food

on a JAR scale as “too weak,” “too strong” or “just-about-right” along a particular continuum such as tenderness or crispness. Data that are normally distributed around the center of the scale (i.e. the “just-about-right” point) are indicative of an optimized level of the continuum or attribute. It is important in this technique to examine the distribution of the raw data. For example, a consumer panel might contain one segment that prefers products with one set of sensory characteristics and another segment that prefers the same product with a completely different set of sensory characteristics. This is illustrated in Fig. 2.2 which is a distribution of scores on a JAR scale for the crispness of cheese sticks. In this example, the appropriateness of the level of crispness in a fried cheese stick appetizer was assessed with a panel comprising 180 individuals. The data clearly shows that it is not distributed around the just right score, although the mean value of scores would be close to the JAR point. Instead, the distribution of the data is bimodal with roughly equal numbers of consumers who found the product to be too soggy or too crispy. The appropriateness of the cheese texture for the same product was evaluated by the same consumer panel and the distribution of scores is given in Fig. 2.3. For cheese texture, there was a much greater consensus among consumers as to the appropriateness of the level of cheese firmness. This is in sharp contrast to results from Fig. 2.2. These two examples illustrate the importance of data distribution assessment of consumer data before more advanced analyses are performed. This is especially true when means are to be used for further analyses such as special cases of preference mapping. These methods will be discussed later in this chapter.

60

Number of responses

50 40 30 20 10 0 Much too soggy

Too soggy

Just about right Too crisp Crispness JAR scale

Much too crisp

Fig. 2.2 Distribution of the JAR scores for crispness of a fried cheese stick product.


39

140

Number of responses

120 100 80 60 40 20 0 Much too soft

Too soft

Just about right

Too firm

Much too firm

Cheese texture JAR scale

Fig. 2.3 Distribution of JAR scores for the cheese texture of a fried cheese stick product.

2.4 Understanding consumer preferences Moskowitz and Jacobs (1987) described several methods for developing an understanding of the importance of texture in determining the relative importance of food texture to the product acceptance. The simplest method they described is the direct rating of importance. With this method, consumers are asked to rate the importance of various product attributes, including texture, on a scale from 0 to 100. By comparison of mean values for the various attributes, one can evaluate if texture plays an important role in determining overall acceptance. However, this method has limitations in that it does not involve product testing and relies on the a priori judgement of the subject. It is similar to that employed by Szczesniak and Kahn (1971). A more popular approach has been to correlate attribute ratings to acceptance of foods. Several approaches have been described. Researchers often look at correlations between acceptance scores and intensity ratings in an attempt to assess the relative importance of specific texture attributes in consumer acceptance. Although many have used correlations to understand consumer response, there are some limitations to this approach. This is due to the fact that correlations evaluate linear relationships, and it is well known that attribute intensity and acceptance are not necessarily linearly related. Although it is probably true that, in cultures such as that of the USA, the sweeter the cola beverage the more acceptable it is, this is not necessarily true for most food products.

40 Texture in food

2.4.1 Linear relationships Example: Poultry breast meat One example of linear relationship between perceived intensity and acceptance of specific attributes is given in Fig. 2.4. In this consumer study, 80 panellists were presented with ten treatments of poultry breast meat (i.e. deboned at various times post mortem) representing a vast array of tenderness intensities. Among other questions, consumers were asked to rate the acceptance of the product’s texture on a nine-point hedonic scale (1 = dislike extremely and 9 = like extremely) and their perceived intensity of the various treatments on a nine-point category scale anchored at the extreme categories with extremely tough and extremely tender. Results show that the relationship between perceived intensity and acceptability was linear and that the more tender the product, the more acceptable it was to this group of consumers. This is not to say that in a different context, the results could not have been different. In this study, no common additives designed to enhance the tenderness of poultry meat were added. One can imagine that a product so tender that it became “mushy” could be produced and that its acceptance would be lower than for products of optimal tenderness. However, in many practical cases related to texture attributes of food the relationship between acceptance and perceived intensity will be linear.

2.4.2 Non-linear relationships When the relationship is known to be non-linear, it is often parabolic where the liking for a product will first increase with increasing levels of the

8.0

R 2 = 0.88

Perceived tenderness intensity

7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

Acceptance of poultry breast meat tenderness

Fig. 2.4 Relation between the acceptance of poultry breast meat tenderness and its perceived tenderness intensity.


41

attribute, peak and then decrease as the intensity of the attribute continues to increase. The peak has been described as the bliss point, and the shape of the curve, known as the inverted U or L function, relating hedonic response to stimulus intensity was first proposed by Wundt (Moskowitz and Jacobs, 1987). It should be noted that the relationships between the sensory experience or the hedonic response and the physical stimulus are governed by very different laws as the relationship between perceived intensity and stimulus intensity can usually be modelled by a power law (Stevens, 1975).

2.4.3 Single attribute analysis A common model described by Moskowitz (1980) is of the quadratic form: liking = A + B(intensity) + C(intensity)2

[2.1]

The main advantage of the equation is that it allows liking to follow a parabolic response, but does not force it if the relationship is not of that form. The relative importance of the attribute can be calculated as the absolute value of the partial derivative of Eq. [2.1] and is defined as: importance = |B + C(intensity)|

[2.2]

This equation implies that the importance of a single attribute for liking is dependent on the level of that attribute, which is a far superior approach to that of the simple correlations. One limitation of this approach is that liking is related to a single sensory attribute, disregarding the fact that multiple sensory texture attributes may be present in the product and that they may all influence liking. Even if multiple attributes are used to model liking, the correlation among the predictors is not being considered.

2.4.4 Multivariate analysis techniques Other researchers have considered multivariate analysis techniques to assess the relationship between liking and texture attributes ratings. Although it is not the primary purpose of this chapter to discuss these techniques in detail, some of the basics should be covered as they often help to elucidate the likes or dislikes of consumers for specific food products. Preference mapping techniques have been described in detail in publications such as that of MacFie and Thomson (1999). Their development came from the inability of sensory scientists to fully understand consumer reactions (acceptability) to specific food products. This comes from the simple fact that consumers have a limited vocabulary when it comes to describing perceived sensory attributes of food. As a general rule, they confine themselves to commenting about very general terms or to making vague statements such as “I disliked the texture of this product”. It is obvious that this type of data is not very actionable. To remedy these problems, researchers will usually try to ask more pointed questions about specific product attributes. For example, for

42 Texture in food

dairy beverages or nutritional supplements, one may consider asking about two main texture attributes, thickness/consistency and chalky or smooth. If the attribute ratings are that of perceived intensities or appropriateness level and a sufficient number of products (six or more) are evaluated by consumers, it is then possible to predict the acceptance of a product (i.e. overall acceptance or acceptance of texture) from attribute ratings. Figure 2.5 is an illustration of such a concept. In this study, 10 chocolate nutritional beverages were evaluated by 150 consumers. The objective of the study was to identify the drivers of product acceptance, including important texture attributes. The nine-point hedonic scale was used to assess the overall degree of liking by consumers while the appropriateness of the level of color, chocolate flavour, sweetness, tartness, smoothness and thickness was assessed using JAR scales. Anchors used for the JAR scales are given in Table 2.2. PC2 (20 %, 17 %) 0.8 C Thickness

0.6 H 0.4 B 0.2

I

G 0 –1.2

–1

–0.8

–0.6

–0.4

–0.2 0 –0.2 –0.4

F J

0.2

0.4

Color

Acceptance A Smoothness

0.6 0.8 Sweetness

PC1 (57 %, 59 %)

1

Chocolate

–0.6 Tartness E

–0.8

D

–1 –1.2

Fig. 2.5 Partial least squares regression of acceptability versus diagnostic ratings for 10 chocolate nutritional beverages. Table 2.2 Diagnostic attribute scales used to assess the appropriateness of various attributes of nutritional beverages Color Chocolate Sweetness Tartness Thickness Smoothness

Much too light Much too weak Not nearly sweet enough Not nearly tart/ sour enough Much too thin

Too light

Not sweet enough Not tart/ sour enough Too thin

Much too coarse/chalky

Too coarse/ chalky

Too weak

Just about right Just about right Just about right Just about right Just about right Just about right

Too dark Too strong Too sweet Too tart/ sour Too thick Too smooth

Much too dark Much too strong Much too sweet Much too tart/sour Much too thick Much too smooth


43

Consumer data was then analyzed by partial least squares (PLS) regression to predict consumer acceptance scores from attribute ratings. Results of this analysis are given in Figs 2.5 and 2.6. Figure 2.5 shows the product map, which is a spatial representation of products, acceptance and diagnostic attribute ratings. This map allows the interpretation of several pieces of information. First it allows the examination of the relationship between the acceptance dimensions and the diagnostic ratings. Figure 2.5 is a twodimensional scatter plot of X (attribute ratings) loading weights and Y loadings (acceptance) for two specified components from PLS. It shows the importance of the different variables for the two components selected and can thus be used to detect important predictors and understand the relationships between X (i.e. attribute ratings) and the Y-variable (i.e. acceptance). To interpret the relationships between attribute ratings and the product acceptance, start by looking at your response (Y) variable. Predictors (attribute ratings), projected in roughly the same direction from the center as a response, are positively linked to that response. Predictors projected in the opposite direction have a negative link. Predictors projected close to the center are not well represented in that plot and cannot be interpreted. In our example, the projection of the diagnostic attributes onto the acceptance vector allows the determination of the most important drivers of liking. Attributes which load most highly on the acceptance vector, either positively or negatively, are the most important drivers of acceptance. In our example, the most important drivers seemed to be smoothness and sweetness as positive drivers (i.e. those for which high attribute ratings resulted in greater acceptance) and tartness and thickness as negative drivers (i.e. those for which high attribute ratings resulted in lower acceptance).

Weighted regression coefficients

1.50E-01

1.00E-01

5.00E-02

0.00E+00

–5.00E-02

–1.00E-01 Color

Chocolate

Sweetness

Tartness

Thickness

Smoothness

Fig. 2.6 Weighted regression coefficients for the PLS model predicting overall acceptance from diagnostic attribute ratings for 10 nutritional beverages.

44 Texture in food

This interpretation is confirmed by Fig. 2.6 which is a plot of the corresponding weighted regression coefficients for a PLS model with two factors. Weighted regression coefficients rather than raw are useful when the relative importance of a specific predictive variable needs to be determined. In Fig. 2.6, it is clear that smoothness and sweetness are the most important positive determinants of product acceptance while thickness and tartness are the only negative drivers of acceptance. In addition, predictive attribute variables close to each other in the loading plot (Fig. 2.5) will have a high positive correlation if the two components explain a large portion of the variance of X. The same is true for variables in the same quadrant lying close to a straight line through the origin. Variables in diagonally-opposed quadrants will have a tendency to be negatively correlated. In our example, sweetness and smoothness are highly correlated while chocolate and thickness were negatively correlated. One should be reminded that correlations do not imply causality and that they are often a function of the product set chosen for the experiment. The overlay of product loadings in the space also allows for the interpretation of the product’s sensory dimensions using a multivariate approach. The plot can be used to interpret the products’ sensory properties. Look for variables projected far away from the center. Samples lying in an extreme position in the same direction as a given variable have large values for that variable; samples lying in the opposite direction have low values. For example, product A has the highest smoothness while product C has the highest thickness.

2.5 Challenges to understanding consumer preferences Although this simple example seems to provide the necessary elements for a product developer to optimize this type of product, it is rarely the case in practice. This is due to the fact that, to gain valuable information and a complete picture of the drivers of liking, one has to get several things right in the design of the experiment. First, the right questions need to be asked of consumers, and that is where the greatest challenge lies. The major limitation is that consumers have a very limited vocabulary and that words within that vocabulary do not necessarily have the same meaning for all. The example previously described is a good illustration of this phenomenon. The attribute smoothness and the JAR scale developed to establish its level of appropriateness in the products tested was the result of long debates with the industrial client for which the test was performed. The debate centred around the fact that smoothness and chalkiness are not necessarily mutually exclusive of each other. This seems especially true since the temporal perception of both attributes is not the same. While smoothness is perceived while manipulating the product in the oral cavity, chalkiness is often perceived most intensely after swallowing. In addition, the meaning of chalkiness to consumers can vary greatly as part of the population, at least in the USA, associates chalkiness as a texture


45

attribute while others think most readily of chalkiness as a visual attribute such as that of chalky rice or halibut. It is obviously of utmost importance for consumers to understand the scales they are asked to use, and confusion in terminology is often a difficult problem to overcome. In the previous example, it was decided that the scale anchors should state coarse/chalky instead of simply chalky although one could argue the use of the word coarse rather than gritty. Because of the challenges associated with understanding consumer responses and communicating effectively with them, many have shied away from trying to gather detailed information about the quality of the products tested from consumers. Many studies with consumers are now strictly designed to assess the overall degree of liking of food products. This leaves sensory scientists with little information to understand the underlying reason for the liking or disliking of product sensory qualities by consumers. The researcher is then left with explaining consumer liking from other sources of data.

2.5.1 External preference mapping External preference mapping is a technique designed to explain consumer results from data not generated by consumers (i.e. external data). The most common source of external data is sensory profiling data generated by trained descriptive panels. However, for texture, rheological data can also be used. We will give here several examples of such methods. Example 1: Jasmine rice In Suwansri et al. (2002), external preference mapping principles were applied to understanding the acceptance of Jasmine rice by Asian consumers living in the USA. Since the Asian population in the USA is very discriminating about the quality of the rice they consume, there is a need to assess the likes and dislikes of this population segment and to evaluate the key sensory differences between domestic and imported aromatic rice. The objectives of this study were to assess the acceptability of both domestic and imported Jasmine rice by US Asian consumers and to correlate the US Asian consumers’ acceptance of Jasmine rice to descriptive sensory data so that acceptance drivers could be identified. Fifteen commercial “Jasmine” rices consisting of three domestic varieties and 12 imported products were evaluated by a trained sensory panel and by 105 Asian families. An in-home-use test was used to gather consumer data of the 15 products, including acceptance data of rice texture which is usually a major driver of acceptance. A nine member panel, well trained in descriptive analysis of rice, evaluated five visual, 16 flavour and 11 texture attributes for all 15 Jasmine rice samples. Visual, flavour and texture lexicons were developed by the trained panellists during three orientation sessions (Meullenet et al., 1998). Results showed that this consumer group preferred imported over domestic

46 Texture in food

Jasmine rice. However, the univariate analysis of the descriptive data did not clearly point out the weaknesses of the domestic Jasmine products. The use of external preference mapping clearly identified the differences between domestic and imported products. As can be seen on Fig. 2.7, a bar chart of weighted regression coefficients for the descriptive attributes used to predict texture acceptance scores, there are two main constructs, hardness and stickiness, describing the texture of cooked rice. Manual stickiness, initial cohesion, adhesion to lips, cohesiveness, cohesiveness of mass and toothpull were identified as positive drivers of acceptance. From the texture lexicon, it is clear that these attributes are all related to the stickiness of rice. Hardness and roughness of mass, attributes that are usually highly correlated to each other, were found to be negative drivers of texture acceptance by consumers. These two attributes are indicative of firmness in rice. From this data, it could be implied that Asian consumers of Jasmine rice like it to be sticky and less firm. Although this is true in this particular context, one would have to be cautious about generalizing this type of result. This is definitely true in this instance as Jasmine rice is classified as a long grain rice not particularly known for being sticky. A previous study which was performed with a similar consumer group on a much wider range of rice products indeed drew different conclusions (Meullenet et al., 2000) as both hardness and stickiness (or attributes related to these two constructs) were negative drivers of consumer acceptance. This shows that the results obtained from preference maps are highly dependent on the samples selected for testing. In the case of Suwansri et al. (2002), the samples were carefully selected to represent products from a niche market. This allowed the identification of the sensory weaknesses of Jasmine rice samples produced in the USA and provided insight for rice breeders for improving the existing cultivars.


0.3

0.2

R 2 = 0.61 RMSEP = 0.39 Manual Initial Adhesion stickiness cohesion to lips

Cohesiveness Cohesiveness of mass Toothpull

0.1

0

–0.1 Hardness

Roughness of mass

–0.2 Texture attributes

Fig. 2.7 Weighted regression coefficients for the PLS predicting the acceptance of cooked white rice texture from texture attributes evaluated by a descriptive panel.


47

Example 2: White corn tortilla chips Another example of external mapping applied to understanding the drivers of acceptance for the texture of white corn tortilla chips is that published by Meullenet et al., 2002. In that study, 11 white corn tortilla chip products were evaluated by a group of heavy users of the product. In addition, a descriptive panel profiled the products for appearance, flavor and texture attributes. Figure 2.8 gives the weighted regression coefficients for the prediction of the acceptance of texture in the 11 products by consumers from the texture profiles generated by the panel. A total of 13 attributes were assessed by the descriptive panel. However, it is unlikely that all of these attributes are contributors to liking by consumers. Therefore, for multivariate regression, statistical techniques need to be employed to reduce the number of predictors to those that are significant contributors. The approximate uncertainty variance of the regression coefficients can be estimated and a t-test performed for each element relative to its estimated uncertainty variance, giving the significance level for each parameter. All parameters with p < 0.05 were retained in the model. This allows for removal of independent variables either not influencing the prediction or creating noise in the model, a procedure which reduces “the uncertainty in the prediction models” and, in most cases, improves the validation statistics. In this study, only important variables selected by the Jack-knife optimisation method (i.e. hardness, crispness, loose particles and oily/greasy film) were included in the predictive models to predict consumer acceptance. R2 and RMSEP (root mean square of prediction) were 0.96 and 0.34, respectively, for the optimal PLS model predicting consumer texture acceptance from descriptive texture attributes. The weighted regression coefficients (Fig. 2.8)


0.6 0.4 0.2 0 –0.2 –0.4 –0.6

Hardness

Crispness

Loose particles

Oily/greasy film

Attributes

Fig. 2.8 Weighted regression coefficients for the PLS predicting the acceptance of white corn tortilla chips texture from texture attributes evaluated by a descriptive panel.

48 Texture in food

show that the first bite attributes of hardness and crispness, surface characteristic of loose particles and residual characteristic of oily/greasy film were important contributors to the consumer acceptance of tortilla chip texture. Crispness and loose particles contributed to increased consumer acceptance of texture while hardness and oily/greasy film decreased consumer acceptance. Crispness is one of the most important texture attributes for potato chips (Smith, 1975; Segnini and Dejmek, 1999). Since tortilla chips and potato chips are similar salty snacks, it was expected that crispness of tortilla chips would positively contribute to overall acceptance.

2.6 Future trends Although it is difficult to foresee the future of this type of research, one certain fact is that consumers will remain difficult to understand. This implies that progress toward understanding consumer perception and acceptance of food texture will rely on the development of novel methods of data analysis and of improved methods for probing consumers. Many have documented the limitations of the internationally popular ninepoint hedonic scale developed by Peryam and Girardot (1952). These include the inequality of intervals between the semantic labels (Jones et al., 1955; Moskowitz and Sidel, 1971; Moskowitz, 1980), the presence of the neutral category (neither like nor dislike) and shortcomings of bipolar attitude scales (Olsen, 1999), and the central tendency (i.e. avoidance of end categories) issues (Stevens and Galanter, 1957). In the recent past, several researchers (Schutz and Cardello, 2001; Henderson and Shewfelt, 2002; Villanueva et al., 2002) have proposed alternatives to the scale widely used now for over 50 years. The most significant contribution is probably that of Schutz and Cardello (2001) who used modulus free magnitude estimation to develop a labelled affective magnitude (LAM) scale using the original labels from the nine-point hedonic scale. The results showed that the problems associated with the nine-point hedonic scale were minimised and that the data was shown to closely follow a normal distribution. This recent development provides an effective alternative to the problematic nine-point hedonic scale and the difficult to implement magnitude estimation scales. Another recent development is the application of alternative statistical methodologies for performing preference mapping. Because preference mapping is critical to understanding consumer acceptance of food texture, these methods have the potential to make a significant contribution. The methods described to date mostly rely upon deterministic modelling where the data dealt with is assumed to be normal. Several examples given in this chapter have demonstrated that this assumption is often violated. Consumer acceptance responses are usually ordinal categories. Ordinary least square (OLS) regression can be used for preference modeling when the ordinal response (Y) is treated as a continuous variable. As a result, with OLS


49

regression models, the mean scores of the ordinal response and the information on the structure (frequencies) of the ordinal response are lost. From a research point of view, the information from the structure of the ordinal response is often more meaningful and useful than that from the mean scores of the response. OLS regression requires that the distribution of error be normal. However, for the limited number of response categories used in consumer testing, or if the nine response categories (i.e. nine-point hedonic scale) are collapsed to five categories (i.e. end of scale avoidance), the normal distribution assumption will not be satisfied. In addition, OLS regression may produce extreme predictions that may be out of the categorical range. These drawbacks have limited the use of OLS regression in preference modeling. Proportional odds models (POM) do not have these limitations and could be an alternative for preference modeling. They are widely used in categorical data analysis in health science (Agresti, 2002) and have recently been applied to sensory data from a study on consumer acceptance of canola oil (VaiseyGenser et al., 1994), qualitative studies of food choice (Tepper et al., 1997) and consumer acceptance of oca cultivars (Sangketkit et al., 2000). The major advantages of the proportional odds model analysis are that it can apply to ordinal categorical responses, model the structure (frequencies) of the categorical responses and estimate the mean scores of the responses. Its invariance to choice of response categories is also an advantage (Agresti 2002). However, there exists little information on the application of proportional odds models for internal and external preference modeling besides that published by Meullenet et al. (2003), Malundo et al. (2001) and Jones and Wang (2000). Although PLS regression and POM are two commonly used methods in preference mapping, if the predictor variables are “JAR” scores, these linear models seem not to be appropriate, at least in most cases, because the relationship between the response and the JAR predictors is no longer linear. Multivariate adaptive regression splines (MARS), developed in 1991 by world-renowned Stanford physicist and statistician Jerome Friedman (Friedman, 1991), can automate variable selection as well as model selection. It has been proven effective in a variety of learning problems and is competitive with neural networks and non-parametric regressions (Dwinnell, 2000). MARS can be used to uncover underlying non-linear relationships between response and predictors in a piecewise linear regression function, which could be useful for modeling the JAR data. It is only in very recent years that MARS has become widely known in the data mining and business intelligence communities due to the availability of the commercial MARS software program (MARS, 2001). Recently, MARS was applied to the prediction of consumer acceptance of fried cheese stick texture from JAR data. One of the main advantages of MARS is its ability to determine the effect that JAR scores on both sides of the just right level have on the response variable (i.e. acceptance of texture). Figure 2.9 shows that for crispness, the drop rate was faster over the region

50 Texture in food 3

Texture acceptance

Texture acceptance

4 3 2 1

2

1

0

0 0

1

2 3 Crispness score (a)

4

5

0

1 2 3 4 Cheese texture score (b)

5

Fig. 2.9 Contributions to texture acceptance from crispness (a) and cheese texture (b) JAR scores.

of 3 to 5 than over the region of 3 to 1, suggesting that “too crispy” was more harmful to the acceptance of texture than “not crispy enough”. For cheese texture (i.e. 1 = much too soft/melted, 5 = much too firm/not melted), the drop rate was slower over the region of 3 to 5 than over the region of 3 to 1, implying that “too soft/melted” had more detrimental effects on texture acceptance than “too firm/not melted”. MARS, similarly to PLS regression, has the ability to determine the relative importance of variables toward the prediction of the response variable (100 and 96.73 % for crispness and cheese texture, respectively). Crispness had slightly more contribution to determining texture acceptance than did cheese texture. The R2 between the observed and predicted mean scores of texture acceptance was 0.95, indicating that the fitted MARS model could accurately predict the mean score for texture acceptance.

2.7 Conclusions Although a considerable amount of research has been done on food texture, most of this work has focused on the development of sensory and rheological methods designed to assess the effects of parameters such as processing conditions or formulation on food texture. The number of studies dealing with consumers and their expectation of texture in various food products has been limited. In a sense, texture remains the overlooked attribute (Szczesniak, 1990). However, the techniques used for understanding consumers have vastly improved and, with methods such as preference mapping and the like, there is an opportunity to expand studies focusing on consumers’ perception, expectation and acceptance of and preference in food texture.


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2.8 References AGRESTI A

(2002) Categorical data analysis (Second Edition). New York, Wiley.

BRENNAN J G (1980) Food texture measurement. In Developments in Food Analysis Techniques-

2. Ed. R D King, London, Applied Science Publishers, 1–78. and MARGALEF M I (2001) How Argentine consumers understand the Spanish translation of the 9-point hedonic scale, Food Qual. Pref. 12, 217–21. DWINNELL W (2000) Exploring MARS: an alternative to neural networks, PC AI, 14(1), 21. FRIEDMAN J (1991) Multivariate adaptive regression splines, The Annals of Statistics, 19, 1–141. HENDERSON J D and SHEWFELT R L (2002) Evaluation of scales to measure consumer acceptability, 2002 IFT Annual Meeting Book of Abstract, Anaheim, CA. JONES B and WANG J (2000) The analysis of repeated measurements in sensory and consumer studies, Food Qual Pref, 11, 35–41. JONES L V, PERYAM D R and THURSTONE L L (1955) Development of a scale for measuring soldiers’ food preferences, Food Res, 20, 512–20. MACFIE H J H and THOMSON D M H (1999) Measurement of Food Preferences, Gaithersburg, MD Aspen. MALUNDO T M M, SHEWFELT R L, WARE G O and BALDWIN E A (2001) An alternative method for relating consumer and descriptive data used to identify critical flavor properties of mango (mangifera indica L.), J Sens Stud, 16, 199–214. MARS (2001) MARS User’s Guide, Salford Systems. MEULLENET J F, GROSS J, MARKS B P and DANIELS M (1998) Sensory profiling of cooked rice and its correlation to instrumental parameters using an extrusion cell, Cereal Chem, 75, 714–20. MEULLENET J-F, GRIFFIN V K, CARSON K, DAVIS G, DAVIS S, GROSS J, HANKINS J A, SAILER E, SITAKALIN C, SUWANSRI S and VASQUEZ CAICEDO A L (2000) External rice preference mapping for Asian consumers living in the United States, J Sens Stud, 16, 73–93. MEULLENET J-F, XIONG R, MONSOOR M, BELLMAN-HORNER T, ZIVANOVIC S, DIAS P, FROMM H and LIU Z (2002) Preference mapping of commercial toasted white corn tortilla chips, J Food Sci, 67, 1950–57. MEULLENET J-F, XIONG R, HANKINS J A, DIAS P, ZIVANOVIC S, MONSOOR M A, BELLMAN-HORNER T, LIU Z and FROMM H (2003) Modeling preference of commercial toasted white corn tortilla chips using proportional odds models, Food Qual Pref, 14, 603–14. MOSKOWITZ H R (1980) Psychometric evaluation of food preferences, J Foodservice Systems, 1, 149–67. MOSKOWITZ H R and JACOBS B E (1987) Consumer evaluation and optimizaton of food texture. In Food Texture: Instrumental and Sensory Measurement. Ed. H R Moskowitz, New York, Marcel Dekker Inc, 293–328. MOSKOWITZ H R and SIDEL J L (1971) Magnitude and hedonic scales of food acceptability, J Food Sci, 36, 677–80. OKABE M (1979) Texture measurement of cooked rice and its relationship to eating quality, J Text Stud, 10, 131. OLSEN S O (1999) Strength and conflicting valance in the measurement of food attitudes and preferences, Food Qual Pref, 10, 483–94. PANGBORN R M, GUINARD J X and MEISELMAN H L (1989) Evaluation of bitterness of caffeine in hot chocolate drink by category, graphic and ratio scaling, J Sens Stud, 4, 31–53. PERYAM D R and GIRARDOT N F (1952) Advanced taste-test method. Food Eng., 24, 58–61. SANGKETKIT C, SAVAGE G P, MARTIN R J, SEARLE B P and MASON S L (2000) Sensory evaluation of new lines of oca (Oxalis tuberosa) grown in New Zealand, Food Qual Pref, 11, 189–99. SCHUTZ H G and CARDELLO A V (2001) A labeled affective magnitude (LAM) scale for assessing food liking/disliking, J. Sens. Stud, 16, 117–59. CURIA A V, HOUGH G, MARTINEZ M C

52 Texture in food and DEJMEK P (1999) Relationship between instrumental and sensory analysis of texture and color of potato chips, J Text Stud, 30, 677–90. SHEPHERD R, GRIFFITHS N M and SMITH K (1988) The relationship between consumer preferences and trained panel responses, J Sens Stud, 3, 19–35. SMITH O (1975) Potato chips. In Potato Processing. Eds W F Talbut and O Smith, Westport, CT, Avi Publishing Co, 305–402. STEVENS S S (1975) Psychophysics: an Introduction to its Perceptual, Neural, and Social Aspects, New York, John Wiley. STEVENS S S and GALANTER E H (1957) Ratio scales and category scales for a dozen perceptual continua, J Exp Psych 54, 377–411. SUWANSRI S, MEULLENET J-F, HANKINS J A and GRIFFIN K (2002) Preference mapping of domestic/ imported jasmine rice for US Asian consumers, J Food Sci, 67, 2420–31. SZCZESNIAK A S (1963) Classification of textural characteristics, J Food Sci 28, 385. SZCZESNIAK A S (1971) Consumer awareness of texture and of other food attributes, J Text Stud, 2, 196. SZCZESNIAK A S (1990) Texture: is it still an overlooked attribute? Food Tech, 9, 86–95. SZCZESNIAK AS AND KAHN E L (1971) Consumer awareness of and attitudes to food texture, J Text Stud, 2, 280–950. TEPPER B J, YOUNG S C and NAYGA R M (1997) Understanding food choice in adult men: influence of nutrition knowledge, food beliefs and dietary restraint, Food Qual Pref, 8(4), 307–17. VAISEY-GENSER M, MALCOLMSON L J, RYLAND D, PRZYBYLSKI R, ESKIN NAM and ARMSTRONG L (1994) Consumer acceptance of canola oils during temperature-accelerated storage, Food Qual Pref, 5(4), 237–43. VILLANUEVA N D M, DA SILVA M A A P and PETENATE A J (2002) Performance of the selfadjusting and hybrid hedonic scales in the generation of internal preference maps, 2002 IFT Annual Meeting Book of Abstract, Anaheim, CA. VAN TRIJP H C M and SCHIFFERSTEIN H N J (1995) Sensory analysis in marketing practice: comparison and integration, J Sen Stud, 10, 127–47. YEH L L, KIM K O, CHOMPREEDA P, RIMKEEREE H, YAU N J N and LUNDAHL D S (1998) Comparison in use of the 9-point hedonic scale between Americans, Chinese, Koreans and Thai, Food Qual and Pref, 9, 413–19. SEGNINI S

3 Texture and mastication A. C. Smith, Institute of Food Research, UK

3.1 Introduction Texture perception is an important factor in consumer sensory appreciation (Wilkinson et al., 2000). Bourne (2002) defined the textural properties of a food as the group of physical characteristics that: 1 arise from the structural elements of the food; 2 are sensed by the feeling of touch; 3 are related to the deformation, disintegration and flow of food under force; 4 are measured objectively by functions of mass, time and distance. Brennan (1989) defines perceived texture as the attribute of a substance resulting from a combination of physical properties and perceived by the senses of touch, sight and hearing. Figure 3.1 shows the involvement of the various senses in eating. This chapter considers the sensory and instrumental definitions of texture before describing the different facets of mastication. Aspects of the mastication process are described and various in vivo techniques are described which join, albeit tenuously, sensory and instrumental texture. Muscle activity, acoustic emission, force and displacement have been measured. Subjective tasks such as event recording and time-intensity may be added as part of multi-tasking combined with objective monitoring of the subject while eating. Having discussed the wherewithal of the data acquisition, the next section stands back and considers the principal aspects of chewing, swallowing, salivation and bolus formation. The wider context of texture and mastication is considered, drawing on some current emphases such as flavour and nutrient release, the

54 Texture in food

Observation

Vision

Handling

Consumption – biting – chewing – manipulation – swallowing

Vision, hearing, somesthesis, kinesthesis

Hearing, somesthesis, kinesthesis

Fig. 3.1 The involvement of the senses in texture perception during the process of food consumption. (Reprinted from ‘From food structure to texture’, Wilkinson C, Dijksterhuis G B and Minekus M (2000), Trends in Food Science and Technology, 11, 442–50, with permission from Elsevier).

possibilities for modelling and the advances now possible with modern data analysis. A brief summary of research by commodity and related reviews conclude the chapter. Texture is a key factor in influencing consumer acceptability. Texture comprises visual, followed by tactile and in the mouth senses (Peleg, 1980). Szczesniak and Kahn (1971) described perception in the mouth as a mixture of conscious and unconscious processes, the awareness being accentuated when visual expectations are violated.

3.1.1 Sensory and instrumental texture Instrumental measurements are only as relevant as their predictive power with regard to sensory attributes. Szczesniak (1963) linked texture to sensory, structural and physical parameters.The subtlety is that instrumental measurements are not always focussed on texture but on maturity, in the case of fresh fruits and vegetables, or susceptibility to damage in transport or processing. Sherman (1979) expressed texture as the composite of those properties arising from the structural elements and the manner in which they register with the physiological senses. Jowitt (1974) stated that the appreciation of texture involves the interaction between both motor and sensory components of the masticatory and central nervous systems. Kilcast and Eves (1993) summarise this as ‘the complex reactions occurring during the chewing of food are all integrated by the brain into the sensation perceived as texture’. Peleg (1983) points out that sensory terms can be used interchangeably and cites crunchy, crisp and brittle as overlapping as do firm, tough and hard. Instrumental tests can be fundamental, empirical and imitative (Bourne, 2002). In the context of this chapter on texture and mastication imitative tests are most worthy of emphasis. In selecting fruit ripeness the tactile assessment by squeezing is often used in sorting and grading. The use of dentures or opposing single teeth with a universal test machine are probably the most memorable imitative tests. However, there is often failure of a

Texture and mastication

55

single instrumental measurement as a reliable texture descriptor. Mohsenin and Mittal (1977) commented that sensory tests generally correlated better with large strain failure instrumental tests than with small strain tests.

3.2 The mastication process Mastication needs to be seen as part of the wider oral processing of food, which can be divided into motility and secretory contributions. Oral processes affect breakdown of food in the mouth, and thence sensory perception, and depend on the properties of the food and their time dependence. The motility effect operates through lower jaw, tongue, cheeks and lips. Motility is needed to transport food to the pharynx for swallowing and to reduce particle size and to form a bolus for swallowing. If it is just placed in the mouth, this involves the tongue itself and pushing it against the palate. The tongue, cheeks and lips are able to push the food between particular teeth. Three stages have been identified (Hiiemae et al., 1996; Lund, 1991): Stage I involves transport from the front of the mouth to the premolars and is characterised by low amplitude jaw movements; Stage II reduces particle size; Stage III is pre-swallowing and the food moves to the back of the tongue by tongue–palate interaction. Food clearance uses tongue and jaw movements to remove food at the end of mastication and involves swallowing (Wilkinson et al., 2000). Swallowing occurs intermittently in a chewing sequence. Oral secretion of saliva is by the salivary glands located under the tongue, between the jaw bones, at the lower jaw and beneath the ear. Saliva is mixed with food during mastication to form a bolus for swallowing. Mastication is a process combining simultaneous food comminution and lubrication, although the formation of a cohesive bolus is seen as important (Prinz and Lucas, 1997). When food is introduced into the mouth, it is moved by the tongue and then pressed against the palate which serves to indicate surface morphology. Moistening with saliva and minor deformation give way to incision and chewing and the food is deformed and may be fractured (Lillford, 2001). The subject of oral motility in relation to food structure makes use of methods from oral health and speech therapy. Hence some studies have compared speech and mastication.

3.2.1 Physiology Oral processing of foods involves initial ingestion, incision and repetitive chewing and swallowing. The incision and chewing constitutes mastication. Mastication involves teeth, gums, palate, cheeks, tongue and lips and the movements of lower (mandible) and upper (maxilla) jaws together with the secretion from the salivary glands. The tongue has an important role in deciding whether particle comminution is sufficient and moist enough to swallow (Wilkinson et al., 2000).

56 Texture in food

Oral sensitivity varies with position in the body and after the finger tips, the tongue, peridontal membrane, lips and palate are particularly acute. Perception of texture and mouthfeel involves three distinct groups (Guinard and Mazzucchelli, 1996) of mechanoreceptors: 1 in the palate, tongue and gums; 2 in the peridonytal membrane surrounding the roots of the teeth; 3 of the muscles and tendons involved in mastication. All mechanoreceptors have characteristic nerve endings. The importance of physiology is at the scale of the receptor behaviour and also at the larger scale of oral processes and motility.

3.2.2 Jaw and teeth movement The teeth play an important part at different stages of oral processing. The first bite with the incisors is the part of the eating process which has been best emulated by texture measuring devices since it is closest in action to that of a unidirectional single deformation test in a universal test machine. The main chewing stage or mastication involves jaw movement and action of the teeth to break down foods. In some stages of jaw movement, such as moving the food into the mouth and manipulation to the molars, the teeth may not reach occlusion. Speeds are variable over a large range, but the teeth may move vertically and horizontally or with a myriad of combinations leading to compressive, tensile and shear forces. Food is bitten often at the extremes rapidly with low forces or slowly with high force. The lower jaw is attached to the head by the temporomandibular joint which enables a wide range of movements. Depression (mouth opening), elevation (mouth closing), protrusion (jaw moving forward) and retrusion (jaw moving backward) and lateral movements are possible (Boyar and Kilcast, 1986).

3.2.3 Muscle activity The muscle actions corresponding to the jaw movements described above are described in detail by Boyar and Kilcast (1986) and involve the masseter, temporal, pterygoid, mylohyoid, digastric and geniohyoid muscles (Fig. 3.2). Muscle fatigue is a phenomenon that is well charted, although it is rarely described in the context of the masticatory muscles.

3.3 Measuring mastication The complexity of eating or even the chewing phase makes high demands on a texture measuring instrument. Here there is a gulf with universal test machines which operate at one rate and in one direction or even instrumental tests such as texture profile analysis (TPA). One would ideally want to make measurements


57

Temporalis

Masseter

Temporalis

Lateral pterygoid

Mylohyoid

Medialpterygoid

Temporalis

Geniogossus Geniohyoid Digastric

Fig. 3.2 Lateral view of the skull showing the muscle attachments. Inset: muscle attachments to the medial surface of the mandible. (Adapted from Romanes G J (1967), Cunningham’s Manual of Practical Anatomy, vol. 3: head and neck and brain, with permission from Oxford University Press).

of structure, displacement and force on the subject. A logical argument is to make objective measurements on people while they are chewing or eating. However, the act of measurement may affect the observations and hence should be as unintrusive as possible. Various textural techniques used to monitor mastication have been reviewed by Boyar and Kilcast (1986). Among these techniques, electromyography (EMG), the measurement of the electrical activity of muscles, has found wide application. EMG is a potential link between purely instrumental measures of food mechanical properties and sensory evaluations of consumers. It is complementary to recording sounds emitted during consumption (Section

58 Texture in food

3.3.6) and tracking displacement using kinesiology (Section 3.3.5). Although forces between teeth and dentures have been measured, the local displacement information necessary to produce the same type of force-displacement data as generated by a universal test machine are not usually available simultaneously.

3.3.1 Electromyography (EMG) Electromyography is a technique to characterise the chewing patterns of foods which differ in texture (Agrawal et al., 1997; Brown et al., 1994; Kilcast and Eves, 1991). It forms something of a bridge between instrumental tests and sensory assessments since it is an objective measurement carried out on human subjects. Brown (1994) has shown the use of EMG to characterise consistent differences in chewing patterns between individuals, and reviewed its use in the literature. EMG characteristics depend strongly on muscle fibre length, position of the electrodes relative to the muscle fibres, electrode area and inter-electrode distance and fat thickness between skin and muscles (Lateva et al., 1993; Dimitrova et al., 2001). When the muscle lies close to the surface of the skin the EMG activity can be related to specific muscles using surface electrodes. Other electrodes need to be implanted for deeperlying muscles. In many studies the temporal and masseter muscles are used (Brown, 1994; Kilcast and Eves, 1993), although Plesh et al. (1987) and Hiiemae et al. (1996) describe the monitoring of the digastric muscle. In the study of surface muscles it is non-invasive, but Hiiemae et al. (1996) used unipolar needle electrodes to record from geniohyoid/genioglossus muscles. The EMG signal is amplified and recorded and then analysed as shown in Fig. 3.3. Filtering of the signal in various ways can take place to avoid mains Time intensity (T-I) slider

Data collection computer

Event marker button

ADC interface

Polygraph (4-channel amplifier)

EMG electrodes (left & right temporal and masseter)

Fig. 3.3 EMG, time intensity and swallow button block circuit diagram. (ADC: analogue to digital converter.)


59

electricity spikes and other undulations. A large amount of data is possible with monitoring of four muscles and other time-dependent signals. Kilcast and Eves (1993) describe the integration of the signal of each muscle and the extraction of various gradients, heights and areas. Brown (1994) used software to pool and rectify the signals from four muscles. This aided identification of chews and swallow events. Subsequent data analysis extracted parameters: chew time, number of chews, chew rate and chew work. In combination with kinesiology (Section 3.3.5), the chew work was divided into that occurring during jaw closing in the vertical direction and subsequent horizontal movement at the end of the chew stroke. Swallows were also identified from EMG traces by the shape of the EMG chew bursts. Jack et al. (1993) defined the following from their integrated EMG response: number of chews, duration of muscle activity up to first swallow, duration of muscle activity after first swallow, height of first peak and also mean peak amplitude, total of peak amplitudes and also maximum peak amplitude, and chew frequency. Plesh et al. (1987) defined cycle duration, burst duration and interburst duration. Edlund and Lamm (1980) evaluated ‘masticatory efficiency’ as integrated maximum values of EMG activity in volts for the temporal and masseter muscles of five subjects. The mean values ranged from 384 µV for apple to 690 µV for white bread, with 612 µV obtained for silicone-based dental test material Optosil. Horio and Kawamura (1989) found that the mean EMG amplitude from the masseter muscles of 29 subjects decreased from about 818 µV to 187 µV as the TPA (see Bourne 2002) shear force fell from 6.3 kg to 0.1 kg. The TPA ‘gumminess’, defined as the chewing energy to bite through a cross-section of the sample, fell non-uniformly from 0.74 to 0.22 (no units). Plesh et al. (1993) defined a ‘kinematic index of stiffness’ for jaw movements as the three-dimensional speed divided by the three-dimensional path length, and found it decreased with increasing chew rate.

3.3.2 Other-myographys This section completes the picture for the related muscle measurement methods, although these are used in the context of muscles more commonly studied by occupational therapists. EMG signals are affected by muscle fatigue. The frequency spectrum becomes narrower with fatigue, losing parts of the highfrequency content (Herzog et al., 1994). Other ‘-myographys’ behave differently. Acoustic myography (AMG) in which muscle sounds are recorded from the vibrations of contracting muscle applied to quadricep muscles. Vibromyography (VMG) (Matheson et al., 1997) uses an accelerometer to measure lateral oscillations during contraction. The mean power frequency does not change, with fatigue, leading to the claim that it is a measure of intrinsic mechanical activity (Zwarts and Keidel, 1991). Interestingly VMG was found to discriminate better the absolute forces between subjects than EMG. In comparison with EMG, where the median frequency decreased continuously,

60 Texture in food

an abrupt drop in the median frequency of the VMG signal occurred after fatigue. This change in signal was readily observed in the time domain for the raw data leading to identification of the onset of fatigue (Herzog et al. 1994).

3.3.3 Time-intensity and multi-tasking Time-intensity (T-I) techniques involve the recording of specific sensory attributes as a function of time (Lee and Pangborn 1986). Cliff and Heymann (1993) have reviewed the use of T-I techniques and their use for sensory flavour and texture. Wilson and Brown (1997) used combined EMG and TI in a study of mastication and flavour release from gels of differing mechanical properties, where they observed that the act of swallowing was often associated with a marked increase in flavour perception. Studies by Sprunt et al. (2002) and Wright et al. (2003) considered mastication and flavour release from gels incorporating different concentrations of flavour. The dual-attribute time-intensity (DATI) method has been developed recently (Duizer et al. 1996a; Duizer et al., 1997) for the collection of the perception of two attributes simultaneously. These authors suggested that the use of DATI should improve evaluation of interactions, allowing foods to be assessed in a more realistic manner. Duizer et al. (1996b) have studied the relationship between T-I, EMG and instrumental texture measurements of beef tenderness. More recently, Brown and Braxton (2000) have used combined EMG and T-I together with recording of jaw movement patterns in a study on biscuits, relating dynamics of food breakdown to perceptions of texture and preference. In this case T-I was used to monitor the wetness of the biscuit sample. Brown et al. (1996) studied the tenderness of meat and trained panellists recorded it as a function of time using a sliding potentiometer in conjunction with EMG. Significant correlations were obtained between perceived tenderness and the masticatory muscle chewing work rather than chewing time or number. Brown et al. (1996) comment that flavour perception starts at zero for a given stimulus, follows a smooth increase as the concentration of the stimulus is released and falls smoothly back to zero with increasing time. In contrast, texture perception may increase sharply on ingestion and may be little changed up to swallowing. Some researchers have investigated the concept of multi-tasking (asking panellists to perform at least two sensory assessment procedures at once). Larson-Powers and Pangborn (1978) asked panellists to use a foot-pedal to initiate chart recording when carrying out time-intensity measurements on beverages and gelatins. In evaluating tactile properties of skincare products, trained panellists can be asked to evaluate absorbency while rubbing at a rate marked to a metronome (Civille and Dus, 1991). A swallow button was used by Hiiemae et al. (1996) who also used videotape recording. Jack et al. (1994) used the event marker of their time-intensity


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computer module to mark chewing strokes in combination with EMG. Brown et al. (1996) used a microphone strapped over the larynx at the same time as EMG and T-I studies. EMG and T-I can be combined with a swallow indicator button for panellists consuming confectionery gels of differing flavour levels. There are opportunities for the subject to make subjective measurements as well, such as T-I curves and swallowing events (Fig. 3.4) (Sprunt et al., 2002). This non-invasive combination allows synchronous collection of chewing activity, swallowing events and T-I data. The extracted parameters from each recording session (with units) are as follows: T-I area, T-I duration, T-I maximum intensity, number of chews, time of last chew, chewing rate, number of swallows, time of first swallow, time of last swallow and swallow rate (Sprunt et al., 2002). 3.3.4 Force measurement Force transducers have been used in dental prostheses to measure bite forces directly. Yeh et al. (2000) used a cross-arch transducer and found that it was directly correlated with salivary flow. Van Eijden (1991) and Waltimo and Kononen (1994, 1995) have made bite force measurements, including the contribution from single and multiple teeth. Mioche et al. (1993) and Peyron et al. (1994) used an intra-oral load cell. Kilcast and Eves (1993) compared EMG integrated peak height with force for a subject biting on a force transducer using the molar teeth, which yielded a good linear relationship (Fig. 3.5). Tornberg et al. (1985) used 10 strain gauges in a dental prosthesis which enabled measurement of deformation rates in the order of 2000–4000 mm min–1 and the maximum strain for different samples.

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3.3.5 Displacement and kinesiology Several studies have investigated oral motility from studies of tongue and jaw movements. Movement of the lower jaw can be studied by tracking

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Fig. 3.4 Recording of EMG signals from left (L) and right (R) masseter (Mass) and temporalis (Temp) muscles, Time-Intensity (T-I) curve for flavour, and swallows indicated by a swallow button shown as voltage spikes (channel ‘5’).

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a magnet attached to the lower incisors, termed kinesiography. Bellisle et al. (2000) described ‘edograms’ which use a strain gauge resting on the subject’s cheek via a light headset to detect jaw movements, corresponding to chewing. Simultaneously they measured swallowing (deglutition), using a water-filled ballon mounted against the patient’s throat with a collar, the volume of which reduced and the resulting increased pressure was detected with a pressure transducer. The authors also describe the use of videoing subjects eating to deduce eating parameters for foods of different physical form, described in their case as traditional, sandwich and semi-liquid. Horio and Kawamura (1989) used a Kinesiograph (KI) to measure vertical movements of the lower jaw at the same time as making EMG measurements for the first five chewing strokes. They defined a tooth contact period and degree of jaw opening. Plesh et al. (1986, 1987, 1993) used EMG and a KI to study the jaw movements of subjects chewing gum, at individuals’ preferred rates, and also at rates prescribed using a metronome. The Myotronics Kinesiograph measured three-dimensional movement of a magnet on the lower central incisors using three magnetometers mounted on a light frame. They allowed subjects to chew at their preferred rate and also in time with a metronome at faster rates. Various parameters were identified from the KI traces, such as cycle duration, duration of opening, duration of closing and duration of occlusal. Brown et al. (1998) and Brown and Braxton (2000) also used EMG and KI and synchronised the two techniques for various foods. They were then able to apportion which phase of jaw movement corresponded to which part of the muscle work (Fig. 3.6) (see Section 3.3.1).


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Fig. 3.6 Typical jaw movement profile for a single chew. During the closing movement EMG activity may occur at the same time as the initiation of closing or may be slightly delayed. The portion of jaw closing associated with EMG activity was divided into vertical and closing. Horizontal closing was determined as any movement which occurred when the teeth were less than 1 mm away from the minimum vertical separation, and may be associated with lateral movement. (Reprinted from ‘Dynamics of food breakdown during eating in relation to perceptions of texture and preference: a study on biscuits’, Brown WE and Braxton D (2000), Food Quality and Preference, 11, 259–67, with permission from Elsevier).

Hiiemae et al. (1996) used a Sirognathograph with a magnet glued to the lower central incisors and detected movement in three directions, with one set of subjects depressing a swallow button and another set being recorded on videotape. Togashi et al. (2000) used a magnet ‘pasted’ to the gum of a lower molar. Three mutually perpendicular Hall probes resulted in the threedimensional tracking of tooth movement. They divided mastication movements into two parts, a rhythmical chewing period followed by an irregular period in preparation for swallowing. Subsequently Agrawal et al. (2000) made measurements of height and width of the chewing loop and closing angle of the jaw from Sirognathograph traces. Peyron et al. (1997) used an infrared tracking system using three cameras with four infrared emitting diodes mounted on a framework attached to the subject’s forehead and a fifth on the chin. From data analysis the movement of the chin relative to the skull was obtained. Ostry and Flanagan (1989) used a linear voltage displacement transducer (LVDT) with the transformer fixed relative to the skull with a modified hockey helmet and the core linked to the subject’s chin. The LVDT was

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oriented to capture the principal direction of motion of the jaw. These authors compared jaw movement in speech with mastication (of rubber tubes). They found amplitudes, velocities and times to be greater for mastication. They measured speaking and mastication rates at the subject’s preferred rate and at an imposed faster rate.

3.3.6 Acoustic emission and monitoring Mechanoreceptors are complemented with auditory cues. Vickers (1985, 1988) has reviewed the evaluation of crispness and the hypothesis that auditory sensations are involved in the perception of crispness (Vickers and Bourne, 1976). They concluded that the number of emitted sounds per unit biting distance and the loudness of the sounds changed with perceived crispness.One means of assessing sensory attributes such as crispness is to measure the sounds produced during compression of foods. This may occur with instrumentally or manually deformed samples or by holding a microphone against the outer ear (Vickers, 1985). The signal may be played back through a frequency analyser and the data can be presented as amplitude–time curves at different frequencies. Studies have indicated that vibratory stimuli can lead to the distinguishing of crisp and crunchy foods (Vickers, 1985). Although crispness and crunchiness were closely related sensations, crisper sounds were higher in pitch and louder than the crunchier sounds (Vickers, 1984). Crisp products are characterised by sudden, clean and total fractures. Loudness, crunchiness and crispness were judged to be very closely related. A complementary area of science is gnathosonics, the study of sounds from teeth occlusion. Occlusal disorders have been monitored using equipment from stethoscope to microphone, the permanent record being termed an occlusogram. Watt (1976) classified sounds into categories depending on the duration of the occlusal sound. Malocclusion was diagnosed from longer periods of sound emission during voluntary tooth contact. As described above (Section 3.3.2) AMG ‘listens’ to contracting muscles (Herzog et al., 1994).

3.3.7 Comparisons with physical properties The first bite is often used as the focus for relating food physical properties and sensory attributes. Peyron et al. (1997) emphasise that biting may be an isolated voluntary act or the first step of the masticatory process. A number of studies have compared in vivo mastication with aspects determined using laboratory equipment. Olthoff et al. (1986) used a pneumatic bite simulator with a crosshead speed of 30 mm s–1 and measured force for different angled cusp-shaped probes together with the particle size distribution of various foods and the model material Optosil. In the case of Optosil in vivo tests were carried out and the particle size distribution measured after different numbers of chewing strokes. Fragmentation into smaller particles was found with in vivo biting.


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Ostry and Flanagan (1989) compared thin and thick walled rubber tubes. The duration of the jaw movement and its amplitude were less and the maximum velocity higher for the thinner-walled tube which had a lower stiffness (greater compliance). Kemsley et al. (2003) have recorded the EMG response for opening and closing elastic elements held between the incisors. The root-mean-square (RMS) voltage across the concatenated signals from the masseter-right and masseter-left channels (Fig. 3.4) was calculated for all measurements. It was plotted (in beats per minute) versus chew rate in Fig. 3.7(a) and (b). Elements with stiffer springs (Peg L) resulted in signals with generally greater amplitude, although they can lead to muscle tiredness, and the authors elected not to use these in the subsequent multi-volunteer study. The RMS voltage for less stiff elements (Peg W) averaged across all volunteers and sessions is shown in Fig. 3.7(c). The trend of increasing EMG response with chew rate is clear. The average energy needed to compress the elements had been calculated from force–displacement data obtained with a universal test machine. Turning to the relationship between mastication and instrumental texture of foods, Agrawal et al. (1997, 1998, 2000) point to the importance of the

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engineering properties of toughness and modulus of elasticity in determining the rate of breakdown of foods (Section 3.4.4), pattern of mandibular movements and muscular activity measured by EMG in mastication. Engineering properties fall within the class of fundamental texture measurement (Section 3.1.1), and these studies are noteworthy in defining a fragmentation index, (toughness/modulus)0.5 from a fracture mechanics point of view.

3.3.8 Monitoring subjects Kilcast and Eves (1993) have described aspects of reproducibility in EMG experiments in a study of 30 subjects chewing fruit pastilles. They compared the integrated peak height as a function of time and divided the responses into three groups. Grouping of volunteers on the basis of chewing efficiency as judged by almond attrition and chewing gum weight loss was reported by Brown and Braxton (2000) (Section 3.4.1). Kilcast and Eves (1993) also compared the response of five subjects on three separate occasions, this time with toffee. Initial differences in integrated peak height gave way to similar results in terms of the rate of decrease of height and similar total chewing times.

3.4 Chewing, swallowing, salivation and bolus formation One aspect of chewing function is to quantify it, and a number of authors have reported on the definition of ‘masticatory efficiency’. One approach is to identify a test substance. Dahlberg (1942) listed a number of requirements of a test material: 1 it should resemble an ordinary food in that it is crushed by the alveolar cusps of the teeth or, at the other extreme, not be difficult for those with a poor dentition; 2 it should not swell or dissolve in water or saliva and should be such that its pulverisation can be established; 3 it should not break along cleavage surfaces or be tough or sticky; 4 it should be capable of standardisation, non-perishable and of good or indifferent taste. Dahlberg (1942) concluded that a hardened gelatine was suitable. Peanuts were favoured from the 35 foods tested by Yurkstas and Manly (1950). Heath (1982) observed that chewing gum bolus has different shapes that relate to the chewer’s dentition. Liedberg and Owall (1995) described a masticatory test using two-coloured chewing gum. Colour mixing and shape indices were found to depend on dental status. Interestingly they compared comminution of foods with kneading and shaping in determining a swallowing threshold. Horio and Kawamura (1989) concluded that number of chewing strokes could stimulate the swallowing process, regardless of comminution.


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3.4.1 Chewing Chewing activity appears as a burst in the EMG output from each of the monitored muscles. Horio and Kawamura (1989) found that the chewing force and movements were strongly influenced by the texture of food, highlighting its ‘hardness’. They measured the number of chewing strokes and chewing time until the final swallow and classified their subjects into two groups from these data. As a precursor to EMG studies, chewing efficiency was measured and used to divide subjects into groups by Braxton et al. (1996) and Brown and Braxton (2000). They used two techniques: 1 chewing gum, in which the weight loss from a stick of gum was measured after 100 chews as used earlier by Heath (1982) 2 chewing almonds, in which the median size for a whole, blanched nut after 10 chews was measured using the diameters from a scanned image. The EMG work for gum was also used to normalise EMG work obtained for other foods (Brown et al. 1994). Edlund and Lamm (1980) evaluated ‘masticatory efficiency’ as integrated maximum values of EMG activity in volts for the temporal and masseter muscles of five subjects. After the main chewing activity has finished some lower-level EMG activity may continue, due to muscle movements associated with mouth clearance. Swallowing (see Section 3.4.2) may be seen as longer EMG bursts due to teeth clenches during the chewing sequence as reported by Brown et al. (1994). As described in Section 3.3.1 above, a number of time and number parameters are used to define chewing. Hiiemae et al. (1996) compared the number of chewing cycles to the first swallow and the masticatory sequence duration for banana, peeled and unpeeled apple and ginger nut biscuits and concluded that they were determined by initial food consistency.

3.4.2 Swallowing In an exploration of EMG in assessing chewing behaviour for different foodstuffs, Brown et al. (1994) described the measurement of the timing and number of swallows, identifying a swallowing event as a short pause in the chewing sequence associated with a burst of activity from the masticatory muscles as the teeth are clenched. However, they were unable to measure these events for individuals who swallow with little involvement of the masticatory muscles (termed ‘visceral’ swallowing). Hiiemae et al. (1996) compared video evidence, a swallow button and inspection of the EMG output. In a later combined EMG and T-I study, Brown et al. (1996) used a miniature microphone placed over the larynx to measure swallowing, but only reported the final swallow at the end of mastication. A number of other techniques for measuring human swallowing events have appeared in the literature. Piezoelectric sensors have been used to measure laryngeal movements during swallowing of liquids (Pehlivan et al., 1996), and have also been used in combination with EMG activity of submental and cricopharyngeal muscles

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(Ertekin et al., 1996). Commercial devices exist for measuring the ear’s tympanic membrane displacement during swallowing (Marchbanks, 1984). Sonotubometry is another method, whereby changes in sound pressure level due to eustachian tube opening are measured in the external auditory meatus, while a constant sound source is applied via the nostril (Munro et al., 1999). However, none of these methods has been shown to be effective for swallowing measurement during mastication of solid foods, where both a large amount of laryngeal and tympanic membrane displacement and inner-ear noise can be associated with mandibular movements and chewing activity. As a combined technique to couple with T-I, it is also desirable that any swallow measurement device should be as non-invasive as possible, to avoid distraction of the panellist. Some of the techniques are described in Section 3.3.3 above. At the end of the chewing sequence mouth clearance occurs, typified by irregular patterns of jaw movement (Hiiemae et al., 1996). They comment that there is little masseter muscle activity compared to that of the geniohyoid and genioglossus.

3.4.3 Salivation Saliva has a lubrication effect and also has a role in taste perception as well as containing enzymes which digest lipids and starches, counter microorganisms and buffer acids in the mouth. Saliva mixed with food initiates digestion and facilitates swallowing through formation of a bolus. If a food is dry it is difficult to swallow, e.g. the party game of how many cream crackers can be eaten. A key factor in determining salivary rate is the gustatory stimulus. Salivary flow rate has been found to vary considerably depending on the type of foodstuff being consumed (Guinard et al., 1998), being strongly influenced by food texture and water content as well as flavour type and level (Prinz and Lucas, 1997; Watanabe and Dawes, 1988a,b). Total salivary flow rates as a result of eating have been published by a number of workers. Hoebler et al. (1998) found that a basal flow rate of 0.8–0.9 g/min rose to 1.1 g/min for cooked spaghetti and 1.3 g/min for white bread. In a study of oil/water emulsions containing vanillin and limonene, Mialon and Ebeler (1997) found flow rates ranging from 1.4 g/min to 5.61 g/min for their 14 volunteers. For wines of varying composition, Fischer et al. (1994) found flow rates equivalent to 1.8–5.98 g/min after 30 seconds in the mouth. Watanabe and Dawes (1988a) studied a variety of foods, and found mean salivary flow rates of 0.72 ml/min with no oral stimulation and 3.15 ml/min for boiled rice, rising to 4.94 ml/min for rhubarb pie and 7.07 ml/min for oral infusion with 260 mmmol/l citric acid.

3.4.4 Structural change during mastication Methods for expressing food breakdown in terms of comminution typically focus on measurement of particle size after a given number of chewing


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strokes. Multiple sieving techniques used to measure particle size distributions have been described by Lucas and Luke (1983) for chewed carrot samples, Edlund and Lamm (1980) for Optosil, and Olthoff et al. (1984) for both Optosil and peanuts. Mowlana et al. (1995) described a rapid optical scanning method to measure two-dimensional surface area of chewed almonds, Vanderbilt et al. (1993) used a similar device to compare different types of particle size distributions in foods and Agrawal et al. (1997) used image analysis to study fragmentation due to chewing for a variety of foodstuffs. The latter also related breakage of food particles to material properties and discussed a fragmentation index based on toughness and stiffness of the food (Section 3.3.7). More recently, Hoebler et al. (1998, 2000) have measured particle sizes of chewed and minced bread and pasta using laser light diffraction and image analysis. These authors also measured saliva content of the chewed foods, drawing attention to its importance in bolus formation. The resulting salivary impregnation for white bread to give an adequate water content for swallowing was found to be up to five times as great as that for pasta (Section 3.3.7). Some methods for measuring surface areas of foodstuffs have been reviewed by Mohsenin (1970). These include an airflow planimeter to measure flat surface area of leaves, peeling strips from the outer surfaces of fruits and vegetables for direct measurement of total outer layer, and calculations based on measured outer linear dimensions assuming idealised shapes (e.g. apples and eggs). Methods suitable for irregularly shaped soft samples such as chewed gels were not described. Adamson (1982) has reviewed surface area determination by adsorption of substances on solid surfaces, where the adsorbate forms a surface coating that may be one or more molecular layers thick. Among these are the well-known gas adsorption isotherm (BET) method, and adsorption of dyes on impermeable solids. The surface area is then determined by a consideration of quantity of adsorbed substance. However, gels may have surfaces permeable to water, so an adsorbate such as a dye used for coating may penetrate the outer surface layer. Sprunt and Smith (2002) presented a method to give total surface area of chewed gelatin gels by absorption of aqueous amaranth dye into the gel surface. This method was used to measure profiles of surface area, gel weight and saliva weight as a function of time for panellists chewing gels throughout the mastication sequence. These gels tended to congeal on mastication, so the fragments would not be easily separable for individual coating or particle size analysis. The method described should be suitable for any similar gels that can be coated by dye absorption, but since specific dye permeation quantity will be affected by the factors discussed above, a calibration graph of absorbed dye versus surface area would need to be constructed for each separate type and composition of gel. Figure 3.8 shows results for surface area with gel weight and saliva weight as a function of number of chews. Swallow positions were seen as sharp vertical drops in all three curves. Swallowing brings about an abrupt decrease in gel surface area and gel

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weight from removal of gel mass. Saliva weight also drops significantly on each swallow as would be expected. The original (no. of chews = 0) gel mean surface area and weight are shown on the upper two curves. A sample is shown taken approximately half way to the first swallow, in this case at 17 chews. This gives an indication of the build-up of surface area and any change in solid weight as the gel is initially chewed. Although this study obtained total salivary flow rates indirectly from weights by subtraction, other studies have measured saliva flow rate directly from particular ducts. For example Guinard et al. (1998) used a suction cap over the orifice of Stenson’s duct.


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3.4.5 Bolus formation The particle size of a friable food must be reduced to a size that can be accepted by the pharyngeal and esophageal passageways for comfortable swallowing. The end product of mastication is the bolus (Hutchings and Lillford, 1988), although authors comment that some foods form a bolus and some do not (Lucas and Luke 1983). Hutchings and Lillford (1988) report a classification into at least three types of bolus: 1 a subdivision model where the teeth subdivide the sample into smaller pieces for swallowing singly or gathered together in foods such as cheese and carrot which are the cited examples; 2 rolled bolus applicable to anisotropic foods such as meats; 3 shattered foods comprising many small pieces as in biscuits and snacks. Prinz and Lucas (1997) comment that formation of a bolus is instrumental in the initiation of swallowing.

3.5 Future trends 3.5.1 Texture related to flavour and nutrient release The organoleptic quality of fruits and vegetables is influenced not only by texture, but also by the taste and aroma (Boyar and Kilcast, 1986; Piggott, 2000). The taste of an ingested plant tissue will be dependent on the extent to which flavours and volatiles within the food are released into the mouth, enabling them to come into contact with saliva and then sensory cells. The most relevant cell-wall influence relates to the fracture or separation of plant cells, and therefore to texture. Cell rupture releases cell contents into the saliva as is the case in crisp, juicy apples. In some foods, cell rupture will also be instrumental in flavour creation. Where tissue fracture involves cell separation, as in thermallysoftened vegetables or over-ripe, mealy fruits, a degree of encapsulation will occur. This is why mealy apples are generally perceived to have a dry mouthfeel. In such circumstances, the opportunity for the juice (cell contents) to reach the sensory receptors responsible for taste will depend on the rate of diffusion across the intact cell wall into the surrounding saliva in addition to the ability of the saliva to transport them to the receptors. Flavour is the combination of taste and odour influenced by sensations of pain, heat and cold and by tactile sensation. Aroma, taste, texture and mouthfeel account for the major stimuli that make up flavour (Taylor, 1996). Chemicals from the food come into contact with sensors in the nose and mouth and interact with mucous membranes. Plant cell walls in tissues and organs affect the chewing process and interact with the mouth lining. Studies of chewing using EMG can be combined with volunteer’s direct recording of time-intensity signals with swallowing recorded indirectly with a throat microphone or directly with a swallow button on the volunteer’s console. The T-I device can be used to record sensory or quality attributes, including flavour intensity.

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Again, indirect measurement of volatile flavour can be carried out. Most analyses of volatile flavours have been carried out on whole foods by extracting all aroma compounds by distillation or extraction. However, physical changes occur in the mouth with regard to both texture and flavour. Surface area of food may increase initially and then decrease during bolus formation. Exhaledbreath sampling during eating using mass spectrometry techniques is an objective approach to quantify aroma release, often in combination with generation of T-I profiles by the subject (Taylor, 1996). Modelling of flavour release is outside the scope of this chapter, but the interdisciplinarity of the approach requires information from different areas of science, notably T-I studies of flavour intensity together with chewing data: number of chews and chew times, swallow times and surface area change with chewing (Harrison et al., 1998; Wright et al., 2003). The arguments about flavour release also carry over to release of nutrients and impinge on digestion. The structure and physical properties of the food following comminution and bolus formation, and then swallowing are expected to be important. In fact Prinz and Lucas (1997) argue that models of the digestive system are incomplete without specification of the food being swallowed. 3.5.2 Modelling Ledley (1971) reviewed the analysis of forces on teeth and design of denture occlusal surfaces. Starting with flat-surfaced teeth he progressed to real anatomical-shaped teeth with cusps. Numerical force analysis was carried out for different chew angles and occlusal shapes and then extended to stability of complete dentures. A very interesting current area of research is that being carried out by Professors MR Heath, M Hector and PS Wright (Queen Mary University of London, School of Medicine and Dentistry) and RH Crompton (University of Liverpool) who are collaborating on the use of finite element stress analysis to create a 3D model of food reduction, capable of predicting differences in food failure from tooth morphology. In combination with instrumented dental prostheses to measure in vivo bite forces in humans, they propose to develop and verify a general model of food comminution applicable to prediction of food texture perception and design of dental prostheses. Another modelling area is that of neural networks that produce motor patterns and receive sensory patterns. Otten (1988) describes the modelling of neurons with responses that depend on firing rates. A pattern generator was simulated in a neural network consisting of a matrix of 15 by 15 neurons. 3.5.3 Bringing together sensory and fundamental tests Attempts to correlate physical measures of food properties with sensory quality do not account for the mastication process. Instrumental measures do not imitate oral motion, rates of force application and the effects of temperature and saliva (Pierson and Le Magnen, 1970). The distinction between structure


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and texture is often ignored (Cardello, 1994) and the terms are used interchangeably. In other disciplines, texture is purely a structural, or topological term. The blurring of perceptional and instrumental terms has been highlighted and “may impede progress in either field”. This has been a recurring theme. Szczesniak (1963) separates textural attributes into three broad categories: mechanical, geometrical and compositional. The Scott-Blair approach divides instrumental methods of texture measurement into three types: empirical, imitative and fundamental (Brennan and Jowitt, 1977; Szczesniak, 1963). Empirical tests have been developed from practical experience and are often marked out as arbitrary, poorly defined, lacking an absolute standard and effective only for a limited number of foods (Bourne, 1994). Imitative tests are often seen as a subset of empirical tests that subject the food to a process that partially mimics the consumer. Empirical tests cannot easily be expressed in fundamental terms and are dependent on test geometry, friction and sample size (Peleg, 1983). Fundamental tests are more rigorously defined, usually in engineering units, whereas empirical tests are often more successful than their fundamental counterparts. Many fundamental tests use low stresses which do not cause the material to break or fail; they also use rectilinear motion, whereas the movement of the teeth is along an arc and much faster than speeds in the universal test machine. Physical tests often produce single values while the consumers may change rates and manipulate the food during mastication. How a food deforms during mastication depends on mechanical property contributions at the different levels of structure and their interaction. In line with this philosophy, the human mouth can be instrumented to make objective measurements during eating (Kilcast and Eves, 1991). Recent work studied EMG responses simplified by dispensing with the food stimulus and the associated phenomena of particle breakdown and saliva stimulation. Instead, volunteers simulated an aspect of chewing by making jaw movements to elastically deform calibrated springs held between their incisors. Moreover, the volunteers were asked to coordinate their movements with the sound produced by a metronome, operated at a number of predetermined rates (Kemsley et al., 2003). Advances in the understanding of texture depend on a multidisciplinary approach as claimed by several reviewers (Heath and Lucas, 1987; Wilkinson et al., 2000). Three main areas are sensory, physiology and, more broadly, physicochemical properties comprising rheology (including mechanical properties) and structure.

3.5.4 More data analysis The development of EMG took place in the early days of computing power. Much of the data was discarded or averaged or simplified in order to be analysed with what are now viewed as low-level PCs. The current surge in computing power and the advent of parallel computing has opened up new opportunities, demonstrated by the following example (Kemsley et al., 2002).

74 Texture in food

Multivariate modelling (Kemsley et al., 2002) was used to analyse individual EMG recordings, which were Fourier-transformed to give a power spectrum in the frequency domain. Examination of the raw data, and of subsets of power spectra, was carried out using Matlab 6.1 (The Mathworks, Inc.) running on a desktop computer. Algorithms were written in-house to identify bursts of activity corresponding to chews, and to carry out distributional analysis of the intervals between chews. A real Fourier transform algorithm was used, provided in the IMSL Fortran 90 MP Library (Visual Numerics Inc.) supplied with Visual Fortran 6.1 (Compaq Computer Corp). Before performing principal component analysis (PCA), data points corresponding to the d.c. component were discarded, the required number of data points from each channel was concatenated, and the data area-normalised. The sum of the vector elements was set equal to unity, a transformation which has been demonstrated to emphasise band shape differences between spectra. Correlation matrix PCA was used throughout. Matrices of power spectra too large to be processed on a desktop computer were analysed on the SGI Origin2000 (Silicon Graphics Inc., Mountain View, CA, USA), at Computer Services for Academic Research (CSAR, Manchester, UK). Algorithms to perform PCA were written in Fortran 90, incorporating LINPACK library functions. The low-frequency ( 65%) increases the ‘hard-to-cook’ problem. Also, the level of divalent cations in the cooking water and phytic acid content of the beans appear to be crucial factors (Crean and Haisman, 1963; Kyriakidis et al., 1997). Although solutions to the problem have been proffered, the underlying chemistry is not completely understood (Liu, 1995). A number of reactions have been implicated, including cross-linking of calcium with cell wall pectin, and the formation of lignins. The distribution of calcium within the bean is dependent on the presence of complexing agents, such as the naturally occurring phytic acid. On breakdown of the phytic acid by the enzyme phytase, the calcium is potentially available to bridge the pectin and to restrict cell separation on cooking. Lignin accumulates in the cell walls and middle lamella of beans

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Texture in food

stored under high-temperature and high-humidity conditions (Hincks and Stanley, 1987). As a result of its hydrophobic nature, it may enhance the restraining effect of calcium on pectin by leading to a reduction in the hydration of the cell wall polysaccharides. Asparagus Texture is the most important parameter for evaluating the quality of fresh and canned asparagus (Asparagus officinalis). Post-harvest cutting, handling and storage leads to an undesirable increase in toughness, within hours of harvesting the spears, caused by lignification of the fibrovascular bundles. Toughening occurs mainly in the lower portion of the stem, particularly in the outer tissues (Rodriguez-Arcos et al., 2002) (see Fig. 14.4). The asparagus loses textural acceptability whether it is exposed to sunlight or held in the shade, although the rate of toughening is higher in spears held in the sun or cut ‘all green’ (Powers and Drake, 1980). Low-temperature storage, at about 4 ºC, greatly reduces the lignification rate.

Strength (N/mm2)

4

3

2

1

0 Upper

Middle 2000 Stem section

Lower

Upper

Middle 1999 Stem section

Lower

Strength (N/mm2)

4

3

2

1

0

Fig. 14.4 Transverse puncture strength of fresh (ⵧ), cooked (䊏), stored ( ), and stored and cooked ( ) asparagus sections determined over two consecutive seasons. (Standard deviations are shown.) (From Rodriguez-Arcos et al., 2002).

Raw materials quality and the texture of processed vegetables

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NIR spectroscopy is an accurate (and non-invasive) method of determining the amount of neutral and acid detergent fibre that forms during asparagus storage (Garrido et al., 2001). However, a link between the fibre formation and perceived texture deterioration has yet to be established. 14.4.2

Phenolic cross-link forming vegetables

Chinese waterchestnut The Chinese waterchestnut (Eleocharis dulcis) is the corm of a sedge that grows in water and is commonly used in Asian foods. The edible part consists of starch-rich, non-lignified storage parenchyma interspersed with vascular strands. In the raw state, it has a very crisp texture, and this is retained on cooking and even after extended heat treatments, such as in canning. The reason for this textural stability is that the water chestnut cells do not separate on cooking. Evidence has been presented that this is due to ferulic acid and its dimer, diferulate, forming thermally stable cross-links between polysaccharides within the wall and between cells (Parr et al., 1996). Chufa Chufa (Cyperus esculentus) is a perennial plant with grass-like stems and leaves, found throughout the tropics and in warm, temperate areas. It is valued for its small, fleshy underground tubers. These are very crisp in the raw state and, like the Chinese waterchestnut, retain their crisp texture on cooking. Ferulic acid cross-links between structural polysaccharides have been suggested as being the cause of the textural stability (Parker et al., 2000). Sugarbeet A similar texture stabilising mechanism appears to apply in the case of the sugarbeet (Beta vulgaris) which does not completely soften after heating at 100 ºC for several hours (Waldron et al., 1997). This is in contrast to the behaviour of beetroot that softens rapidly due to cell separation. In the case of sugarbeet, it has been proposed that the texture retention is related to the degree of ferulic acid cross-linking between pectic polysaccharides. However, borate ester crosslinks (Ishii and Matsunaga, 2001) may also be involved. Sweet potato Holding sweet potatoes under chill conditions and then at higher temperatures can cause a textural disorder known as ‘hardcore’ that is strongly cultivar dependent (Buescher and Balmoori, 1982). It appears, after cooking, as hard tissue that is not softened by extending the cooking time. Phenylpropanoid metabolism is stimulated during cold-temperature treatments of sweet potato (Rhodes et al., 1981), suggesting that ferulic acid cross-linking reactions may occur and particularly rapidly when the temperature is raised. The observation that sweet potato peroxidase has a high substrate specificity for ferulic acid (Leon et al., 2002) is in agreement with this hypothesis.

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Texture in food

Carrot Phenolic compounds bound to cell walls in carrots (Daucus carota) have been shown to be mainly hydroxybenzoic acid with only a minor contribution from ferulic acid (Beveridge and Harrison, 2001). The cell wall phenolics varied widely between cultivars and increased on storage. However, these elevated levels were not correlated with increased carrot toughness. As the normal behaviour of carrot is to soften on cooking, it may be suggested that texture stabilisation due to ferulic acid cross-linking between cells does not occur. Lignin is formed in cut carrots (Murakami and Takeyama, 1978), but no link with the texture of heat-processed products appears to have been studied.

14.5 Future trends 14.5.1 Vegetable texture improvements through breeding and genetic engineering Conventional breeding is based on crossing two cultivars with complementary traits and selecting within the segregating population. The development of improved hybrids using this process is very time-consuming and breeding has therefore concentrated on key agronomic traits. Breeding of vegetables specifically to obtain improved textural properties on processing has not been carried out to any significant extent. Although lead-in times can be dramatically reduced using genetic engineering approaches, the controversy surrounding genetically engineered foods needs to be resolved first. This controversy led to the removal of GM tomato puree from the UK market despite it being of better flavour and consistency, as well as being cheaper, than the non-GM puree. However, GM studies directed towards the development of more precise techniques for the manipulation and expression of transgenes should lead to new cultivars that have enhanced textural attributes being grown under controlled conditions. Genetic studies are currently being employed to help uncover the genes that encode some of the cell wall-related biosynthetic and hydrolytic enzymes, and structural proteins. The latter have tended to be overlooked in the efforts to understand the functionality of the starches, polysaccharides and phenolic components of plants. For example, the hydroxyproline-rich glycoproteins, or extensins, a family of structural proteins present in plant cell walls, have long been considered to contribute to fruit and vegetable texture. They are essential and integral components of the macromolecular cell wall complex whose close association with sclerenchyma, lignin and fibres suggests that they play a structural role. Research is required to determine whether extensin synthesis leads to a stronger wall, and to see if an alteration of the amount in the plant cell wall affects its strength and rigidity. If it does, it may be possible to alter the properties of plant cell walls by genetically, or otherwise, manipulating the amount and type of extensin that is synthesised in the wall,


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thereby regulating processes in ripening/maturation in order to improve postharvest textural characteristics. The finding of a ferulic acid-specific peroxidase in sweet potato (Leon et al., 2002) could lead to studies with other vegetables designed to enhance the formation of stable diferulate crosslinks. Essential to such studies would be the knowledge gained on the phenylpropanoid pathway in plants.

14.5.2 Texture changes in organically grown vegetables As well as cultivar, the textural characteristics of vegetables can depend on the fertiliser type and method of application, climatic factors and soil type. It is expected that organic treatments with their variation in available nutrients will therefore have a significant effect on vegetable dry matter and consequently on texture. With few exceptions, such as the work on potatoes described in Section 14.2.2, little research has been published on the effect of organic treatments on the textural quality of processed vegetables. Further studies are therefore required before recommendations can be made on optimal organic treatments.

14.5.3 Vegetable texture maintenance through treatment of the raw material Heat processing in water or brine leads to hydration of starch and cell wall polysaccharides and results in a soft textured vegetable that easily forms a paste during mastication. If the heat process could be reduced significantly or eliminated, a texture more resembling the raw material could feasibly be achieved. Non-thermal processing techniques, such as those based on highintensity electric field pulses or high-pressure treatments, are therefore receiving considerable attention. Studies on low heat input processes are likely to continue in the future. Further developments are likely in modified atmosphere storage of raw vegetables that could have significant effects on the texture of the processed products. In particular, the use of ethylene inhibitors is expected to have a considerable impact on current techniques for controlling rate of ripening/maturation and, if applied to vegetables for processing, could have a beneficial impact on final product texture.

14.6 Sources of further information and advice The following books are suggested as supplementary reading to this chapter: • Dennis C and Arthey V D (1999) Vegetable Processing, New York, VCH Publishers Inc. • Eskin N A M (1989) Quality and Preservation of Vegetables, Boca Raton, FL, CRC Press.

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• Hägg M, Ahvenainen R, Evers A M and Tiilikkala K (1999) Agri-Food Quality II: Quality Management of Fruits and Vegetables, Cambridge, The Royal Society of Chemistry. • Rubatzky V E and Yamaguchi M (1997) World Vegetables: Principles, Production and Nutritive Values, Second Edition, New York, Chapman & Hall. • Springett M B (2001) Raw Ingredient Quality in Processed Foods: The Influence of Agricultural Principles and Practice, Gaithersburg, MD, Aspen Publishers Inc.

14.7

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of green pea texture, J Food Qual, 24(2), 91–110. and NEALE R J (1999) Characterisation of starches from West African yams, J Sci Food Agric, 79(15), 2105–12. GARRIDO A, SANCHEZ M T, CANO G, PEREZ D and LOPEZ C (2001) Prediction of neutral and acid detergent fiber content of green asparagus stored under refrigeration and modified atmosphere conditions by near-infrared reflectance spectroscopy, J Food Qual, 24(6), 539–50. GRIZOTTO R and DE MENEZES H C (2002) Effect of cooking on the crispness of cassava chips, J Food Sci, 67(3), 1219–23. HALL C B (1987) Firmness of tomato fruit tissues according to cultivar and ripeness, J Amer Soc Hort Sci, 112(4), 663–5. HINCKS M J and STANLEY D W (1987) Lignification: evidence for a role in hard-to-cook beans, J Food Biochem, 11(1), 41–58. HOEBERICHTS F A, VAN DER PLAS L H W and WALTERING E J (2002) Ethylene perception is required for the expression of tomato-ripening related genes and associated physiological changes even at advanced stages of ripening, Postharv Biol Tech, 26(2), 125–33. ISHII T and MATSUNAGA T (2001) Pectic polysaccharide rhamnogalacturonan II is covalently linked to homogalacturonan, Phytochem, 57(6), 969–74. JACKMAN R L and STANLEY D W (1995) Creep behaviour of tomato pericarp tissue as influenced by ambient temperature ripening and chilled storage, J Tex Studs, 26(5), 537–52. KERTESZ Z I, TOLMAN T G, LOCONTI J D and RUYLE E H (1940) The use of Calcium in the Commercial Canning of Whole Tomatoes, New York State Agric Expt Stn, Geneva, NY, Tech Bull No 252. KLAMCZYNSKA B, CZUCHAJOWSKA Z and BAIK B-K (2001) Composition, soaking, cooking properties and thermal characteristics of starch of chickpeas, wrinkled peas and smooth peas, Int J Food Sci Tech, 36(5), 563–72. KOZHEVNIKOV G O, PROTSEROV V A, WASSERMAN L A, PAVLOVSKAYA N E, GOLISCHKIN L V, MILYAEV V N and YURYEV V P (2001) Changes of thermodynamic and structural properties of wrinkled pea starches (Z-301 and Paramazent varieties) during biosynthesis, StarchStärke, 53(5), 201–10. KRAMER M, SANDERS R, BOLKAN H, WATERS C, SHEEHY R E and HIATT W R (1992) Postharvest evaluation of transgenic tomatoes with reduced levels of polygalacturonase: processing, firmness and disease resistance, Postharv Biol Technol, 1(3), 241–55. KYRIAKIDIS N B, APOSTOLIDIS A, PAPAZOGLOU L E and KARATHANOS V T (1997) Physicochemical studies of hard-to-cook beans (Phaseolus vulgaris), J Sci Food Agric, 74(2), 186–92. LEON J C, ALPEEVA I S, CHUBAR T A, GALAEV I YU, CSOREGI E and SAKHAROV I YU (2002) Purification and substrate specificity of peroxidase from sweet potato tubers, Plant Sci, 163(5), 1011–19. LIU K (1995) Cellular, biological, and physicochemical basis for the hard-to-cook defect in legume seeds, Crit Rev Food Sci Nutr, 35(4), 263–98. LIU Q, WEBER E, CURRIE V and YADA R (2003) Physicochemical properties of starch during potato growth, Carbohydrate Polymers, 51(2), 213–21. MARTENS H J and THYBO A K (2000) An integrated microstructural, sensory and instrumental approach to describe potato texture, Lebensm-Wiss u-Technol, 33(7), 471–82. MCCANN M C, BUSH M, MILIONI D, SADO P, STACEY N J , CATCHPOLE G, DEFERNEZ M, CARPITA M C, HOFTE H, ULVSKOV P, WILSON R H and ROBERTS K (2001) Approaches to understanding the functional architecture of the plant cell wall, Phytochem, 57(6), 811–21. MCCARTNEY L, ORMEROD A P, GIDLEY M J and KNOX J P (2000) Temporal and spatial regulation of pectic (1—>4)-β-D-galactan in cell walls of developing pea cotyledons: implications for mechanical properties, The Plant Journal, 22(2), 105–13. MCCOMBER D R, HORNER H T, CHAMBERLIN M A and COX D F (1994) Potato cultivar differences associated with mealiness, J Agric Food Chem, 42(11), 2433–9. FARHAT I A, OGUNTONA T

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and BLANSHARD J M V (1996) Effect of solvent extraction on the gelatinisation properties of flour and starch of five cassava varieties, J Sci Food Agric, 72(3), 329–36. MORRISON I M, COCHRANE M P, COOPER A M, DALE M F B, DUFFUS C M, ELLIS R P, LYNN A, MACKAY G R, PATERSON L J , PRENTICE R D M, SWANSTON J S and TILLER S A (2001) Potato starches: variation in composition and properties between three genotypes grown at two different sites and in two different years, J Sci Food Agric, 81(3), 319–28. MURAKAMI H and TAKEYAMA S (1978) Accumulation of lignin in cut edible parts of vegetables, J Japan Soc Food Nutrn, 31(1), 91–4. NJOROGE C K, KERBEL E L and BRISKIN D P (1998) Effect of calcium and calmodulin antagonists on ethylene biosynthesis in tomato fruits, J Sci Food Agric, 76(2), 209–14. PARKER M L, NG A, SMITH A C and WALDRON K W (2000) Esterified phenolics of the cell walls of Chufa (Cyperus esculentus L.) tubers and their role in texture, J Agric Food Chem, 48(12), 6284–91. PARR A J, WALDRON K W, NG A and PARKER M L (1996) The wall-bound phenolics of Chinese waterchestnut (Eleocharis dulcis), J Sci Food Agric, 71(4), 501–7. POWERS J R and DRAKE S R (1980) Effect of cut and field-holding conditions on activity of phenylalanine ammonia-lyase and texture in fresh asparagus spears, J Food Sci, 45(3), 509–10. REEVE R M and BROWN M S (1968) Histological development of the green bean pod as related to culinary texture: 2. Structure and composition at edible maturity, J Food Sci, 33(3), 327–31. RHODES M J C, WOOLTORTON L S C and HILL A C (1981) Changes in phenolic metabolism in fruit and vegetable tissues under stress. In Recent Advances in the Biochemistry of Fruits and Vegetables. Eds J Friend and M J C Rhodes, London, Academic Press, 191– 220. RIVERO R M, RUIZ J M, GARCIA P C, LOPEZ-LEFEBRE L R, SANCHEZ E and ROMERO R (2001) Resistance to cold and heat stress: accumulation of phenolic compounds in tomato and watermelon plants, Plant Sci, 160(2), 315–21. RODRIGUEZ-ARCOS R C, SMITH A C and WALDRON K W (2002) Mechanical properties of green asparagus, J Sci Food Agric, 82(3), 293–300. ROSE J K C, LEE H H and BENNETT A B (1997) Expression of a divergent expansin gene is fruitspecific and ripening-regulated, Proc Natl Acad Sci USA, 94(11), 5955–60. RUBATZKY V E and YAMAGUCHI M (1997) World Vegetables: Principles, Production and Nutritive Values (Second Edition). New York, Chapman and Hall. SCANLON M G, PRITCHARD M K and ADAM L R (1999) Quality evaluation of processing potatoes by near infrared reflectance, J Sci Food Agric, 79(5), 763–71. SCHUCH W, KANCZLER J, ROBERTSON D, HOBSON G, TUCKER G, GRIERSON D, BRIGHT S and BIRD C (1991) Fruit quality characteristics of transgenic tomato fruit with altered polygalacturonase activity, HortSci, 26(12), 1517–20. SCHWEIKERT C, LISZKAY A and SCHOPFER P (2000) Scission of polysaccharides by peroxidasegenerated hydroxyl radicals, Phytochem, 53(5), 565–70. SEALY L, RENAUDIN S, GALLANT D J, BOUCHET B and BRILLOUET J M (1985) Ultrastructural study of yam tuber as related to postharvest hardness, Food Microstruc, 4(1), 173–81. SISTRUNK W A, GONZALEZ A R and MOORE K J (1989) Green beans. In Quality and Preservation of Vegetables. Ed. NAM Eskin, Boca Raton, FL, CRC Press, 185–215. SKRABANJA V, LILJEBERG H G, HEDLEY C L, KREFT I and BJÖRCK I M (1999) Influence of genotype and processing on the in vitro rate of starch hydrolysis and resistant starch formation in peas (Pisum sativum L.), J Agric Food Chem, 47(5), 2033–9. SOZZI G O, TRINCHERO G D and FRASCHINA A A (1999) Controlled-atmosphere storage of tomato fruit: low oxygen or elevated carbon dioxide levels alter galactosidase activity and inhibit exogenous ethylene action, J Sci Food Agric, 79(8), 1065–70. STANLEY D W and AGUILERA J M (1985) A review of textural defects in cooked reconstituted legumes: the influence of structure and composition, J Food Biochem, 9(4), 277–323.


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15 Improving the texture of processed vegetables by vacuum infusion R. Saurel, University of Lyon, France

15.1

Introduction

Vacuum technology, which is also called ‘vacuum infusion’ or ‘vacuum impregnation’ (VI), is considered to be a pre-treatment for processed fruit or vegetables which improves their quality by incorporating functional ingredients such as acids, preservatives, water activity depressors or firming agents in the product structure (Saurel, 2002). Vacuum infusion technology is based upon hydrodynamic mass transfers promoted by pressure change, and it consists of putting the food product in the impregnation solution under vacuum before restoring the atmospheric pressure. This allows the occluded gas initially occupying the fruit or vegetable pores to be replaced by the impregnation solution in a quick and simple way. The treatment seems generally to adapt well to porous products and can be applied to whole or cut fruits and vegetables. Particular attention has been given to the use of this treatment to minimise the problems of post-harvest or processing-related deterioration of vegetable products. It is well established that processing treatments for fruits and vegetables that are designed to preserve them in various forms (fresh, frozen, pasteurised or dried) affect the initial organoleptic qualities such as texture, colour or flavour. With regard to the stability of vegetable texture, firmness improvement is the most common objective of vacuum technology application. For this purpose, the solutes used are firming agents such as calcium salts, gelling hydrocolloids or certain enzymes. The effect of these texture agents is indeed optimal, thanks to their large penetration into the internal porous structure of fruit or vegetable pieces. In addition, the diversity of ingredients used as infusion aqueous solutions makes it possible to generate novel structural effects and thus design new processed products.

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The aim of this chapter is first to give the description and the model of transfer which occurs during vacuum treatment and the consequent modification of the structural properties of products. Secondly, the different mechanisms involved in texture modification are discussed in the light of current or proposed future applications in the fruit and vegetables sector.

15.2 Vacuum infusion technology 15.2.1 Mass transfer phenomena Comprehension and control of the mass transfer resulting from the application of VI technology are particularly important with respect to the structural effect required for the food product. Indeed, the nature of the mass transfer determines the quantity of the reactive substance which penetrates into the product, its distribution in the plant structure and the possible structural modifications generated by the vacuum step. Compared to a classical diffusion process (candying, salting, soaking, osmotic dehydration…) which is carried out by simple dipping or prolonged immersion of the product in the solution for several hours or days, VI treatment has the advantage of a fast penetration – only a few minutes – of the active substance directly into the internal structure of the product. The mass transfer occurring during the vacuum treatment is mainly governed by a hydrodynamic phenomenon which is due to pressure changes. When a vacuum pulse is applied, trapped gases are expanded and partially removed from the food matrix during the vacuum application. After atmospheric pressure is restored, a positive pressure differential results which allows penetration of the liquid into the free voids in the structure until internal and external pressure equilibrium is reached. The time taken to reach a vacuum usually depends on the efficiency of the vacuum system (pump, closed volume of apparatus...) and lasts at best for only a few seconds. In most cases, products have to be maintained under vacuum for a few minutes to ensure good extraction of internal gases, but this step could be unnecessary if degassing is completed during the pressure drop. At the end of the treatment, vacuum release is generally obtained instantly. Fito and Pastor (1994) and Fito (1994) gave a clear description and model of the mass transfer phenomena – referred to as the ‘hydrodynamic mechanism’ (HDM) – observed in vacuum technology. Intercellular spaces in plant products are described as a set of elementary cylindrical pores being occupied by an ideal gas undergoing isothermal compression (Fig. 15.1). The penetration of solution into the ideal rigid pores breaks down into two stages. In the first part of the treatment, corresponding to atmospheric immersion and vacuum application, the pores fill by capillary action. Secondly, when restoring normal pressure, the resulting driving force induces liquid flow in the pores. The quantity of external liquid transferred can be almost as great as the available void space in the food structure. The impregnated sample volume fraction

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Gas

Liquid

Solid (a)

(b)

Residual gas

(c)

(d)

Fig. 15.1 Main stages during vacuum infusion of porous food immersed in a liquid. The situation in an elementary ideal pore (adapted from Fito, 1994): (a) the capillary effect under normal pressure; (b) degassing under vacuum conditions; (c) capillary effect under reduced pressure; (d) HDM at restored normal pressure.

(X) (m3 impregnated solution/m3 initial sample), usually measured by a gravimetric method, has been modelled on the basis of the HDM and the Hagen-Poiseuille equation. At its simplest X is a function of the product effective porosity (εe) and the compression rate (r = (P2 + Pc)/P1; P1 is the applied vacuum pressure, P2 is the restored atmospheric pressure, Pc is the capillary pressure). Thus, Fito and colleagues established that the simple


367

expression for the volume fraction occupied by the liquid in the fruit or vegetable product after vacuum infusion is: X = εe · (1 – 1/r)

[15.1]

Capillary pressure appears to be negligible with respect to the driving force imposed on the system when the work is carried out at sufficiently low pressure (lower than 600 mbar according to Fito, 1994). Effective porosity is thus determined from an experimental procedure involving calculating the slope of the linear function obtained by adjusting the X versus 1 – 1/r curve (Fito, 1994; Del Valle et al., 1998a), and it is close to the percentage of sample volume initially occupied by the gases (Calbo and Sommer, 1987). In the case of fruit and vegetables, the porosity values were found to be extremely variable depending on the raw materials: for example average εe values are 0.20 for apple and 0.05 for apricot. Moreover, the effective porosity will depend not only on the type of fruits or vegetables, but also on their variety and their maturity (Del Valle et al., 1998a). Several authors (Fito et al., 1996; Salvatori et al., 1998) reported that the HDM mechanism is accompanied by deformation of the food matrix which influences the final liquid uptake and affects the mechanical properties of the product after treatment. The deformation phenomenon corresponds firstly to an extension of the internal occluded air volume inside the product when degassing at the time the vacuum is created, and secondly to a partial retraction in pore volume caused by structure relaxation at the time of return to atmospheric pressure. The total structure relaxation in the fruit pores may not be instantaneous, but it does not exceed a period of five minutes. As a function of the viscoelastic properties of the internal structure and the cohesive forces in plant cellular tissue, the deformation–relaxation phenomenon correlated with the pressure driving forces, resulting in variation of the effective porosity value and of the quantity of infused liquid in the product. A more complete and accurate equation was proposed by Fito et al. (1996) to take account of this phenomenon as follows: X = εe · (1 – 1/r) + γ + (1/r) · γ1

[15.2]

where γ1 and γ are the relative deformations of sample volume measured respectively at the end of the vacuum step (Fig. 15.1a) at the end of the process (Fig. 15.1d). At relatively low pressure, r is high and it may be accepted that: X ≅ εe . (1 – 1/r) + γ

[15.3]

Experimental values of the different parameters of the model are available for different fruit and vegetable varieties (Table 15.1). The data indicate that some poorly porous products like mango, peach or carrot could be impregnated with a significant quantity of liquid owing to the deformation phenomena and to the loss of native liquid – as indicated in Table 15.1 by negative X1 value – that is replaced by the solution. In other cases, the deformation

Peach (Miraflores) Pear (Passa Crassana) Pineapple (Espanola Roja)

0.9 ± 0.2 –4 ± 0.3 –6 ± 0.02

5.4 ± 0.5 2 ± 0.3 2 ± 0.02

Slice (t = 1 cm)

Dice (t = 2.5 cm) Slice (t = 1 cm) –6.5 ± 0.6

-0.2 ± 0.2

6.8 ± 0.6

1.8 ± 0.4

–

–

–1.3 ± 0.2

–

–

2.8 ± 0.2

–2.7 ± 0.3

2.8 ± 0.2

–2.29 ± 0.13

–5.0 ± 0.4

2.1 ± 0.4

2.0 ± 0.3

–4.2 ± 0.3

1.7 ± 0.3

Slice (d = 2 cm, t = 2 cm) Rectangle (2 × 7 cm) –

–

–

Slice (d = 2 cm, t = 3 cm) Slice (d = 2 cm, t = 2 cm) Slice (d = 2 cm, t = 2 cm) Slice (d = 2 cm, t = 2 cm) Half (d = 5–6 cm) Slice (d = 2 cm, t = 3 cm) Quarter 13.5 ± 1.3

5.3 ± 1.2

2.3 ± 0.4

2.2 ± 0.7

2.1 ± 0.4

14 ± 0.03

–0.4 ± 0.2

8.9 ± 0.4

5.7 ± 0.8

5.3 ± 0.9

5.6 ± 1.0

40 ± 0.05

5 ± 0.2

14.2 ± 0.5

0.89 ± 0.14

6.0 ± 0.7

1.4 ± 0.8

0.8 ± 0.5

8.9 ± 1.2

12.2 ± 1.8

15 ± 2

–6.0 ± 0.5

–2.4 ± 1.0

–0.6 ± 1.2

18.7 ± 0.7

1.2 ± 0.6

3.7 ± 1.3

3.4 ± 0.5

2.9 ± 0.4

21 ± 0.04

6 ± 0.3

5.9 ± 0.4

0.5 ± 0.5

8.6 ± 1.2

5.6 ± 0.5

15.9 ± 1.2

15.5 ± 1.1

16.6 ± 1.2

18.4 ± 1.8

εe

–

–

2.6 ± 0.5

–

–

9.9 ± 1.3

2.3 ± 0.8

–

–

25.4 ± 1.4

21.6 ± 1.0

23.8 ± 1.0

–

Real porosity

Fito et al. 1996) Salvatori et al. 1998) Salvatori et al. (1998) Salvatori et al. (1998) Fito et al. (1996) Fito et al. (1996) Salvatori et al. (1998) Salvatori et al. (1998) Fito et al. (2001a) Fito et al. (2001a) Salvatori et al. (1998) Fito et al. (2001a) Fito et al. (2001a)

References

Apple (G. Smith) Apple (G. Smith) Apple (Red Chief) Apple (Golden) Apricot (Canino) Banana (G. Cavendish) Kiwi (Hayward) Mango (T. Atkins) Melon (Inodorus) Orange peel

X*

Shape (d = diameter, t = thickness)

Product γ

Table 15.1 Some Fito model parameters and vacuum impregnation responses of some fruits and vegetables. Vacuum pressure: 50 mbar

X1 *

Texture in food

γ1

368

Whole (d = 5–6 cm) Whole (d = 2–2.5 cm) Dice (t = 2.5 cm) Dice (t = 1.5 cm) Slice (d = 4.3 cm, t = 1 cm) Dice (t = 2.5 cm) Slice (d = 4.3 cm, t = 1 cm) Whole (d = 3.3 ± 0.2 cm) Whole (d = 3–4 cm) Square piece (2.5 cm side) Square piece (2 cm side, t = 1 cm) Dice (t = 2.5 cm) Slice (d = 4.3 cm, t = 1 cm)

–2.1 ± 0.2 – –4.6 ± 0.8 –18 ± 3 –5.79 ± 0.08 –9.9 ± 0.9 –8.9 ± 0.5 –3 ± 2 – –21 ± 5 –48 ± 4 –3.8 ± 0.6 –3.43 ± 0.03

2.9 ± 0.4 – 1.3 ± 0.6 1.2 ± 1.1 1.7 ± 0.1 –1.8 ± 0.7 0.7 ± 0.2 1.9 ± 0.5 – 7±4 3±1 2.3 ± 0.6 3.2 ± 0.8

3±2

5±2

9±1

8±4

20 ± 3

49 ± 2

30 ± 4

25.7 ± 2.6

–4.8 ± 1.7 10 ± 5

34 ± 2

52 ± 1

24 ± 3

5.8 ± 0.3

16 ± 3

0.0 ± 1.1

1.6 ± 0.5

–37 ± 5

3.3 ± 0.4

3.0 ± 0.6

7±2

4.2 ± 1.1

1.4 ± 3.6 3.1 ± 1.3

0.2 ± 0.7

–4.0 ± 0.6

5±2

16 ± 3

41 ± 2

20.0 ± 0.5

34.4 ± 1.8

37 ± 3

54 ± 1

64 ± 4

2.6 ± 0.9

13 ± 3

4.47 ± 0.03

3.0 ± 3.4

4.8 ± 0.3

18 ± 2

18 ± 2

–

10.3 ± 0.5

–

37 ± 2

–

59.6 ± 0.5

–

0.3 ± 0.8

6±2

–

6.3 ± 1.6

*X1 and X are volume fractions of initial sample impregnated at the end of the vacuum step and at the end of the atmospheric step.

Zucchini

Mushroom (Agaricus b.) Mushroom (Gurelan 55) Oyster mushroom Oyster mushroom Zucchini

Eggplant

Eggplant

Carrot

Carrot

Strawberry (Chandler) Strawberry (Chandler) Beetroot

Salvatori et al (1998) Fito et al. (1996) Gras et al. (2002) Gras et al. (2002) Gras et al. (2003) Gras et al. (2002) Gras et al. (2003) Gras et al. (2002) Fito et al. (1996) Gras et al. (2002) Gras et al. (2003) Gras et al. (2002) Gras et al. (2002)

Improving the texture of processed vegetables by vacuum infusion 369

370

Texture in food

phenomena tend to reduce the product porosity. Taking account of these variations, the porosity estimated by the previous models is defined more precisely as the sample volume fraction available for the HDM mechanism and depends on the vacuum conditions (Mujica-Paz et al., 2003). Following the calculation of X by the Eqs (15.2) and (15.3), the content of the active substance in the product after the infusion step is determined from the concentration of the substance in the solution and the density of this solution (Fito et al., 2001a, b; Roa et al., 2001). The limitation of the previously proposed Eq. (15.2) is that the model was strictly derived from capillary flow theory and established for isotonic solutions. This gives rise to two problems. The first is that this approach is not adapted in the case of the infiltration of non-Newtonian liquids or high-viscosity solutions which induce significant flow pressure drop. Moreover, the compressed structure may generally relax following the vacuum step, thus promoting suction of the external solution. This liquid intake may be inhibited in high-viscosity solutions because of the equilibrium between the relaxation force of the matrix and the friction force, thus resulting in sample deformation instead of impregnation. In this case, product volume relaxation may occur when the sample is removed from the viscous solution, thus gaining ambient air instead of liquid (Barat et al., 2001). The influence of viscosity is particularly important because the dissolution of solutes such as gelling agents leads to very viscous liquid whose flow into pores appears limited. Figure 15.2, reporting results obtained in our laboratory, shows the effect of the solution viscosity – adjusted with low methylated (LM) pectin – on the X values of 30

25

X (%)

Effective porosity εe = 0.3 X = εe . (1 – 1/r) · [1 – 0.078 Ln (µ)] 20

15

10

0

100

200

300

400

500

600

700

µ, viscosity (mPa.s)

Fig. 15.2 Effect of viscosity on the impregnated sample volume fraction X of apple slices (diameter 20 mm, thickness 8 mm) after vacuum infusion at 20 °C in water and different pectin solutions. Vacuum treatment conditions are 50 mbar for 1 min 15 s.


371

apple cylinders chosen as a model fruit during vacuum treatment. The decrease of X values with viscosity indicates a significant effect on the hydrodynamic mass transfer which could not be predicted from the previous model. We propose to take account of the viscous effect during HDM by correcting the driving force with a viscosity-dependent term. Thus, neglecting the final deformation value γ in the case of apple, the expression for X becomes: X = εe . (1 – 1/r) . [1 – k Ln(µ)]

[15.4]

where µ is the solution viscosity (mPa.s) and k is a specific empirical factor for the product and the solute considered. The second problem is that the Fito model is not able to represent directly the mass transfer phenomena when hypertonic solutions are used. This is due to osmotic phenomena occurring simultaneously with the hydrodynamic mechanism which could affect the penetration of external liquid. The osmotic phenomena decrease the mass gain of the product measured after vacuum impregnation because of the water loss due to cell plamolysis, and seem to block partially the liquid flow in the pores due to structure collapse (Barat et al., 2001). With vacuum pressure, the composition and concentration of the aqueous solutions represent the main governing factors that modify the liquid intake in porous fruits or vegetables. The other variables upon which the vacuum process depends, i.e. temperature of impregnation solution, time to achieve vacuum, time maintained under vacuum, time to restore atmospheric pressure, have not received a great deal of systematic study. After restoring normal pressure, the time during which the product is maintained in the solution is also non-negligible with respect to the relaxation time in the pores and the flow kinetic of liquid. However, in most cases no evolution was reported after a five minutes period (see above). The few existing data concerning the effect of temperature showed that a slight variation in mass transfer rate was induced (Hoover and Miller, 1975). In practice, temperature conditions are limited when nearing the liquid boiling point under vacuum, for example close to 46 °C for water at 100 mbar. Finally, it can be suggested that the temperature effect on liquid viscosity or food matrix elasticity certainly has a role in vacuum technology.

15.2.2 Structural modifications As mentioned before, some fruits or vegetables tend to lose native liquid during processing at very low pressure, thus revealing cell structure damage due to mechanical strain. Thus, the deformation–relaxation phenomenon could induce irreversible effects, involving in some cases rigidity loss due to embrittlement or rupture in the cell walls junctions, as pointed out by several authors. From microscopic observations of kiwi fruit before and after vacuum treatment with glucose solutions, Muntada et al. (1998) noticed that the size of the cells in the infused plant tissue and their arrangement were preserved

372

Texture in food

even if ruptures in the cellular walls were observed. This was in agreement with the previous work of Bolin and Huxsoll (1987) on apple, which showed that vacuum impregnation causes the rupture of a non-negligible number of cells and structure collapse. When isotonic solutions are used as impregnation media, the intercellular spaces are filled with the liquid and cell turgor is maintained. With hypertonic solutions, as generally used in the osmotic dehydration (OD) process, microscopic observations show plasmolysis of the cells in all cases. Nevertheless, the structural change, which influences the final mechanical properties of osmo-dehydrated products, develops differently according to whether OD is preceded by a vacuum step (pulsed vacuum OD, PVOD) or is carried out at atmospheric pressure. In the classical OD process, water loss mainly occurs cell to cell by osmotic mechanisms and during the cell shrinkage cell wall significantly deforms while remaining bonded to the plamalemma. On the other hand, in PVOD treatment, plamalemma separates from cell wall throughout shrinkage and the liquid phase flows into the cell cavity from the intercellular spaces through the permeable cell wall. In this case, cell cohesiveness and cell wall integrity are enhanced and a less deformed shape of cells can be distinguished (Muntada et al., 1998; Salvatori et al., 1998; Barat et al. 1999). All of the phenomena involved in vacuum application (pores filling, pores deformation, cell damage) have consequences for the rheological properties of the pre-treated products. Del Valle et al. (1998b) noticed some undesirable structure changes as a result of vacuum infiltration of water, as indicated by a reduction in the textural attributes of cylindrical apple samples measured by compression-to-failure tests. As the absolute pressure level decreased from 59.9 to 9.3 kPa, fracture-point (the minimal required force to cause failure) values decreased from 46 to 32% of the value measured for an untreated control sample. The relative ‘fragility’ of impregnated samples, which was related to cell de-bonding or cell fracture, was estimated to be proportional to the vacuum pressure and the infiltrated liquid quantity. Vacuum infusion at 50 mbar of cylindrical apple samples with hypotonic or isotonic solution does not significantly change the maximum stress value, assessed by stress/relaxation tests, compared with that obtained for fresh apple (Martinez-Monzo et al., 1998). Nevertheless, the use of an isotonic pectin solution produces a significant decay in the maximum force. The high viscosity of this solution made the flow of the liquid into the apple pores difficult when atmospheric pressure was restored. Thus the change in pressure resulted in a deformation of the cellular structure of the sample, affecting mechanical behaviour. Moreover vacuum impregnation with hypertonic solutions strongly decreases the maximum stress value due to both the loss of cell turgor that causes cell de-bonding and the reduction of the mechanical modulus of vegetal tissue. After VI treatment, samples became all the more ‘less elastic–more viscous’ because the solutions are hypertonic. The ‘more viscous’ feature observed for VI samples must be partially explained by the


373

out-flow of the impregnated liquid through the fruit pores during the compression/relaxation test. The structural modifications observed in the VI process appear relatively complex and mainly depend on the following factors: vacuum pressure, osmolarity and viscosity of the solution, and mechanical properties of the vegetable products. In order to minimise the deterioration of cellular tissue, we would thus recommend working as far as possible with an isotonic solution which is not very viscous and at moderate vacuum pressure (absolute pressure between 50 and 100 mbar). This is a compromise solution between the uptake of a pre-determined quantity of solute and the possible denaturation of the fresh food structure. As will be emphasised hereafter, the damage to food structure at the infusion stage may be masked by reinforcing the cell wall structure by calcium or by strengthening the wall with gelling agents or other solutes, which could improve the texture of the products at the processing stage still more. The paradox of the vacuum technique becomes clear when considering the negative effect of the deformation–relaxation phenomenon: the moderate loss of integrity which is a consequence of the vacuum treatment can be compensated to a large extent by the active role of the transferred solutes.

15.3 Applications to improve texture The use of vacuum technology has been proposed as a pre-treatment in many processing and product applications: post-harvest storage, frozen fruits or vegetables, blanched, canned, cooked, osmo-dehydrated products, and so on. The major way in which vacuum technology has been exploited is the modification of food structure in order to improve the strength and firmness of products after physical treatment for preservation and/or during storage. Different ingredients or additives are used which have specific effects on food texture.

15.3.1 Structuration mechanisms involved and advantages The main changes affecting the mechanical behaviour of plant tissues during thermal treatments (freezing, blanching, cooking, pasteurisation, sterilisation) are generally attributed to the following phenomena: • cell membrane alteration resulting in loss of cell turgor, juice loss and softening; and • de-bonding or rupture of cell wall decreasing cell wall resistance. It is possible to partially compensate for these types of damage by infusing specific solutes to strengthen the structure of vegetable products. Thus, we can distinguish four possible actions to improve texture, which gain advantage from the vacuum technology application.

374

Texture in food

• The protective action of solutes through a physicochemical mechanism. The work of Martinez-Monzo et al. (1998) seems to be a particularly representative example of this possibility, offering promising prospects for the development of a pre-treatment that will modify the initial composition of porous fruit, making it more resistant to damage caused by the freezing–thawing process. The infusion of concentrated cryoprotectant solutions (low molecular weight solutes) into apple pieces before freezing significantly reduced the freezable water content. This may contribute to a decrease in the damage produced by ice crystals because of the reduction in their volume fraction. After impregnation with modified grape must as the chosen cryoprotectant, cryo-scanning electron microscopy observations of the cellular structure of the apple showed that the formation of ice crystal was similar in intercellular space and inside the vacuole, but no apparent disturbances in the cell (size, shape and intracellular arrangement) were detected. • The role of physiologically active solutes in the case of fresh and postharvested fruits or vegetables. Solutes such as cations or plant hormones act as membrane regulators, but they are of limited interest in the case of living plant tissue. • The strengthening of the cell wall structure by calcium addition which may be amplif ied by the action of endogenous or exogenous pectinemethylesterase. This appears to be a more efficient mode for thermally processed products and will be largely described hereafter. • The formation of an additional gel network in the pores or in the intracellular spaces filled with thickening or gelling hydrocolloids. The efficiency of this process depends strongly on the porosity of the product since significant penetration rates of gelling agent solution are required to generate texture modification. As the different actions directed at structure protection and texture improvement described above are often linked or combined, we will specify the preservation mechanisms of solutes for the different applications presented in the following sections.

15.3.2 Preserving fruit firmness by calcium or polyamines infusion Post-harvested products The process of dipping whole fruits in aqueous preservative solutions, which is improved by vacuum application, has been used to prolong the postharvest shelf life of many products: apples (Scott and Wills, 1977, 1979; Lidster et al., 1986), lemons (Valero et al., 1998a, b), avocados (Wills and Sirivatanapa, 1988), mangoes (Tirmazi and Wills, 1981), tomatoes (Wills and Tirmazi, 1979), strawberries (Ponappa et al., 1993). The compounds


375

used in the impregnation solution are usually calcium salts (mostly calcium chloride) and many plant hormones (polyamines). Vacuum infusion seems to be used as an alternative to the pressure infiltration process (Poovaiah, 1986; Wang et al., 1993). The benefit of calcium application is generally related to the ability of the cation to interact with cell membranes and walls, as well as to its regulatory role at the metabolic level. The beneficial effects of calcium enrichment of whole fruit after harvest have multiple causes (Poovaiah, 1986; Stow, 1989; Glenn and Poovaiah, 1990; Picchioni et al., 1998). First, calcium plays a special role in maintaining the middle lamella and the cell wall rigidity in fruits and other storage organs by interacting with the pectic acid in the structure to form calcium pectate. Extensive crosslinking may facilitate packing of pectic polymers and form a cell wall network that increases mechanical strength and restricts access to hydrolytic enzymes such as polygalacturonase. Second, calcium interacts with the cellular membrane by modifying its structure and it exerts a regulating role on its permeability and on the transport of some substances involved in product ripening and senescence. Thirdly, many enzymatic reactions (e.g. polypeptides phosphorilation by protein kinase) would be calcium-dependent. The presence of impregnated calcium thus allows the cell wall resistance and cell turgor that are mainly responsible for fruit firmness during storage and/ or ripening to be maintained. Through this multiple action, calcium acts favourably to delay senescence and to control physiological disorders during fruit or vegetables storage. Vacuum infiltration of calcium applied to various apple varieties (Gravenstein, Cox’s Orange Pippin) and harvests made it possible to decrease the physiological disorders after three weeks, storage at ambient temperature (around 20 °C) while puncture testing indicated a significant gain in firmness (Scott and Wills, 1977). Poovaiah (1986) found that the firmness of Golden Delicious apples stored for 15 weeks at 0 °C was improved after vacuum infusion in a 3–4% calcium chloride solution. At the same time, the ascorbic acid content was enhanced up to two-fold, while carbon dioxide production and ethylene evolution appeared to be significantly reduced. After six months, storage at 2 °C, Golden Delicious apples infiltrated with CaCl2 (4% v/w) solution using a combination of vacuum (–28 kPa) and pressure (28 kPa) were firmer and had greater tensile strength than untreated fruit (Glenn and Poovaiah, 1990). Tissue firmness values from puncture testing and tensile strength values were correlated with the calcium content of fruit samples, thus indicating an uneven distribution of calcium within the fruit. Although the infiltration was not uniform, firmness improvement was mainly explained by the increased structural integrity of the middle lamellar region maintaining cell cohesiveness. Vacuum infiltration of polyamines, which are positively charged molecules, could play the same role as calcium in delaying softening and senescence of plant products, due to their ability to bind the cell wall or to stabilise the membrane, and to their implication in physiological processes. The similar action of these compounds is illustrated by the work of Valero et al. (1998a,

376

Texture in food

b) who compared the effect of different impregnation runs with calcium and with polyamines on the preservation of lemons during the ripening stage. It was shown that vacuum infiltration of putrescine or gibberellin increased the firmness of whole lemons preserved for 21 days at 15 °C up to 50% at the same time as it delayed the colour changes in unripe-picked fruits. Finally, the work of Lidster et al. (1986) displayed the potential of postharvest vacuum infusion in solutions containing flavonoid glycosides (quercetin, rutin) and phenolic acid (chlorogenic acid) to retard fruit softening of ‘Spartan’ and ‘Golden Delicious’ apples held at 20 °C and 0 °C. This effect was mainly explained by the inhibitory properties of these compounds on β-galactosidase. The delay in ripening and senescence would thus make it possible for many products to be maintained for longer in the distribution chain even at ambient temperature, which is particularly interesting for most developing countries where little or no refrigeration is available. Minimally and thermally processed products Ponappa et al. (1993) compared the effect of vacuum impregnation with calcium and with different polyamines on the preservation of fresh strawberry slices during four and nine days’ storage at 20 °C and 1 °C, respectively. Among the polyamines studied in this work, spermidine and spermine had a greater effect than putrescine but appeared to be less effective than calcium in maintaining the firmness of the fruits especially at the highest temperature studied. Recently, Fito et al. (2001a) used vacuum infusion in order to design fresh vegetal food fortified with nutrients or physiogically active compounds such as calcium. Following this approach, Gras et al. (2003) have studied the interactions of the cation with the cellular matrix when isotonic sucrose and calcium lactate solution were impregnated in different vegetables pieces (carrot, eggplant and oyster mushroom). The stress–strain curves registered during compression tests indicated that the calcium-infused samples of carrot and eggplant presented a less viscoelastic, harder texture response compared to the VI control without calcium lactate. In addition, sample volume recovery after compression was greatly reduced by the Ca presence thus indicating irreversible fractures in the cell network due to its higher rigidity and fragility. In contrast, the calcium infusion did not modify the oyster mushroom microstructure and mechanical response since mushroom cellular wall does not contain pectin. Del Valle et al. (1998b) have studied the effect of different pre-treatments (blanching and calcium infiltration) on the texture of apple cylinders osmodehydrated in sucrose solutions. The authors reported that the infiltration of calcium solutions partially compensated for the undesirable textural changes caused by the vacuum treatment (see Section 15.2.2). Samples subjected to a vacuum treatment with 2% CaCl2 retained texture during osmotic dehydration better than any other sample (untreated control or blanched apple with or without calcium) which was explained by a more extensive crosslinking of


377

demethoxylated pectin by a large excess of calcium present in the intercellular spaces. Moreover, it was qualitatively assessed that vacuum treated apple cylinders retained their external geometry and colour better than any other. Calcium lactate infusion in fresh whole or sliced strawberries improved their texture and reduced their weight loss measured after canning (Main et al., 1986) owing to the presence of calcium which reinforces the cell wall structure by forming pectates. In addition, Main et al. (1986) showed that calcium impregnation on whole or sliced strawberries prior to freezing only slightly improved the resistance of thawed fruit to shear. The low effectiveness of calcium in improving firmness was explained by insufficient demethylation of endogenous pectins in the fruit for the purpose of pectate formation. When the freezing/defrosting cycle was followed by heat treatment, the effect on texture was greater owing to increased enzymatic demethylation activated during temperature rise. The improvement of texture by calcium infusion was also observed by French et al. (1989) with canned apricot – Patterson cultivar fruits – even if the chelator effect of exogenous or endogenous citrate tended to limit calcium effectiveness, especially on low maturity fruits because of their stronger acidity.

15.3.3 Modifying texture by enzyme addition The vacuum infusion of enzymes in the structure of fruits and vegetables has been mentioned in connection with designing enzymatically modified food (McArdle and Culver, 1994; Baker and Wicker, 1996; Culver et al., 2000). Enzymatic modification of the internal characteristics of intact fruit or vegetables by vacuum infusion leads to an interesting transfer/reaction process in food matrix engineering. The applications of enzyme vacuum infusion appear to be numerous, depending on the specific activity and function of the enzyme: peeling, firming or softening, generating volatile aroma from glycosidic precursors, removal of off-flavours, degradation of non-digestible or toxic components, and so on. A more advanced application now in commercial use is the use of infused pectinases and cellulases for easier peeling of citrus fruits (Rouhana and Mannheim, 1994; Pretel et al., 1997). The applications involving structure modification have been studied successfully and the vacuum infusion of exogenous PME in fruit was found to be effective in increasing firmness in thermally processed foods. PME is a cell wall-bound enzyme in fruits and vegetables, which deesterifies pectin. In post-harvest ripening of fruits, PME activity precedes depolymerization by polygalacturonase, resulting in fruits softening. However, the PME is postulated to increase firmness of fruits and vegetables by demethylation of endogenous pectin and subsequent chelation of divalent cations by ionised carboxylic acid groups on adjacent pectic acid chains. In the presence of calcium, the firming effect is proportional to the natural PME activity preceding the thermal treatment, and it can be reinforced by vacuum–assisted infusion of exogenous PME. As reported by Javeri et al.

378

Texture in food

(1991) for blanched (95 °C, 30 s) or blanched-canned (104 °C, 12 minutes) peaches, vacuum-infused citrus PME and calcium increased the firmness of these thermally-processed products up to a value nearly four times that of un-infused controls (Javeri et al., 1991). Vacuum treatment combining exogenous citrus PME and calcium chloride infusion was applied successfully to whole strawberries prior to freezing (Suutarinen et al., 2000, 2002a; b). Texture tests assessing compression force and area of deformation curve were performed in an Ottawa cell system to compare the firmness improvement of enzymatically treated berries notably with that of fruits treated with calcium only or untreated. The pre-freezing vacuum treatments with PME and calcium had the most significant influence on the firmness of frozen strawberries. Furthermore, jams made from the frozen strawberries vacuum treated with CaCl2 and PME had significantly higher firmness values than the reference samples (approximately twice as great). Fourier transform infrared microscopy and bright-field microscopy studies proved that the pectin, protein and structural carbohydrates components of the strawberries treated with PME were more stable than those of the controls. Vacuum treatment with CaCl2 and PME seemed also to result in interesting improvements in sensory attributes such as wholeness of berries and redness of the colour of the jam. Recently, vacuum impregnation with a commercial fungal PME preparation (Rapidase® FP Super) and calcium was proposed as a means of increasing the firmness of pasteurised 1 cm apple cubes, strawberry halves and whole raspberries (Degraeve et al., 2003). In order to check the effectiveness of the VI pre-treatment, the fresh fruits were infused following an atmospheric dipping procedure or vacuum-treated with solutions containing water, calcium alone, or calcium and PME. The classical atmospheric procedure consisted of putting the fruits in a 35°Brix sucrose syrup with or without the firming agents during the pasteurisation cycle. Classically-prepared and vacuumimpregnated samples were both pasteurised in a 35°Brix sucrose syrup representing a simplified standard recipe for fruit preparation. The heat treatment was carried out by maintaining the blends during 20 minutes at 40 °C to eventually activate PME followed by holding the fruit preparation at 85 °C for about 15 minutes to both pasteurise the fruit preparation and inactivate the enzyme. The compression force on the pasteurised fruits separated from the syrup was measured in an Ottawa cell system after 48 hours storage at 4 °C. The firmness of the fruits treated with PME and calcium was always found to be higher than that of the controls. The results (Fig. 15.3) indicated that the effectiveness of vacuum-assisted impregnation strongly depends on the plant tissue considered. Raspberry, having a polydrupe structure, is poorly porous and very fragile, and a high degree of softening of the fruit structure was observed after vacuum application at 50 mm Hg; this observation points to atmospheric treatment being more advantageous in improving firmness in raspberries. The two types of pretreatment when applied to Granny Smith apples resulted in similar firmness;


379

40 35

Firmness (kg)

30 25 20 15 10 5 0 Apple

Raspberry

Control

CaCl2

Strawberry

CaCl2 + PME

(a) 35

Firmness (kg)

30 25 20 15 10 5 0 Apple

VI: control

Raspberry

VI: CaCl2

Strawberry

VI: CaCl2 + PME

(b)

Fig. 15.3 Firmness of pasteurised strawberry halves, apple cubes and whole raspberries treated by a classical (a) or a vacuum-assisted (b) infusion procedure. (Reprinted from Journal of Food Science, 68(2), 715–21© Institute of Food Technologists, Chicago, Ill, 2003).

the mechanical strength and good thermal resistance of apple and the small size of the fruit pieces used were mentioned to explain this behaviour. Vacuum infusion was more efficient for strawberry halves, which have fragile and intermediary porous structure, because it favoured the penetration of the firming agents in the fruit pores. The authors, who also studied the influence of infusion solution composition on strawberry firmness, proved that thresholds exist in the solution concentration of both PME and CaCl2 at which maximum firmness is reached. These correspond to respective thresholds in PME and calcium contents in the fruit. The PME content necessary for maximum firmness varied logically inversely with the reduction in activation time applied during the heating operation.

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The use of exogenous PME to modify cell wall structure of fruits or vegetables is a promising tool for thermally-processed products since the enzyme has to be activated and inactivated by heating. In this case, the processed food does not require any specific reference on the label (i.e. as food ingredient or additive) to enzyme use. Furthermore, the use of enzyme with vacuum technology is adapted to vegetable products with low porosity since the enzymatic firming process generally requires a low level of PME activity. To reach a definitive conclusion on this advantage, the factors limiting PME penetration in plant tissue under vacuum application are being further studied at the University of Lyon (Guillemin, 2003).

15.3.4 Processed fruit firming by infusion of gelling agents The infusion of gelling agents in the fruit or vegetable structure allows texture improvement either by generating intercellular bridges in the pores complementing the plant cell wall network or by forming bonds between the added hydrocolloids and the cell wall components. However, the intercellular gel can modify the texture response only where the porous structure is high and the solute gain is significant, especially as mass transfer phenomena are generally limited by the high viscosity of gelling agent solutions. In addition, gel formation for many hydrocolloids (pectin or alginate) works with calcium, and the use of the cation is usually needed. Because of this the combination of the two reagents in the same solution is not appropriate due to the risk of causing thickening or gel formation before infusion. Thus two vacuum infusion cycles are generally necessary: the first with the solution containing the gelling agent to fill a large fraction of pores and the second with the calcium to complement the residual free spaces. Otherwise, infused gelling agents could react with the endogenous calcium or with the calcium present in the food medium – e.g. a sauce or a syrup – where the fruit or vegetable pieces would be incorporated. Other medium conditions, such as concentration, temperature or pH, exist to make the gelling effective. Different mechanisms are thus involved depending on the hydrocolloid type. This complexity makes the control of the application more difficult, as was the case for the firming agents proposed in the previous sections. Among the well-known applications, the vacuum treatment of button mushroom with xanthan gum before blanching and canning has been shown to improve the weight yield and the organoleptic quality of the final product (Gormley and Walshe, 1986). Xanthan impregnation tended to decrease the shrinkage of mushroom during the blanching/canning cycle and thus to reduce the product weight loss. The pre-treatment with xanthan led to a more acceptable and less tough texture of canned mushrooms. This ‘softening’ effect of the vacuum treatment on canned mushroom is a desirable feature since canned mushrooms often have a ‘hard’ texture. The benefit is due presumably to the thickening property of the xanthan gum solution (0.5–1% w/w) which occupies the wide-open hyphae structure of mushroom and prevents expulsion during


381

blanching and retorting. Some of the xanthan molecules might also be bound by the mushroom proteins. Demeaux et al. (1988) indicated that in terms of weight loss reduction of canned mushroom, the use of gelling agents such as egg white proteins is much more effective than xanthan gum which does not gel. The freezing/thawing cycle applied to fruits or vegetables causes substantial damage to the cellular structure, that is denaturation of the membranes and rupture of the cell walls by ice crystals, leading to loss of turgor and rigidity. This generally results in a strong juice exudation when defrosting the product. With the aim of limiting these problems, Barton (1951) attested that fresh fruits mixed with sugar and gelling agents and consequently submitted to a vacuum step give frozen/defrosted products with better organoleptic quality. In the case of strawberry slices, as proposed by Barton, the use of pectin and alginate before freezing made it possible to maintain the shape, weight and colour of the fruit to a greater degree than was the case for untreated fruit this was particularly so when high methylated (HM) pectin was used. Preliminary vacuum impregnation of strawberries in solutions containing gelling agents was proposed by Cierco (1994) as a new method for improving the quality of frozen fruits. Using this process, frozen/thawed strawberries were obtained which maintained the features and the taste of fresh ones even after several years’ storage at –20 °C and that are usable for traditional pastry-making. More recently, Matringe et al. (1999) showed the possibility of introducing various gelling hydrocolloids (gelatine, pectin, alginate and starch) through the application of vacuum on fresh apple pieces before freezing. Texture measurements by compression test on apple samples infused in that way showed firmness improvement with certain gelling agents just after vacuum treatment (Fig. 15.4). If the gelling agent uptake is sufficient, a structuring 140

Firmness (N)

120 100 80 60 40 20 0

Co

nt

ro

W at l

er

Ge

Al

Pe

cti

n

gin

5%

+

3%

Ca

Cl

2

lat

in

+

Ca

Cl

2

1,

7%

*

5%

1, 7%

*

Fig. 15.4 Firmness of apple slices (1 cm thickness, 2 cm diameter) after vacuum impregnation with water and different texture agents. Compression speed: 0.5 mm/s. Relative deformation: 15%. *double impregnation

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Texture in food

effect is also observed on the frozen-defrosted product. An example of this texture modification is presented in Fig. 15.5. The ‘cuttability’ – defined as the force to cut a 1 cm thick apple cube measured by a texture analyser equipped with a blade – of samples impregnated with gelatine appeared to exhibit behaviour similar to that with a simple hydrocolloid gel. Indeed, apple dices treated with gelatine before freezing definitely showed higher gel strength (the slope of the curve is steeper). Then, the frozen impregnated sample showed a tendency to be cut like a gel (there is a breaking point before the end of the measurement), which was completely different from the control case for which the gel strength value corresponded only to a continuous crushing. The measurement of gel strength under compression gave similar results (Fig. 15.5). These phenomena were explained by the formation of gel-filled intercellular spaces predominating over the softened structure of defrosted apple. Thereafter, work carried out on pasteurised fruit preparations for dairy products described in the FAIR European programme, referenced in Section 15.5, showed significant texture improvement for products containing pear or strawberry pieces enriched with pectin or alginate. The improved organoleptic qualities of the processed products were validated by sensory analysis (Cattaneo et al., 2000; Avitabile Leva et al., 2000). 300

300 Vacuum infused Non-infused

Force (g)

250

Compression test

250

200

200

150

150

100

100 ‘Cuttability’

50

50

0 0.0

2.5

5.0

0 7.5

Distance (mm)

Fig. 15.5 Texture analysis profiles of frozen-defrosted 1 cm3 apple cubes, vacuum infused with gelatine and non-infused, representing shearing force (‘cuttability’) or strength force under compression versus distance.


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15.4 Future trends At the time of writing, industrial application of vacuum impregnation is to the best of our knowledge still being developed. This can be explained by the great variety in the response of fruits and vegetables to the vacuum process, which is due to their structure properties being extremely variable. Indeed, no ready-made formula can be applied to a raw material without acquiring, as a preliminary, data relating to the porosity, mass transfer, and resistance of the plant tissue to mechanical strains exerted during pressure changes, and without having checked the effectiveness of the numerous firming agents available. In addition, the qualitative and commercial added value which results from any texture improvement has to be balanced against the additional cost generated by the pre-treatment application, which includes investment, lengthening of operating time and consumption of energy and ingredients. Some new lines of research might develop which could widen the scope of the innovative applications offered by the vacuum technique with respect to the texture improvement of foodstuffs. It could be interesting to investigate the co-impregnation of two or several ingredients which act differently and synergistically on the product structure as a means of enhancing the firming effect. For example, we might propose to combine the impregnation of PME, which acts to reinforce the cell wall structure, with the infusion of a gelling agent which stabilises the solution occupying the fruit pores. Another interesting subject is the combination of vacuum treatment with an emerging technology (high pressure, microwave, electric f ields, ultrasound…) with a view to enhancing synergistically the effectiveness of these procedures. A well-known example is the use of vacuum infusion before the osmotic dehydration treatment of fruits in concentrated syrup. As already indicated in our chapter, the pre-treatment induces new structure effects on the osmo-dehydrated products in addition to the modification of the mass transfer kinetics. Other applications have been reported, such as the vacuum impregnation of vegetable pieces with salt solutions prior to ohmic treatment in order to modify their electric conductivity and thus optimise the heating conditions (Wang and Sastry, 1993). Lastly, we should mention a few works dealing with meat or cheese processing (Pavia et al., 1999; Chiralt et al., 2001) which mention the beneficial effect on product structure of vacuum brining techniques.

15.5 Sources of further information and advice Department of Food Technology Director: Pr P Fito Universidad Politecnica de Valencia PO Box 22012 46071 Valencia, Spain

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Tel: +34 96 387 7360 Fax: +34 96 387 7369 Research Laboratory in Food Engineering Contact: Dr R Saurel IUT A - University of Lyon 1 Rue Henri de Boissieu 01060 Bourg-en-Bresse, France Tel: +33 (0)4 74 45 52 52 Fax: +33 (0)4 74 45 52 53 European AAIR project F-FE 253/97 ‘Texture of heat processed fruits’ Contact: Dr S A Jones Leatherhead Food Research Association Randalls Road, Leatherhead Surrey KT227RY, United Kingdom Tel: +44 1372 376761 Fax: +44 1372 386228 European FAIR demonstration project CT 98-3814 ‘Improvement of processed fruit and vegetable texture by using a new technology: vacuum infusion’ TMI International 20, Bd Eugene Deruelle 69432 Lyon cedex 03, France Tel: +33 (0)4 72 84 04 82 Fax: +33 (0)4 72 84 04 85

15.6 References AVITABILE LEVA L, MARABOLI A, CATTANEO T M P

and TORREGGIANI D (2000) Improvement of processed pear ingredients using vacuum infusion: influence on the quality characteristics of pear yoghurt. In Book of Abstracts (edited by Dipartimiento di Colture Arboree): 8th International Pear Symposium, September 4–9, Ferrara, Bologna, Italy. BAKER R A and WICKER L (1996) Current and potential applications of enzyme infusion in the food industry, Trends Food Sci Technol, 7(9), 279–84. BARAT J M, ALBORS A, CHIRALT A and FITO P (1999) Equilibrium of apple tissue in osmotic dehydration: microstructural changes, Drying Technol, 17(7&8), 1375–86. BARAT J M, FITO P and CHIRALT A (2001) Modeling of simultaneous mass transfer and structural changes in fruit tissues, J Food Eng, 49, 77–85. BARTON R R (1951) Improving the quality of frozen premier strawberries, J Amer Soc Hort Sci, 58, 95–8. BOLIN H R and HUXSOLL C C (1987) Scanning electron microscope/image analyser determination of dimensional postharvest changes in fruit cells, J Food Sci, 52(6), 1649–50. CALBO A G and SOMMER N F (1987) Intercellular volume and resistance to air flow of fruits and vegetables, J Amer Soc Hort Sci, 112(1), 131–4. CATTANEO T M P, AVITABILE LEVA A and TORREGGIANI D (2000) Improvement of processed strawberry ingredients using vacuum infusion: influence on the quality characteristics of strawberry yoghurt, Acta Horticulturae, 567(2), 787–90.

Improving the texture of processed vegetables by vacuum infusion CHIRALT A, FITO P, BARAT J M, ANDRES A, GONZALES-MARTINEZ C, ESRICHE I

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and CAMACHO M M (2001a) Use of vacuum impregnation in food salting process, J Food Eng, 49(2&3), 141–51. CIERCO M (1994) Pre-freezing treatment of strawberries and their use as fresh strawberry, French patent application (in French), FR 94-13864. CULVER C A, BJURLIN M A and FULSHER R G (2000) Visualizing Enzyme Infusion into apple tissue, J Agric Food Chem, 48(12), 5933–5. DEGRAEVE P, SAUREL R and COUTEL Y (2003) Vacuum impregnation pretreatment with pectinmethylesterase to improve firmness of pasteurized fruits, J Food Sci, 68(2), 716–21. DEL VALLE J M, ARANGUIZ V and DIAZ L (1998a) Volumetric procedure to assess infiltration kinetics and porosity of fruits by applying a vacuum pulse, J Food Eng, 38(2), 207– 21. DEL VALLE J M, ARANGUIZ V and LEON H (1998b) Effects of blanching and calcium infiltration on PPO activity, texture, microstructure and kinetics of osmotic dehydration of apple tissue, Food Res Int, 31(8), 557–69. DEMEAUX M, SONNERAT P and LORIENT D (1988) Localization and behaviour study of egg white proteins incorporated in cultivated mushrooms (in French), Sciences des Aliments, 8, 269–83. FITO P (1994) Modelling of vacuum osmotic dehydration of food, J Food Eng, 22(1–4), 313–28. FITO P and PASTOR R (1994) Non-diffusional mechanisms occurring during vacuum osmotic dehydration, J Food Eng, 21(4), 513–19. FITO P, ANDRES A, CHIRALT A and PARDO P (1996) Coupling of hydrodynamic mechanism and deformation-relaxation phenomena during vacuum treatments in solid porous foodliquid systems, J Food Eng, 27(3), 229–40. FITO P, CHIRALT A, BETORET N, GRAS M, CHAFER M, MARTINEZ-MONZO J , ANDRES A and VIDAL D (2001a) Vacuum impregnation and osmotic dehydration in matrix engineering: Application in functional fresh food development, J Food Eng, 49(2&3), 175–83. FITO P, CHIRALT A, BARAT J M, ANDRES A, MARTINEZ-MONZO J and MARTINEZ-NAVARRETE N (2001b) Vacuum impregnation for development of new dehydrated products, J Food Eng, 49(4), 297–302. FRENCH D A, KADER A A and LABAVITCH J M (1989) Softening of canned apricots: a chelation hypothesis, J Food Sci, 54(1), 86–9. GLENN M G and POOVAIAH B W (1990) Calcium-mediated postharvest changes in texture and cell wall structure and composition in Golden Delicious apples, J Amer Soc Hort Sci, 115(6), 962–8. GORMLEY T R and WALSHE P E (1986) Shrinkage in canned mushrooms treated with xanthan gum as a pre-blanch soak treatment, J Food Tech, 21, 67–74. GRAS M, VIDAL-BROTONS D, BETORET N, CHIRALT A and FITO P (2002) The response of some vegetables to vacuum impregnation, Innov Food Sci Emerg Tech, 3(3), 263–9. GRAS M, VIDAL D, BETORET N, CHIRALT A and FITO P (2003) Calcium fortification of vegetables by vacuum impregnation. Interactions with cellular matrix, J Food Eng, 56(2&3), 279–84. GUILLEMIN A (2003) PhD in progress, Université Claude Bernard LYON 1, France. HOOVER M W and MILLER N C (1975) Factors influencing impregnation of apple slices and development of a continuous process, J Food Sci, 40, 698–700. JAVERI H, TOLEDO R and WICKER L (1991) Vacuum infusion of citrus pectinmethylesterase and calcium effects on firmness of peaches, J Food Sci, 56(3), 739–42. LIDSTER P D, DICK A J, DEMARCO A and MCRAE K B (1986) Application of flavonoid glycosides and phenolic acid to suppress firmness loss in apples, J Amer Soc Hort Sci, 111(6), 892–6. MAIN G L, MORRIS J R and WEHUNT E J (1986) Effects of pre-processing treatments on the firmness and quality characteristics of whole and sliced strawberries after freezing and thermal processing, J Food Sci, 51(2), 391–4.

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MARTINEZ-MONZO J, MARTINEZ-NAVARETTE N, CHIRALT A

and FITO P (1998) Mechanical and structural changes in apple (var. Granny Smith) due to vacuum impregnation with cryoprotectants, J Food Sci, 63(3), 499–503. MATRINGE E, CHATELLIER J and SAUREL R (1999) Improvement of processed fruit and vegetable texture by using a new technology «vacuum infusion», Proceedings of the International Congress “Improved traditional foods for the next century”, 28–29 October, Valencia, Spain, 164–7. MCARDLE R N and CULVER C A (1994) Enzyme Infusion: a developing Technology, Food Tech, November, 85–9. MUJICA- PAZ H, VALDEZ-FRAGOSO A, LOPEZ-MALO A, PALOU E and WELTI -CHANES J (2003) Impregnation properties of some fruits at vacuum pressure, J Food Eng, 57(4), 305– 14. MUNTADA V, GERSCHENSON L N, ALZAMORA S M and CASTRO M A (1998) Solute infusion effects on texture of minimally processed kiwifruit, J Food Sci, 63(4), 616–20. PAVIA M, TRUJILLO A J, GUAMIS B, CAPELLAS M and FERRAGUT V (1999) Changes in microstructural, textural and color characteristics during ripening of manchego type cheese salted by brine vacuum impregnation, Int Dairy J, 9(2), 91–8. PICCHIONI G A, WATADA A E, CONWAY W S, WHITAKER B D and SAMS C E (1998) Postharvest calcium infiltration delays membrane lipid catabolism in apple fruit, J Agric Food Chem, 46(7), 2452–7. PONAPPA T, SCHEERENS J C and MILLER A R (1993) Vacuum infiltration of polyamines increases firmness of strawberry slices under various storage conditions, J Food Sci, 58(2), 361–4. POOVAIAH B W (1986) Role of calcium in prolonging storage life of fruits and vegetables, Food Tech, May, 86–9. PRETEL M T, LOZANO P, RIQUELME F and ROMOJARO F (1997) Pectic enzymes in fresh fruit processing: optimisation of enzymatic peeling of oranges, Proc Bioch, 32(1), 43–9. ROA V, TAPIA M S and MILLAN F (2001) Mass balances in porous foods impregnation, J Food Sci, 66(9), 1332–6. ROUHANA A and MANNHEIM C H (1994) Optimisation of enzymatic peeling of grapefruit, Lebensm Wiss und Technol, 27(2), 103–7. SALVATORI D, ANDRES A, CHIRALT A and FITO P (1998) The response of some properties of fruits to vacuum impregnation, J Food Proc Eng, 21, 59–73. SAUREL R (2002) The use of vacuum technology to improve processed fruit and vegetables, In Fruit and Vegetable Processing Ed. W Jongen Cambridge, Woodhead, 363–80. SCOTT K J and WILLS R B H (1977) Vacuum infiltration of calcium chloride: a method for reducing bitter pit and senescence of apples during storage at ambient temperatures, Hort Sci, 12(1), 71–2. SCOTT K J and WILLS R B H (1979) Effects of vacuum and pressure infiltration of calcium chloride and storage temperature on the incidence of bitter pit and low temperature breakdown of apples, Aust J Agric Res, 30, 917–28. STOW J (1989) The involvement of calcium ions in maintenance of apple fruit tissue structure, J Exp Bot, 40(218), 1053–7. SUUTARINEN J, HONKAPÄÄ K, HEINIÖ R L, AUTIO K and MOKKILA M (2000) The effect of different prefreezing treatments on the structure of strawberries before and after jam making, Lebensm Wiss und Technol, 33(3), 188–201. SUUTARINEN J, HONKAPÄÄ K, HEINIÖ R L, AUTIO K, MUSTRANTA A, KARPPINEN S, KIUTAMO T, LIUKKONEN-LILIA H and MOKKILA M (2002a) Effects of calcium chloride-based prefreezing treatments on the quality factors of strawberry jams, J Food Sci, 67(2), 884–94. SUUTARINEN J, HONKAPÄÄ K, HEINIÖ R L, MUSTRANTA A, LIUKKONEN-LILIA H and MOKKILA M (2002b) Modeling of calcium chloride and pectin methylesterase prefreezing treatments of strawberries and jams, J Food Sci, 67(3), 1240–48. TIRMAZI S I H and WILLS R B H (1981) Retardation of ripening of mangoes by postharvest application of calcium, Trop Agric, 58(2), 137–41.

Improving the texture of processed vegetables by vacuum infusion VALERO D, MARTINEZ-ROMERO D, SERRANO M

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and RIQUELME F (1998a) Influence of postharvest treatment with putrescine and calcium on endogenous polyamines, firmness, and abscissic acid in lemon (Citrus lemon L. Burm Cv. Verna), J Agric Food Chem, 46(6), 2102–9. VALERO D, MARTINEZ-ROMERO D, SERRANO M and RIQUELME F (1998b) Postharvest gibberellin and heat treatment effects on polyamines, abscisic acid and firmness in lemons, J Food Sci, 63(4), 611–15. WANG W C and SASTRY S K (1993) Salt diffusion into vegetable tissue as a pretreatment for ohmic heating: electrical conductivity profiles and vacuum infusion studies, J Food Eng, 20(4), 299–309. WANG C Y, CONWAY W S, ABOTT J A, KRAMER G F and SAMS C E (1993) Postharvest infiltration of polyamines and calcium influences ethylene production and texture changes in Golden Delicious apples, J Amer Soc Hort Sci, 118(6), 801–6. WILLS R B H and SIRIVATANAPA S (1988) Evaluation of postharvest infiltration of calcium to delay the ripening of avocados, Aust J Exp Agric, 28, 801–4. WILLS R B H and TIRMAZI S I H (1979) Effect of calcium and other minerals on ripening of tomatoes, Aust J Plant Physiol, 6, 221–7.

16 Improving the texture of frozen fruit: the case of berries M. Suutarinen and K. Autio, VTT Biotechnology, Finland

16.1 Introduction: the effects of freezing and thawing on berry texture Freezing and subsequent frozen storage causes excessive softening of berries, and frozen strawberries are characteristically very soft and moist. The great susceptibility of strawberries to textural damage is due to their low solids content, large cells and thin cell walls. During freezing the ice crystals formed rupture the parenchyma cell walls and induce loss of turgor. These structural characteristics account for the loss of instrumental firmness. The right choice of cultivar is important in producing a high-quality frozen product (Oswin, 1979) and so grading involving possible defects, size and texture is necessary before freezing. The rate of freezing is a critical issue with regard to tissue damage. The fast freezing method allows better retention of texture, and higher texture values can be obtained with freezing in liquid nitrogen and plate freezing. However, berries immersed in liquid nitrogen may crack due to ultra-rapid freezing. Slow freezing results in formation of large ice crystals and significant damage to cell walls. Disruption of the intercellular structure will also release enzymes and substrates which will cause further problems with the flavor and color. High pressure offers new opportunities to treat samples at temperatures below zero. By using high pressure, the freezing point of water can be lowered to –22 ºC allowing storage of foods in liquid state without freezing. The cost-effectiveness can be greatly increased by using pressure-shift freezing and pressure-induced thawing. In the latter case the high-pressure treatment is used only for freezing and thawing.

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16.1.1 Sensory perception of texture The sensory quality of fresh strawberries is influenced by cultivar, maturity, site, season and agronomic practice. The texture profiling technique has been used to assess sensory perception of the texture of strawberries (Szczesniak and Smith, 1969). Fresh berries are firm, plump, low in cohesiveness and moderately juicy. Sensory firmness, cohesiveness and degree of fibrousness decrease with increased ripeness. Not all berries have good freezing characteristics and berries with good quality should be chosen for freezing. Strawberries retain much of their natural flavor and color under freezing, frozen storage and thawing but suffer serious softening in texture and release more juice than the fresh product. This loss in sensory firmness can be characterized by a moist, soft and limp appearance, poor shape retention and a tough texture of the interior fibers. 16.1.2 Microstructure Figure 16.1 shows the cross-section of a strawberry. Strawberry is composed of different tissues which differ in terms of chemical structure and microstructure. Vascular tissue composed of long fibers and pith forms the skeleton of the strawberry structure. Epidermal cells form the outer layer. Vascular bundles beginning from the achenes and connecting to the pith play a very important role in the texture of strawberries. The second layer is composed of hypodermal cells and the third layer of cortical cells (cortex). In cortical cells the pectins in the middle lamella have an important role in cementing the cells together. In plant tissue, the maintainance of shape is based on turgor pressure within individual cells. Freezing and thawing has a highly detrimental effect on cortical parenchyma cells which have large cells Epidermis Hypodermis

Vascular tissue Cortex

Achenes

Pith

Fig. 16.1 Cross-section of strawberry demonstrating the location of different tissues. (Reprinted with permission from Suutarinen et al., 2002b).

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and thin cell walls. The degree of cortical cell wall rupture during freezing determines the extent of textural change. Frozen berries also showed plasmolyzed cells. 16.1.3 Mechanical properties Measuring the mechanical properties of strawberries by normal compression is difficult, since the shape and size of strawberries vary a lot. It is also impossible to take a cylindrical sample from strawberry, as the structure is very unhomogenous. Puncture testing has most often been used in strawberry texture measurement (Bourne, 1979). By utilizing a puncture-penetration method it has been possible to measure both the skin toughness and flesh firmness of different strawberry varieties before freezing and after freezing and thawing and to grade species according to their suitability for freezing (Lacroix et al., 1985). The skin toughness of fresh samples for 25 different strawberry varieties varied from 3.19–6.19 N, and after freezing (–50 °C/20– 25 min), frozen storage (three months at –30 °C) and thawing (at 3 °C for 48 h) from 1.32–2.92 N. In the same way, flesh firmness of fresh berries was in the range 5.34–15.28 N. After freezing and thawing it was 1.75–6.28 N indicating significant softening of the berry skin and flesh due to freezing, frozen storage and thawing. The flesh firmness of fresh berry correlates with that of thawed sample (0.88) suggesting that berries most suitable for freezing can be graded according to firmness. Fresh strawberries cultivated in the field gave firmness values of 30 N, whereas for fresh strawberries cultivated in the greenhouse the corresponding value was 10 N (Agnelli and Mascheroni, 2002). Strawberries with high initial firmness showed a better texture when they were frozen in liquid nitrogen following storage in an air-blast freezer. 16.1.4 Changes in water binding properties The juice loss from strawberries has been measured by quantifying the amount of moisture released to filter paper during mechnical mastication by the Texturometer (Szczesniak and Smith, 1969). Frozen berry releases juice in great quantity. It has been demonstrated in one study that the texture of fresh samples is correlated with the drip loss during thawing (Lacroix et al., 1985). Freezing method and field (F)/greenhouse (G) cultivation had very significant influence on water drip (Agnelli and Mascheroni, 2002). Up to 60% reduction in drip loss was obtained by freezing the field cultivated strawberries in liquid nitrogen following storage in an air-blast freezer.

16.2 Maintaining texture: conventional pre-freezing treatments The two most common problems in frozen fruits and vegetables are: chemical reactions that cause changes in flavor, color, texture, and nutrition; and physical


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damage that causes loss of turgor and texture changes (Haard, 1997). Loss of tissue firmness, disruption of the cell membrane, and excessive softness are the major consequences which need to be avoided (Rahman, 1999). Loss of membrane semipermeability and disruption of cellular compartments can be minimized by rapid freezing rate, low storage temperature, and slow thawing (Haard, 1997). The use of pre-freeze treatments can help to reduce (or avoid) detrimental phenomena, either by inactivating the deterioration reactions, or by reducing the water content in the material (Torregiani, 2000). Prior to freezing, the fresh fruit is normally washed to improve the microbiological standard and physical appearance. A fast continuous wash will minimize the leaching of color and flavor. A close inspection should be carried out to remove any foreign material that might still be adhering to the fruit, together with any blemished pieces. After washing, fruits can be individually quick frozen (IQF) or they can be packed in sugar or syrup or they can be puréed before freezing (Burrows, 1996).

16.2.1 Blanching Most fruits do not require blanching, although those susceptible to enzymatic browning benefit from inactivation of polyphenoloxidase (Haard, 1997). This is achieved by denaturating the proteins that would otherwise take part in reactions leading to deterioration (Torregiani, 2000). Blanching at 70–105 °C is associated with destruction of enzyme activity. Blanching can be achieved by immersion or steaming, and high-pressure steam blanching is more energy efficient than water blanching. Wrolstad et al. (1980) found a positive effect of microwave blanching on the color and composition of strawberry concentrate. It is important that cooling is carried out shortly after blanching for products which are to be frozen. The advantages of blanching may be slightly offset by the loss of nutritional values that occurs during the operation. Therefore, blanching times should be kept as short as possible (Burrows, 1996). The activities of most enzymes are greatly dependent on pH of the tissue or the blanching water. Additives, such as citric acid, sodium chloride, and carbonates, can be used in water, depending on the purpose (Rahman, 1999). According to the structural changes recorded by a scanning electron microscope (SEM), in blanched strawberry tissues cellular membranes are broken, decreasing the cell turgor pressure. Hot water blanching results in a very severe ultrastructural disorganization of the cell walls: fibrillar organization is lost, the cell wall appears perforated, and the cells are difficult to identify because of the structural disruption of the walls. Cell walls exhibit a severe loss of material and extreme swelling. Steam blanching, on the contrary, maintains the original arrangements of cells, although cell walls appear more porous. In the steam-treated sample examination of tissues with a transmission electron microscope (TEM) indicates electron-dense cell walls, a network of microfibrils, and a pectin matrix very similar to those of the fresh fruit, although the middle lamella has been partially lost (Alzamora et al., 2000).

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16.2.2 Dipping Softening caused by freezing–thawing can sometimes be minimized by dipping the tissue in different solutions before freezing. Much of the softening results from degradation of the middle lamella of the walls of cortical cells with increased release of pectins from the cell walls. In many fruit, such as tomato, endo-polygalacturonase begins water solubilization of pectins while exopolygalacturonase completes hydrolysis (Perkins-Veazie, 1995). Firmness changes in strawberries during senescence and in the absence of polygalacturonase have been linked to changes in ionic stability of the middle lamella. Divalent calcium ions (Ca2+) normally occur between the cells, where they form crosslinks between the carboxyl groups of adjacent polyuronide chains (Van Buren, 1979; Main et al., 1986). The structure of the pectin “gel” in the middle lamella is generally described by the “egg box” model in which pectins have few “hairy regions” and a low methoxyl rate. For calcium to be an effective firming agent, a low degree of methoxylation must be present. As a result of the formation of calcium pectate in the cell wall, calcium may decrease softening by cell-wall macerating enzymes produced by plant pathogens. If the activity of pectinesterase is enhanced only during heating, the exogenously supplied calcium will be able to form calcium pectate, which will increase resistance to decay. Addition of calcium actually decreases pectinesterase activity (Sams et al., 1993). Examination of the cell wall of the strawberry fruit has indicated very low levels of PME (pectin methylesterase) (Jones, 1996). In addition, to enhance the activity of pectinesterase naturally present in fruit, commercially available PME preparations can be used. PME catalyzes cleavage of the ester bonds between the methyl and the carboxyl groups of pectic substances, thus forming anionic COO– groups with which calcium ions can form salt bridge crosslinks. Calcium pectate is thus formed, which is assumed to anchor the pectic substances, resulting in an overall increase in firmness (Baker and Wicker, 1996). Pre-treatments could be combined with heat treatment. Heat allows demethylation of pectin by PME (Sams et al., 1993). The treatment of strawberries at 45 °C for 3 h delayed ripening and fruit decay under ambient conditions (20 °C for two to four days). The most dramatic effects under refrigeration conditions were delayed anthocyanin degradation and reduced viable fungi counts (Vicente et al., 2002). Alonso et al. (1997) found that thermal and calcium pre-treatment affected texture, pectinesterase and pectic substances of frozen sweet cherries. Thermal pretreatment at 50 °C for 10 min followed by immersion in 100 mM CaCl2 prevented freezing-induced loss of firmness. Thermal pre-treatments reduced the degree of pectin esterification and increased both the concentration of divalent cations in the cell wall and the pectinesterase activity bounded to the cell wall. Immersion in CaCl2 increased concentration of Ca2+ cations in the cell wall and enhanced the effect of thermal pre-treatments on pectinesterase activity.


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In many cases the frozen product is protected by a suitable glazing compound. A glaze acts as a protective coating against the two main causes of deterioration during storage: dehydration and oxidation. It protects against dehydration by preventing moisture from leaving the product and against oxidation by physically preventing air contact with the product. Oxidation can also be minimized if the glaze carries a suitable antioxidant. The different glazes available include inorganic salts of sodium acid phosphate, sodium carbonate, and calcium lactate, alginate solution, ascorbic and citric acids, glutamic acid, and monosodium glutamate, and other edible coatings, such as corn syrup solids (Rahman, 1999). El Ghaouth et al. (1991) applied a chitosan-based coating to strawberries. The coating modified the internal fruit atmosphere and decreased water evaporation, resulting in delayed strawberry ripening. Starch-based coatings were applied to extend storage life of strawberries in a cold chamber at 0 °C. Coatings made with starches with a higher amylose content decreased water vapor permeability (WVP) and weight losses and retained fruit firmness for longer periods than coatings formulated from medium amylose content starches. Coatings with sorbitol showed lower WVPs than glycerol ones (García et al., 1998). Other studies have been carried out treating strawberries with pectin, alginate, starch, gelatine or polyamine (Ponappa et al., 1993; Cierco, 1994; Pontes et al., 1996), sugar and soybean flour (Nobutsugu, 1984), as well as dry sucrose, sucrose solution, and sucrose or glucose syrup (Armbruster, 1967; Garrote and Bertone, 1975; Deng and Ueda, 1993; Sormani et al., 1999), before freezing and thawing or before refrigeration. These pre-treatments and calcium chloride pre-treatments (Polesello and Crivelli, 1971; García et al., 1996), calcium chloride pre-treatments with PME (Grassin and Fauquembergue, 1994), calcium chloride pre-treatments with PME in a vacuum (Suutarinen et al., 2000, 2002a), or of calcium lactate or other calcium salt pre-treatments in a vacuum before freezing and thawing (Main et al., 1986; Garcia-Berbari et al., 1998), enhanced firmness independent of the species and variety of fruits and also increased their soluble solids content. Pretreatments proved to be effective in reducing liquid and ascorbic acid loss in thawed strawberries. The treatments did not affect the sensory quality of the fruits.

16.3 Maintaining texture: alternative pre-freezing treatments 16.3.1 Microwave blanching Microwave processing can offer several advantages when compared to conventional heating methods. These include speed of operation, energy savings, precise control, and faster start-up and shut-down times. Microwave blanching can fulfil one or more of several purposes:

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1. inactivation of enzymes prevents discoloration or development of unpleasent taste during storage; 2. improved texture due to liberation of water; 3. expulsion of air, which is confined to plant tissues, and reduction of oxidation during frozen storage; 4. microbial status is improved because vegetative cells, yeast and mold are killed; 5. cooking time of the finished products is shortened. When water or steam is used for heating, leaching of vitamins, flavors, colors, carbohydrates, and other water-soluble components takes place. If products are going to be frozen after blanching, a chilling step will generally take place before transporting the product into the freezer. If this cooling is done with cold water, additional leaching takes place. The ripening level of fruit to be processed is critical in microwave treatment. More information about the effects of microwave energy on quality indicators is still needed (Cano, 1994). Microwave blanching of strawberry concentrate (fruits were heated to an internal temperature of 82–88 °C for 3–4 min) resulted in improved color stability and had a protective effect on anthocyanin pigment, reactive phenolics, and ascorbic acid (Wrolstad et al., 1980).

16.3.2 Partial air drying Partial dehydration is generally achieved by air drying. The resulting process is termed dehydrofreezing. The advantages over conventional freezing include: (1) energy savings, since the water load to the freezer is reduced, as well as reduced transport, storage and wrapping costs; (2) improved quality and stability (color, flavor), as well as better thawing behavior (lower drip loss). When using partial air drying, food ingredients of high water activity (aw > 0.96) are generally obtained, since water removal is limited to 50–60% of the original content. To avoid browning during air drying, blanching or other treatments, such as dipping in antioxidant solutions (ascorbic or citric acid, sulphur dioxide), can be used (Torregiani, 2000). Microwave-assisted air drying methods such as microwave vacuum drying, microwave freeze-drying and microwave atmospheric pressure drying for dehydration of fruits can also be used (Funebo, 1997). Partial water removal from the food prior to the freezing process leads to the concentration of components in the cytoplasm of cells, the decrease of the freezing point, and promoted supercooling. Thus, there are relatively fewer large ice crystals, and a lower ratio of ice crystals to unfrozen phase, with a consequent reduction in textural changes (Torregiani, 2000). Partial removal of water through air dehydration of strawberry slices maintains better tissue organization compared to strawberry frozen without pre-treatment. A good agreement was obtained between structural and textural changes observed after both pre-dehydration and freeze-thawing of strawberry slices (Sormani et al., 1999). Changes in mechanical properties due to freezing–


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thawing in fresh and pre-dried strawberries were studied in order to quantify the possible cryoprotective effect of osmotic treatment with sucrose solutions. Good mechanical properties were retained to a higher degree by air drying than osmotic drying; however, after freezing–thawing these differences were not significant (Chiralt et al., 2001).

16.3.3 Osmotic dehydration Air drying can be substituted by (or combined with) osmotic dehydration as a pre-freeze treatment (Torregiani, 2000). This process involves placing the solid food (whole or in pieces) into hypertonic solution, i.e. a high-concentration sugar or salt solution. Osmotic dehydration can be applied either as a separate process or as a processing step in alternative processing schemes leading to a variety of end products (Lazarides, 1994). The selection of the solute for the osmotic solution is based on the sensory characteristics of the product; the cost of the solute or solutes; and the molecular weight of the solute. The most common solutes used for osmotic dehydration are sodium chloride, sucrose, lactose, high-fructose corn syrup, and glycerol (Barbosa-Cánovas and Vega-Mercado, 1996). In osmotic dehydration, operating temperatures range from 30 to 80 °C. In practical conditions, with anosmotic treatment of 1–2 h at ambient temperature, a solid gain of up to 5–10% can be attained. This gain corresponds to a 50–100% increase, if referred to an initial soluble solid content of 10% (Torregiani, 2000). In the process of osmosis, water diffuses through the membrane from the dilute to the concentrated solution until an equilibrium concentration is reached. The solute is unable to diffuse through the membrane in the reverse direction, or can do so only very slowly, so that the major result of this process is a transfer of water to the concentrated solution. Transfer of water by osmosis is applicable to fruit pieces, since they contain sugars and other solutes in dilute solution, and their cellular surface structure acts as an effective semipermeable membrane (Ponting et al., 1966). Application of ultrasound during treatments, or of a high-intensity electrical field pulse or an ultrahigh hydrostatic pressure to the material prior to osmotic treatment have been used to improve mass transfer rate (Rastogi et al., 1999). Sucrose, corn starch syrup at various fructose/glucose ratios, concentrated fruit juices and other mono- and di-saccharides have been used as osmotic solutions (Torregiani, 2000). By immersing fruit pieces in a concentrated sugar solution, water can be removed to the extent of over 50% of the initial weight of the fruit. The added sucrose acts as a dehydrating agent in order to decrease the water content of strawberries. Water is transferred out of the fruit into the syrup matrix and sucrose diffuses into the berries (Ponting et al., 1966). Sodium chloride (NaCl) was employed to assess the possibility of increasing the process rate without affecting the sensory acceptability of osmotically treated apple samples. Salt concentration improved water loss at equilibrium but showed a negative interaction effect with sucrose concentration. Salt and

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sucrose concentrations had a synergistic effect on soluble solid impregnation; on the other hand, sugar concentration was shown to reduce salt gain in the fruit samples (Sacchetti et al., 2001). The same phenomenon was found with strawberries that had been treated with CaCl2 and sucrose solution (Suutarinen et al., 2002a). This fact could be explained by the existence of instantaneous interactions at the food–solution interface. The addition of an NaCl level (0.5%) caused a reduction in product acceptability very close to that determined by the addition of a sucrose level (5%). Salt gain was not sufficient to balance the sweetness of the product, but an addition of NaCl may help to attenuate the excessive sweetness of products processed with high sucrose concentration (Sacchetti et al., 2001). Reduced water content will decrease structural collapse of berries during freezing. Lower drip losses ensure better fruit appearance, flavor, aroma and rheological behavior in cooking. Owing to the soluble solid intake, the overall effect of osmosis is a decrease in water activity, with only a limited increase in texture. Fruit texture is partly associated with the plasticizing and swelling effect of water on the pectic and cellulosic matrix of the fruit tissues. Hence, texture depends primarily on the insoluble matter and water content, rather than on the soluble solids and water activity. In this way, low water activities may be achieved while maintaining an acceptable consistency (Torregiani, 2000). Viberg et al., (1998) investigated volume and density changes during osmotic processing of strawberries in aqueous sucrose solutions (20–85% w/w) and granulated sucrose. Best results were obtained by pre-treatment in 60% (w/w) sucrose solution; this resulted in increased density with only a small decrease in volume. Shrinkage was greatest when osmotic pre-treatment with granulated sucrose was used. The volume of pre-treated strawberries was not altered significantly by subsequent thermal processing. Early penetration studies showed that the rate of solute penetration is directly related to the solution concentration and inversely related to the size of the sugar molecule (Lazarides, 1994). Air drying alone, or in combination with osmotic drying, resulted in the greatest texture improvement of the strawberry slices after thawing (Sormani et al., 1999). Application of selective coatings to fruits could result in a desirable reduction in solute uptake rate during osmotic drying. Strawberries were coated with 4% aqueous solutions of either low methylated pectin, potato starch, or a mixture of pectin and potato starch and osmotically dried in 61.5% saccharose solution at 30–80 °C. Lowest water loss and solid gain was observed in strawberries coated with potato starch solution. This coating showed the best potential for use on frozen stawberries prior to osmotic drying (Ogonek and Lenart, 2001). Vacuum osmotic dehydration When pressures lower than atmospheric pressure are used, vacuum osmotic dehydration (VOD) occurs (Fito et al., 1994). The air content of some fruit tissues adversely affects the processability as well as the colour and flavor of


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the final product (Escriche et al., 2000), and strawberries contain 18–22 vol. % air. Vacuum techniques have been used to increase the incorporation of sugar and firming agents in strawberries (Kolev et al., 1983). Fito (1994) described a rapid mass transfer phenomenon which occurs when porous structures are immersed in a liquid phase under vacuum (hydrodynamic mechanism). This involves the in-flow of the external liquid through the capillary pores. Temperature and pressure changes also impose in the system. Reduced pressure is imposed in a solid–liquid system, followed by restoration of atmospheric pressure. During the vacuum step, the internal air in the fruit pores expands and partially flows out. In the atmospheric step, the residual air is compressed and the external liquid flows into the pores. Several advantages are observed for VOD: a faster kinetic for water loss, principally during the first period of drying; and a sugar gain similar to that obtained for osmotic drying. In fruits, the product processed by VOD showed better sensory perception of texture than could be achieved at the same temperature by osmotic drying. More stable products with a lower level of browning and oxidation can be produced by VOD (Fito et al., 1994), Neither the duration under vacuum nor that at atmospheric pressure has an influence on the deformation and impregnation levels of the fruits in the examined time scales of 5–15 min (Salvatori et al., 1998). However, pressure changes can promote deformation of the fruit because of the viscoelastic properties of its solid matrix. A high-pressure variation is due to rapid vacuum release which can reduce the effectiveness of the process by crushing some tissues. Even when the vacuum is released gradually, tissues can be seen to compress. Ideally, vacuum release should be sufficiently slow to permit the porous tissue to recover its original shape while absorbing the solution (Baker and Wicker, 1996). The use of a vacuum impairs the overall appearance of the fruit somewhat, but this may not be noticeable in a jam-type product (Main et al., 1986), Limited cell wall porosity and high molecular weight of macromolecules limit the effectiveness of osmotic diffusion. Treatment of soybean cells with pectin esterase enlarges the trans-wall channels without affecting cellular viability, indicating that cell wall pectins are responsible for the sieving effect (Baron-Epel et al., 1988). A vacuum infusion of PME is not successful in all applications. An impermeable skin or lack of interior voids may minimize the effects of infusion (Baker and Wicker, 1996). Pulsed vacuum osmotic dehydration The main advantage of vacuum osmotic drying compared to osmotic drying lies in the mass transfer due to the hydrodynamic mechanism and to the corresponding increase produced in the solid–liquid interface surface. In view of the fact that the most important hydrodynamic mechanism effect is very quick and it occurs just when the system is returned to atmospheric pressure, a new procedure – pulsed vacuum osmotic dehydration (PVOD) – was designed to carry out vacuum osmotic drying. Through this procedure,

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short periods (e.g. 5 min) of vacuum treatment were applied to apple slices, while they were immersed in the osmotic solution (sucrose solution in water at 65% (w/w)). In this way, the filling of the food pores with the same osmotic solution was induced at the beginning of the treatment. During the pulsed vacuum osmotic drying a great proportion of the volume of pores was occupied by osmotic solution, the residual volume of air being small. When shrinking of pores occurs, the large liquid plug at the entry to the pore prevents the air from escaping (Fito et al., 1994). Pulsed vacuum osmotic dehydration with 65 °Brix sucrose solution following steam blanching was found to be the most effective treatment of strawberries in decreasing water activity, due to the maximum sucrose gain achieved. Firmness and color were adversely affected, although they remained within reasonable values, and the microbiological quality of these strawberries was optimal (Moreno et al., 1998).

16.3.4 Immersion chilling and freezing Immersion chilling and freezing (ICF) consists of dipping food materials in a chilled aqueous liquid (< 5 °C), also called aqueous freezant (AF) (Lucas et al., 1999). ICF is quite similar to osmotic dehydration in that both involve direct contact between food pieces and a concentrated solution. However, ICF is carried out at lower temperatures ranging from –20 °C to 0 °C whereas operating temperatures range from 30 °C–80 °C in the case of osmotic dehydration (Torregiani, 2000). During immersion, simultaneous mass and heat transfer take place at the solid/liquid interface and within the material. Mass transfer includes cross-solute gain and water loss, and it proceeds in two stages. While freezing (primary stage) mass transfer rate is high, of the same order of magnitude as observed when working at temperatures superior to 0 °C (osmotic dehydration). Once freezing is completed the product may be stored in the AF (secondary stage) for several hours or days. Mass transfer rate is lower than in the primary stage, but long-term storage leads to subsequent impregnation and dehydration levels (2–10% for solute gain, 2–39% for water loss) (Lucas et al., 1999). The concentration, molecular mass and combinations of the dissolved solutes added to the solution determine the temperature range which will keep the solution in the liquid state. Binary solutions comprising 23% sodium chloride or 40% ethanol allow the use of temperatures as low as –20 °C and –30 °C respectively. Because of the low operating temperatures and the freezing process occuring inside food during ICF, mass transfer rates are much lower than in osmotic dehydration, ranging from 1–7% w/w water loss, and 0.5–1% w/w solute gain. ICF should be considered as a quick pre-cooling state, associated with a light surface formulation effect. Coating the frozen product with the remaining ICF solution can also help to improve food color, slow down food deterioration during cold storage, or bring new properties to food (Torregiani, 2000).


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Evolution of mass transfer during the secondary stage was assessed against time (6 h, 21 days). Apple cylinders were soaked in four different aqueous freezing agents (binary monophasic NaCl-water liquid; biphasic binary NaClwater solution mixture; two ternary NaCl-sucrose-water liquids) (–17.8, –17.4, –5 °C). Results demonstrated that over the whole process (primary and secondary), mass transfer was lower when the AF temperatures were lower (–17.8 °C). The addition of sucrose in the AF led to a subsequent increase in water loss (as high as 29% initial material (i.m.) after 21 days), but did not significantly affect salt gain levels over 21 days’ storage. Only the use of a biphasic mixture could limit mass transfer (less than 3.5% i.m. after 21 days) (Lucas et al., 1999).

16.3.5 High pressure High isostatic pressure (HP) ranges between 50 and 1000 MPa. The benefit of using HP instead of heat for preservation is that aroma, color and nutritional value of the fresh product are better maintained, if the pressure treatment can be carried out at a reduced temperature. HP also offers opportunities in food processing because it influences the texture of foods containing cell structures. The presence of water in foods to be used in high-pressure processing is essential (at least 40% w/v water) for the process to be effective. HP treatment can also simplify the preparation of jams (400–600 MPa at room temperature for 10–30 minutes, refrigerate for a two-month shelf life) (Williams, 1994). An integrated approach is necessary to study the effect of HP on food. For example, the inactivation kinetic of enzymes depends on the medium in which the enzymes are treated. In addition the optimal pressure–time– temperature combinations can also differ with respect to different product characteristics. The preservation of porous fruits and vegetables is often problematic, since they contain significant air voids within the tissue. During pressure-processing the air is not removed from the product. In systems subjected to pressure, any air present in a porous material will be almost completely compressed, which can cause an irreversible collapse of the tissue. The oxygen dissolved in the product causes trouble also for keeping quality. Various oxidative reactions (enzymatic or non-enzymatic), by which the quality of the product deteriorates, can take place in the product during storage. Examples of reactions affecting product quality are discoloration, formation of off-flavours and decomposition of vitamin C (Matser et al., 1998). Strawberry slices were treated in a liquid medium containing the PME enzyme (200 µL/100 g fresh strawberries; PME activity 100 000 nkat/ml) and calcium ions (1% CaCl2 solution) under vacuum (13.3 kPa for 10 min) prior to high-pressure processing. It was possible to improve the firmness of the foodstuff while simultaneously deaerating the product. It has been reported that high-pressure treatment (50 MPa/min up to 500 MPa; temperature 25 °C and holding time 15 min) may even increase the activity of PME (Stute et al.,

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1996). A slight increase in firmness of berries was found when berries were soaked in a liquid containing either enzyme or calcium ions. The best result was obtained when both enzyme and calcium ions were present in the soaking liquid, giving a firmness that was about nine times higher than that for the berries that were not pre-treated at all (Stolt et al., 2001).

16.4 Application: frozen berries and jams 16.4.1 Potential techniques for berries Berries can be frozen in syrup or as individual berries. As individual berries, they may be frozen on a tray or individual quick frozen (IQF) on a belt in an air-blast or cryogenic freezer. Depending on the final product, different freezing procedures might be appropriate (Reid, 1996). Freezing berries by fluidization yields products of the same quality as does liquid nitrogen freezing, but requires less time (Miller and Butcher, 2000). Strawberries for commercial preserves, jam, and jelly manufacture are usually washed and capped, frozen with either 5:1 or 4:1 fruit:sugar ratio, and then thawed before processing (Sistrunk et al., 1982). The appearance of the frozen berries is influenced by the method of mixing the sweetener with berries rather than the sweetener itself (Aref et al., 1956). One possible way to pre-treat berries is to use calcium, which fortifies the fruit by changing the pectin structure (Poovaiah, 1986; García et al., 1996). The use of PME enzyme has proved to be an efficient way to produce fruit jams and jellies without, or with greatly reduced, sugar and pectin addition (Grassin and Fauquembergue, 1994). When fruits or fruit pieces are pretreated with calcium and PME before heat treatment the integrity and firmness of fruits are preserved as much as possible (Coutel and Dale, 1998). According to microscopical studies the pre-treatments with calcium chloride and crystallized sucrose as well as with CaCl2 and PME in a vacuum influence the microstructure of strawberry tissues. These pre-treatments especially affect pectin, protein, lignin and structural carbohydrates in the vascular tissue and cortex when compared to the untreated reference samples. The use of a vacuum appears to make the cortex and pitch absorb the pre-treatment solutions more efficiently thus improving the stabilization particularly of pectin and structural carbohydrates (Suutarinen, 2002). Micrographs of the strawberry cortical and vascular tissues of the untreated reference and the CaCl2– and PME-treated strawberries in a vacuum after pectin staining, are shown in Fig. 16.2 (Suutarinen et al., 2000). Firmness of thawed strawberries pretreated with CaCl2 and PME in a vacuum is more than twice as high as that of other pre-treated or untreated berries (Fig. 16.3) (Suutarinen et al., 2002a). High hydrostatic pressure thawing (600 MPa, 25 and 50 °C, 15 min) of frozen strawberries increases sucrose uptake in strawberry slices (21%) as well as in whole fruit (140%), reaching maximum sucrose contents of 45.6


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Fig. 16.2 Micrographs of strawberry (a) cortical and (b) vascular tissues. Pectin (marked with an arrow) appears pink. Bar is 50 µm (Suutarinen et al., 2000).

and 34.7 °Brix respectively. This process can be compared with thawing of strawberries at atmospheric pressure prior to thermal processing (92 °C, 20° min) (Eshtiaghi and Knorr, 1996).

16.4.2 Potential techniques for jams The four essential ingredients in manufacturing jams are fruit, pectin, sugar, and acid. Optional ingredients include spice, buffering agents, preservatives, and anti-foaming agents. The exact process selected will depend upon the type of product to be manufactured, the raw materials available, and the scale of production. Traditionally, all of the ingredients are blended together as the first stage of processing; however, with modern demands for a high degree of consistency in quality, it has become common to add some critical ingredients such as citric acid and volatile flavoring at later stages in the process. Most modern plants are based on low temperature or vacuum evaporation, which may necessitate the addition of an extra pasteurizing stage to give a product of suitable microbiological quality to allow prolonged storage (Baker et al., 1996b). The pre-freezing treatments of berries are particularly interesting for the jam-making industry because fresh berries are not available for jam-making in all seasons. Furthermore, harvesting conditions and the size and chemical structure of berries selected for jam-making vary. For industrial jam-making

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Fig. 16.3 Firmness of pretreated thawed strawberries. Error bars are shown as means ± SD (n = 6). a-e Means with the same letter are not significantly different as determined by Tukey’s test (p < 0.05), 1a. Reference, 1b. Water in a vacuum, 2a. CaCl2, 2b. CaCl2 in a vacuum, 3a. CaCl2 + PME, 3b. CaCl2 + PME in a vacuum, 4a. CaCl2 + sucrose, 4b. CaCl2 + sucrose in a vacuum, 5a. CaCl2 + PME + sucrose, 5b. CaCl2 + PME + sucrose in a vacuum, 6. CaCl2 + PME + sucrose-solution before freezing (Reprinted with permission from Suutarinen et al. (2002a).

it is of primary importance to ensure both consistent jam-making conditions and the integrity of berries (Suutarinen, 2002). Firmness of jam strawberries pre-treated with CaCl2 and PME in a vacuum was higher than that of untreated berries or berries pre-treated with different methods (Suutarinen, 2002 b)/(Fig. 16.4). Dipping of strawberries into a CaCl2 solution with PME in a vacuum resulted in a significantly different sensory profile compared to that found in the other jams. The sensory attributes such as wholeness of the berries (p < 0.001), firmness, clarity and evenness of the jam medium (p < 0.001), softness of the berries (p < 0.001) and faultlessness of odour and flavor (p < 0.001) in particular showed statistically significant differences among the strawberry jams. Sensory quality was perceived to decrease during four months of storage, even though the shapes of the sensory profiles of the studied jams did not change significantly from those evaluated after two weeks’ storage (Suutarinen et al., 2002a). For achieving highquality jams, the pre-treatment time should be short (about 5–15 min), the temperature low (below 20 °C), the vacuum level high (pressure less than 10 kPa), the CaCl2 concentration moderate (about 1%) and the dosage of PME comparatively low (about 50–100 µkat/kg strawberries). The pre-treatment studies allows several different strawberry varieties to be used in industrial production rather than its being dependent on a single variety. A further advantage is the enrichment with calcium ions. This is advantageous for

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Fig. 16.4 Firmness of jam strawberries and jam medium after two weeks of storage. Error bars are shown as means ± SD (n = 6), a-e Means with the same letter are not significantly different as determined by Tukey’s test (p < 0.05). 1a, Reference. 1b. Water in a vacuum. 2a, CaCl2. 2b, CaCl2 in a vacuum. 3a CaCl2 + PME. 3b, CaCl2 + PME in a vacuum. 4a, CaCl2 + sucrose. 4b, CaCl2 + sucrose in a vacuum. 5a, CaCl2 + PME + sucrose. 5b, CaCl2 + PME + sucrose in a vacuum. 6, CaCl2 + PME + sucrosesolution before freezing. 7, CaCl2 + PME + sucrose-solution before jam-making. (Reprinted with permission from Suutarinen et al., 2002a).

many people requiring dietary calcium, e.g. people having lactose intolerance. Further, it appears that the vacuum reduces the air content in the strawberries, so that there is less air which may cause foaming in jam-making (Suutarinen, 2002). In addition to strawberries, the studied pre-treatment could also be applied to other soft berries such as raspberries and cloudberries. It is suitable for use before freezing or for application to frozen berries before jam-making (Suutarinen, et al., 2001). The pre-treatment is suitable for exported berries, which can be treated before freezing and transport to foreign countries (Suutarinen 2002). The vacuum equipment includes a pump, an air-pressure chamber and pipes. Costs of the pre-treatment equipment and chemicals will be reasonable. A pump which can be used to decrease the pressure of a 200 L chamber to 7 kPa with a suction effect of 100 m3/h would cost about 1300–2000 EUR (Busch Vakuumteknik Oy, Finland, year 2001). Equipment can be designed for batch or continuous processing. Chemicals including CaCl2 (1%) and PME enzyme (50 µkat/kg strawberries) would cost about 45 cent per 100 kg strawberries. The cost of food-grade CaCl2 is about 3 EUR per kg (Telko Oy, Finland) and that of PME (50 µkat/kg strawberries) (Novo Shape) is about 54 EUR per L (S.O. Strömberg Ky, Finland), The amount of pre-treatment solution needed for 100 kg strawberries is 120 L. The solution must be changed every 2 h, which means that the total

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amount of solution per 8 h day will be 500 L. One pretreatment will take about 15 min. About 30 batches will be treated per day, which means about 3000 kg of strawberries per day. About 8.5 L extra pre-treatment solution must be added into the chamber after each treatment to compensate for solution absorbed into the berries and for losses during pouring. From this it can be concluded that chemical costs for the treatment of 3000 kg strawberries would be about 27 EUR per day (Suutarinen, 2002). High-pressure treated fruit preparations have been commercially available in Japan since 1990, but limited information regarding the processing of such products remains limited. Horie et al. (1991) reported on a strawberry jam developed by pressurization at 400–600 MPa, which maintains its original fresh fruit color and flavor and sustains 95% of the vitamin C content of the fresh fruits. The pressure-processed jam is preferrable to a heat-processed jam. Dissolved oxygen and residual enzyme activities in pressure-treated preparations are given as the reason for color and flavor changes during storage. Consequently, chilled storage is suggested by the authors. Arai (1992) reports the production of high-pressure jam in which frozen strawberries or raspberries are mixed with 45–65% saccharides (sucrose or glucose), 0.1– 0.5% pectin and 0.02–0.1% sour agent. The mixture is then kept at over 200 MPa (normally 300–800 MPa) to afford a high-quality jam. Pre-treatment in the form of CaCl2 and PME solution in a vacuum can be used for strawberries and similar berries having a sensitive and soft texture before subjecting them to HP treatment (Stolt et al., 2001). In this case, the berries do not need to be drained at all, or are only partially drained after the pre-treatment. Sugar, salt or other agents including gelling and/or thickening agents, optionally in liquid medium, are then added to the pre-treatment medium. These agents may also be added before the actual pre-treatment. The berries are then packaged in a flexible container and subjected to a pressure of 400–700 MPa at 0–40 °C for 1–60 min. Firmness measurements of HP-treated strawberry slices soaked in a liquid containing CaCl2 and PME gave a result that was about nine times higher than that of berries which were not pre-treated at all. Watanabe et al. (1991) described a process in which strawberry paste is freeze concentrated, using ice nucleation active bacteria suspended in the juice previously separated from the pulp, and subsequently pressurized at 400 MPa for 5 min at room temperature. The resulting product is superior in brightness and red colour as well as in maintaining the original flavor compared to the heat-processed controls. Quality of the pressure-treated jam is higher immediately after processing as compared to the heat-treated one and could be maintained under lowtemperature conditions for two to three months. Storage at room temperature results in discoloration, off-flavor formation and decomposition of sucrose and vitamin C. Dissolved oxygen and residual enzyme activities are the causative factors for quality changes in pressure-treated jams (Kimura et al., 1994). Optimal pressure for freeze concentration and high hydrostatic pressure-


405

treated strawberry jams is 200 MPa for preservation of anthocyanins in jams (Gimenz et al., 2001). Dervisi et al. (2001) studied the rheological properties and color of strawberry jams with various concentrations of pectin (0.1–10% weight basis) using high-pressure processing at 400 MPa for 5 min. Results suggested that optimum pectin concentration for colour retention and texture quality was between 2.5 and 5%. Shi et al. (1996) introduced a new jam manufacturing process using osmotic drying of strawberries (in 80 °Brix sucrose solution at 35–40 °C for 2 h) prior to addition at room temperature to sugar-pectin solution, potassium sorbate and citric acid. The jam was stored for up to three months in the dark at 5, 25 or 35 °C. Color, flavor, and taste scores of jam made in this way were significantly higher than those of commercial jam.

16.5 Future trends High pressure offers new opportunities for food processing and preservation, not only at temperatures above zero but also at those below zero (Denys et al., 2002). The advantage obtained by using high pressure is the prevention or retardation of the damages caused by ice crystals. By using high pressure, the freezing point can be lowered to –22 °C allowing sub-zero storage of foods in the liquid state without freezing. Studies with strawberries have shown that their fresh taste, texture and color can be maintained for weeks (Deuchi and Hayashi, 1991). Sudden pressure release of a product initially kept at sub-zero conditions in liquid state promotes rapid ice nucleation and formation of smaller ice crystals (Denys et al, 2002). This type of process is called pressure-shift freezing. In the same way, a frozen product can be forced to the liquid state by applying high pressure which allows faster thawing, and this process is called pressure-induced thawing (Deuchi and Hayashi, 1992). The quality changes during storage are, however, also dependent on subsequent frozen storage conditions. Usually pressure-shift freezing allows uniform and rapid nucleation and smaller ice crystal size distribution. The main advantages of pressure-induced thawing generally include reduction of the time required to thaw and smaller drip losses. The high-pressure equipment is used only for freezing and thawing. This is more cost-effective than using high-pressure throughout the entire freezing period.

16.6 References and MASCHERONI R H (2002) Quality evaluation of foodstuffs frozen in a cryomechanical freezer, J Food Engineering, 52(3), 257–63.

AGNELLI M E

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ALONSO J, CANET W

and RODRIQUEZ T (1997) Thermal and calcium pretreatment affects texture, pectinesterase and pectic substances of frozen sweet cherries, J Food Sci, 62(3), 511–15. ALZAMORA S M, CASTRO M A, VIDALES S L, NIETO A B and SALVATORI D (2000) The role of microstructure in the textural characteristics of minimally processed fruits. In Minimally Processed Fruits and Vegetables. Fundamental Aspects and Applications. Eds S M Alzamore, M S Tapia and A López-Malo, Gaithersburg, Aspen Publishers, 153–71. ARAI S (1992) Production of jam. Int. patent appl. JP patent 900202236 900730. AREF M, SIDWELL A P and LITWILLER E M (1956) The effect of various sweetening agents on frozen strawberries for preserve manufacture, Food Tech, 10(7), 293–7. ARMBRUSTER G (1967) Cellular and textural changes in three varieties of strawberries as a result of pre-freezing treatments, J Amer Soc Hortic Sci, 91, 876–80. BAKER R A and WICKER L (1996) Current and potential applications of enzyme infusion in the food industry, Trends in Food Sci and Techn, 7(9), 279–84. BARBOSA-CÄNOVAS G V and VEGA-MERCADO H (Eds) (1996) Dehydration of Foods, New York, Chapman and Hall. BARON-EPEL O, GHARYAL P K and SCHINDLER M (1988) Pectins as mediators of wall porosity in soybean cells, Planta, 175, 389–95. BOURNE M C (1979) Texture of temperate fruits. J Text Studies, 10(1), 25–44. BURROWS G (1996) Production of thermally processed and frozen fruit. In Fruit Processing. Ed. D Arthey and P R Ashurst, London, Chapman & Hall, 135–64. CANO M P (1994) Combined microwave/freezing methods to improve preserved fruit quality. In Minimal Processing of Foods and Process Optimization – an Interface. Eds R P Singh and F A R Oliveira, London, CRC Press, 135–52. CHIRALT A, MARTINEZ-NAVARRETE N, MARTINEZ-MONZO J, TALENS P, MORAGA G, AYALA A and FITO P (2001) Changes in mechanical properties throughout osmotic processes. Cryoprotectant effect, J Food Eng, 49(2–3), 129–35. CIERCO M (1994) Process for freezing of strawberries, their use in a similar manner to fresh strawberries. Int. patent appl. FR 94 13864, Institut National de la Propriété Industrielle. COUTEL Y A G and DALE R H S (1998) A method for fruit processing, Int. patent appl. Patent 98/52423, Gist-Brocades Co. DENG H and UEDA Y (1993) Effects of freezing methods and storage temperature on flavor stability and ester contents of frozen strawberries, Engei Gakkai Zasshi (in Japanese), 62, 633–9. DENYS S, SCHLÜTER O, HENDRICKX M E G and KNORR D (2002) Effects of high pressure on water-ice transitions in foods. In Ultra High Pressure Treatments of Foods. Eds M E G Hendrickx and D Knorr, New York, Kluwer Academic/Plenum Publishers, 215–48. DERVISI P, LAMB J and ZABETAKIS I (2001) High pressure processing in jam manufacture: effects on textural and colour properties, Food Chem, 73(1), 85–91. DEUCHI T and HAYASHI R (1991) Pressure application to thawing of frozen foods and to food preservation under subzero temperature. In High Pressure Science for Food. Ed. R Hayashi, Kyoto, San-Ei Suppan, 101–10. DEUCHI T and HAYASHI R (1992) High pressure treatments at subzero temperature: Application to preservation, rapid freezing and rapid thawing of foods. In High Pressure and Biotechnology. Eds. C Balny, R Hayashi, K Heremans and P Mason, Montrouge, John Libbey Eurotaxt, 353–5. EL GHAOUTH A, ARUL R, PONNAMPALAM R and BOULET M (1991) Chitosan coating effect on storability and quality of fresh strawberries, J Food Sci, 56(6), 1618–31. ESCRICHE I, CHIRALT A, MORENO J and SERRA J A (2000) Influence on blanching-osmotic dehydration treatments on volatile fraction of strawberries, J Food Sci 65(7), 1107–11. ESHTIAGHI M N and KNORR D (1996) High hydrostatic thawing for the processing of fruit preparations from frozen strawberries, Food Biotech, 10(2), 143–8. FITO P (1994) Modelling of vacuum osmotic dehydration of foods, J Food Eng, 22(1–4), 313–18.

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and CHIRALR A (1994) Vacuum osmotic dehydration of fruits. In Minimal Processing of Foods and Process Optimization – an Interface. Eds R P Singh and F A R Oliveira, London, CRC Press, 107–21. FUNEBO T (1997) Microwave assisted air dehydration of fruits and vegetables – a literature review, SIK-Rapport. GARCÍA J M, HERERA S and MORILLA A (1996) Effects of postharvest dips in calcium chloride on strawberry, J Agric Food Chem, 44(1), 30–33. GARCÍA M A, MARTINO M N and ZARITZKY N E (1998) Plasticized starch-based coatings to improve strawberry (Fragaria x Ananassa) quality and stability, J Agric Food Chem, 46(9), 3758–67. GARCIA-BERBARI S A, NUNES-NOGUEIRA J N and SILVA-CAMPOS S D (1998) Effect of different pre-freezing treatments on the quality of frozen strawberry variety Chandler, Ciénc Tecnol Aliment, 18(1), 82–6. GARROTE R L and BERTONE R (1975) Chemical evaluation and suitability for freezing of strawberry varieties, Revista del ITA (1), 81–99. GIMENEZ J, KAJDA P, MARGOMENOU L, PIGGOTT J R and ZABETAKIS I (2001) A study on the colour and sensory attributes of high hydrostatic pressure jams as compared with traditional jams, J Food Sci Agric, 81(13), 1228–34. GRASSIN C M T and FAUQUEMBERGUE P C L (1994) Use of pectinesterase in the treatment of fruit and vegetables. Int. patent appl. WO patent 94/12055, Gist-Brocades Co. HAARD N F (1997) Product composition and the quality of frozen foods. In Quality in Frozen Food. Eds M C Erickson and Y-C Hung, New York, Chapman & Hall, 275– 95. HORIE Y, KIMURA K, IDA M, YOSHIDA Y and OHKI K (1991) Jam preparation by pressurization, Nippon Nogeikagaku Kaishi (in Japanese), 65(6), 975–80. JONES S A (1996) Optimisation of texture in heat processed fruits – Flair-Flow seminar, VTT Biotechnology and Food Research, Espoo, Finland, 24th of May 1996. KIMURA K, IDA M, YOSIDA Y, OHKI K, FUKUMOTO T and SAKUI N (1994) Comparison of keeping quality between pressure-processed jam and heat-processed jam: Changes in flavor components, hue, and nutrients during storage, Biosci Biotech Biochem, 58(8), 1386– 91. KOLEV D, KAFEDZHIEV I and ATANASOV I (1983) Technology of vacuum degassing of apples, Konservna Promishlenost (in Bulgarian), 12, 26–7. LACROIX C, CASTAIGNE F and ROUTHIER B (1985) Evaluation du comportement textural à la congélation de diverses espèces de fraises, Lebensm-Wiss und –Technol, 18(1), 35–42. LAZARIDES H N (1994) Osmotic preconcentration: developments and prospects. In Minimal Processing of Foods and Process Optimization – an Interface. Ed. Singh R P and F A R Oliveira, London, CRC Press, 73–85. LUCAS T, FRANCOIS J, BOHUON P and RAOULT-WACK A L (1999) Factors influencing mass transfer during immersion cold storage of apples in NaCl/sucrose solutions, LebensmWiss und –Technol, 32(6), 327–32. MAIN G L, MORRIS J R and WEHUNT E J (1986) Effect of preprocessing treatments on the firmness and quality characteristics of whole and sliced strawberries after freezing and thermal processing, J Food Sci, 51(2), 391–4. MATSER A M, KNOTT E R and BARTELS P V (1998) High pressure preservation of mushrooms. In Fresh Novel Foods by High Pressure. Ed. K Autio, VTT Symposium 186; Helsinki 21–22 September 1998. Espoo: Technical Research Centre of Finland, 123–30. MILLER J P and BUTCHER C (2000) Freezer technology. In Managing Frozen Foods. Ed. C J Kennedy, Cambridge, CRC Press, 159–93. MORENO J, CHIRALT A, ESCHRICHE I and SERRA J (1998) Stabilizing effect of combined enzyme inactivation/osmotic dehydration methods minimally processed strawberries, Alimentaria (in Spanish) (295), 49–52. NOBUTSUGU K (1984) Improved jam. Int. patent appl. JP patent 59106258.

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OGONEK A and LENART A (2001) Influence of selective edible coatings on osmotic dehydration

of strawberries, Zywsnosc, 8(3), 62–74. (1979) Quick frozen fruit. Quality assessment of raw material and frozen product, Technical-Memorandum-Campden-Food-Preservation-Research-Association, 221, 1–20. PERKINS-VEAZIE P (1995) Growth and ripening of strawberry fruit. In Horticultural Reviews. Ed J Janick, London, John Wiley and Sons, 267–97. POLESELLO A and CRIVELLI G (1971) Addition of calcium salts to fruits as pretreatment for quick freezing, Ind Agr (in Italian), 9, 413–22. PONAPPA T, SCHEERENS J C and MILLER A R (1993) Vacuum infiltration of polyamines increases firmness of strawberry slices under various storage conditions J Food Sci, 58(2), 361– 4. PONTES J, MC NICHOL R J and KERBY N (1996) Treating organic tissue. Int. patent appl. WO patent 96/41542, Mylnefield Research Services Ltd. PONTING J D, WATTERS G G, FORREY R R, JACKSON R and STANLEY W L (1966) Osmotic dehydration of fruits, Food Tech, 20(10), 1365–8. POOVAIAH B W (1986) Role of calcium in prolonging storage life of fruits and vegetables, Food Tech, 40(5), 86–9. RAHMAN M S (1999) Food preservation by freezing. In Handbook of Food Preservation. Ed M S Rahman, New York, Marcel Dekker Inc., 259–84. RASTOGI N K, ANGERSBACH A and KNORR D (1999) Mechanism of mass transfer during osmotic removal of water from plant materials. In Improved Traditional Foods for the Next Century. Eds F Toldrà, D Ramó and J L Navarro, Proceedings of the International Congress organised by D G XII- European Comission and the Instituto de Agroquímica y Tecnología de Alimentos (C.S.I.C.), Valencia 28–29 October, 366–71. REID D S (1996) Fruit processing. In Processing Fruits: Science and Technology. Vol 1. Biology, Principles, and Applications. Eds L P Somogyi, H S Ramaswamy and Y H Hui, Lancaster, Technomic Inc, 169–83. SACCHETTI G, GIANOTTI A and DALLA ROSA M (2001) Sucrose-salt combined effects on mass transfer kinetics and product acceptability. Study on apple osmotic treatments, J Food Eng, 49(2–3), 163–73. SALVATORI D, ANDRÉS A, CHIRALT A and FITO P (1998) The response of some properties of fruits to vacuum impregnation, J Food Proc Eng, 21(1), 59–73. SAMS C E, CONWAY W S, ABBOTT J A, LEWIS R J and BEN-SHALOM N (1993) Firmness and decay of apples following postharvest pressure infiltration of calcium and heat treatment, J Amer Soc Hortic Sci, 118(5), 623–7. SHI X Q, CHIRALT A, FITO P, SERRA J, ESCOIN C and GAQUE L (1996) Application of osmotic dehydration technology on jam processing, Drying Tech, 14(3–4), 841–57. SISTRUNK W A, MORRIS J R and KOZUP J (1982) The effect of chemical treatments and heat on color stability of frozen machine-harvested strawberries for jam, J Amer Soc Hort Sci, 107(4), 693–7. SORMANI A, MAFFI D, BERTOLO G and TORREGIANI D (1999) Textural and structural changes of dehydrofreeze-thawed strawberry slices: effects of different dehydration pretreatments, Food Sci Tech Int, 5(6), 479–85. STOLT M, SUUTARINEN M and AUTIO K (2001) A process for preserving foodstuff. Int. patent appl. WO patent 01/58286, Technical Research Centre of Finland. STUTE R, ESHTIAGHI M N, BOGUSLAWSKI S and KNORR D (1996) High pressure treatment of vegetables. In High Pressure Chemical Engineering. Eds Rudolf von Rohr and Ch. Trepp, London, Elsevier Science, 271–6. SUUTARINEN M (2002) Effects of prefreezing treatments on the structure of strawberries and jams (Doctor Thesis, VTT Publications 462, Espoo, Finland, Edita Prima Oy Ltd). SUUTARINEN M, HEISKA K, AUTIO K and MOKKILA M (2000) The effect of CaCl2 and PME prefreezing treatment in a vacuum on the structure of strawberries (abstract), In: 4th International Strawberry Symposium Book of Abstracts. Eds T Hietaranta, M–M Linna, July 9–14, Tampere, Finland, Kaarinan Tasopaino Oy Ltd, 28. OSWIN P M

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and MOKKILA M (2001) A process for preparing jam, WO patent 01/30178, Technical Research Centre of Finland. SUUTARINEN M, HONKAPÄÄ K, HEINIÖ R–L, AUTIO K, MUSTRANTA A, KARPPINEN S, KIUTAMO T, LIUKKONEN–LILJA H and MOKKILA M (2002a) Effects of calcium chloride-based prefreezing treatments on the quality factors of strawberry jams, J Food Sci, 67(2), 884–94. SUUTARINEN M, HONKAPÄÄ K, HEINIÖ R–L, MUSTRANTA A, LIUKKONEN–LILJA H and MOKKILA M. (2002b) Modeling of calcium chloride and pectin methylesterase prefreezing treatments of strawberries and jams, J Food Sci, 67(3), 1240–48. SZCZESNIAK A S and SMITH B J (1969) Observations on strawberry texture a three-pronged approach, J Text Studies, 1(1), 65–89. TORREGIANI D, LUCAS T and RAOULT-WACK A–L (2000) The pre-treatment of fruits and vegetables. In Managing Frozen Foods. Ed. C J Kennedy, Cambridge, CRC Press. VAN BUREN J P (1979) The chemistry of texture in fruits and vegetables, J Text Studies, 10(1), 1–23. VIBERG U, FREULER S, GEKAS V and SJÖHOLM I (1998) Osmotic pretreatment of strawberries and shrinkage effects, J Food Eng, 35(2), 135–45. VICENTE A R, MARTINEZ G A, CIVELLO P M and CHAVES A R (2002) Quality of heat-treated strawberry fruit during refrigerated storage, Postharvest Biol Technol, 25(1), 59–71. WATANABE M, ARAI E, KUMENO K and HONMA K (1991) A new method for producing a nonheated jam sample: the use of freeze concentration and high-pressure sterilization, Agric Biol Chem, 55(8), 2175–6. WILLIAMS A (1994) New technologies in food preservation and processing: part II, Nutrition & Food Sci, (1), 20–23. WROLSTAD R E, LEE D D and POEI M S (1980) Effect of microwave blanching on the color and composition of strawberry concentrate, J Food Sci, 45(6), 1573–7.

17 Improving the texture of processed fruit: the case of olives I. Mafra, University of Porto and M. A. Coimbra, University of Aveiro, Portugal

17.1 Introduction: the texture of table olives The table olive is the fruit of varieties of the cultivated olive tree, Olea europaea. The origins of cultivation of the olive tree lie rooted in legend and tradition. It probably started about 5000–6000 years ago within a wide strip of land by the eastern Mediterranean Sea and in the adjacent zones comprising Asia Minor, part of India, Africa and Europe (Fernández Díez, 1971). World production of table olives is about 1.3 million tonnes, of which 43% are produced in America, 36% in Europe, and 7% in Arab countries (http:// www.asemesa.es). In relation to the 12 million tonnes of olives for olive oil production, table olives represent about one tenth of the world olive production. The olive fruit is a drupe, similar to other drupes or stoned fruits. However, the olive differs from all other drupes in its chemical composition due to its relatively low sugar content, 2–5% versus around 12%, high oil content, 20–30% versus 1–2%, and its characteristic strong bitter taste which is caused by oleuropein (Table 17.1) (Garrido Fernández et al., 1997). The length of the fruit is usually between 2 and 3 cm and its transverse diameter between 1 and 2 cm. Its total weight may vary between 0.5 and 20 g but generally falls within the range 3–10 g (Fernández Díez, 1983). The texture of the edible flesh is very variable, depending upon variety, oil content, stage of maturity, type of culture, soil, climate, and other factors which influence the chemical composition of the fruit. The component parts of olive are the epicarp layer which has a continuous well-developed cuticle, the mesocarp layer which constitutes between 70 and 90% of the fruit, and the woody endocarp enclosing the embryo. The mesocarp and epicarp are composed of parenchymatous cells involved in a thin cell wall and associated by the middle lamella. In the epicarp, the cells are

Improving the texture of processed fruit: the case of olives 411 Table 17.1 Composition of the olive fruit mesocarp (Garrido Fernández et al., 1997) Component

Proportion (%)

Moisture Lipids Reducing sugars, soluble Non-reducing sugars Mannitol Raw fibre Proteins (N × 6.25) Ash Organic acids and their salts Phenolic compounds Pectic substances Other compounds

60–75 10–25 3–6 ≤ 0.3 0.5–1.0 1–4 1–2 < 1.0 0.5–1.0 2–3 ≤ 0.6 3–7

closely packed without the empty spaces apparent between the mesocarp cells. Changes in the structural arrangement and chemical composition of the epicarp and mesocarp cell walls determine the physical properties of olive tissue. A study of the mechanical properties of Hojiblanca and Douro varieties showed that in a tensile test the epicarp (skin) is stronger and stiffer than the mesocarp (flesh) (Georget et al., 2001). This was also reflected in the strain at failure of the two tissues, the epicarp being less deformable than the mesocarp. Table olives are included in pickled products, which are defined as those products whose preparation and preservation are carried out by a combination of salting, fermentation and/or acidification (Garrido Fernández et al., 1997). This system of processing allows the preservation of an otherwise perishable raw material over extended periods of time. The combined effects of the salt, low pH and organic acidity often allow the preservation to be carried out without heat treatment, depending on the type of processing. The natural bitterness of the fruit can be eliminated, or at least reduced, by processing to make it acceptable for human consumption. The processing is also responsible for softening of the tissue, which is desirable if the raw fruit is too hard, but is a problem when the olives become too soft. The type and extent of processing should then be suited to the characteristics of the raw fruits, in order to obtain a final product with an appropriate sensory profile, in which the texture characteristics are of great importance. Olives are among the fruits with an appreciable fibre content (Fernández Díez, 1985). The dietary fibre content, that is the total polysaccharide and lignin which, when eaten, is not digested by endogenous secretions of the digestive tract, represents around 12% of the weight of some processed varieties, although it can reach 20% in dried olives (Jiménez et al., 2000). On a fruit basis, the amount of dietary fibre is approximately constant, depending on the size of the fruit and the flesh to pit ratio. However, on a pulp basis, the relative content of dietary fibre might change due to variation

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in the moisture and fat contents caused by the type of processing and stage of ripening of the raw fruits.

17.2 Factors affecting the texture quality of raw olives The chemical composition and physical properties of the olive are important factors in determining the quality of the final product, and these are strongly influenced by both the variety and the time of harvest. In the following subsections, some relevant parameters related to the texture of raw olives intended for the production of table olives are discussed with regard to the effect of ripening and the variety.

17.2.1 Ripening During ripening, the fruit surface colour changes progressively from green to pale-green, straw yellow, pink, purple–pink (cherry) and black. Normally the fruits reach their maximum size when they change their surface colour from slightly pink to purple–pink or black. During development and ripening, other chemical changes take place, such as increase in oil content, decrease in water and reduction in sugars. Changes in cell wall polysaccharides play a major role in bringing about alterations in olive fruit texture during ripening. Olive pulp cell walls are composed mainly of pectic polysaccharides rich in arabinose, glucuronoxylans and cellulose, while xyloglucans, mannans and glycoproteins occur as minor components (Coimbra et al., 1994, 1995) (Fig. 17.1). Studies carried out on olives of Douro variety (Mafra et al., 2001), a Portuguese variety suitable for black oxidised olives, showed that, on a dry pulp basis, an overall decrease in pectic polysaccharides occurs between the green and the cherry stages due to the degradation of the galacturonan moiety as well as the arabinan side chains (Fig. 17.1a). The change in pectic polysaccharides was followed by a decrease in their degree of methylesterification. The amounts of glucuronoxylans and cellulose were also reduced. Higher solubilisation was observed in all polymers between the cherry and black stages. The same study, but on a fruit basis (Fig. 17.1b) (Mafra, 2002), also indicated the decrease in pectic polysaccharides between green and cherry; however, between cherry and black, a considerable increase in pectic polysaccharides was observed. This increase was related to the synthesis of new polysaccharides to match the increase in cellular volume and amount of fruit pulp, which is observed with ripening (John and Dey, 1986). The increase in xyloglucan content corresponded to the decrease in cellulose, which indicated an increase in the solubility of glucans with ripening, rather than an increase in xyloglucan content per fruit. Also, per fruit, the amount of glucuronoxylans tends to remain constant with ripening.

Improving the texture of processed fruit: the case of olives 413

The study of olive pulp cell wall polysaccharides from the two different harvests showed that the changes with ripening are not always so pronounced from green to cherry and to black stages. One great difference between the

g/kg dry pulp

50

g/kg dry pulp

40 30

25 20 15 10 5 0 Galacturonans Arabinans

20 10 0

Pectic Glucuronoxylans Xyloglucans polysaccharides

Mannans

Cellulose

(a) 30 mg/fruit

25

mg/fruit

20 15

12 10 8 6 4 2 0 Galacturonans Arabinans

10 5 0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

(b) 40 20

mg/fruit

35

mg/fruit

30 25 20

15 10 5 0 Galacturonans Arabinans

15 10 5 0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

(c) Green

Cherry

Black

Fig. 17.1 Composition of olive pulp cell wall polysaccharides of Douro variety in three stages of ripening, 1997 harvest, expressed as (a) g.kg–1 of dry pulp and (b) as mg per fruit, (c) 1998 harvest, expressed as mg per fruit (Mafra, 2002).

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two harvests was the fact that the extension of the changes and/or degradation of polysaccharides in one harvest (Fig. 17.1b) was higher than in the other one (Fig. 17.1c). In green olives of Douro variety (Mafra et al., 2001), the thin-walled parenchyma cells are uniform and tightly packed (Fig. 17.2b). Tissue fracture involves cell walls breaking, both in epicarp and mesocarp (Fig. 17.2a), but cell separation at the middle lamella level is not observed. The fracture surface of cherry olives consists of a mixture of broken and separated cells and intact cells. In black olives, the region of broken cells is smaller and involves only the epicarp and the first layers of the mesocarp (Fig. 17.2c), with cell separation (Fig. 17.2d). The study of the mechanical properties of olives of Douro variety (Mafra et al., 2001) showed that green olives are the most difficult to penetrate (Fig. 17.3a) and the least deformable (Fig. 17.3b), whereas black olives are the easiest to penetrate and the most deformable. Both puncture and compression tests showed tissue softening induced by ripening. The study of the effect of ripening on Hojiblanca olives, a Spanish variety also used to produce black oxidised olives, indicated that pectic polysaccharides were widely affected (Jiménez et al., 2001a). These authors reported the

(a)

(c)

(b)

(d)

Fig. 17.2 SEM photographs of broken surface olives, adapted from Mafra et al. (2001): (a) green olives, overview, showing tissues fracturing through the cells; (b) details of green olive parenchyma cells tightly packed (cell adhesion); (c) black olives, overview, showing tissues mainly fracturing along the middle lamellae; (d) details of the black olive parenchyma showing the dissolution of pectic polysaccharides (cell separation).

Improving the texture of processed fruit: the case of olives 415 10

Deformation (mm)

5

Force (N)

4 3 2 1 0

8 6 4 2 0

Green

Cherry (a)

Black

Green

Cherry (b)

Black

Fig. 17.3 (a) Puncture test force (penetrometer) and (b) compression test deformation (durometer) of green, cherry and black olives of Douro variety, adapted from Mafra et al. (2001).

release of galacturonans as ripening progresses together with the debranching of the rhamnogalacturonan between cherry and black stages. Between green and cherry there was also a decrease in the yield of tightly bound hemicellulosic polysaccharides together with a general decrease in molecular weight of hemicellulosic polysaccharides (Jiménez et al., 2001b). Between cherry and black fruits, the most significant modifications were those that took place in hemicellulosic polysaccharides and cellulose. The study of the mechanical properties of olives of Hojiblanca and Douro varieties in different stages of ripening showed that the epicarp of green olives of both varieties is stronger than the mesocarp for 25–30 orders of magnitude (Fig. 17.4a, b), whereas for the cherry and black stages, the epicarp became stronger than the mesocarp by 100 orders of magnitude (Georget et al., 2001). The ripening affects the strength of the mesocarp more than that of the epicarp. The decrease of tissue strength occurs mainly in the mesocarp of the olive when the fruit turns from green to cherry. Figure 17.4 (c, d) shows also that the strain at failure, measured by the ratio of the change in length to the original length, is higher in the mesocarp tissues than in epicarp, but is not significantly influenced by the stage of ripening of the olive. These data show that the flexibility for elongation of the mesocarp is higher than that of the epicarp. Another mechanical characteristic measured in the olive was the stiffness (Fig. 17.4e, f). The stiffness is 100–200 orders of magnitude higher in epicarp than in mesocarp, and was shown to decrease with ripening in both tissues (Georget et al., 2001). The dissolution and breakdown of polysaccharides of middle lamella and cell walls during ripening of fruits is caused by the action of a diversity of cell wall enzymes. A series of enzymatic activities has been described during the growth and ripening of olive fruits (Fernández-Bolaños et al., 1995). The activity of polygalacturonase is often considered to be responsible for fruit softening and to be many times inversely related to fruit firmness (Brett and Waldron, 1996). Polygalacturonase catalyses the hydrolysis of galacturonans main chain of pectic polysaccharides and is more active in de-esterified pectic polysaccharides than in methylesterified ones. Pectinmethylesterase

416

Texture in food 0.12

Strength (MN m–2)

Strength (MN m–2)

4.0 3.0 2.0 1.0 0.0 Green

Cherry (a) Epicarp

Strain at failure

Strain at failure

0.03

Green

Cherry (b) Mesocarp

Black

Green

Cherry (d) Mesocarp

Black

Green

Cherry (f) Mesocarp

Black

0.20

0.15 0.10 0.05 0.00

0.15 0.10 0.05 0.00

Cherry (c) Epicarp

Black

60 50 40 30 20 10 Green

Cherry (e) Epicarp

Black

Hojiblanca

Estimated modulus (MN m–2)

Green

Estimated modulus (MN m–2)

0.06

0.00

Black

0.20

0

0.09

0.5 0.4 0.3 0.2 0.1 0.0

Douro

Fig. 17.4 Mechanical properties of olives of Hojiblanca and Douro varieties in different stages of ripening, adapted from Georget et al. (2001). (a) Strength of the epicarp; (b) strength of the mesocarp; (c) strain at failure of the epicarp; (d) strain at failure of the mesocarp; (e) stiffness of the epicarp; (f) stiffness of the mesocarp.

catalyses the hydrolysis of esterified galacturonic acid (Fischer and Bennet, 1991). The concerted action of both enzymes causes an extensive degradation of cell wall pectic polysaccharides. Cellulase activity is also present in ripening fruits. The decrease in organisation of the microcrystalline cellulose fibrils allows the access of cellulases, causing a decrease in the molecular weight of these polysaccharides (O’Donoghue et al., 1994). The reported changes in cell wall polysaccharides of olive pulp during ripening may be attributed to the increases of the activities of polygalacturonase, pectinmethylesterase and cellulose, which were found to occur with ripening of olives of Douro variety (Mafra et al., 2000).


17.2.2 Variety The olive oil content, as well as the general composition of the pulp, is highly variable and dependent on a multiplicity of factors, such as climate, soil, and type of culture. According to the principal use of the olive fruit, varieties can be classified in three groups (Garrido Fernández et al., 1997): • fruits for table olive production; • fruits for olive oil extraction; • fruits for both purposes. Within the group of fruits suitable for table olive production, depending on their chemical and physical properties, different types of processing are traditionally used. Characteristics such as the size of the fruits, the flesh to pit ratio, the colour of the epicarp, the texture of the epicarp and mesocarp, the susceptibility to gaseous spoilage and to shrivelling during brine storage and the sensory properties of the final product are factors which determine the choice of the variety. In OLITEXT Project, FAIR CT97 3053 – Improvement of Texture Characteristics of Some European Olive-Fruit Varieties Suitable for TableOlive Purposes (1997–2000), six olive varieties were studied: Douro (D), from Portugal; Hojiblanca (H), from Spain; Conservolia (C) and Thasos (Th), from Greece; Taggiasca (T) and Cassanese (Ca), from Italy. They were processed as black oxidised (D and H), naturally black (C and T), dry-salted (Th), and boiled, salted and oven-dried (Ca) table olives. This European Union funded project aimed: 1. to increase the understanding of the chemical and biochemical processes that occur during the production of processed olives in relation to product quality (texture) as modulated by the stage of ripening and agronomic factors; 2. to investigate the effects of debittering treatments on final olive texture; 3. to use the knowledge gained to develop and/or improve production methods and thereby optimise table-olive texture. This chapter focusses on the results obtained in this project. The study of cell wall polysaccharides of raw olive pulp of different varieties at a ripe to very ripe black stage showed that Douro and Taggiasca had a very similar polysaccharide composition (Table 17.2). They were rich in pectic polysaccharides (45–50%) and contained 25–29% cellulose, 12–13 % glucuronoxylans, and 9–10 % xyloglucans. Cassanese was also rich in pectic polysaccharides (46%), and contained 25% cellulose, 16% xyloglucans, and 12% glucuronoxylans. Conservolia and Thasos contained 34–36% pectic polysaccharides, 26–27% cellulose, and 18–20% xyloglucans. Glucuronoxylans accounted for 20% in Conservolia and 14% in Thasos.

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Table 17.2 Relative amount (%) of olive pulp cell wall polysaccharides of raw black olives of different varieties

Pectic polysaccharides Glucuronoxylans (Xylo)glucans Mannans Ara-rich glycoproteins Cellulose

Douro (D 97)

Douro (D 98)

Taggiasca (T)

Conservolia Cassanese Thasos (C) (Ca) (Th)

49 13 9 1 1 27

45 12 10 3 1 29

50 13 10 1 1 25

34 20 18 2 0 26

46 12 16 1 0 25

36 14 20 2 1 27

17.3 Influence of processing on table olives The three main methods used for preparation of table olives are: • green pickled olives in brine, produced according to the Sevillian style; • black oxidised olives in brine, produced according to the Californian style; • naturally black olives in brine, produced according to the Greek style. According to the different regions and traditions, other types of table olives processing are used on a small scale.

17.3.1 Green pickled olives The processing of pickled green olives according to the Sevillian Style is widely used in Portugal and Spain. In this procedure, the fruits are collected at a mature green stage (prior to ripening) and are treated anaerobically with a sodium hydroxide solution (lye) for several hours to remove the bitter glucoside oleuropein. The treatment is likely to inactivate all biochemical activity within the fruits. The strength of the alkali (approximately 2% w/w) depends on the fruit size, temperature and stage of ripening. Alkali treatment is terminated when two thirds of the pulp have been penetrated. The fruits are then washed several times with water and subjected to a lactic fermentation in a brine solution (7–10% NaCl) for between two and four months. After fermentation, the green olives are packed for sale (Fernández-Díez, 1985). According to Coimbra et al. (1996), the major structural modification in the cellular matrix with processing to pickled green olives according to the Sevillian Style was due to the degradation of pectic polysaccharides. This modification was reflected both in pectic polysaccharides associated with Ca2+ and in pectic polysaccharides solubilised with alkali solutions, and could be the principal factor in the alteration of olive pulp texture with processing. A study carried out on processing of Hojiblanca variety (Jiménez et al., 1995) reported that the lye treatment had a significant effect on pectic polysaccharides, altering their solubility in aqueous solutions. Fermentation produced a marked degradation of hemicellulosic polysaccharides and cellulose,


and probably an interchange in solubility. These authors also reported that the greatest change in texture measured by a texturometer was observed after lye treatment and, to a smaller extent, after fermentation. They suggested that the loss of texture during the lye treatment, washing and soaking in brine could be due to the variations in polysaccharide solubility, disorganisation of the cell walls, attributed to the breakdown of the different kinds of bonds, and the decrease in cellular turgidity caused by the disorganisation of the plasma membrane. After equilibrium in brine, there was an increase in firmness attributed to the neutralisation of pectic polysaccharides, negatively charged because of the lye treatment. Storage in brine solution caused changes neither in polysaccharide composition nor in texture. The alkali reagent dissolves the epicuticular wax coating and decreases the thickness of the cuticle. This was shown by Marsilio et al. (1996), who also observed changes in pectic polysaccharides of cell walls and intercellular cohesion. The effect depends on the degree of ripening but, above all, on the alkali concentration. The alkali dissolves the pectic polysaccharides leaving the cellular microfibrils as an empty network with a weak structure, which in turn results in loss of firmness and often in collapse. More pronounced changes were noted in soft pulp of Ascolana and Caroleo varieties due to their thinner cell walls and smaller amount of pectic polysaccharides in the middle lamella. The cell structure of the variety Intosso, which has a higher amount of pectic polysaccharides, was less damaged by the lye treatment.

17.3.2 Black oxidised olives In black oxidised processing in the Californian style, the olives, mostly green and cherry, are normally stored in brine with 5–10% NaCl for between two and six months, depending on the needs of production. The brine may be acidified to pH 4 with lactic acid and acetic acid and kept in anaerobic or aerobic conditions. To improve texture, calcium chloride could be added during this period. Once the fresh or stored fruits are sorted and occasionally graded, they are treated with a series of dilute sodium hydroxide solutions and exposed to air between treatments. In the USA, three to five lyes are usually used, while in Portugal and Spain two to three are considered enough. The lye treatments are normally adjusted since the first one penetrates the skin while the remaining lyes are permitted to penetrate the pulp progressively until the last one reaches the stone. The lye concentration varies generally from 1.5 to 2.0%, and the first lye normally has the highest concentration. The duration and concentration of lye should be adjusted to the temperature conditions and fruits used, i.e. previous storage in brine, size and stage of ripening. After each alkaline treatment the fruits are placed in water and oxidised by injecting air under pressure into the immersed olives. After the lye treatments and oxidation, the olives are washed several times with water to remove most of the residual lye to reach a final pH around 7. Generally, 0.1% (w/w) of ferrous gluconate is added to the last wash to stabilise the

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Texture in food

colour produced by oxidation of polyphenols during air injection. The olives are then placed in 3–5% brine during one to three days to equilibrate the sodium chloride content in the flesh. A bulk pasteurisation to minimise the action of some aerobic bacteria during brining is optional. The olives are then packed and sterilised (Garrido Fernández et al., 1997). The effect of the black oxidising process on cell wall polysaccharides and texture has recently been a subject of study in olives of Douro variety. The results of the study in green olives showed that brine storage increased, on a fruit basis, the amount of pectic polysaccharides, glucuronoxylans and cellulose (Fig. 17.5a) (Mafra, 2002). The maintenance of cell wall polysaccharides in NaCl solutions containing CaCl2 might be the result of charge stabilisation conferred by Na+ and Ca2+, and mainly of the ability of Ca2+ to form complexes with pectins (Jiménez et al., 1997), allowing them to form gels (Cardoso et al., 2003). The increase in polysaccharides was attributed (probably) to 45 40 mg/fruit

35

mg/fruit

30 25 20


15 10 5 0

Pectic Glucuronoxylans Xyloglucans polysaccharides (a) Green olives

Mannans

Cellulose

45 40 mg/fruit

35

mg/fruit

30 25 20


15 10 5 0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

(b) Black olives Raw

Brine

Lye

Final product

Fig. 17.5 Composition of olive pulp cell wall polysaccharides of (a) green and (b) black olives of Douro variety, expressed as mg per fruit, along the black oxidising process (Mafra, 2002).


biosynthesis of new polysaccharides during the long storage period. The alkali treatment had two effects: it caused degradation and loss of polysaccharides and, on the other hand, it increased their retention in the cell walls. This retention could be due to the ionisation of the hydroxyl groups of cellulose which could prevent the diffusion of negatively charged pectic polysaccharides enmeshed within the swollen cellulose matrix, attenuating their loss. The final thermal treatment by sterilisation caused mainly the loss of hemicellulosic polysaccharides and cellulose. The combined effects of black oxidising processing and ripening on olives of Douro variety were also reported (Mafra, 2002). The storage in brine also contributed to stabilisation of pectic polysaccharides in cherry and black olives; however, there was a decrease in cellulose, especially in black olives, in contrast to the increase observed in green olives. This fact might be the result of a higher biosynthetic activity in green olives and/or a higher hydrolytic activity in black ones. The lye treatment had more effect on the cell wall polysaccharides of black olives as they all decreased, indicating that the degradation caused by processing depended on the stage of ripening of raw fruit. The black oxidised processing increased the differences observed in cell wall polysaccharides of fresh olives at different stages of ripening (Fig. 17.5b). The study of the compression test of olives at different stages of ripening and processing indicated an increase in the deformation during ripening as mentioned above, mainly between green and cherry (Fig. 17.6). After brine storage, it seems that the increase in polysaccharides referred above does not correspond to a great improvement in texture. The lye treatment seemed to decrease the deformation of green and black olives, probably due to the 7

Deformation (mm)

6 5 4 3 2 1 0 Green Raw

Cherry Brine

Lye

Black Final product

Fig. 17.6 Compression test deformation (durometer) of green, cherry and black olives of Douro variety in the different stages of processing to black oxidised olives. (Results from Istituto Sperimentale per la Elaiotecnica, Pescara, OLITEXT Report).

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Texture in food

insolubilisation of polysaccharides. The deformation of the final products of green and cherry olives increased, probably due to the increase in the solubilisation of cell wall polysaccharides after equilibrium of charges in brine and due to sterilisation. The mechanical properties of black oxidised olives of Hojiblanca variety processed in different stages of ripening have been studied by Georget et al. (2003). These authors used the approach of separating the epicarp from the mesocarp and demonstrated that the strength of the epicarp decreased significantly in black oxidising processing, whereas the flesh became stronger after successive treatments with brine, lye, and brine and heat. Most treatments resulted in reduced strength and stiffness of the skin. However, brine storage enhanced the strength of the flesh. Treatment in alkali enhanced the stiffness and the strength of the flesh, which is consistent with the higher retention of cell wall polysaccharides mentioned above in Douro variety (Mafra, 2002). The final heat treatment step resulted in a decreased strength, which is consistent with the increased cell wall polysaccharides solubility. According to the study of Georget et al. (2003), the stage of maturity of the fruit seemed to play a more significant role in governing the mechanical properties of the flesh than those of the skin.

17.3.3 Naturally black olives in brine Naturally black olives in brine according to the Greek style are obtained from fruits harvested when fully ripe or slightly before full ripeness is reached. The olives are placed in NaCl brine with a concentration varying normally between 8 and 14%, allowing a spontaneous and slow fermentation as the diffusion of components through the skin is very slow. As no lye treatment is used in the preparation of this product, oleuropein and other fruit components are only partially and slowly leached into the brine. The initial flora is normally composed of gram-negative bacteria, yeasts, moulds and, sometimes, lactic acid bacteria. During the first days of fermentation the gram-negative bacteria disappear, while yeasts reach their maximum levels (Garrido–Fernández et al., 1997). Depending on the variety, temperature and salt concentration a lactic fermentation might occur (Tassou et al., 2002). The process is considered to be over when fermentable substrates are exhausted, which in most regions of Greece happens after eight months or later. However, this period might change depending on several factors, such as the variety and olive size, the salt concentration and the temperature. After fermentation the olives are exposed to the air to improve colour. Conservolia and Taggiasca are varieties which can be used to produce naturally black olives in brine. Naturally black olives of Conservolia variety originate in Greece, are large in size (6.1 g/fruit), have a high flesh to pit ratio, but are sensitive to softening during processing and storage. Olives of Taggiasca are cultivated in the western side of Liguria (Northwest of Italy), are small in size (2.2 g/fruit) and used for both olive oil extraction and table


olive production. These two varieties allow a lactic fermentation if the conditions of temperature and NaCl concentration are favourable. The study of the effect of fermentation on cell wall polysaccharides of Conservolia indicated an increase, on a fruit basis, in pectic polysaccharides (galacturonan moiety) and xyloglucans, with a slight decrease in glucuronoxylans (Fig. 17.7a). The changes in cell wall polysaccharides of naturally black olives of Taggiasca variety (Fig. 17.7b) indicated a slight increase in pectic polysaccharides, an increase in xyloglucans and a decrease in glucuronoxylans and cellulose. Pectic polysaccharides of Taggiasca variety became more soluble in aqueous solutions as a result of processing, while pectic polysaccharides of Conservolia did not show a shift in solubility after processing. The mechanical properties of Taggiasca and Conservolia indicate that the epicarp is stronger and stiffer than the mesocarp, as observed in Douro and 40 mg/fruit

20

mg/fruit

30

15 10 5 0

20

Galacturonans Arabinans

10

0 Pectic Glucuronoxylans Xyloglucans polysaccharides (a) Conservolia

Mannans

Cellulose

15 mg/fruit

6

mg/fruit

10

4 2 0 Galacturonans Arabinans

5

0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

(b) Taggiasca Unprocessed

Processed

Fig. 17.7 Composition of olive pulp cell wall polysaccharides of (a) Conservolia and (b) Taggiasca varieties raw and after natural fermentation. (Results from Universidade de Aveiro, OLITEXT Report).

424

Texture in food

Hojiblanca varieties. The compression test of olives of Conservolia variety indicated that with processing the tissues became softer. The same values for Taggiasca variety do not indicate much change. These findings suggest that, as with the changes observed during ripening, an increase in cell wall polysaccharides does not necessarily correspond to an improvement in texture. The activities of cell wall degrading enzymes, such as polygalacturonase, pectinmethylesterase, cellulase and proteolytic, were higher in Taggiasca than in Conservolia, which is in accordance with the higher degree of solubilisation of cell wall polysaccharides in Taggiasca than in Conservolia (Universidade de Aveiro, Portugal, OLITEXT final report). The naturally black processing of olives in brine decreased the activity of all these enzymes in both varieties, especially in Taggiasca.

17.3.4 Other types of processing Other types of processing include naturally black olives dried in salt which are generally obtained from raw black olives or from olives partially ovendried. The olives are arranged in alternating layers of fruits and dry salt, or dry salt can be sprinkled over them. They retain a degree of bitterness and a characteristic fruity flavour and are generally packed in plastic bags, sometimes with potassium sorbate. A small amount of olive oil may be added to cover the surface of the fruits (Garrido Fernández et al., 1997). Dry-salted olives of Thasos are a traditional preparation of naturally black olives cultivated mainly on the island of Thasos in Northern Greece (Panagou et al., 2002). The traditional processing method consists of placing the olives in concrete tanks in layers with coarse salt in a proportion of 40%. Due to the high osmotic pressure exerted by the salt, olives lose water and other watersoluble substances and become shrivelled. Much of the oleuropein is removed from the product while the remaining bitterness is masked by the salt present in the flesh. The longer the fruits remain in the tanks, the saltier they become, obtaining olives with a low water activity. The amount of cell wall polysaccharides of dry-salted olives of Thasos variety did not change much, indicating only slight increases in the amounts of pectic polysaccharides, glucuronoxylans and cellulose per fruit (Fig. 17.8). However, the pectic polysaccharides were affected as the amount of galacturonans increased while arabinan side chains decreased. The solubility of pectic polysaccharides in aqueous solutions was strongly affected, increasing after processing. The study of the mechanical properties of dry-salted olives indicates an increase in the firmness of processed olives (Institute of Food Research, Norwich, UK, OLITEXT final report). That was verified by the measurement of the strength, strain at failure and stiffness of the mesocarp and epicarp. With processing, the strength, the strain at failure, and the stiffness increased both in the mesocarp and epicarp. The changes observed in texture might be the result of the stabilisation of the galacturonan moiety by the Na+, as was

Improving the texture of processed fruit: the case of olives 425 30 20 15 10 5 0

mg/fruit

25

mg/fruit

20 15

Galacturonans Arabinans

10 5 0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

Thasos Unprocessed

Processed

Fig. 17.8 Composition of olive pulp cell wall polysaccharides of Thasos variety in raw and dry salted. (Results from Universidade de Aveiro, OLITEXT Report).

the case with the textural changes observed in green olives after equilibrium in brine (Jiménez et al., 1997).

17.4 Improving texture Calcium is known to have a very strong texture-increasing effect by retarding softening during storage. Pectic polysaccharides of middle lamella are crosslinked with calcium ions forming structures called the “egg-box” model (Brett and Waldron, 1996). Jiménez et al. (1997) studied the combined effects of Ca2+, Na+ and pH on the texture of olives processed according to the Sevillian style. These authors reported that the increase in Ca2+ at pH 11 showed a logarithmic increase of firmness, which changed to linear at pH 3. Na+ increased firmness linearly at pH 11 but was ineffective at pH 3. The high concentration of Na+ at pH 11 changed the increase in firmness caused by Ca2+ to a linear correlation. The presence of Na+ caused a minimal displacement of Ca2+ while Na+ was displaced by Ca2+. The ability of Ca2+ to form complexes with pectic polysaccharides allowed a more effective stabilisation of cell walls than that conferred by Na+, as this was only able to stabilise charges. The effect of Ca2+ on naturally black olives in brine of Conservolia variety according to the Greek style was also studied during the OLITEXT project. CaCl2 treatment resulted in a significant Ca2+ uptake in the flesh of olives, and this had a positive effect on texture improvement which was detected mainly by objective measurements (Fig. 17.9). Another parameter studied was the effect of temperature during fermentation of naturally black olives. Increasing the temperature to 25 ºC accelerated the debittering process, allowing smaller periods of fermentation.

426

Texture in food 160

Force (kg)

120

80

40

0 Olive with skin Reference

Olive without skin Calcium

Fig. 17.9 Effect of CaCl2 treatments on olive pulp mechanical properties, Conservolia variety. Maximum forces required to shear 100 g of depitted olives in a Kramer Cell. (Results from Institute of Technology of Agricultural Products – ITAP, Athens, OLITEXT Report).

The degradation of cell wall polysaccharides caused by ripening seems to be very relevant to the textural properties of table olives. The choice of the exact ripening stage for olive processing is a key factor in texture improvement.

17.5 Future trends The increasing interest of the consumer in “natural” products with minimal processing and loss of soluble constituents and nutrients involves changes in the technologies which can offer the guarantee of preservation, hygiene and genuineness of food products. This may arouse interest in preparing ovendried table olives variously seasoned with olive oil and natural aromatic herbs. In this view of the continuing consumer interest, these products may be successful into meeting the consumer demands in terms of dietetic, gustative, social and cultural expectations. The effect of oven-drying processing on the textural properties of Cassanese variety olives according to the Ferrandina method was a subject of study by the OLITEXT Project (Marsilio et al., 2000). The oven-drying processing of raw olives or those stored in brine consists of a quick immersion into boiling water (blanching) for 6 min, salting of the fruit with NaCl for three days, and finally drying the fruits on a wooden trellis in an air oven at approximately 50 ºC in order to reduce the water content of the final product. The effect of oven-drying processing according to the Ferrandina method is reflected in the decrease in uronic acids (Ur.Ac.) and arabinose (Ara), the major sugar components of pectic polysaccharides (Fig. 17.10). No significant differences were observed in hemicellulosic sugar residues, such as xylose (Xyl) or glucose (Glc).

Improving the texture of processed fruit: the case of olives 427 20

mg/fruit

200 150 100 50 0

mg/fruit

15

10

Polymeric material

Polysaccharides

5

0 Rha

Ara Unprocessed

Xyl

Man Cassanese CWM Ferrandina

Gal

Glc

Ur.Ac

Industrial

Fig. 17.10 Sugar composition of cell wall material (CWM) of Cassanese variety, raw and oven-dried according to Ferrandina and industrial methods. (Results from Universidade de Aveiro, OLITEXT Report).

The microstructural analysis of parenchyma cells of raw olives of Cassanese revealed uniform and tightly packed thin-walled cells (Fig. 17.11a, b) (Marsilio et al., 2000). Heat treatment caused gross changes in the parenchyma tissue structure of the fruit as a result of changes in chemical constitution of the middle lamella, which affected its adhesive properties, leading to cell wall separation (Fig. 17.11c, d). Examination of fracture surfaces showed that most cells remained intact, suggesting that tissue failure occurred by cell to cell rupture and that the resulting decrease in firmness of olive fruit was related to the formation of soluble pectic polysaccharides. Oven-dried olive tissues clearly showed cell wall disruption and the presence of some cavities (Fig. 17.11e, f), explained as a gelation process of the network of cellulose microfibrils, hemicelluloses and pectic polysaccharides (Marsilio et al., 2000). The study of mechanical properties indicated that heat treatment induced loss of firmness in comparison with raw samples, while during salting there was a slight increase of firmness (Fig. 17.12). The oven-drying step brought about further firming of the tissues (Marsilio et al., 2000). Based on the Ferrandina method, an alternative procedure was tried on an industrial scale. This industrial scale processing was a major modification compared to the traditional method: the first stage comprised three cuts for each olive, followed by immersion in water for 15–20 days. In the second stage, salt (8–10%, w/w) was added to the olive fruits and then left for about three days for salting-debittering. After these treatments, the product was dried in an oven at around 45 °C. This product showed no significant degradation of cell wall polysaccharides (Figure 17.10), although loss of cellular content was noticed, possibly due to the rupture of olive epicarp and soaking in water and salt. It was classified organoleptically as a slightly firm and crispless product, without flavour defects, which was considered satisfactory by the sensory panel (Figure 17.13).

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Texture in food

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 17.11 SEM images of Cassanese variety, adapted from Marsilio et al. (2000). (a) unprocessed olives, overview, showing tissues fracturing through the cells; (b) details of green olive parenchyma cells tightly packed (cell adhesion); (c) heated olives, overview, showing tissues fracturing through the middle lamellae; (d) details of parenchyma showing the cell separation, (e) oven-dried olives, overview, showing tissue damaging, (f) details of parenchyma, showing cell disruption.

These are only a few examples of the first attempts to produce table olives that can safeguard olive oil quality and its antioxidant potential in table olives, as well as olive dietary fibre characteristics. These are major challenges for food chemists and food technologists in the near future.

17.6 Sources of further information and advice Fernández-Díez M J (1985) Table Olive Biotechnology (Spanish), Consejo Superior de Investigaciones Cientificas (CSIC), Instituto de la Grasa e seus Derivados, Madrid-Seville, Spain.


Firmness (% deformation)

40

30

20

10

0 Fresh

Heated

Salted

Oven-dried

Fig. 17.12 Compression test deformation (durometer) of Cassanese variety in the different steps of processing according to the Ferrandina method, adapted from Marsilio et al. (2000). Colour 10 Overall judgement

8

Odour

6 4

Detach p/s

Metallic

2 0 Woody

Rancid

Crisp

Fusty Firm

Fig. 17.13 Sensory profile of industrially processed Cassanese variety based on Ferrandina method. (Results from Istituto Sperimentale per la Elaiotecnica, Pescara, OLITEXT Report).

Garrido Fernández A, Fernández-Díez M J and Adams M R (1997), Table Olives, London, Chapman & Hall. International Olive Oil Council, Madrid. OLITEXT Participants Prof. Manuel António COIMBRA, Universidade de Aveiro, PT – 3810-193 AVEIRO – Chemistry of polysaccharides. Dr Keith W. WALDRON and Dr Andrew SMITH, Institute of Food Research, Norwich Research Park, GB – NORWICH NR4 7UA – Texture, tissue mechanics. Prof. Giorgio BIANCHI, Dr Vincenzo MARSILIO and Dr Barbara LANZA, Istituto Sperimentale per la Elaiotecnica, Contrada Fonte Umano, Città S. Ângelo, IT – 65013 CITTÁ S.ANGELO (Pe) – Olive science, phenolics, microstructure, mechanics, sensory analysis.

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Dr Antónia HERÉDIA and Dr Rafael GUILLÉN, Instituto de la Grasa, Av. Padre Garcia Tejero, 4, ES - 41012 SEVILLA – Chemistry of polysaccharides. Dr Konstantinos KATSABOXAKIS, Institute of Technology of Agricultural Products. S Venizelou 1, GR - 14123 ATHENS – LYKOVRISSI – Olive microbiology, olive processing. Prof. Nicola UCCELLA, CIRASAIA - Università della Calabria, Arcavacata di Rende, IT - 87030 RENDE/COSENZA – Phenolic compounds, nutraceuticals. Mr Gil MAÇARICO, Maçarico Lda, Av. Cidade de Coimbra, Praia de Mira, PT - 3070 MIRA – Table olives producer. Mr Nelos GEORGOUDIS, D.E. Georgoudis Co., Riga Fereou 119 PO Box 1169, GR - 381.10 VOLOS – Table olives producer. Dr Mauro AMELIO, Fratelli Carli S.p.A., Via Garessio 11 – 13, IT - 18100 IMPERIA – ONEGLIA – Olive oil and table olives producer. Mr Luis REJANO ZAPATA, Agro-Sevilla Aceitunas Soc. Coop. Andaluza, Carretera Sevilla-Malaga km 124.3, La Roda de Andalucia, ES - 41590 SEVILLA – Table olives producer.

17.7

References

and WALDRON K W (1996) Physiology and Biochemistry of Plant Cell Wall, London, Chapman & Hall. CARDOSO S M, COIMBRA M A and LOPES DA SILVA J A (2003) Calcium-mediated gelation of an olive pomace pectic extract, Carbohydrate Polymers, 52, 125–33. COIMBRA M A, WALDRON K W and SELVENDRAN R R (1994) Isolation and characterisation of cell wall polymers from olive pulp (Olea europaea L.), Carbohydrate Research, 252, 245–62. COIMBRA M A, RIGBY N M, SELVENDRAN R R and WALDRON K W (1995) Investigation of the occurrence of xylan-xyloglucan complexes in the cell walls of olive pulp (Olea europaea), Carbohydrate Polymers, 27, 277–84. COIMBRA M A, WALDRON K W, DELGADILLO I and SELVENDRAN R R (1996) Effect of processing on cell wall polysaccharides of green table olives, Journal of Agricultural and Food Chemistry, 44, 2394–401. FERNÁNDEZ-BOLÃNOS J, RODRÍGUEZ R, GUILLÉN R, JIMÉNEZ A and HEREDIA A (1995) Activity of cell wall-associated enzymes in ripening olive fruit, Physiologia Plantarum, 93, 651– 8. FERNÁNDEZ-DÍEZ M J (1971) The olive. In The Biochemistry of Fruits and their Products. Ed. A C Hulmed, London and New York, Academic Press, 255–79. FERNÁNDEZ-DÍEZ M J (1983) Olives, In Biotechnology – A Comprehensive Treatise in 8 Volumes. Eds H-J Rehm and G Reed, Weimheim, Florida and Basileia, Verlag Chemie, 379–97. FERNÁNDEZ-DÍEZ M J (1985) Table Olive Biotechnology (Spanish), Consejo Superior de Investigaciones Cientificas (CSIC), Instituto de la Grasa e seus Derivados, MadridSeville. FISCHER R L and BENNET A B (1991) Role of cell wall hydrolases in fruit ripening, Annual Review of Plant Physiology and Plant Molecular Biology, 42, 675–703. GARRIDO FERNÁNDEZ A, FERNÁNDEZ DÍEZ M J and ADAMS M R (1997) Table Olives, London, Chapman & Hall. BRETT C T

Improving the texture of processed fruit: the case of olives 431 GEORGET D M R, SMITH A C

and WALDRON K W (2001) Effect of ripening on the mechanical properties of Portuguese and Spanish varieties of olives (Olea europaea L.), Journal of the Science of Food and Agriculture, 81, 448–54. GEORGET D M R, SMITH A C, WALDRON K W and REJANO L (2003) Effect of ‘Californian’ process on the texture of Hojiblanca olive (Olea europaea L) harvested at different ripening stages, Journal of the Science of Food and Agriculture, 83, 574–9. JIMÉNEZ A, GUILLÉN R, SÁNCHEZ C, FERNÁNDEZ-BOLÃNOS J and HEREDIA A (1995) Changes in the texture and cell wall polysaccharides of olive fruit during “Spanish green olive” processing, Journal of Agricultural and Food Chemistry, 43, 2240–46. JIMÉNEZ A, HEREDIA A, GUILLÉN R and FERNÁNDEZ-BOLÃNOS J (1997) Correlation between soaking conditions, cation content of cell wall, and olive firmness during “Spanish green olive” processing, Journal of Agricultural and Food Chemistry, 45, 1653–8. JIMÉNEZ A, RODRÍGUEZ R, FERNÁNDEZ-CARO I, GUILLÉN R, FERNÁNDEZ-BOLÃNOS J and HEREDIA A (2000) Dietary fibre content of table olives processed under different European styles: study of physico-chemical characteristics, Journal of the Science of Food and Agriculture, 80, 1–6. JIMÉNEZ A, RODRÍGUEZ R, FERNÁNDEZ-CARO I, GUILLÉN R, FERNÁNDEZ-BOLÃNOS J and HEREDIA A (2001a) Olive fruit cell wall: degradation of pectic polysaccharides during ripening, Journal of Agricultural and Food Chemistry, 49, 409–15. JIMÉNEZ A, RODRÍGUEZ R, FERNÁNDEZ-CARO I, GUILLÉN R, FERNÁNDEZ-BOLÃNOS J and HEREDIA A (2001b) Olive fruit cell wall: degradation of cellulosic and hemicellulosic polysaccharides during ripening, Journal of Agricultural and Food Chemistry, 49, 2008–13. JOHN M A and DEY P M (1986) Post harvest changes in fruit cell wall, Advances in Food Research, 30, 139–93. MAFRA I, REIS A, BARROS A S, NUNES C, GUEDES S, VITORINO R, SARAIVA J and COIMBRA M A (2000) Ripening-related changes of olive fruit cell wall polysaccharides and associated enzymes in two consecutive harvests, 10th International Symposium in Plant Polysaccharides, 23–26 August, Wageningen, Wageningen University. MAFRA I, LANZA B, REIS A, MARSILIO V, CAMPESTRE C, DE ANGELIS M and COIMBRA M A (2001) Effect of ripening on texture, microstructure and cell wall polysaccharide composition of olive fruit (Olea europaea), Physiologia Plantarum, 111, 439–47. MAFRA I (2002) Effect of ripening and processing in cell wall polysaccharides of olive pulp (Portuguese) (PhD Thesis, Aveiro, Universidade de Aveiro). MARSILIO V, LANZA B and DE ANGELIS M (1996) Olive cell wall components: physical and biochemical changes during processing, Journal of the Science of Food and Agriculture, 70, 35–43. MARSILIO V, LANZA B, CAMPESTRE C and DE ANGELIS M (2000) Oven-dried table olives: textural properties as related to pectic composition, Journal of the Science of Food and Agriculture, 80, 1271–6. O’DONOGHUE E M, HUBER D J, TIMPA J D, ERDOS G W and BRECHT J K (1994) Influence of avocado (Persea americana) Cx-cellulase on the structural features of avocado cellulose, Planta, 194, 573–84. PANAGOU E Z, TASSOU C C and KATSABOXAKIS K Z (2002) Microbiological, physicochemical and organoleptic changes in dry-salted olives of Thassos variety stored under different modified atmospheres at 4 and 20 °C, International Journal of Food Science and Technology, 37, 635–41. TASSOU C C, PANAGOU E Z and KATSABOXAKIS KZ (2002) Microbiological and physicochemical changes of naturally black olives fermented at different temperatures and NaCl levels in the brines, Food Microbiology, 19, 605–15.

18 Improving the texture of bread S. P. Cauvain, CCFRA, UK

18.1

Introduction

Bread is the product of complex interactions between the main raw materials (wheat flour, water, yeast, salt and other functional ingredients) and different processing methods to convert the dough mass into loaf sized units. An overview of the most common breadmaking processes (Cauvain, 2000) and details of the technology involved in modern bread production (Cauvain and Young, 1998) can be obtained elsewhere. There is no such thing as a ‘standard’ bread product with many shapes and sizes of products having evolved throughout the long history of breadmaking. Along with variations in shape, size, environment and cultural inputs have come differences in texture. Interest in bread texture arises because of its direct link with shelf life, eating qualities and flavour. Thus, an understanding of what contributes to bread texture has a direct impact on the sensory pleasures that we can derive from eating bread and other related bread products. The textural variation that one sees with bread and other related products is considerable, and it arises from a number of sources. In order to understand how we measure, adjust and can improve bread textural properties we must first decide which ones are the most important to us. This is not an easy task because of the great variety that we see in such products. One way in which we may attempt to classify the family of bakery products is illustrated in Fig. 18.1. The classification is based on two key formulation issues: flour to sugar and flour to fat ratios. These have been chosen for the illustration because of their role in the inhibition of gluten protein formation in the manufacture of baked products. Gluten formation in baked products occurs when flour and water are mixed together. Gluten is hydrated wheat protein, and its properties have the

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Fig. 18.1 Family of baked products.

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Improving the texture of bread 433

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special role of being able to trap air bubbles during mixing and carbon dioxide gas from yeast fermentation or baking powder reaction in baked products. The function of the gases is critical in creating an expanded structure, thereby modifying product eating qualities and digestibility. In the case of sugar and fat additions higher levels will restrict gluten formation, hence the inclusion of their relationship with flour as defining characters in baked product quality in Fig. 18.1. Baking always involves the loss of water from the ‘raw’ to the baked product (Cauvain and Young, 2000). The baking process therefore has a direct impact on the final moisture content of the product and, in turn, on its eating character. This aspect of baked product character is also illustrated in Fig. 18.1 by the use of solid and broken lines to define the boundaries of the various product types. Those baked products with moisture contents of less than 15% are indicated with broken lines, while those which are above this moisture content are indicated by the solid lines. In general, bakery products with below 15% moisture can be considered as hard and crisp, with those above 15% being soft and tender. Fat too will directly contribute to soft and tender eating characters independent of moisture content.

18.2 Textural characteristics of bread and other cereal-based foods All baked products comprise an outer zone – the crust – and an inner zone – the crumb. The formation of the crust occurs in the oven and it is characterised by being darker in colour and lower in moisture content than the inner crumb. These differences result in very different textural characteristics in the baked product and are part of what determines whether the final product has the ‘right’ quality. In addition to the obvious colour and less obvious moisture content differences, there may be differences in the physical structure between the crust and the crumb. To better understand what characterises bread crust and crumb we can assess the differences by examining cross-sections of typical breads. The darker colour of the crust zone has already been commented on. If the products are freshly baked then we will also observe that the crust is hard to the touch but breaks readily if we try to bend it. Looking very closely just underneath the coloured crust area we are likely to see a zone of dense structure extending 1 or 2 mm towards the product centre. The inner crumb, by contrast, is lightly coloured and has a distinct aerated and cellular structure not unlike that of a bath sponge. In fact cereal scientists often refer to baked products as being ‘sponges’ in the sense that all of the cells which comprise the crumb are interconnected (Cauvain and Young, 2000). When the inner crumb is pressed it feels soft, and when the pressure is released the fresh bread crumb will spring back to it original shape before compression.

Improving the texture of bread

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Two types of bread products are illustrated in Fig. 18.2 to show how the key characteristics may vary with product type. At the top is the representation of a ‘sandwich’-type loaf characterised by a thin crust and a relatively fine (small average cell size) and uniform crumb structure. On the bottom is the representation of a baguette characterised by a relatively thick crust, a more open (larger average cell size) and random cell structure. All of the differences between the two types of loaf arise from differences in dough processing and baking conditions designed to contribute the relevant product character rather than from the ingredients or formulation used. The main objective attributes which characterise fresh bread are summarised for sandwich and baguette-type products in Table 18.1. There are many types

Fig. 18.2 Sandwich and baguette-type breads.

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Table 18.1 Key properties of sandwich and crusty-type breads

Crust thickness (mm) Crust moisture (%) Crumb cell size (mm)* Crumb moisture (%)

Sandwich-type

Crusty-type

1.0 15.0 1.0–2.5 40.5–41.5

1.5–2.5 13.0 2.0–6.0 29.5–30.5

* Data obtained using C-Cell imaging equipment (Calibre International Ltd, Warrington, UK)

of fermented bread, cake, pastry and biscuit products in the ‘world’ of bakery products and all may be characterised to a greater or lesser degree by the properties listed in Table 18.1.

18.3 Definitions of texture Some of the key textural properties relevant to cereal-based foods have already been introduced. Collectively they may be summarised as follows.

18.3.1 Moistness This is directly related to product moisture content, but absolute levels are determined by the ‘traditional’ properties sought in the bakery food concerned. Thus, breadcrumb is expected to be moist eating but the crust is expected to be dry. Loss of moisture from the crumb renders the product stale on the taste buds of the consumer, and yet the same descriptor would be used for a high moisture content (soft) crust on a baguette. The shelf life of baguette can be measured in hours as the crust gains moisture from the crumb, but the same judgement may not be applied to sandwich-type bread where the shelf life is measured in days. Moistness is seen as a positive character in cakes.

18.3.2 Firmness or hardness These descriptors are generally used to describe a loss of softness in the breadcrumb. This loss of softness may arise from two main sources; a loss of moisture from the crumb or the underlying retrogradation of starch (Pateras, 1998). Hardness may also be considered as a negative attribute for the crust of most bakery products and should not be confused with crispness. While crisp crusts are expected to be hard they are also expected to break or shatter readily. Hard crusts on the other hand tend to require considerable chewing force or time. Hardness is most commonly the positive character sought in low moisture content products such as biscuits and pastries and is seen as a negative character in cakes.


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18.3.3 Softness While this descriptor may be seen as a positive attribute in breadcrumb it will be seen as a negative attribute for crusty bread products, such as baguette, or as a positive attribute for the crust of sandwich bread. Softness is seen as a positive property in cakes but as a negative character for biscuits and pastries.

18.3.4 Cohesiveness The crumb of all bread types is expected to form a ball readily in the mouth and to require some effort in chewing. This character is controlled in part by moisture content and in part by the strength of the network surrounding the holes (cells) in the crumb. Loss of moisture contributes to crumbliness, as do the underlying staling processes. Cohesiveness is generally seen as a positive character in all types of baked products though chewiness should be avoided.

18.3.5 Springiness Fresh bread crumb is expected to be springy following removal of any compressing force. Crumb springiness is related to the strength of the crumb cell wall network. During storage breadcrumb loses its springiness as a result of the underlying staling processes.

18.3.6 Staleness An all-embracing term which describes the collective changes to bread texture as it loses its ‘fresh-baked’ character. Staleness covers many different aspects of changes in bread character with time after baking. While there are some underlying common changes which contribute to staleness, many others are bakery product specific. This means that a statement of the product type being considered should always be used to qualify any comments on bread texture.

18.4 Measuring texture As with most foods, the textural characteristics of bread and other cereal products are most commonly described in terms of their sensory properties. While meaningful to individuals or, on occasions, trained panels, sensory descriptions are of limited value to technologists and scientists involved in quality assessment. In order to be able to measure improvements in bread quality we must first be able to define the baseline character and find some means of measuring both the magnitude and the direction of quality change as the result of our attempts at improvement.

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Subjective descriptions of sensory properties can be used (Hansen and Setser, 1990), but more commonly some form of objective measurement is required. It is useful, though not always necessary, if the subjective sensory and objective measurements can be linked or capable of cross-referencing. The work of Szczesniak et al. (1963a,b) is an early example of how mechanical parameters and sensory methods of texture evaluation can be correlated. Two key bread characteristics associated with freshness are the softness of the crumb and its ability to recover after the deforming force has been removed. These are most readily assessed with the fingers as shown by the common ‘squeeze’ test applied to packaged bread by consumers. Experience, and sub-conscious training by others, leads individuals to reject packaged bread which is firm to the touch or remains ‘squashed’ after the squeeze test. The sensory character experienced through the fingers and with the eyes can be readily confirmed in the mouth. It is not surprising, therefore, that many of the objective tests which are employed to evaluate bread and other cerealbased foods are based on some form of compression or deformation test.

18.4.1 Compression/deformation tests Objective compression or deformation testing may be carried out in one of two ways; either the food is subjected to a standard compression force and the distance through which it is compressed is measured, or the food is compressed through a standard distance and the force required to achieve this is measured. Both methods have been used in the assessment of cerealbased foods. Owing to its cellular structure bread crumb does not obey Hooke’s Law, but it does have a stress/strain relationship similar to that shown in Fig. 18.3. This means that Young’s modulus (stress/strain) varies with the amount of strain, the latter being measured as fractional compression. Compression testing of bread became common because of the potential to express data in terms of fundamental measurements.

18.4.2 Compressimeter and Cone Indenter Compression tests for bread take a number of forms. Early forms of objective measurement compressed a sample of bread crumb of known thickness between two flat, parallel plates using a standard weight applied for a fixed period of time and recording the distance travelled by the upper plate. The apparatus used (known as a Compressimeter) was designed to have little friction, often moving under the weight of < 2 g on the upper plate (Platt and Powers, 1940). A second form, commonly used with compression testing of bread, was a cone with a defined angle. The mechanism of operation was similar to that of the Compressimeter, but in this case the compressing weight was carried on a pan suspended below the bread sample with the cone pressing downwards through the bread crumb. The apparatus used was known as a Cone Indenter (Cornford, 1969).

Improving the texture of bread 1000

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Stress–strain curve for bread crumb

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Stress–strain curve for bread crumb (Cornford, 1969).

In the case of both the Compressimeter and the Cone Indenter, additional qualitative information could be obtained by measuring the recovery or springiness of the sample. Carefully removing the compressing force and measuring the height to which the sample recovered within a fixed period of time, typically 1 min, achieved this. Thus, a combination of compression and recovery data provided information relevant to the sensory compression recovery tests commonly applied by consumers. Indeed some companies considered such data to be valuable enough to develop ‘Squeezometers’, capable of compressing whole loaves, as a more direct mimic of consumer requirements. The early forms of bread compression equipment have now largely been superseded by the development of motorised equipment linked with data acquisition and analysis by computer program. The advantage of the motorised equipment is that the compressive force is applied at a fixed rate until the required degree of compression has been achieved. Using the approach it has become common to refer to softness or firmness as the force (in Newtons) required to compress a sample to a given degree. Commonly used degrees of compression with bread are 25 or 40% of sample thickness (AACC, 1987). 18.4.3 Multiple compression tests Multiple compression tests may also be used to determine a range of bread crumb properties. Texture Profile Analysis (TPA) is a common multicompression technique used with bread crumb. In TPA the sample is subjected

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Texture in food

to two compressions in quick succession with withdrawal of the compressing force after each compression. An example of a TPA trace for bread and cake crumb is illustrated in Fig. 18.4. In essence TPA was designed to simulate the processes of biting and chewing in the mouth. The textural parameters of TPA were first defined by Szczesniak (1963a, b). Later Bourne (1978) defined the same parameters with an Instron Universal Texture Machine. Modern instrumental measurements of TPA are based on their pioneering work. 18.4.4 Puncture test The hardness or crispness of bread crusts may be assessed using some form of puncture test. The shape of the probe used for such tests is in the form of either a needle or a small diameter cylinder. Cauvain (1991) used a needle probe to examine the changes in hardness of all of apple pie components with storage time. Measurements of lid pastry crispness, filling firmness and base pastry softness were made in a single pass though the sample. The loss of crispness in the pastry arose from moisture migration from the filling to the lid and base pastry. 18.4.5 Other product parameters Bread texture is directly related to a number of other product parameters, and attempts to measure textural changes should take these into account. Indeed in many cases it is necessary to correct measured texture values so that the contributions of individual ingredients or processes changes can be truly evaluated. The main properties which should be considered in this context are moisture content, density and porosity. Moisture content The common method for measuring sample moisture content is by a form of oven drying method (Cauvain and Young, 2000). In general, the higher the 6

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Fig. 18.4 Example of a TPA trace for bread and cake crumb at 25% compression.


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moisture content of a baked product the lower will be its hardness value (i.e. it will be softer). Density In the case of a whole loaf it is relatively easy to measure loaf weight and volume, the latter being carried out using some form of seed displacement apparatus (Cornford, 1969). In general the greater the product volume the lower will be its hardness value (i.e. it will be softer). However, in the case of many breads the crumb density across a given cross-section is not uniform. For such samples it would be ideal to know the density at the point of testing. This may be achieved using a coring technique such as that described by Cauvain (1991). Using a known bread slice thickness and a cylindrical cutter of known diameter it is a simple matter to weigh the core and calculate its density from its dimensions. Cellular structure Until recently this has not been a bread property which could be readily and objectively measured. The development of the image analysis equipment known as C-Cell (developed by CCFRA and available from Calibre Control International Ltd, Warrington, UK) has provided a tool with the capacity to measure many features of cell size and distribution and cell-wall thickness. Such data can be used to provide a more complete picture of how bread texture properties may be changed or controlled.

18.4.6 Influence of raw materials The textural properties of all cereal-based products are strongly influenced by the quality of the ingredients used and how they are combined in the formulation. In bread the key textural features come from the development of a wheat protein (gluten) network in the dough. The gluten network traps small air bubbles and retains them in the dough where they will later be expanded by the carbon dioxide gas produced from bakers’ yeast fermentation (Cauvain, 1998a). Bakers refer to the formation of the gluten structure from wheat flour, water, yeast and other functional ingredients as ‘development’ and they commonly refer to the ‘gas retention’ properties of the dough. Improvements in dough gas retention yield larger volume and therefore softer loaves which are seen as fresher by the consumer. The formation of a suitable gluten structure with good gas retention properties is then essential to improving bread texture. Many ingredients can and do contribute to the necessary improvements. They include the following. Flour properties Gas retention and therefore loaf volume are improved by increasing the flour protein content. In this case the improvements in texture, usually crumb softness, come from the increase in volume rather than the flour protein per

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Texture in food

se. The protein used may be indigenous (that is derived from the wheat) or it may be added in the form of dried gluten. Cauvain and Mitchell (1986) showed that additions of dried gluten yielded softer bread crumb; however, they did not adjust their data for crumb density. The quality of flour proteins and their ability to retain gas also varies and may have an influence on bread texture. Other flour properties likely to influence bread texture through their volume effect include the bran content of white flours and the level of enzymic activity present (usually cereal alpha-amylase). Bread improvers A wide range of functional ingredients may be added to dough in order to improve its breadmaking qualities (Williams and Pullen, 1998). Since many of the ingredients have a direct effect on dough gas retention they will by implication affect product texture. In a few cases, changes from the effects of functional ingredients on bread texture may occur independently of other product character changes but most commonly texture changes are the result of changes in product moisture content, density or crumb porosity, or any combination of the three. The most common texture change observed by using bread improvers is an increase in the initial softness of the bread. Such changes are seen with the addition of the oxidant ascorbic acid, fat, emulsifiers, such as di-acetyl tartaric esters of monoglycerides, sodium steoryl-2-lactylate, and a range of active enzymes preparations, such as fungal alpha-amylase (Cauvain and Mitchell, 1986).

18.5 Influence of processing and storage 18.5.1 Textural changes in bread during processing Since bread texture is strongly influenced by product density and crumb porosity it is reasonable to assume that those aspects of dough processing which change bread volume and cell structure are important influences. Dough mixing, and in particular the level of energy imparted, is an integral part of dough development. It is well known that increasing the level of energy transferred to the dough during mixing increases bread volume and crumb softness. This is an especially important relationship in no-time dough making processes, such as the Chorleywood Bread Process (CBP) (Cauvain, 1998b). Indeed by using the CBP it is possible to get the same bread volume and crumb softness with a lower protein flour than would be required with other breadmaking processes. The combination of the size of the holes (cells) in the crumb and the thickness of the crumb (cell walls) is a key factor in determining the texture of bread. In no-time dough processes like the CBP the final cell structure in the crumb is largely determined by the sizes of the gas bubbles incorporated


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during dough mixing. The distribution of gas bubbles sizes in the dough leaving the mixer is a function of the mixer design and the conditions under which it is operated. In the case of CBP it is possible to control gas bubble populations in the dough during mixing by varying the pressure in the mixer headspace. Fine, uniform cell structures are encouraged by using pressure below atmospheric while open, more random structures can be obtained using pressures greater than atmospheric (Cauvain et al., 1999). In the socalled Pressure-Vacuum mixers it is possible to vary pressure during mixing and thus directly affect bread cell structure (Cauvain, 1995). Variations in cell size in the loaf crumb influence texture. In general, a fine uniform crumb structure is accompanied with thin cells walls which confer softness to the crumb. If the dough has been suitably developed then the crumb is strong enough to recover from modest deformations, whether sensory or mechanical. On the other hand, larger cell sizes with thick cell walls tend to give the crumb a firmer character. Changes may occur to gas bubble populations during processing which have an adverse effect on bread texture. The processing stage with the greatest potential for introducing textural defects is when the dough pieces are finally shaped (moulded). At this time the rheological properties of the dough are critical in ensuring that the gas bubbles created during the mixing stage are retained in their required form. If the dough rheological properties are unsuitable then the gluten membranes may be damaged during moulding which may lead to loss of gas bubbles or coalescence of smaller ones to form undesirable larger ones (Cauvain and Young, 2000). Dough damage during moulding is most commonly manifested as dark coloured and firm patches in the crumb. The discolouration and firmness of the crumb are a direct result of the thicker cells walls which have formed. Cells of larger size characterise many crusty products such as baguette. In this case the openness of the crumb structure encourages the formation of a crisp or hard outer crust. The open cell structure required may be achieved by changing the dough processing conditions. The most common approach is to encourage some yeast fermentation before the dough enters the final shaping or moulding stage. It is important with such processing conditions that the large gas cells which have formed are retained in the dough, otherwise product quality will be lost. The formation of a crisp crust on baguette and other crusty product is also encouraged by the application of steam in the early stages of the baking process (Cauvain and Young, 2000). The application of moisture and heat encourage enzymic activity which converts the starch into dextrins, confers gloss and contributes to the brittleness of the crust.

18.5.2 Textural changes in bread during storage Freshly baked bread has very different characteristics to that which has been stored for short periods of time. The nature and magnitude of the changes depends on the conditions under which the product has been kept. If held

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unwrapped then the products in most cases will dry out as moisture evaporates from the product to the surrounding atmosphere. The rate at which moisture is lost from the product depends in part on the differential in moisture content between product and atmosphere, and it proceeds faster when the moisture content of the atmosphere is lower. A further factor controlling moisture loss from baked products is the water activity (aw); the lower the aw the lower the rate at which the product will lose moisture (Cauvain and Young, 2000). Wrapping bread will cause it to lose moisture more slowly; however, in this case the shelf life of the product will be limited by the occurrence of mould growth (Pateras, 1998). The appearance of mould on the surface of the bread product is possible because the aw is high enough to permit its growth, typically 0.90–0.98 (Cauvain and Young, 2000). At the end of baking the moisture content and aw of bread crust is usually too low to permit mould growth. During storage, moisture moves from the moist crumb zone to the drier crust. In unwrapped bread the moisture evaporates to the atmosphere, but for wrapped bread an equilibrium is reached between the crumb, crust and atmosphere in the wrapper surrounding the bread. Collectively the changes result in a reduction of the crumb moisture content and an increase in that of the crust. In addition to creating the potential for mould growth, the absorption of moisture by the bread crust causes it to lose its crispness and go ‘soft’. This change reduces the sensory pleasure experienced by the consumer, especially if the expectation is that the crust should be hard (e.g. as with baguette), and the product is seen as being ‘stale’. It is common practice to reduce the loss of crispness of bread crust by wrapping the product in a perforated film. The small holes in the wrapper allow some of the moisture that migrates from the moist crumb to evaporate from the crust which allows the latter to remain hard and crisp. However, the overall effect of the moisture loss is for the crumb to quickly dry out and become hard. In composite products there is considerable potential for moisture migration to and from the bread crumb and the other materials which may be used. There are few components of sandwiches which have a higher water activity than bread. One well-known example is lettuce, and sandwiches made with lettuce frequently go ‘soggy’ during storage, that is the bread crumb moisture content increases. In many other cases the high water activity of bread crumb means that the moisture may be lost to the other sandwich components and the bread will go hard. The application of butter or margarine to the surface of the bread represents an attempt to ‘waterproof ’ the surface of the bread crumb and minimise the migration of water to or from the bread component. Bread staling may be described as the loss of ‘oven-freshness’. It encompasses a number of different changes: • loss of crumb and crust moisture, especially if the product is unwrapped; • loss of crust crispness, more likely to occur if the product is wrapped; • increases in product crumbliness, commonly related to moisture content;


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• increases in crumb firmness; • changes in taste, usually a loss of; • changes in aroma, usually a loss of. Even when bread products are wrapped to prevent moisture losses during storage there is progressive increase in the firmness of the crumb with increasing storage time. This intrinsic firming is the change most commonly referred to as ‘staling’ in the scientific literature and arises because of changes in the crystalline structures of the starch component of the product (Pateras, 1998). Starch in wheat flour undergoes a number of changes during the breadmaking process and during storage. The re-crystallisation process which occurs during storage is known as ‘retrogradation’ and is responsible for the moistureindependent firming of bread crumb. The magnitude and rate of the retrogradation process depend on a number of formulation factors and the conditions of storage. While there are a number of potential ameliorating measures which may be applied, they merely slow down the retrogradation process. Refrigeration and deep freezing may be used to delay both the microbial spoilage and staling processes. The rate of bread staling increases as the storage temperature decreases with the maximum staling rate occurring at around 4 °C. This is unfortunate because refrigerated temperatures are used to restrict microbial growth in sandwiches, and this is the very temperature at which the bread component will stale most rapidly. This effect explains, in part, why bread flavour is not always distinct when white bread is used and may go someway to explaining the popularity of wholemeal or other ‘enriched’ breads. While deep freezing will prevent microbial activity and bring bread staling to a halt, the very act of freezing and thawing is the equivalent of 24 h of bread staling (Pence and Standridge, 1955) because the product must pass twice through the temperature of optimum staling, once on cooling and once on thawing.

18.6 Improving texture The opportunities for improving bread texture can be considered under three main headings: • changing bread structure; • ameliorating storage changes; • using alternative production technologies. The profound influence of changes in bread volume, density and cell structure on the textural properties of bread have already been discussed above and a number of potential ingredient effects introduced. In most cases improvements are aimed at produced a softer bread crumb at the start of shelf life so that, even if the staling rate remains constant during storage, the impression for

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the consumer is that the product is ‘fresher’ later in life. Examples of such effects may be seen for the use of oxidants and emulsifiers. One aspect of bread technology not often considered in understanding how bread texture may be improved relates to the distribution of density in the cross-section of a slice of bread. Careful sampling of crumb density using a coring technique (Cauvain, 1991) will reveal that the crumb is most dense towards the crust and least dense in the centre. The higher density towards the crust arises because the expanding inner crumb has been compressed against the rigid crust formed in the early stages of baking, especially if the product is baked in a pan (Wiggins, 1998). The low centre crumb density provides low resistance to compression, whether sensory or mechanical. The variation in density in the cross-section of a loaf is now a key aspect of bread texture, and those bread processing technologies which do not achieve this aspect have not remained commercially viable. Uniform cross-section density yields a bread which is firm to the touch and considered to be stale. Some bread processing techniques deliberately set out to achieve variations in cross-section density. One such processing technique is known as ‘fourpiecing’ in which the bubbles in the dough are re-orientated to provide a different orientation in the final product (see Fig. 18.5). This four-piecing technique is most commonly seen in the production of bread for sandwich making. Bread staling cannot be prevented though the rate at which it occurs can be lowered. The mechanism by which this can be achieved involves changing the rate at which starch retrogrades during storage. This reduction in the firming rate can achieved through the optimisation of moisture levels in the baked product (Zelesnak and Hoseney, 1986), the addition of ‘antistaling’ emulsifiers, such as glycerol mono-stearate (Russell, 1983), intermediate thermal stable alpha-amylase enzymes (Si, 1997), lipase enzymes (Leon et al., 2002) treatment with alcohol (Pateras, 1998) or the addition of sugars (I’Anson et al., 1990; Cairnes et al., 1991). The significant effect

Fig. 18.5 Four-piecing of bread dough.


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of sugars is shown by the fact that the maximum staling rate for cakes is around 21 °C. A large number of ‘bakeries’ as seen in convenience stores, garages, restaurants and hotels base their production of ‘fresh’ bread on an interrupted baking process. In the baking industry this is referred to as ‘part-’ or ‘parbaking’. In essence the bread product is set but not coloured significantly during the first bake. This is achieved through the adjustment of baking conditions. The part-baked product is stable and may be stored subject to the usual microbial spoilage conditions. It will stale, that is crustiness will be lost and the crumb will go firm. However, in order to serve or sell the product it must be re-heated and this second baking stage largely reverses the adverse changes which have occurred during storage. The crumb will certainly soften as the starch retrogradation process is reversed. To some extent product crustiness returns and the surface colour will darken so that the product appears similar to that of a freshly baked product. After the second bake staling will once again begin and the rate of crumb firming will occur at a much faster rate than before. This means that the shelf life of part-baked products after second baking may be measured in hours rather than days.

18.7 Future trends Bread has existed as a ‘processed’ food for several thousands of years and evolved into a wide variety of products, but many opportunities for developing new products, textures and eating characters remain. In part these opportunities arise because the cellular structure which characterises bread and other cerealbased foods yields products which may be eaten and enjoyed on their own or as key elements of multi-component foods. Cereal-based foods, and bread in particular, provide a suitable ‘wrapper’ or ‘carrier’ for other food components and this has placed them in a key position in the development of modern convenience foods. In modern breadmaking the cell structure of bread is effectively created in the mixer through the incorporation of small air bubbles in the developing dough. In the later processing stages, the bubble populations will be modified. In the dough which leaves the mixer the air bubbles are at their smallest and greatest in number. During processing, gas bubbles become larger in size and less numerous. The key to creating new bread textures lies in understanding these transitions. Some of the key questions which remain to be answered include: • what is the mechanism by which air bubbles are incorporated into the dough during mixing? • how are they stabilised? • why do their numbers decrease?

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• what are the relative contributions of cell wall material and cells (the holes) to texture? • how can bread be prevented from losing its freshness? A number of factors which control the development of bread texture have been discussed above. However, while there is a significant corpus of knowledge related to bread manufacture there are still significant gaps. The key to bread eating qualities lies in the manipulation of the ingredients and processing methods which are used in bread production. The numbers of potential interactions are very large, and it remains difficult to predict the end result with confidence. Continued research and development into breadmaking is beginning to provide the building blocks of the required knowledge base. The arrival of the Pressure-Vacuum mixer, developed by the Flour Milling and Baking Research Association (FMBRA) at Chorleywood, provides bakers with unique opportunities for creating new cell structures in bread products because of the greatly improved possibilities for manipulating gas bubble populations in the mixed dough. Nor are the opportunities limited to the incorporation of air during dough mixing. As long ago as the 1970s FMBRA showed that the modification of the gas composition of the mixer headspace also contributed to bread crumb structure (Chamberlain and Collins, 1979). The advent of considerably increased computing power has enabled cereal scientists to harness the power of image analysis and to form a better understanding of the foam to sponge transition in bread. To conventional light microscopy of frozen dough sections (Whitworth and Alava, 1999) has been added imaging of bread doughs using X-ray scanning (Cauvain, 1997; Whitworth and Alava, 1999) so that a more complete picture of what bubbles are formed in the dough and how they are changed during processing is emerging. Loss of bread freshness (staling) remains a major concern to bread manufacturers and, while much is known about the mechanisms involved, preventing it remains a significant challenge. The advent of enzyme-based solutions appears to offer new opportunities for solving this age-old problem, but there is a long way to go. On the one hand the opportunities for developing new bread texture appear limitless, but on the other they appear limited by our (still) imperfect knowledge of how to make bread textures. Given the long tradition of breadmaking this may seem surprising. This is not the case to those involved in the study of cereal-based foods. The processes which go to make-up the baking remain a mixture of part science, part technology, part craft and part ‘art’.

18.8 Sources of further information and advice The following texts provide useful sources.


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• Technology of Breadmaking (1998) Eds S P Cauvain and L S Young, Blackie Academic & Professional, London, UK. Fabricacion de Pan (Spanish language version) Acribbia S A, Zaragoza, Spain. Provides a comprehensive review of modern breadmaking practices. • Bakery Food Manufacture & Quality: Water Control & Effects (2000) S P Cauvain and L S Young, Blackwell Science, Oxford, UK. Provides a comprehensive review of the role of water in the manufacture of bakery products and discusses many aspects of bakery product texture formation and modification during storage. • Dough Rheology and Baked Product Texture (1990) Eds H Faridi and J M Fabion, Van Nostrand Reinhold, New York, USA. Provides discussion of the principles involved in the measurement of food texture and dough rheology for baked products. • Breadmaking: Improving Quality (2003) Ed. S P Cauvain, Woodhead Publishing, Cambridge, UK. Reviews key recent research on the ingredient and process factors which determine bread quality. • Baked Goods Freshness (1996) Eds R F Hebeda and H F Zobel, Marcel Dekker, Inc., New York, USA. Considers the technology, evaluation and inhibition of staling in baked products. • Bubbles in Food (1999) Eds G M Campbell, C Webb S S Pandiella and K Niranjan, AACC, St Paul, MN, USA. A compendium of papers presented at an international conference, this book provides an in-depth consideration of the contribution of bubbles to food structure and texture.

18.9 References AACC (1987) Approved Methods of the American Association of Cereal Chemists, St Paul, MN, AACC. BOURNE M C (1978) Texture profile analysis, Food Technology, July, 62–6, 72. CAIRNES P, MILES M J and MORRIS V J (1991) Studies of the effect of the sugars ribose, xylose and fructose on the retrogradation of wheat starch gels by X-ray diffraction, Carbohydrate Polymers, 16(4), 355–65. CAUVAIN S P (1991) Evaluating the texture of baked products, The South African Journal of Food Science & Nutrition, 3(4), 81–6. CAUVAIN S P (1995) Controlling the structure: the key to quality, South African Food Review, 22(2), April/May, 51, 53. CAUVAIN S P (1997) Controlling the structure is the key to quality, Proceedings of the Fiftieth Anniversary Meeting of the Australian Society of Baking, Sydney, Australia, October 16, 6–11. CAUVAIN S P (1998a) Bread – the product. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 1–17. CAUVAIN S P (1998b) Breadmaking processes. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 18–44.

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(2000) Breadmaking. In Cereals Processing Technology. Ed. G Owens, Cambridge, Woodhead Publishing. CAUVAIN S P and MITCHELL T J (1986) Effects of gluten and fungal alpha-amylase on CBP bread crumb properties, FMBRA Report No. 134, Chipping Campden, UK, CCFRA. CAUVAIN S P and YOUNG L S (1998) Technology of Breadmaking, London, Blackie Academic and Professional. CAUVAIN S P and YOUNG L S (2000) Bakery Food Manufacture & Quality: Water Control & Effects, Oxford, Blackwell Science. CAUVAIN S P, WHITWORTH M B and ALAVA J M (1999) The evolution of bubbles structure in bread doughs and its effects on bread structure. In Bubbles in Food. Eds G M Campbell, C Webb, S S Pandiella and K Niranjan, St Paul, MN, Eagan Press, 85–8. CHAMBERLAIN N and COLLINS T H (1979) The Chorleywood Bread Process: the role of oxygen and nitrogen, Baker’s Digest, 53(February), 18–24. CORNFORD S J (1969) Volume and crumb firmness measurements in bread and cake, FMBRA Report No. 25, Chipping Campden, UK, CCFRA. HANSEN L S and SETSER C S (1990) Texture evaluation of baked products using Descriptive Sensory Analysis. In Dough Rheology and Baked Product Texture. Eds H Farid and J M Faubion, New York, Van Nostrand Reinhold, 573–96. I’ANSON K J, MILES M J, MORRIS V J et al., (1990) The effects of added sugars on the retrogradation of wheat starch gels, Journal of Cereal Science, 11(3), 243–8. LEON A E, DURAN E and BENEDITO DE BARBER C (2002) Utilization of enzyme mixtures to retard bread crumb staling, Journal of Agricultural and Food Chemistry, 50(6), 1416– 19. PATERAS I (1998) Bread Spoilage and Staling. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 240–61. PENCE J W and STANDRIDGE N N (1955) Effect of storage temperature and freezing on the firming of a commercial bread, Cereal Chemistry, 32(November), 519–26. PLATT W and POWERS R (1940) Compressibility of bread crumb, Cereal Chemistry, 17, 601– 21. RUSSELL P L (1983) A kinetic study of bread staling by differential scanning calorimetry, Starch/Starke, 35, 277–81. SI J Q (1997) Synergistic effect of enzymes for bread baking, Cereal Foods World, 42(10), 802–3. SZCZESNIAK A S (1963a) Classification of textural characteristics, J Food Science, 28(4), 385–9. SZCZESNIAK A S (1963b) Objective measurement of food texture, J Food Science, 28(4), 410–20. SZCZESNIAK A S, BRANDT M A and FRIEDMAN H H (1963) Development of standing rating scales for mechanical parameters of texture and correlation between the objective and sensory methods of texture evaluation, J Food Science, 28(4), 397–403. WHITWORTH M B and ALAVA J M (1999) The imaging and measurement of bubbles in bread doughs. In Bubbles in Food. Eds G M Campbell, C Webb, S S Pandiella and K Niranjan, St Paul, MN, Eagan Press, 221–32. WIGGINS C (1998) Proving, baking and cooling. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 120–48. WILLIAMS T and PULLEN G (1998) Functional ingredients. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 45–80. ZELESNAK K J and HOSENEY R C (1986) The role of water in the retrogradation of wheat starch gels and bread crumb, Cereal Chemistry, 63(5), 407–11. CAUVAIN S P

19 Analysing and improving the texture of cooked rice S. K. Kim, Dankook University and C. O. Rhee, Chonnam National University, Korea

19.1 Introduction Cultivated rice at present sustains two-thirds of the world’s population. Asian countries produce 91.1% and consume 90.2% of the world production of milled rice (International Rice Commission, 2000). Although there are a number of different markets, rice has traditionally been thinly traded with a market less than 6% of the world production of 397.2 million tonnes of milled rice. Most of this is consumed as cooked rice, and only a small portion (0.86% in the world and 0.67% in Asia) is utilized for processing, such as rice cake in Korea (Kim et al., 2001) and some other products (Juliano, 1998; Kohlwey et al., 1995). Grain quality denotes different properties to different sectors of the rice industry – farmers, processors and millers, retailers, shoppers who buy the milled rice in the market, the consumers themselves, and nutritionists and policymakers (Juliano, 1972). Criteria for price and market quality of milled rice, however, are not directly related to those for cooking and eating quality and for the nutritional quality of the cooked rice. Aging or storage changes should also be considered in the effective implementation of tests for rice quality. The freshly harvested grain undergoes texture changes during the first three months of harvest; hence, it is preferable to carry out milling tests and tests for cooked rice texture on aged rice (Juliano, 1985a). Aspects of aging of rice have been reviewed in depth by Juliano (1985a), Chrastil (1994), and Zhou et al. (2002). The mechanism of aging involving lipids, protein, and starch was first proposed by Moritaka and Yasumatsu (1972). Zhou et al. (2002) modified the mechanism and proposed that alteration of integrity of the cell wall by the release of phenolic acids should

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also be considered in the aging process. Sowbhagya and Bhattacharya (2001) discussed the aging process in relation to pasting properties. The rice produced in Asia is indica and japonica type with a wide range of amylose contents, and preferences for rice type and its amylose content differ from region to region (Juliano, 2001). Moreover, some markets (e.g. India) have a preference for stored rice, while others (e.g. Korea, Japan, China) favor fresh rice. Freshness is considered so highly in the Japanese market that tests are devised for its measurement (Matsukura et al., 2000). Texture of cooked rice has been shown to govern its acceptance by consumers when eaten as the whole grain (Okabe, 1977). Texture can be defined as a multi-dimensional characteristic that humans can perceive, define, and measure (Szczesniak, 1987). Therefore, sensory evaluation is critical, although instrumental measurement of textural properties is also common practice. This chapter gives an overview of rice quality evaluation methods and recent developments in the cooking and eating quality of rice. Studies on criteria and tests for rice grain qualities up to the early 1980s are well documented (Juliano, 1985a). In many countries the cooking process involves pre-soaking the rice in cold or hot water. Although considerable information about the hydration of rice is available in the literature, this has not been highlighted in the reviews on rice quality (Juliano, 1985a; Zhou et al., 2002). The hydration process has therefore been included in this chapter.

19.2 Criteria for evaluating rice quality A recent survey on the criteria and methods used by Asian countries to evaluate the cooking and eating quality of rice in rice-breeding programs is summarized in Table 19.1 (Juliano, 2001). The criteria include physicochemical properties, cooking test, and properties of cooked rice. 19.2.1 Physicochemical properties Measurement of physicochemical properties is an indirect method of estimating eating quality based on chemical composition and gelatinization properties. Among the chemical components of rice, amylose and protein contents are the most important factors influencing eating quality. Amylose content is determined by a simplified colorimetric method (Juliano, 1971), or by nearinfrared reflectance spectroscopy (NIRS) (Delwiche et al., 1996). For the determination of protein content, the Kjeldahl method is most popular, but recently NIRS has also been used (Delwiche et al., 1996). The authentication of rice has been attempted using NIRS (Kim et al., 2003). Okadome et al. (2002) proposed chemometric formulae for predicting amylose and protein contents based on the surface and overall physical properties of single-cooked milled rice as predictive variables. Amylose content is considered to be the single critical factor affecting eating quality (Noda et al., 2003). Preferred

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rice grain quality based on amylose content varies between countries (Juliano, 2001). It was reported that protein content of Japanese milled rice was inversely related to eating quality as determined by sensory evaluation (Ishima et al., 1974). However, Champagne et al. (1999) found that protein content of short- , medium- , and long-grain rice produced in the USA had a poor correlation with sensory attributes. Alkali spreading value, which is related to the gelatinization temperature of raw starch granules, involves incubating six grains of milled rice in 100 mL of 1.7% KOH at 30 °C for 23 h (Little et al., 1958). It measures the degree of spreading using a seven point scale (1 = intact, 7 = greatly dispersed) and corresponds to gelatinization temperature as follows: 1–2, high (74.5– 80 °C); 3, high-intermediate; 4–5, intermediate (70–74 °C); and 6–7, low (< 70 °C). Gel consistency was originally developed to differentiate high-amylose rice with contrasting amylograph pasting viscosities (Cagampang et al., 1973). The method consists of dispersing 100 mg of milled rice flour in 2.0 mL of 0.2 N KOH, heating for 8 min in vigorously boiling water, cooling in an icewater bath, and measuring gel length. The gel consistency values are classified as soft (61–100 mm), medium (41–60 mm) or hard (26–40 mm), which are positively correlated with gelatinization temperature of high amylose rice, and negatively correlated with protein content (Juliano, 1998) and amylograph consistency (Perez, 1979).

19.2.2 Cooking test Batcher et al. (1963) classified the various methods in the world of cooking milled rice into groups: • • • • • •

oven cooking method; cooking in a small amount of water; cooking in a medium amount of water; cooking in a large amount of water; steaming; and cooking in water or steaming with oil added.

However, in rice-breeding programs, rice is cooked by being dropped in boiling water until the center is gelatinized or, as in most of Asia, by being washed, possibly pre-soaked, and then cooked at a fixed water–rice ratio, which varies between countries depending on the amylose content of the rice (Juliano, 2001). Aspects of the cooking properties of milled rice have been reviewed by Juliano (1985b). The methods commonly used for evaluation of rice cooking tests are aroma and measurement of pasting viscosities by either Rapid Visco Analyser (RVA, Newport Scientific Pty Ltd, Australia) or Brabender viscoamylograph (Table 19.1). Viscosity properties measured on a Brabender viscoamylograph

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Table 19.1 Major methods used in Asian rice-breeding programs to evaluate the cooking and eating quality of milled rice (Adopted in part from Juliano, 2001 and modified) Rice property

Frequencya

Methodology

Physicochemical Amylose content

11 (I)

Colorimetric (Juliano, 1971); NIRS (Delwiche et al., 1996) Kjeldahl; NIRS (Delwiche et al., 1996) Little et al. (1958) Cagampang et al. (1973)

Protein content Alkalispreding value Gel consistency Cooking Aroma RVA viscosity Amylograph viscosity Cooked rice Appearance/gloss Hardness

5 (I) 9 (M) 9 (M) 12 (S) 6 (I) 3 (I) 8 (S)/ 2 (I) 7 (S)/ 2 (I)

Texture

6 (S)/ 5 (I)

Stickiness/cohesiveness Taste/flavor Aroma

7 (S) 7 (S) 5 (S)

IRRI (1971) AACC method 61-02; Japanese method (Ohtsubo et al, 1998) Juliano et al. (1985) Toyo Midometer for gloss (Yoon, 2002) Instron (OTMS cell) (Perez and Juliano, 1979) Texture profile analysis (Champagne et al., 1998, 1999; Perdon et al., 1999)

a

Number out of 13 respondents from 11 countries. Symbols in parentheses, I, M and S, are instrumental, manual and sensory methods, respectively.

are similar to those measured with RVA (Blankeney et al., 1991). The Japanese RVA method and the American Association of Cereal Chemists method (AACC, 2000) differ in that the former uses 3.5 g instead of 3.0 g of flour and heats only to 93 °C rather than to 95 °C, but it cooks for longer by prolonging the holding time at 93 °C from 4 to 7 min (Ohtsubo et al., 1998). Since a main component of rice grain is starch, gelatinization properties of rice or rice starch are closely related to eating quality (Ohtsubo et al., 1998). During heating in water, starch granules swell and amylose leaches out. The increase in viscosity observed during heating of starch in water in an amylograph or RVA is mainly contributed by the swollen granules. Breakdown of viscosity is caused by breakdown of gelatinized starch granules, of which the degree of breakdown is dependent on rigidity of the swollen granules. The breakdown of the amylograph is positively correlated with the overall eating quality of Japanese rice (Chikubu et al., 1985). Bhattacharya and Sowbhagya (1978, 1979) recommended running the amylograph at a fixed-peak viscosity rather than fixed-concentration (i.e. 10% solid), since at fixed-peak viscosity the breakdown was the primary index in evaluation of rice varieties differing in amylose contents, and of the change in pasting behavior of rice during aging (Sowbhagya and Bhattacharya, 2001).


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Amylose content is inversely proportional to the degree of granule swelling during pasting (Lii et al., 1966; Sasaki and Matsuki, 1998). Noda et al. (2003) reported that amylose content (3.5–17.2%) of rice starch was significantly positively correlated with RVA peak viscosity, breakdown, set-back, and pasting temperature. Han and Hamaker (2001) reported that the paste breakdown of rice starches with a fairly narrow range of amylose content (15.1–17.9%), but a wide variation in RVA pasting curve, was affected by the fine structure of amylopectin. Proportion of long chains (DPn > 100), which represents the long B chain of amylopectin, was negatively correlated with breakdown and proportion of short chains (DPn = 17), which would be mostly A chains of amylopectin, was positively correlated with breakdown. Martin and Fitzgerald (2002) reported that proteins in rice grain influenced RVA viscosity curves both through binding water, which increased the concentration of the dispersed and viscous phase of gelatinized starch, and through the agency of a network linked by disulfide bonds.

19.2.3 Properties of cooked rice The properties of freshly cooked rice that are important to consumers include intact grains, appearance, gloss, softness, stickiness, taste, and aroma (Table 19.1). These properties are commonly evaluated by sensory test. Instruments are also used to complement sensory scores (Champagne et al., 1999; Ohtsubo et al., 1998). Gloss of cooked rice is measured by a Midometer (Toyo Rice Cleaning Machine Co. Ltd, Japan) in Japan and Korea. Rice (33 g) in a measuring device is cooked in a water bath at 80 °C for 10 min, rested for 2 min at room temperature, and measured for gloss, which is converted to eating quality score (100 point scale). The eating quality score of Korean rice indicates that the score varies by variety and degree of milling as shown in Fig. 19.1 (Yoon, 2002).

19.3 Hydration of rice Soaking of rice in water is one of the unit operations during cooking, parboiling or production of quick cooked rice. Soaking is also an essential step in wetmilling of rice flour (Chiang and Yeh, 2002). During the soaking process water diffuses into the rice and is absorbed by the starch. The absorption of liquid water by rice grain proceeds by a heterogeneous mechanism. Mathematical analysis of non-stationary-state diffusion in solids of arbitrary shape can be simply expressed as follows (Becker, 1960):

m – m0 = k 0 t and

k 0 = 2 ( ms – m0 ) S V π

[19.1]

D

[19.2]

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Texture in food 84 82 C 80 B 78

Eating quality score

76 74 72 A 70 68 66 64 62 60 10

11

12 13 14 15 16 Degree of milliing (%)

17

18

Fig. 19.1 Relationship between degree of milling and eating quality score measured with Toyo Midometer. A = Odaebyeo, B = Hwasungbyeo, and C = Ilpumbyeo. (Adopted from Yoon, 2002).

where m is the average moisture content (g/g) at a given absorption time (t), m0 is the initial moisture content (g/g), ms is the effective surface moisture content (g/g), S is the surface area, V is the volume, and D is the diffusion coefficient. Equation 19.1 indicates that the moisture gain of a sample immersed in water should be approximately proportional to the square root of the absorption time.

19.3.1 Water uptake rate In general, the absorption of water by a given rice is a function of time, temperature, and initial moisture content. In practice, variety, degree of milling, and storage conditions should be considered in evaluating the hydration rate of rice. Varietal differences in hydration rate of japonica type milled rice have been reported (Kim et al., 1984b; Kim et al., 1985; Lee et al., 1983). The equilibrium moisture content (EMC) of milled rice at room temperature is 30–32% for japonica type (Cho et al., 1980; Lee et al., 1983; Suzuki et al., 1977; Takeuchi et al., 1997a), 28–29% for indica type, (Bhattacharya et al., 1972) and 30.4 % (Chiang and Yeh, 2002) and 34–37% for waxy rice (Bhattacharya et al., 1982). It was reported that EMC of milled rice was very highly significantly related inversely to amylose content and directly to kernel chalkiness index (Bhattacharya et al., 1982).


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The diffusion coefficients of milled rice reported in the literature are summarized in Table 19.2. Steff and Singh (1980) and Suzuki et al. (1977) assumed that the rice grain is spherical, and the others assumed it to be prolate spheroid. This gives a difference in the calculations of S and V of rice grain, hence affecting the calculated value of diffusion coefficient. Degree of milling (Yoon, 2002) and defatting (Kim et al., 1986) of milled rice also affect the hydration rate. Zhang et al. (1984) developed a computer-aided model using finite elements to analyze non-linear diffusion in milled rice during soaking at 60 °C. The contour plots of the moisture distribution showed that variation in moisture content was between 35 and 50% at 30 min of soaking, whereas at 60 min the variation was between 40 and 51%. The average mass diffusivity changed from an initial value of 6.4 × 10–7 m2/hr to 4.2 × 10–7 m2/h in 10 min of soaking. This period corresponded to intervals of maximum water uptake, after which the water uptake slowed down as did the change in mass diffusivity. Average mass diffusivity decreased from 6.4 × 10–7 m2/h to 3.0 × 10–7 m2/h as the moisture content increased from 13 to 50%, which indicates that mass diffusivity is a function of concentration. The diffusion coefficients of brown rice at soaking temperature of 30 °C are 1.78–3.68 × 10–6 cm2/min (Song et al., 1988), 3.51 × 10–6 cm2/min (Kim et al., 1984a), 5.52 × 10–6 cm2/min (Steff and Singh, 1980), and 2.31 × 10–6 cm2/min (Han et al., 1996). These results suggest that the diffusion coefficient of brown rice is fairly constant regardless of the variety. The hydration rate of brown rice at 60 °C was reported to be 0.0394–0.0552 min–1/2 (Lee and Kim, 1994), that at 100 °C being 0.0743–0.1419 min–1/2 (Kim and Suh, 1990). Lee and Kim (1994) reported that the water absorption rate of brown rice had a positive correlation with numbers and thickness of aleurone layers of the kernel, implying that the differences in water absorption among brown rice are due to the differences in bran structure of the kernel. Yoon (2002) demonstrated that moisture gain of brown and milled rice soaked in water for 20 min at 30 °C showed a linear relationship with degree of milling and the slope was essentially the same among three rice cultivars. An increase of degree of milling by 1% resulted in an increase of moisture gain by 1.06 times. Moisture gain was highly significantly related inversely to protein and fat contents. Table 19.2 Diffusion coefficients of milled rice at soaking temperature of 30 °C D × 104 (cm2/min)

Number in sample

Reference

2.28–2.64 26.0 90.0 8.46–9.95 1.09–4.61 2.55–4.03

2 1 1 6 21 3

Cho et al. (1980) Steff and Singh (1980) Suzuki et al. (1977) Lee et al. (1983) Kim et al. (1984a) Song et al. (1988)

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Texture in food

The water uptake rate constants of brown and milled rice linearly decrease as storage time increases when rice in laminated film pouch is stored at 4– 30 °C for up to five months (Cho and Kim, 1990, 1993; Han et al., 1996). The change in water uptake rate is more pronounced in milled rice and at elevated temperatures. The EMC remains fairly constant for both brown and milled rice, but the time to reach EMC increases as storage time is prolonged and temperature is elevated (Cho and Kim, 1990, 1993).

19.3.2 Temperature dependence of diffusion coefficient Since diffusion coefficient is influenced by water temperature, diffusion coefficient as a function of temperature can be expressed using Arrhenius relations (Cho et al., 1980): D = D0 exp (–Ea/RT)

[19.3]

where D0 is the diffusion constant, Ea is the activation energy (cal/mol), R is the gas constant (cal/mol·K), and T is the absolute temperature (K). Steff and Singh (1980) reported the temperature dependence of diffusion coefficient (m2/h) of endosperm (D1) and bran (D2) on the assumption that endosperm and bran were homogeneous, isotropic materials and diffusion coefficients were not a function of moisture concentration:

and

D1 = 1.29 × 10–2 exp (–3.43 × 103/T)

[19.4]

D2 = 1.82 exp (–5.40 × 103/T)

[19.5]

where E a for endosperm and bran layer was 6800 cal/mol and 10 700 cal/mol, respectively. Kim et al. (1984b) found that no two rice varieties had the same D among Korean milled rice. The value of D0 ranged from 0.1940 to 2.1173 and Ea from 3600 to 6500 cal/mol with average of 4700 cal/mol. This value agrees well with the findings of Cho et al. (1980) and Lee et al. (1983). Suzuki et al. (1977) reported that Ea of soaking of Japanese rice was approximately equal to 3000 cal/mol. These results imply that Ea of milled rice during soaking at room temperature depends on variety and degree of milling. The temperature dependence of D for Korean brown rice was reported to be (Kim et al., 1984a): D = 0.352 exp (– 4.73 × 103/T)

[19.6]

where D is in cm2/sec and Ea is 9400 cal/mol, which was comparable to 10 700 cal/mol in Eq. (19.5). The D values of brown rice during soaking at 30 °C calculated from Eq. (19.5) and Eq. (19.6) were 5.52 × 10–6 and 3.51 × 10–6 cm2/min, respectively. This result implies that D at the initial stage of soaking of brown rice is controlled by the bran layer, and that D of brown rice may be independent of variety, as discussed earlier.


459

19.3.3 Changes in hardness of rice during soaking Kim et al. (1984a) showed that the changes in hardness of brown rice during soaking at 20–80 °C could be expressed: ln H = – k h t H0

[19.7]

where H0 and H are the hardness of brown rice at soaking time of zero and t (min), and kh is the reaction rate constant. The Ea calculated was 12 000 cal/mol at soaking temperature of 40–60 °C and 6500 cal/mol at 70–80°C, which indicates that Ea of hydration of brown rice changed at 60–70°C. In general, heterogeneous catalytic reactions involve the diffusion of reactants; Ea observed in the diffusion-limited reaction is about one-half of Ea observed in the case of reaction only (Suzuki et al., 1976). The gelatinization temperature of starches of rice employed by Kim et al. (1984a) was reported to be 60–65 °C (Chung et al., 1982). The changes of Ea during soaking, therefore, imply first that the decrease in hardness of brown rice at 40–60 °C was due to the gradual absorption of water by the bran layer resulting in the physical changes, and second that at temperature above the gelatinization temperature the diffusion-limited reaction, i.e. partial gelatinization of endosperm, controlled the changes in hardness.

19.4 Factors affecting cooking quality Cooking of rice is closely related to eating quality because the texture of cooked rice depends on the degree of cooking. Since the texture of cooked rice is a major feature of its quality, the prediction of the behavior of rice during cooking is essential to understanding cooking quality of rice.

19.4.1 Cooking mechanism It is reasonable to consider that the cooking process of rice comprises the gradual absorption of water from the surface to the inner portion of the rice grain and physicochemical changes or reactions of rice constituents with water by heating. Suzuki et al. (1976) adopted the rheological method using the parallel plate plastometer for measurement of degree of cooking of rice at 75–150 °C, on the assumption that the ratio of the soft part to the original volume was convertible into the degree of cooking. One gram of rice with 1.4 g of water in a 12 mm (inner diameter) × 28 mm brass vessel, made capable of enduring inner pressure at 150 °C by sealing with a packing and a screw cap, was cooked in an oil bath. The deformation ratio of cooked rice grains showed a clear linear relationship with cooking time at various cooking temperatures (Fig. 19.2). The deformation ratios with each cooking temperature

460

Texture in food

Deformation ratio (–)

1.0

0.5 150 °C

130120 110 100 °C

80 °C 75 °C

90 °C 0.1 0.5

1

5 10 Cooking time (min)

50

100

Fig. 19.2 Relation between the deformation ratio of cooked rice grains and the cooking time. (Reprinted, with permission, from J of Food Sci, 41(5), 1180–83, ©Institute of Food Technologists, Chicago, Ill, USA 1976).

reached a constant value after some cooking time, which was called the terminal point of cooking. The degree of cooking (α) can be expressed: α=

X – X0 Xe – X0

[19.8]

where X0, X and Xe are the deformation ratios at cooking time zero, t and the terminal point of cooking, respectively. If it is accepted that the deformation ratio is proportional to the degree of cooking, the rate of the uncooked portion of rice gives a straight line with cooking time: ln (1 – α) = – kt

[19.9]

where k is the reaction rate constant. The Ea for cooking calculated was 19 000 cal/mol at temperatures of 75– 100 °C and 8800 cal/mol at 100–150 °C. As explained earlier, heterogeneous catalytic reactions involve the diffusion of reactants: Ea observed in the diffusion-limited reaction is about one-half of Ea observed in case of reaction only, because Ea of diffusion seems negligibly small compared with the value of the reaction. Based on these results, Suzuki et al. (1976) concluded that the cooking rate was limited by the reaction rate of the rice component with water at temperatures below 100 °C, and it was limited by the diffusion of water through the cooked layer toward the interface of uncooked core where the reaction occurs. Cheigh et al. (1978) measured the hardness of milled rice grains during cooking and found that the reciprocal of hardness followed a similar pattern to that shown in Fig. 19.2. Activation energy they found was 17 200 cal/mol at a temperature below 100 °C and 8900 cal/mol at a temperature above 100 °C. Cho et al. (1980) also reported essentially the same Ea values for milled rice as Cheigh et al. (1978). In the case of brown rice, Ea was 15 000–16 000


461

and 8500 cal/mol at cooking temperatures below 100 °C and above 100 °C, respectively (Kim et al., 1984a). Suzuki et al. (1977) studied the cooking rate equations of milled rice by measuring the change in weight of rice accompanying cooking at 70– 98.5 °C, and concluded that the cooking rate was mainly limited by the reaction rate of rice components with water and the equivalent value of Ea of the reaction rate was equal to 20 000 cal/mol, although the cooking rate was relatively influenced by the diffusion rate of water in the cooked rice layer at 98.5 °C. 19.4.2 Effect of degree of milling and soaking on cooking rate Cheigh et al. (1978) reported that the degree of milling did not affect Ea of cooking, but the lower degree of milling resulted in a slower cooking rate, thus increasing the cooking time. Desikachar et al. (1965) reported that removing the outer 1% of the brown rice kernel increased the water absorption during cooking to that of highly milled rice. The wax content of this outer 1% layer was implicated in reducing the rate of water absorption during cooking. Champagne et al. (1990) reported that the removal of the outer 1.3% of rice kernel led to the largest decrease in onset and peak temperature values of differential scanning calorimetry. The pre soaking of rice increases the cooking rate constant of milled rice (Cho et al., 1980; Suzuki et al., 1976) and brown rice (Kim et al., 1984a).

19.4.3 Temperature dependence of terminal point of cooking Kim et al. (1984a) found that the temperature dependence of the terminal point of cooking of brown rice could be expressed as z-value based on cooking temperature and time: z = 4.6 T 2 /Ea = 10/logQ10

[19.10]

Their results are presented in Table 19.3. The z-value at cooking temperature of below 100 °C was about half of that at over 100 °C, which was the opposite to Ea. The data indicate that the cooking process of brown rice could also be explained by z-value.

Table 19.3 Temperature dependence of the terminal point of cooking of brown rice Cooking temperature (°C)

z (°C)

Q10

Ea (cal/mol)

80–100 100–130

43 89

1.71 1.30

13 700 8000

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Texture in food

19.4.4 Diffusion of water during cooking Suzuki et al. (1976) measured the gravimetric change of rice grains during cooking at evaluated temperatures, which was analyzed using a shell and core model, assuming that the gelatinization was much more rapid than the rate of water diffusion in a grain so that the starch gelatinization occurred only at the interface of the uncooked core. They reported that D (cm2/min) of rice was 1.86 × 10–3 at 110 °C, 3.32 × 10–3 at 130 °C, and 5.05 × 10–3 at 150 °C. The gravimetric change of a rice grain which has been immersed in water at 60–80 °C shows that it is the rapid diffusion process which is dominant in the initial stage of the change in moisture content in a grain, but in the middle stage (50–150 min) the rise of moisture content is reduced and approximated to a straight line, of which the gradient may be regarded as an index of the rate of gelatinization (Takeuchi et al., 1997b). Based on this, they proposed the rate of increase in moisture content (M, g water/g rice) referred to as the gelatinization rate as a function of temperature:

dM = 5.208 × 10 –5 ( T – 331) 2 dt

at 331 < T < 355

[19.11]

dM = 0.030 dt

at 355 < T < 373

[19.12]

and

To determine the rate of starch gelatinization which was responsible for the increase in moisture content in the rice grain during cooking, Gomi et al. (1998) observed the water diffusivity of rice starch/water mixture using a pulsed-field-gradient-nuclear magnetic resonance (NMR) method. The water diffusivity decreased as heating time increased. On the assumption that the time course of changes in moisture content followed a first-order process, a rate constant of 10–2 s–1 was obtained at temperatures of 66–80 °C. This value was 100-fold smaller than that reported by Suzuki et al. (1976).

19.4.5 Moisture profile in a rice grain during boiling Takeuchi et al. (1997b) reported the moisture profile in a quenched rice grain after boiling in excess water using an NMR transverse relaxation time (T2 in milliseconds) imaging method at 200 MHz. The NMR image showed that the moisture content (g water/g rice) after 6 min boiling reached 0.6 at the peripheries while the moisture content was less than 0.45 in the middle. The image after 12 min boiling indicated that swelling due to gelatinization was almost completed, although a core of low moisture content of less than 0.55 remained in the middle part. Sometimes a low moisture core was formed not in the middle but somewhat unsymmetrically shifted to the dorsal side. The thicker protein layer on the dorsal surface, which restricted water


463

Moisture content (g water/g sample)

percolation in the rice grain during boiling, was attributed to the unsymmetrical behavior of the rice in moisture content. Kim et al. (1996a) reported that most protein bodies existed in the periphery of the milled rice. The moisture distribution map in partly boiled grains of rice which were cellulase treated to destroy the cell wall showed little difference with nontreated grains (Takeuchi et al., 1997b). Therefore, it seems that the effect of the cell walls on moisture transport during boiling was insignificant. The thickness of the endosperm cell wall was reported to be about 0.5 µm in diameter (Kim et al., 1996a). Takeuchi et al. (1997a) developed a method of quick imaging a onedimensional NMR T2 profile to observe moisture profile in a rice grain. They applied it to the real-time measurement of the moisture profile in a rice grain every two minutes during boiling for 40 min. The change of moisture profile in a rice grain during boiling is shown in Fig. 19.3, in which the moisture content along the lateral line from the dorsal surface to the ventral surface is plotted. The moisture content (g water/g rice) increased rapidly in the ventral side to reach about 0.75–0.80 after 14 min boiling, which means that the rate of moisture increase was 0.10 per min. On the other hand, the rise of the moisture profile was moderate in the dorsal side and in the middle part – 0.04 per min between 12 min and 14 min boiling. The rise further declined to about 0.01–0.02 per min between 14 min and 22 min boiling, and reached 0.80 after 22 min boiling, which remained constant for the remaining heating time. The value of moisture content of 0.8 agreed with the maximum moisture content which was attained when a rice grain was boiled in excess water at 100 °C (Takeuchi et al., 1977b).

0.9 0.8 0.7 0.6 0.5

36 min. 22 min. 14 min.

–2

–1

0

1

2 (min)

12 min.

0.4 –1

0 Position (nm)

1

Fig. 19.3 Real-time NMR imaging observation of the change of moisture profile along the parallelepiped virtually dissected in a rice grain during boiling at 100 °C (Reprinted, from Takeuchi et al. (1997a), J of Food Engineering, 33, 181–92 with permission from Elsevier).

464

Texture in food

19.5 Testing texture quality The sensory test is the most fundamental test of the eating quality of rice. It provides information on appearance, aroma, taste, hardness, stickiness, and overall quality (Table 19.1). 19.5.1 Sensory test The sensory properties of cooked rice are measured by taste panel. The methods commonly used are the preference test (Kim and Kim, 1986; Kim et al., 1995), ranking test in the Philippines (Juliano, 2001), and descriptive test (Champagne et al., 1998; Meullenet et al., 1998, 2000; Sesmat and Meullenet, 2001). The preference test involves scoring the cooked rice quality on 1–9 or 4– 7 point hedonic scales. A control variety may be used as a reference. The sensory method used in Japan is –3 to + 3 with 0 for the control. The ranking test for preference and acceptability using a 30-member consumer panel is used in the Philippines. The descriptive test is objective and promising but is labor-intensive and time-consuming. The sensory texture attributes in the evaluation of cooked rice include clumpness, stickiness, hardness and moistness in Korea (Kim and Kim, 1986; Kim et al., 1995) and hardness and stickiness in Japan (Ohtsubo et al., 1998) for short-grain japonica rice, and hardness, stickiness, cohesiveness, roughness, toothpack, and loose particles in the USA for short- , medium- and long-grain rice (Champagne et al., 1999; Muellenet et al., 1998, 2000; Sesmat and Muellenet, 2001). The most common sensory texture attributes are therefore hardness and stickiness, which can be applied to all cooked rice regardless of rice types and can also be easily measured with an instrument. Stickiness is a very important sensory property in many Asian countries. For those countries where flaky or non-sticky rice is preferred, stickiness is an undesirable property of rice. For example, medium- and short-grain classes in the USA are generally referred to as the sticky rice and correspond to the japonica varieties. On the other hand, long-grain rices tend to be bland, flaky, and dry when cooked and correspond generally to the indicas. The sensory texture attributes are affected by factors such as variety (Kim et al., 1995; Perez et al., 1993a), amylose content (Perdon et al., 1999; Windham et al., 1997), protein content (Windham et al., 1997), gelatinization temperature (Del Mundo, 1989), post-harvest processing conditions (Champagne et al., 1998; Lyon et al., 1999; Rousset et al., 1995), the rice to water ratio during cooking (Kim et al., 1995), and cooking method (Kim and Kim, 1986). Cooked rice with low amylose is soft and sticky, while rice with high amylose is firm and fluffy (Perdon et al., 1999). The sensory characteristics such as color, shininess, clumpiness, softness, stickiness, and ease of swallowing of rice cooked with a pressure cooker are significantly greater than those with an electric cooker (Kim and Kim, 1986). Lyon et al. (1999) reported that sensory properties relating to stickiness had significant positive correlation


465

with amylose content and negative correlation with protein content. Kim et al. (1995) reported that hardness and stickiness of cooked rice were affected by its moisture content. Meullenet et al. (2000) investigated the effect of storage conditions of long-grain rough rice on sensory profiles of cooked rice using sensory descriptive methods. Storage temperature (4, 21, and 38 °C) and duration (up to 36 weeks) significantly affected adhesiveness to lips, an indicator of rice stickiness. Increasing storage temperatures decreased rice stickiness. Rice stickups reached a maximum after 20 weeks of storage and decreased significantly after 36 weeks of storage. Cooked kernel hardness decreased with increasing storage moisture content (10–14%) and reached a maximum between 15 and 22 weeks of storage depending on the rough rice storage moisture content.

19.5.2 Physical properties of cooked rice Among various physical properties, hardness and stickiness are the most frequently tested parameters (Table 19.1). Hardness and stickiness of cooked rice measured with an Instron food tester correlate significantly with amylose content, but because stickiness is easily predictable based on amylose content (r = –0.92, p < 0.01), texture measurement concentrates on hardness (r = 0.77, p < 0.01) (Perez and Juliano, 1979). The instruments used to determine the hardness and texture profile of cooked rice in rice-breeding programs (Table 19.1) are the Instron food tester equipped with an Ottawa texturemeasuring system extrusion cell (Perez and Juliano, 1979; Perez et al., 1993b) and TA-XT2 texture analyzer (Stable Miro System, UK) (Champagne et al., 1998, 1999; Perdon et al., 1999). Perez et al. (1993b) reported that Instron hardness of cooked rice was inversely related with RVA breakdown. The basic principle involved in the determination of hardness is based on the maximum force to press on the cooked rice. Stickiness is the force required to pull a device imbedded in or pressed on the cooked rice (Lee and Peleg, 1988). The force is sometimes treated as adhesiveness according to the texture profile analysis. A typical texture profile analysis curve obtained using a TA-XT2 texture analyzer is shown in Fig. 19.4 (Champagne et al., 1998, 1999). Mossman et al. (1983) developed a method to determine the cooked rice stickiness with an Instron tester using a 2 g sample, which could be distinguished easily between long grain and sticky varieties, and among sticky varieties (Fellers et al., 1983). The stickiness was affected by the water to rice ratio during cooking (Mossman et al., 1983) and by hot-air treatment of rice (Fellers et al., 1983). When a medium rice was treated with a hot air (204 °C) blast, stickiness measured by Instron decreased as toasting time increased. Fellers et al. (1983) found that Instron stickiness of heat-treated rice was positively correlated with organoleptic stickiness scores.

466

Texture in food 25

20 H1

Height of first curve

Force (kg)

15

10

Area first bite Area second bite

5 A1 0 D1 (4.9 mm)

A2 A3

D2 Area of negative curve

–5

Fig. 19.4 Typical texture analysis (TPA) curve of 1-g rice samples. TPA text parameters: force–distance test, compression plate set at 5 mm to travel 4.9 mm at 1 mm/sec. Attributes on curves: H1, hardness (kg), measurement of force at peak of first curve; A1, area under first curve; A2, area under second curve; A4, measured from first data point to probe reversal point; A5, measured from first probe reversal point to point where force returns to zero; A2/A1, cohesiveness, ratio of area under curves A2/A1; A3, adhesiveness, area of negative force curve, representing work to separate plunger from sample on upstroke after first curve; D2/D1, springiness, ratio of D2 to D1, where D1 is total distance (4.9 mm) traveled by plunger on downstroke and D2 is distance traveled on downstroke by plunger from point of sample contact to end of downstroke. (Source: Champagne et al., 1998, 1999).

Lee and Peleg (1988) employed a Roano Surface Tensiometer (Biolar Corp., USA) to determine the attractive forces between individual grains which were mounted one on top of the other in parallel portion, and demonstrated that the attractive forces could be used as a physical criterion to distinguish between sticky and flaky rice cultivars. Increase in the water to rice ratio during cooking resulted in an increase in the attractive forces, which was most probably associated with extractability. Kim et al. (1991) reported that the solubles and soluble amylose content were negatively correlated with Instron hardness and positively correlated with Instron adhesiveness of cooked rice. The Texturometer (Zenken Co., Japan) has been used to measure hardness and stickiness of cooked rice in Japan (Suzuki, 1979) and in Korea (Lee et al., 1989). A new technique for evaluating palatability of cooked rice using a Texturometer was proposed by Okabe (1977), who found that adhesive power/hardness or –H/H of three cooked rice grains agreed well with the sensory evaluation. Lee et al. (1989) reported that Texturometer adhesiveness was negatively correlated with amylose content and Texturometer hardness


467

was positively correlated with protein content, which agrees with the results obtained by sensory evaluation (Lyon et al., 1999). Okadome et al. (1999) measured surface hardness and adhesiveness of a single-cooked rice grain at low compression (25% deformation) using a Tensipressure (Myboy System, Taketome Electric Co., Japan). They demonstrated that surface hardness could be an effective tool for differentiating the effect of protein contents on hardness of cooked rice. Surface hardness had a higher positive correlation with protein content (r = 0.80, n = 55) than amylose content (r = 0.44, n = 55). Adhesiveness decreased as the amylose content increased. However, the difference in stickiness among cooked rice samples could be detected better by the surface adhesion distance. The overall hardness at high compression test (90% deformation) was highly correlated with Texturometer hardness. The stickiness of high-amylose cooked rice (26.4–29.7%) could be detected with a Tensipresser, but not with Texturometer. The possibility of predicting cooked rice quality using NIR analysis was reported by Windham et al. (1997) and Barton et al. (1998). The effect of molecular structure of rice starch on Instron hardness and adhesiveness of cooked rice was reported by Kang et al. (1994, 1995). The inherent viscosity, number-average degree of polymerization and molecular weight size of amylose showed a positive correlation with hardness, but a negative correlation with adhesiveness of cooked rice (Kang et al., 1994). The inherent viscosity and average chain length of amylopectin had a positive correlation with hardness and a negative correlation with adhesiveness of cooked rice (Kang et al., 1995).

19.5.3 Effect of storage of cooked rice on texture Cooked rice texture changes with storage (Lima and Singh, 1993; Perdon et al., 1999; Perez et al., 1993). Perez et al. (1993) used an Instron to measure the hardness of staled cooked rice which was cooked at a constant water to rice ratio of 2.0. The results showed that the hardness of freshly cooked, staled, and staled and reheated samples was dependent on variety and linearly correlated with amylose content. Perdon et al. (1999) reported that cooked rice firmness increased, while stickiness decreased, during storage, and that starch retrogradation, measured with a differential scanning calorimeter, had a direct linear relationship with firmness, which was independent of variety (a medium-grain rice low in amylose and a long-grain rice high in amylose), storage temperature (–13.3 and 20 °C), and storage duration (24–96 h). The decrease of rheometer stickiness and ratio of stickiness to hardness as storage time increased at 4 °C was reported by Kim and Kim (1996). Lee et al. (1993) stored the cooked rice in an electric cooker at 60, 70, and 80 °C for up to 12 hr and examined the changes in sensory characteristics. The higher storage temperature resulted in lower glossiness, firmness, moistness, and cohesiveness, and higher adhesiveness and off-flavor. The

468

Texture in food

overall desirability decreased as storage time increased at all storage temperatures. Kim et al. (1996b) analyzed the firming rate of cooked rice during storage. The initial hardness of cooked rice decreased as the moisture content of cooked rice increased from 57.5% to 69.5%. The activation energy and Q10 for firming of cooked rice were –4.07 × 103 cal/mol and 1.26, respectively. These factors were independent of variety and moisture content.

19.5.4 Correlation of sensory and instrumental texture attributes Chikubu et al. (1985) demonstrated that overall eating quality, assessed by sensory evaluation, of cooked Japanese rice could be estimated using a multiple regression equation, which was based on protein content, amylograph viscosities, and cooking property. The eating quality was negatively correlated with protein and starch-iodine blue value of residual liquid after cooking, and positively correlated with amylograph viscosities (maximum viscosity, minimum viscosity, and breakdown). The equation based on these five variables had the coefficient of determination of 70.14%. The palatability of cooked rice by sensory evaluation was highly correlated with the estimated value from the equation (r = 0.84, p < 0.01). Based on the concept of Chikubu et al. (1985), Satake (Satake Corp., Japan) developed a Rice Taster which converts various physicochemical parameters of rice into taste scores based on correlations between NIR measurements of key constituents (i.e. amylose, protein, moisture, and fat acidity) and preference sensory scores. Champagne et al. (1996) examined the applicability of a Satake Neuro Fuzzy Rice Taster to quality evaluation of US medium-grain rice. They found that the effect of amylose on Rice Taster score was small, and low-amylose rice of less than 18% fell outside the range of calibration. In general, a low-amylose, low-protein, high-moisture rice scores high because it generally produces a cooked rice that is softer and stickier, characteristics deemed desirable by the Japanese palate. The score of US rice was lower than that of the high-scoring Japanese cultivar Koshihikari. This might be due, in part, to the higher protein content of US rice. Lowering protein values by 0.3–0.4 with deep-milling increased the score values by about five points. Champagne et al. (1999) reported that protein content of milled rice had a poor correlation with sensory attributes. These results clearly indicate that a taste analyzer calibrated using preference sensory scores can only assess whether the rice has the quality characteristics deemed desirable by the target population represented by the sensory panel. Champagne et al. (1999) assessed textural properties of 87 samples representing short- , medium- , and long-grain rice cultivars by descriptive sensory and instrumental texture profile (TPA) analyses and related them to RVA measurements. The results showed that none of the cooked rice textural attributes, whether measured by sensory analysis or TPA, were modeled by RVA with high coefficient of determination. Sensory texture attributes, such


469

as cohesiveness of mass, stickiness, and initial starch coating, and the TPA attribute adhesiveness had the strongest correlation with RVA measurements. Set-back explained most of the variance attributed to models describing these attributes. Inclusion of amylose and protein contents in regression analysis did not strengthen models. Meullenet et al. (1998) examined a correlation between sensory texture of one medium-grain and two longgrain cooked rice and instrumental parameters using an extrusion cell, and found that sensory characteristics most effectively predicted were hardness (R2 = 0.62) and toothpack (R2 = 0.70). Sesmat and Meullenet (2001) demonstrated that seven sensory texture attributes (cohesiveness of bolue, adhesion to lips, hardness, cohesiveness of mass, roughness of mass, toothpull, and toothpack) were satisfactorily predicted from a single compression test (90% deformation) using Texture Analyzer by Partial Least Squares Regression optimized by a stepwise method.

19.6 Problems and challenges Juliano (2001) summarized three major problem areas that challenge rice researchers in Asia and the world in general as they try to improve instruments to complement sensory evaluation of raw and cooked milled rice: 1. how to make sensory evaluation more sensitive and reproducible and take into account regional variations in preference; 2. how to make instrument methods which can simulate sensory panels while remaining rapid, accurate, and economical; and 3. how to better understand the relationship among grain properties and sensory quality of the rice grain. The question is ‘is it possible to set up a standard method of sensory evaluation of rice to cover regional variations in preference’? Some countries have a preference for stored rice (e.g. Indica), while others such as Korea, Japan and China favor fresh rice. Champagne et al. (1999) stated that preference sensory scores reflect the quality characteristics of cooked rice deemed desirable by the target population represented by the sensory panel. As far as sensory evaluation of rice is concerned, it seems that Japanese scientists operate a firm standard procedure. The sensory test measures appearance, aroma, taste, hardness, stickiness, and overall quality (Ohtsubo et al., 1998). The sensory texture attributes of hardness and stickiness are rapidly and accurately measured using a Texturometer with three cooked rice grains or a Tensipresser with one grain in Japan. The Texturometer has been successfully applied to Korean rice. Recently scientists in the USA employed a Texture Analyzer using a single compression test (Sesmat and Meullenet, 2001) or an extrusion cell (Meullenet et al., 1998), NIR analysis (Barton II et al., 1998; Delwiche et al., 1996; Windham et al., 1997), RVA (Champagne et al., 1999), and Japanese Taste Analyzer (Champagne et al., 1996) to

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predict sensory texture attributes of cooked rice, but the results are still inadequate. It seems that there is no question that amylose content is a single critical factor in governing rice texture. However, some say that protein content is also important (e.g. Japan), but others do not agree (e.g. USA). In Korea, breeding efforts to produce a high-yield Tongil rice (a hybrid of japonica and indica) with good eating quality did not succeed, despite the fact that amylose content and the textural properties of the cooked rice were almost identical to those of Korean japonica cultivars (Lee et al., 1989). Studies on the contribution of molecular structure of amylose and amylopectin to the texture of cooked rice remain to be elucidated. Since cooking methods affect the texture, standardization of the cooking method in evaluating sensory texture as well as of the instrumental measurements of texture would be worthwhile.

19.7 Sources of further information and advice Readers are recommended to consult an excellent monograph on Rice Chemistry and Technology, published by the American Association of Cereal Chemists (Juliano, 1985a). The third edition of the monograph is expected to be available in 2004. The major rice research bodies include the Philippine Rice Research Institute (Laguna, Philippines), the National Food Research Institute (Tsukuba Science City, Japan), and the US Department of Agriculture. Names and organizations of Asian scientists associated with rice-breeding programs are available in an article (Juliano, 2001). Statistics on rice production and trade in the world are available from the International Rice Commission, FAO. Most research articles on rice have appeared in Cereal Chemistry, Journal of Cereal Science, Journal of Food Science, and Journal of Texture Studies. Readers who are interested in specific topic should consult the references cited in this chapter.

19.8 References American Association of Cereal Chemists (2000) Approved Methods of the AACC, Method 61–02, St Paul, MN, AACC. BARTON II F E, WINDHAM W R, CHAMPAGNE E T and LYON B G (1998) Optical geometries for the development of rice quality spectroscopic chemometric models, Cereal Chem, 75, 315–19. BATCHER O M, STALEY M G and DEARY P A (1963) Palatability characteristics of foreign and domestic rices cooked by different methods, Rice J, 66(9), 19–24. BECKER H A (1960) On the asorption of liquid water by the wheat kernel, Cereal Chem, 37, 309–23. BHATTACHARYA K R and SOWBHAGYA C M (1978) On viscograms and viscography, with special reference to rice, J Texture Studies, 9, 341–51.


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and SOWBHAGYA C M (1979) Pasting behavior of rice: a new method of viscography, J Food Sci, 44, 797–800, 804. BHATTACHARYA K R, SOWBHAGYA C M and INDUDHARA SWAMY Y M (1972) Interrelationship between certain physicochemical properties of rice, J Food Sci, 37, 733–5. BHATTACHARYA K R, SOWBHAGYA C M and INDUDHARA SWAMY Y M (1982) Quality profile of rice: A tentative scheme for classification, J Food Sci, 47, 564–9. BLANKENEY A B, WELSH L A and BANNON D R (1991) Rice quality analysis using a computer controlled RVA. In Cereals Internationals. Eds D J Martin and C W Wrigley, Melbourne, Roy. Aust. Chem. Inst., 180–82. CAGAMPANG G B, PEREZ C M and JULIANO B O (1973) A gel consistency test for eating quality of rice, J Sci Food Agric, 24, 1589–94. CHAMPAGNE E T, MARSHALL W E and GOYNES W R (1990) Effects of degree of milling and lipid removal on starch gelatinization in the brown rice kernel, Cereal Chem, 67, 570–74. CHAMPAGNE E T, RICHARD O A, BETT K L, GRIMM C C, VINYARD B T, WEBB B D, MCCLUNG A M, BARTON II F E, LYON B G, MODENHAUER K, LINSCOMBE S, MOHINDRA R and KOHLWEY D (1996) Quality evaluation of US medium-grain rice using a Japanese taste analyzer, Cereal Chem, 73, 290–94. CHAMPAGNE E T, LYON B G, MIN B K, VINYARD B T, BETT K L, BARTON II F E, WEBB D B, MCCLUNG A M, MOLDENHAUER K A, LINSCOMBE S, MCKENZIE K S and KOHLWEY D E (1998) Effects of postharvest processing on texture profile analysis of cooked rice, Cereal Chem, 75, 181–6. CHAMPAGNE E T, BETT K L, VINYARD B T, MCCLUNG A M, BARTON II F E, MOLDENHAUER K, LINSCOMBE S and MCKENZIE K (1999) Correlation between cooked rice texture and rapid visco analyzer measurements, Cereal Chem, 76, 764–71. CHEIGH H S, KIM S K, PYUN Y R and KWON T W (1978) Kinetic studies on cooking rice of various polishing degrees, Korean J Food Sci Technol, 10, 52–6. CHIANG P Y and YEH A I (2002) Effect of soaking on wet-milling of rice, J Cereal Sci, 35, 85–94. CHIKUBU S, WATANABE S, SUGIMOTO T, MANABE N, SAKAI F and TANIGUCHI Y (1985) Establishment of palatability evaluation formula of rice by multiple regression analysis, J Jpn Soc Starch Sci, 32, 51–60. CHO E J and KIM S K (1990) Changes in physicochemical properties of brown and milled rices during storage, J Korean Agric Chem Soc, 33, 24–33. CHO E J and KIM S K (1993) Effects of storage temperature on the physicochemical properties of milled rice, J Korean Agric Chem Soc, 36, 146–53. CHO E K, PYUN Y R, KIM S K and YU J H (1980) Kinetic studies on hydration and cooking of rice, Korean J Food Sci Technol, 12, 285–91. CHRASTIL J (1994) Effect of storage on the physicochemical properties and quality factors of rice. In Rice Science and Tchnology. Eds W E Marshall and J I Wadsworth, New York, Marcel Dekker Inc., 49–81. CHUNG H M, AHN S Y and KIM S K (1982) Comparison of physicochemical properties of Akibare and Milyang 23 rice starch, J Korean Agric Chem Soc, 25, 67–74. DEL MUNDO A M, KOSCO D A, JULIANO B O, SISCAR J J H and PEREZ C M (1989) Sensory and instrumental evaluation of texture of cooked and raw milled rices with similar starch properties, J Texture Studies, 20, 97–110. DELWICHE S R, MCKENZIE K S and WEBB D B (1996) Quality characteristics in rice by nearinfrared reflectance analysis of whole grain milled samples, Cereal Chem, 73, 257– 63. DESIKACHAR H R S, RAGHAVENDRA RAO S N and NANTHACHAR T K (1965) Effect of degree of milling on water absorption of rice during cooking, J Food Sci Technol, 11, 110–12. FELLERS D A, MOSSMAN A P and SUZUKI H (1983) Rice stickiness. II. Application of an Instron method to make varietal comparisons and to study modification of milled rice by hotair treatment, Cereal Chem, 60, 292–5. BHATTACHARYA K R

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and WATANABE H (1998) The rate of starch gelatinization as observed by PFG-NMR measurement of water diffusivity in rice starch/water mixtures, J Food Eng, 36, 359–69. HAN X Z and HAMAKER B R (2001) Amylopectin fine structure and rice starch paste breakdown, J Cereal Sci, 34, 279–84. HAN J G, KANG K J, KIM K and KIM S K (1996) Water absorption of stored brown rice in laminated film pouch, J Korean Soc Food Sci Nutr, 25, 643–8. International Rice Commission (2000) Food Balance Sheet, Rome, FAO. International Rice Research Institute (1971) Annual report for 1970, Los Baños, Philippines, IRRI, 204. ISHIMA T, TAIRA H, TAIRA H and MIKOSHIBA K (1974) Effect of nitrogenous fertilizer application and protein content in milled rice on organoleptic quality of cooked rice, Shokuhin Sogo Kenkysho Kenkyu Hokoku, 29, 9–15. JULIANO B O (1971) A simplified assay for milled rice amylose, Cereal Sci Today, 16, 334– 40, 360. JULIANO B O (1972) Quality vital in rice marketing, Ricemill News, 9(5), 23–4. JULIANO B O (1985a) Criteria and tests for rice grain qualities. In Rice Chemistry and Technology. Ed. B O Juliano, St Paul, MN, AACC, 443–524. JULIANO B O (1985b) Cooperative tests on cooking properties of milled rice, Cereal Foods World, 30, 651–6. JULIANO B O (1998) Varietal impact on rice quality, Cereal Foods World, 43, 207–22. JULIANO B O (2001) Asian perspective on rice sensory quality, Cereal Foods World, 46, 531–5. JULIANO B O, PEREZ C M, ALYOSHIN E P, ROMANOV V B, BEAN M M, NISHITA K D, BLAKENEY A B, WELSH L A, DELGADO L L, EL BAYÂ A W, FOSSATI G, KONGSEREE N, MENDES F D, BRILHANTE S, SUZUKI H, TADA M and WEBB B D (1985) Cooperative study on amylography on milled rice flour for pasting viscosity and starch gelatinization temperature, Starch/Stärke, 37, 40–50. KANG K J, KIM K, KIM S K and MURATA A (1994) Relationship between molecular structure of amylose and texture of cooked rice of Korean rices, J Applied Glycosci, 41, 35–40. KANG K J, KIM K, and KIM S K (1995) Relationship between molecular structure of amylopectin and texture of cooked rice, Korean J Food Sci Technol, 27, 105–11. KIM H Y and KIM K O (1986) Sensory characteristics of rice cooked with pressure cookers and electric cookers, Korean J Food Sci Technol, 18, 319–24. KIM M H and KIM S K (1996) Influence of cooking condition and storage time after cooking on texture of cooked rice, J Korean Soc Food Nutr, 21, 63–8. KIM S K and SUH C S (1990) Water uptake rate of brown rice at 100 °C, J Korean Agric Chem Soc, 33, 261–3. KIM K J, PYUN Y R, CHO E K, LEE S K and KIM S K (1984a) Kinetic studies on hydration of Akibere and Milyang 23 brown rice, Korean J Food Sci Technol, 16, 297–302. KIM S K, JEONG S J, KIM K, CHAE J C and LEE J H (1984b) Tentative classification of milled rice by sorption kinetics, J Korean Chem Soc, 27, 204–10. KIM S K, HAN K Y, PARK H H, CHAE J C and REE J H (1985) Hydration rate of milled rice, J Korean Agric Chem Soc, 28, 62–7. KIM S M, KIM K O and KIM S K (1986) Effect of defatting on hydration of Akibare (Japonica) and Milyang 30 (J/Indica) rice, Korean J Food Sci Technol, 18, 110–13. KIM K, KANG K J and KIM S K (1991) Relationship between hot water solubles of rice and texture of cooked rice, Korean J Food Sci Technol, 23, 498–502. KIM W J, CHANG N Y, KIM S K and LEE A R (1995) Sensory characteristics of cooked rices differing in moisture contents, Korean J Food Sci Technol, 27, 885–90. KIM S K, CHANG B S and LEE S J (1996a) Ultrastructure of compound starch granules and protein bodies of starchy endosperm cell in rice, Agric Chem Biotechnol, 39, 379–83. KIM S K, LEE A R, LEE S K, KIM K J and CHEON K C (1996b) Firming rates of cooked rice differing in moisture contents, Korean J Food Sci Technol, 28, 877–81.

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and EUN J B (2001) Rice cake production using flake rice and medium-grain brown rice, Food Sci Biotechnol, 10, 315–22. KIM S S, PHYU M R, KIM J M and LEE S H (2003) Authentication of rice using near-infrared reflectance spectroscopy, Cereal Chem, 80 346–49. KOHLWEY D E, KENDALL J H and MOHINDRA R B (1995) Using the physical properties of rice as a guide to formulation, Cereal Foods World, 40, 728–32. LEE S J and KIM S K (1994) Bran structure and water uptake rate of Japonica and Tongiltype brown rices, Agric Chem Biotechnol, 37, 94–9. LEE S J and PELEG M (1988) Direct measurement of the attractive force between individual cooked rice grains of sticky and flaky cultivars, J Food Sci, 53, 1113–15. LEE S O, KIM S K and LEE S K (1983) Kinetic studies on hydration of traditional and highyielding rice varieties, J Korean Agric Chem Soc, 26, 1–7. LEE B Y, YOON I H, TETSUYA I, IKUJI K and TETSUJIRO O (1989) Cooking quality and texture of japonica-indica breeding type and japonica type, Korean rice, Korean J Food Sci Technol, 21, 613–18. LEE Y J, MIN B K, SHIN M G, SUNG N K and KIM K O (1993) Sensory characteristics of cooked rice stored in an electric rice cooker, Korean J Food Sci Technol, 25, 487–93. LII C Y, TSAI M L and TSENG K H (1996) Effect of amylose content on the rheological property of rice starch, Cereal Chem, 73, 415–20. LIMA I and SINGH R P (1993) Objective measurement of retrogradation in cooked rice during storage, J Food Quality, 16, 321–37. LITTLE R P, HILDER G B and DAWSON E H (1958) Differential effect of dilute alkali on 25 varieties of milled white rice, Cereal Chem, 35, 111–26. LYON B G, CHAMPAGNE E T, WINDHAM W R, BARTON F E, WEBB D B, MCCLUNG A M, MOLDENHAUER K A, LINSCOMBE S, MCKENZIE K S and KOHLWEY D E (1999) Effect of degree of milling, drying condition, and final moisture content on sensory texture of cooked rice, Cereal Chem, 76, 56–62. MARTIN M and FITZGERALD M A (2002) Proteins in rice grain influence cooking properties!, J Cereal Sci, 36, 285–94. MATSUKURA U, KANEKO S and MOMMA M (2000) Method for measuring the freshness of individual rice grains by means of a color reaction of catalase activity, J Jpn Soc Food Sci Technol, 47, 523–8. MEULLENET J-F C, GROSS J, MARKS B P and DANIELS M (1998) Sensory descriptive texture analyses of cooked rice and its correlation to instrumental parameters using an extrusion cell, Cereal Chem, 75, 714–20. MEULLENET J-F, MARKS B P, HANKINS J-A, GRIFFIN V K and DANIELS M J (2000) Sensory quality of cooked long-grain rice as affected by rough rice moisture content, storage temperature, and storage duration, Cereal Chem, 77, 259–63. MORITAKA S and YASUMATSU K (1972) Studies on cereals. X. The effect of sulfhydryl groups on storage deterioration of milled rice, Eiyo To Shokuryo, 25, 59–62. MOSSMAN A P, FELLERS D A and SUZUKI H (1983) Rice stickiness. I. Determination of rice stickiness with an Instron tester, Cereal Chem, 60, 286–92. NODA T, NISHIBA Y, SATO T and SUDA I (2003) Properties of starches from several lowamylose rice cultivars, Cereal Chem, 80, 193–7. OHTSUBO K, TOYOSHIMA H, and OKADOME H (1998) Quality assay of rice using traditional and novel tools, Cereal Foods World, 43, 203–6. OKABE M (1977) Studies on eating quality of cooked rice, New Food Ind, 19, 65–71. OKADOME H, TOYOSHIMA H and OHTSUBO K (1999) Multiple measurements of physical properties of individual cooked rice grains with a single apparatus, Cereal Chem, 76, 855–60. OKADOME H, TOYOSHIMA H, SHIMIZU N, AKINAGA T and OHTSUBO K (2002) Chemometric formulas based on physical properties of single-cooked milled rice grain for determination of amylose and protein contents, J Food Sci, 67, 702–7. PERDON A A, SIEBENMORGEN T J, BUESCHER R W and GBUR E E (1999) Starch retrogradation and texture of cooked milled rice during storage, J Food Sci, 84, 828–32.

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(1979) Gel consistency and viscosity of rice, Proceedings of the Workshop on Chemical Aspects of Rice Grain Quality, Los Baños, Laguna, Philippines, IRRI, 293– 302. PEREZ C M and JULIANO B O (1979) Indicators of eating quality for non-waxy rices, Food Chem, 4, 185–95. PEREZ C M, JULIANO B O, BOURNE M C and MORALES A A (1993a) Hardness of cooked milled rice by instrumental and sensory methods, J Texture Studies, 24, 81–94. PEREZ C M, VILLAREAL C P, JULIANO B O and BILIADERIS C G (1993b) Amylopectin-staling of cooked nonwaxy milled rices and starch gels, Cereal Chem, 70, 567–71. ROUSSET S, PONS B and PILANDON C (1995) Sensory texture profile, grain physicochemical characteristics and instrumental measurements of cooked rice, J Texture Studies, 26, 119–35. SASAKI T and MATSUKI J (1998) Effect of wheat starch structure on swelling power, Cereal Chem, 75, 525–9. SESMAT A and MEULLENET J F (2001) Prediction of rice sensory texture attributes from a single compression test, multivariate regression, and a stepwise model optimization method, J Food Sci, 66, 124–31. SONG B H, KIM D Y and KIM S K (1988) Comparison of hydration and cooking rates of brown and milled rices, J Korean Agric Chem Soc, 31, 211–16. SOWBHAGYA C M and BHATTACHARYA K R (2001) Changes in pasting behaviour of rice during ageing, J Cereal Sci, 34, 115–24. STEFF J F and SINGH R P (1980) Diffusivity of starchy endosperm and bran of fresh and rewetted rice, J Food Sci, 45, 356–61. SUZUKI H (1979) Use of the texturometer for measuring the texture of cooked rice, Proceedings of the Workshop on Chemical Aspects of Rice Grain Quality, Los Baños, Laguna, Philippines, IRRI, 327–41. SUZUKI K, KUBOTA K, OMICHI M and HOSAKA H (1976) Kinetic studies on cooking of rice, J Food Sci, 41, 1180–83. SUZUKI K, AKI M, KUBOTA K and HOSAKA H (1977) Studies on the cooking rate equations of rice, J Food Sci, 42, 1545–8. SZCZESNIAK A S (1987) Correlating sensory with instrumental texture measurements – an overview of recent developments, J Texture Studies, 20, 97–110. TAKEUCHI S, FUKOUKA M, GOMI Y, MAEDA M and WATANABE H (1997a) An application of magnetic resonance imaging to the real time measurement of the change of moisture profile in a rice grain during boiling, J Food Eng, 33, 181–92. TAKEUCHI S, MAEDA M, GOMI Y, FUKUOKA and WATANABE H (1997b) The changes of moisture distribution in a rice grain during boiling as observed by NMR imaging, J Food Eng, 33, 281–97. WINDHAM W R, LYON B G, CHAMPAGNE E T, BARTON II F E, WEBB B D, MCCLUNG A M, MODELHAUER K A, LINSCOMBE S and MCKENZIE K S (1997) Prediction of cooked rice texture quality using near-infrared reflectance analysis of whole-grain milled samples, Cereal Chem, 75, 626–32. YOON S H (2002) Physicochemical properties of rice differing in milled degrees, (M S Thesis, Dankook University, Seoul, Korea). ZHANG T Y, BAKSHI A S, GUSTAFSON R J and LUND D B (1984) Finite element analysis of nonlinean water diffusion during rice soaking, J Food Sci, 49, 246–50, 277. ZHOU Z, ROBARDS K, HELLIWELL S and BLANCHARD C (2002) Ageing of stored rice: changes in chemical and physical attributes, J Cereal Sci, 35, 65–78. PEREZ C M

20 Improving the texture of pasta B. A. Marchylo and J. E. Dexter, Canadian Grain Commission and L. J. Malcolmson, Canadian International Grains Institute

20.1

Introduction

Pasta comes in diverse shapes and sizes but, in contrast to Asian noodles, which in some cases may have similar appearance, it is generally prepared by extruding semolina dough through a die. Asian noodles are prepared by passing common wheat dough through sheeting rolls. The texture properties of cooked pasta are the primary factor in overall assessment of pasta quality and play a dominant role in influencing consumer acceptance. The main textural properties which are important in cooked pasta include firmness and elasticity, surface integrity and absence of a sticky surface. These properties can be measured by sensory evaluation or by instrumental methods. Pasta textural properties can be influenced by the raw materials used to prepare the final product. The protein content and protein quality of the main ingredients, i.e. the semolina, farina or flour, are fundamentally associated with cooked pasta textural properties. Consumer preference for pasta textural properties varies around the world, although the Italian tradition of ‘al dente’ eating properties, characterized by high degrees of firmness and elasticity, generally first comes to mind. Although dried pasta continues to be the most prominent product used around the world there is a noticeable trend, especially for consumers in urban centres, towards buying convenience foods containing pasta that are ready to serve and easy to prepare. This trend has implications for pasta texture since the production of many of these foods involves heating and freezing which can affect the firmness and other textural attributes of the cooked product.

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20.1.1 Defining pasta The general category of ‘paste products’ includes Asian noodles and pasta. Some forms of Asian noodles and pasta have similar appearance, but they are distinguished by differences in manufacturing process and raw material preference. Asian noodles are prepared by passing dough through sheeting rolls (Hatcher, 2001). The main raw material is common wheat flour. Asian noodles are diverse, differentiated based on ingredients and processing following sheeting. Asian noodles may or may not contain alkaline salts, and may be marketed fresh, dried, partially cooked, or ‘instant’ (pre-cooked by boiling, steaming or frying) (Miskelly, 1998). The vast majority of pasta products are formed by extruding dough through a die (Marchylo and Dexter, 2001). Pasta comes in diverse shapes and sizes classified into long goods (spaghetti, linguine, vermicelli, etc.) and short goods (elbow macaroni, shells, etc.). Length and shape are determined by die configuration and length of time between extrusion and cutting. Most pasta products are dried following extrusion. Industrially produced fresh pasta is a relatively small market, but is becoming more popular. Forming by sheeting rather than extrusion is more common for fresh pasta. Agnesi (1996) has described the history of pasta and the evolution of pasta manufacturing. She concluded that pasta originated in Sicily in the early Middle Ages and spread north. Initially pasta was sheeted, cut into strips and marketed fresh. Eventually it was discovered that the coastal climate of Italy was ideal for drying, and dried pasta quickly became popular because of its storage stability. Mechanization of pasta manufacturing began during the 18th and 19th centuries with the invention of hydraulic presses and kneaders. Drying cabinets became available in the early 20th century, but pasta manufacturing remained a batch process until the 1930s when continuous extrusion using an extrusion auger was introduced. The modern continuous pasta-manufacturing process has been described in detail by many authors (Antognelli, 1980; Baroni, 1988; Dalbon et al., 1996; Marchylo and Dexter, 2001; Milatovic and Mondelli, 1991), so will be only briefly summarized here. A schematic representation of a continuous long goods pasta line with a conventional press is shown in Fig. 20.1. Water, semolina and optional ingredients are initially mixed in a paddle mixer for lines with conventional presses. Mixing is under vacuum to minimize both oxidation of the yellow pigments that are natural components of semolina, and to prevent formation of air bubbles in the final product. The resulting dough is a loose crumble with lumps of up to 3 cm in diameter. The dough then enters an extrusion worm. A relatively recent innovation is the Polymatik press introduced by Bühler (Marchylo and Dexter, 2001). Rather than using a paddle mixer, the Polymatik mixes and develops dough in 20 seconds by a twin-screw kneader system that feeds into the extrusion chamber. The extrusion worm moves the dough forward, compresses it into a homogeneous mass and forms the desired shape by forcing it through a die. The pasta is then dried under carefully controlled environmental conditions.

1

Press/ spreader

Drying zone no. 2

Stick stacker/Stick magazine

Drying zone no. 1

Water

Stripper/Saw

Drying zone no. 3

Cooling zone

1

Fig. 20.1 A simple schematic representation of a long goods pasta line. Adapted from a diagram kindly provided by Bühler AG, Uzwil Switzerland. Arrows indicate the path taken along the production line. Symbols in dryers represent fans.

Die

Extrusion chamber

Semolina mixer

Improving the texture of pasta 477

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20.1.2 Defining the texture of cooked pasta The texture of cooked pasta is the primary criterion for assessing the overall quality of pasta, and plays a dominant role in influencing consumer acceptance. The term ‘texture’ is somewhat misleading since most food products, including pasta, exhibit more than one textural parameter. Thus, it is preferable to use the term ‘textural properties’ since this term implies more than a single parameter. Szczesniak (1963) classified the textural characteristics of food into mechanical and geometric properties and those related to the fat and moisture content of a food. Mechanical characteristics are those parameters related to the reaction of food to stress. They include five primary parameters (hardness, cohesiveness, viscosity, springiness, adhesiveness) and three secondary parameters (fracturability, chewiness, gumminess), which are composites of primary parameters. Geometrical characteristics are related to the geometrical arrangement of the food matrix and are divided into two classes: those related to particle size and shape and those related to particle shape and orientation. This classification system was developed in order to serve as the basis for sensory and instrumental measurements of food texture. It is the foundation of the texture profile analysis (TPA), a comprehensive approach for measuring the texture properties of a food. Based on the classification system proposed by Szczesniak(1963) cooked pasta can be defined by the mechanical parameters listed in Table 20.1. Overall, the main textural properties important in cooked pasta include firmness and elasticity (the ‘al dente’ property), surface integrity and absence of a sticky surface.

20.2 Measuring the texture of cooked pasta The textural properties of cooked pasta can be measured by sensory evaluation or by instrumental methods. Although there are distinct advantages for each technique, both must be carefully standardized in order to provide meaningful and reproducible results. Furthermore, standardization of the cooking procedure Table 20.1

Mechanical properties of cooked pasta. (Adapted from Szczesniak, 1963)

Hardness/firmness Cohesiveness Elasticity/springiness Adhesiveness Chewiness

The force necessary to attain a given deformation The extent to which a material can be deformed before it ruptures The rate at which a deformed material returns to its nondeformed state after the deforming force is removed The work necessary to overcome the attractive forces between the surface of the food and the surface of other materials which come into contact with the food The energy required to reduce the food to a state ready for swallowing; a product of hardness, cohesiveness and elasticity

Improving the texture of pasta

479

is also required since a number of factors have been found to impact on the final texture of pasta. The most important factors to be considered are cooking time, water to pasta ratio, hardness and pH of cooking water, and time elapsed between draining of cooked pasta and testing (Menger, 1979). Time elapsed after draining has been shown to affect pasta firmness and stickiness (Voisey et al., 1978b, Dexter et al., 1983a) whereas water hardness has been found to impact on surface stickiness (Alary et al., 1979; D’Egidio et al., 1981; Dexter et al., 1983b; Malcolmson and Matsuo, 1993; Menger, 1982). Cooking time and water to pasta ratio influence all textural parameters; generally, for experimental purposes, a water to pasta ratio of 10:1 is used and pasta is cooked to optimum, defined as the cooking time corresponding to the disappearance of the centre core.

20.2.1 Sensory method Sensory evaluation is considered to be the most reliable method for measuring the textural properties of cooked pasta since panellists have the ability to measure the overall textural characteristics of cooked pasta. In contrast, instrumental methods can measure only a limited number of characteristics, which may not necessarily relate to sensory judgements. To be meaningful, instrumental measurements must be validated by calibrating against sound sensory measurements. Despite the advantages associated with sensory methods, sensory evaluation procedures are often criticized as being subjective techniques. Part of the problem lies with the failure to acknowledge that two distinct types of sensory tests exist. Product-orientated tests involve the use of selected and trained panellists under controlled testing conditions to evaluate the quality attributes of a product. These tests are objective since they meet the criteria of objectivity: freedom from personal bias and repeatability. Consumer-orientated tests involve the use of consumer panellists to determine product acceptability or degree of liking. These tests by their very nature are subjective since it is the subjective information (personal likes and dislikes) that is of interest. Thus, if sensory tests are done under controlled testing conditions, utilizing trained panellists and appropriate sensory methodologies, the procedures are objective. Only a few studies have been undertaken that involve a comprehensive sensory assessment of cooked pasta texture. Larmond and Voisey (1973) used a trained panel to assess the firmness, chewiness, gumminess, adhesiveness and individuality of spaghetti strands. Panellists were able to distinguish differences among the spaghetti samples for all parameters. When these results were compared with consumer acceptability tests, it was found that consumers preferred spaghetti that was firm, chewy, and maintained individuality and was not gummy or adhesive. Further analysis suggested that firmness and gumminess were sufficient to predict consumer acceptability. In another study, trained panellists were able to distinguish differences among spaghetti samples in firmness, springiness, adhesiveness, and rate of breakdown

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(Voisey et al., 1978a). Attempts to quantify stickiness have involved both oral (Kovacs et al., 1997; Voisey et al., 1978b) and non-oral (Voisey et al., 1978a) procedures including visual and tactile assessments. The International Standards Organization (ISO, 1985) has developed a standard method (TC 34 SC4 7304) using trained assessors for evaluating the firmness and surface condition (stickiness) of cooked spaghetti. A series of reference photographs are used to estimate surface condition. The general appearance, degree of swelling and stickiness are taken into account for assessment of the overall rating. Scores for firmness and surface condition are assigned using nine point rating scales.

20.2.2 Scoring method D’Egidio and Nardi (1996) described a scoring method used in Italy for assessing cooked pasta quality. Experts (at least three persons) evaluate stickiness, bulkiness and firmness using scales ranging from 1 to 100. Evaluations of stickiness and bulkiness are done both visually and manually, while firmness is evaluated orally. An overall quality score is determined by calculating the mean of each of the three scores and summing. An overall score greater than 80 indicates excellent quality while a score below 40 indicates poor-quality pasta. Menger (1985) developed an extensive scoring system for assessing the quality of raw and cooked pasta. Trained panellists assessed 20 factors, six related to the uncooked product, 17 to the cooked product. The scoring system was weighted such that properties of uncooked pasta accounted for 30% of the final score and cooked quality accounted for 70%. Factors such as: retention of shape, surface characteristics, bite/firmness, odour and taste are judged in the assessment of cooked pasta quality. Malcolmson (1991) used a trained texture profile panel to evaluate firmness, elasticity, chewiness, cohesiveness, tooth pack and stickiness of spaghetti using the definitions given in Table 20.2. Unstructured line scales, 15 cm in length, were used by panellists to record their rating of each attribute. Table 20.2 Sensory definitions used in the evaluation of cooked spaghetti. (Adapted from Malcolmson 1991) Parameter

Definition

Firmness Elasticity

The force required to bite through the sample The degree to which the sample returns to its original state after being compressed slightly The degree to which the sample holds together after chewing The amount of energy required to chew the sample to the state ready for swallowing The degree to which the strands adhere to each other and when lightly touched with a finger The degree to which the sample packs around the teeth during and after chewing

Cohesiveness Chewiness Stickiness Tooth pack


481

20.2.3 Instrumental methods An extensive effort has been made to establish instrumental methods for measuring the textural properties of cooked pasta since sensory techniques can be time-consuming, expensive and may exhibit poor reproducibility if proper procedures are not followed. In addition, the use of sensory panels to evaluate a large number of samples or to evaluate samples when sample size is limited is not feasible. Nevertheless, to verify that instrumental values have meaning in terms of sensory ratings of texture, an association between the sensory and instrumental measurements must be confirmed. Because the perception of texture involves a complex response to a number of physical and physicochemical properties of food, it is extremely difficult to replicate instrumentally the responses provided by a sensory panel. This suggests that it is unrealistic to assume that one instrumental method can measure all of the textural properties of a food. Indeed, Bourne (1982) has stated that there is no instrument available that can match the sophistication, sensitivity, and range of mechanical motions of the mouth or that can promptly change speed and mode of mastication in response to the sensations received during the previous chew. A spaghetti tenderness testing apparatus was developed by Matsuo and Irvine (1969) to simulate a bite test by applying a continuously increasing force to a cutting edge resting on a sample of spaghetti. A tenderness index was derived from the slope of the linear portion of the curve, which was indicative of the time it took the loaded cutting edge to cut through the sample. Results were found to correlate with sensory measurements of firmness (Matsuo and Irvine, 1974). Furthermore measurement of other spaghetti textural parameters of “doughiness”, “chewiness” or “springiness” was facilitated by adapting the testing apparatus to use a blunt-edged blade to simulate compression and then measure recovery (Matsuo and Irvine, 1971). Resulting compressibility–recovery curves provided an indication of these parameters. Subsequently, cooking quality was specified by using a single number, the Cooking Quality Parameter, derived from the ratio of Recovery/ Tenderness Index × Compressibility (Dexter and Matsuo, 1977). A number of texture testing instruments have been developed commercially for evaluating the textural properties of food. Of these, the Instron Universal Tester (Instron Corp., Canton, MA) and its hybrids such as the TA.XT2 texture analyser (Texture Technologies Corp., Scarsdale, NY) have been the most widely adopted. The basic components of these types of instruments include a drive mechanism for deformation and a recording system of force, time and compression rate. Tests can be performed in tension or compression and a wide variety of test cells can be employed enhancing its versatility. Compression tests Walsh (1971) first described a compression test using the Instron. Firmness was expressed as the amount of work in g-cm required to shear a strand of spaghetti. Results were found to correlate with sensory measurements of

482

Texture in food

firmness. Oh et al. (1983) adapted this technique for noodles by shearing three strands with a Plexiglas tooth that was bevelled on both sides of the contact surface. Good correlations were established with sensory measurements of firmness and chewiness. The American Association of Cereal Chemists (2000) adopted a compression method for pasta based on this work that has been widely accepted. Five strands of spaghetti are sheared and firmness is expressed as the energy (work) in g-cm to shear one strand of spaghetti. Since variations exist within and among strands, it is important that more than one strand of spaghetti is measured. For short goods pasta products, a single-blade test is not applicable. A multi-blade shear cell such as the Kramer cell is more appropriate. Spaghetti firmness measured by the Instron Universal Tester (Instron) was compared with the storage modulus and dynamic viscosity obtained by dynamic rheometry (Edwards et al., 1993). A strong correlation was found indicating the sensitivity of dynamic rheometry to changes in pasta firmness. Several workers have developed methods for measuring the compressibility and elasticity of pasta. Dalbon et al. (1985) used the Instron to measure compressibility and recovery (a measure of elasticity) of spaghetti using a flat plunger. The strands were compressed to a fixed load. Compression was defined as the relationship of the diameter of the compressed spaghetti to the original diameter multiplied by 100. Recovery was defined as the relationship of spaghetti diameter after recovery to the diameter of the compressed spaghetti. Oh et al. (1983) described a similar method for measuring the compressibility and recovery of noodles using a plunger with a flat surface. Instron measures were found to correlate with sensory measures of firmness and chewiness. Elasticity and breaking strength of cooked pasta can also be measured using a tension test. However, tension tests present operational difficulties due to the difficulties in gripping the ends of the pasta strands and ensuring that breakage occurs along the extended region of the sample. Maximum stress values obtained by tension tests give an indication of the cooked pasta’s resistance to break and the distance to break values indicate its extensibility (Smewing, 1997). Although Voisey and Larmond (1973) established a correlation between tensile readings and sensory measurements of firmness and chewiness, they found instrumental shear readings obtained from a multi-blade shear compression cell were more closely related to sensory results. This can be attributed to the shearing and compression forces that take place during mastication rather than the application of tensile forces. The viscoelastograph (Chopin/Tripette et Renaud, Villeneuve La Garem, France) has also been used to measure compressibility and recovery of cooked pasta (Ames et al., 1998b, 1999; Autran et al., 1986; Delcour et al., 2000). With this instrument, the sample is compressed between two plates with a constant load applied perpendicularly and then removed. Changes in thickness during and after loading, as well as the deformation and the capacity to


483

return to the initial form, are recorded. Compressibility, consistency and relative recovery are obtained from the curve and an index of viscoelasticity can be calculated from compressibility and relative recovery values (D’Egidio and Nardi, 1996). Based on findings by D’Egidio et al. (1993) the viscoelastograph is considered a suitable technique to predict sensory firmness of pasta, particularly when pasta is dried at high temperatures. An instrumental method for measuring surface stickiness has been difficult to establish due to a number of complicating factors including: presence of water on the cooked surface; change in surface properties as time elapses between draining and measuring; difficulty in restraining the sample for measurement; and selection of the correct compression force and probe retraction speed (Smewing, 1997). Difficulty has also been encountered in correlating proposed instrumental methods with sensory judgements of stickiness. Voisey et al. (1978b) reported the use of a multi-strand test fixture mounted on the Instron to assess the stickiness of cooked spaghetti. Ten strands were mounted on a serrated base plate and were compressed to a fixed compression force with a plunger with a flat surface. After allowing the spaghetti to relax, the plates were pulled apart and the maximum tensile force was used as the index of stickiness. This required a load cell with two outputs to record both the compressive forces required to push the plates together and the tensile forces to pull the plates apart. Stickiness readings were found to be related to non-oral sensory assessments of stickiness. Dalbon et al. (1985) and Wood et al. (2001) measured the stickiness of spaghetti by compressing to a fixed force using a flat plunger. Stickiness measurement was derived from the area under the force–distance curve. Guan and Seib (1994) designed a multi-faced probe, a sample restraining device and a sample holder for use in measuring pasta stickiness. Stickiness was defined as the peak tensile force and total tensile work required to separate the probe from the strand surface. Dexter et al. (1983b) reported a method for measuring stickiness using the Grain Research Laboratory (GRL) Compression Tester. A number of strands were compressed with a flat plunger and, upon lifting the plunger, the force of adhesion of the spaghetti to the plunger was measured. Kim et al. (1989) adapted this technique for use with the Instron but encountered some difficulties with the measurement which they attributed to surface water released from the spaghetti during compression. According to the authors, a low stickiness score could mean either a water-logged (over-cooked) spaghetti with water released during compression or a firm spaghetti that was not sticky. It is possible that the use of a lower compression force may have overcome this problem. Although Malcolmson (1991) found instrumental measurements using the Instron and the GRL tenderness testing apparatus highly correlated with sensory measurements of firmness, elasticity and chewiness, poor correlations were found to exist between instrumental tests and sensory measurements of cohesiveness, tooth pack and stickiness.

484

Texture in food

Several researchers have found that the amount of residue in the cooking water and the amount of rinsed material collected from the surface of pasta are good indicators of cooked pasta texture. The amount of residue in the cooking water denotes the degree of breakdown of the pasta during cooking and is referred to as cooking loss. The residue can be determined by evaporating the cooking water by either heating or freeze-drying or by measuring the absorption of the iodine-amylose complex (Matsuo et al., 1992). The amount of total organic matter (TOM) that can be isolated from the surface of spaghetti strands by exhaustive rinsing has been reported by D’Egidio et al. (1982) to be a reliable means of predicting overall cooking score and stickiness. Dexter et al. (1985) found good correlations between TOM values and instrumental values of firmness, stickiness and resilience of cooked spaghetti. The TOM method was subsequently accepted as a standard method by the International Association for Cereal Science and Technology (ICC, 1992).

20.3 Influence of raw materials The primary ingredients of traditional pasta comprise durum wheat semolina or flour, common wheat farina or flour, or various combinations of these, plus water (Milatovic and Mondelli, 1991). The main ingredient of premium quality pasta is 100% durum wheat semolina. Good quality pasta also can be made with durum wheat flour or a blend of semolina and durum wheat flour (granular), although inclusion of durum wheat flour results in poorer cooking quality, especially an increase in surface stickiness (Dexter et al., 1981a). Pasta can be produced using common (soft or bread) wheat farina or flour, but it is generally inferior in cooking quality since it is less firm and more sticky compared to pasta made from durum wheat (Dexter et al., 1981a, 1983a; Kim et al., 1989). Pasta made from 100% durum wheat semolina maintains texture better when over-cooked than pasta made from common wheat farina (Fig. 20.2). Farina, however, is used quite extensively when the price of durum wheat is high or when the quality of the end product is less important. Textural characteristics associated with high quality pasta include the absence of a sticky surface such that it does not stick together after cooking and ‘al dente’ (literally “to the tooth”) eating properties that are characterized by high degrees of firmness and elasticity (Antognelli, 1980). The production of acceptable quality pasta begins with durum or common wheat of good physical quality. Besides the impact of the milling process, some physical defects can influence cooked pasta texture. For example, sprouting due to damp harvest conditions results in high levels of the starch degrading enzyme α-amylase. Many manufacturers of premium pasta specify very low levels of sprout damage along with high Falling Number values because they believe that starch degradation due to α-amylase will cause greater loss of solids during cooking, increased surface stickiness and softer cooked pasta texture. However, there is no firm scientific evidence that

Cooking quality parameter (s/m × 10–6)


485

25

15

5

12

16

20 24 Cooking time (min)

28

100% HRS 60% durum 100% durum

Fig. 20.2 Change in cooking quality during overcooking of pasta made with hard red spring wheat (HRS), durum wheat and a blend of hard red spring (40%) and durum (60%).

α-amylase has an adverse effect on pasta cooking quality unless sprout damage is very severe (Dexter et al., 1990).

20.3.1 Protein content Protein content and protein quality are fundamentally associated with cooked pasta textural quality and are considered the most important of all the grain components that influence cooking quality (Autran et al., 1986; D’Egidio et al., 1990; Matsuo et al., 1982). Consequently, a minimum hard vitreous kernel (HVK) content is an important physical characteristic because of its relationship to protein content. Generally, as HVK content increases and non-vitreous (commonly referred to as starchy or mealy kernels or yellow berry because of their opaque yellow appearance) kernel content decreases, protein content will increase (Dexter et al., 1988). The primary importance of protein content in determining cooked pasta texture is well documented (Autran et al., 1986; D’Egidio et al., 1990; Dexter and Matsuo, 1977). As protein content increases, cooked pasta becomes firmer and more resilient (Fig. 20.3). High protein pasta is also less sticky, although the relationship to protein content is not as strong (Dexter et al., 1983a). Pasta of high protein content will also remain firm when kept in warm water after cooking before it is served. Because of its fundamental impact on the textural properties of cooked pasta, protein content will continue to be the primary quality factor for pasta production in the future (Marchylo et al., 1998; Marchylo and Dexter, 1996).

486

Texture in food

Cooking score (units)

70

50

30

10

12

14

16

Protein content (%)

Fig. 20.3 Impact of durum wheat protein content on cooking score (cooking quality). Data taken from Canadian Grain Commission harvest survey reports.

Gluten Gluten strength, which is related to protein composition, i.e. protein quality, is also universally acknowledged as an important prerequisite for making good-quality pasta (Ames et al., 1999). It should be recognized, however, that the widespread acceptance of high- and ultra-high temperature (HT and UHT, respectively) drying, has made it possible to produce pasta products with reasonable textural characteristics when using weaker gluten strength semolina. As noted by D’Egidio et al. (1990), higher drying temperatures have decreased the importance of gluten strength for cooking quality. A pasta manufacturer who uses HT or UHT drying systems must be concerned primarily with protein content. The processor knows that, as the protein content of semolina increases, there will be a corresponding increase in product quality. A qualification, however, is that semolina possessing strong gluten characteristics will exhibit less sticky dough with better extrusion properties and superior cooked spaghetti textural characteristics compared to weak gluten semolina of comparable protein content. Cooked pasta textural attributes impacted by gluten quality include bite/elasticity, firmness, hardness, bulkiness, smoothness or mouthfeel during mastication and chewiness (Cole, 1991). Scientific evidence obtained so far indicates that the continuity and strength of the protein network is directly related to the textural characteristics of the cooked spaghetti (Zweifel et al., 2003). These characteristics are influenced by total protein content, since as protein content increases so does the extent of the network. Protein quality or composition is believed to affect the properties of the protein network. Some gluten protein components are more effective than others in forming a good network and influencing the plasticity and elasticity of the resultant dough and the extent of the protein network around starch granules. As discussed by Ames et al. (1998a), initial studies identified the presence of the γ-gliadin band 45 and the absence of γ-gliadin 42 as a


487

marker for pasta cooking quality (Kosmolak et al., 1980). The positive effect of γ-gliadin 45 on gluten strength and cooking quality has been attributed to its genetic linkage with the low-molecular-weight (LMW) glutenin subunits (GS) identified as LMW-2 controlled at the Glu-B3 locus (Payne et al., 1984; Pogna et al., 1988). Studies have classified durum wheat according to several different LMW subunit models (Payne et al., 1984; Ruiz and Carillo, 1995a). These models have been inadequate, however, because they are a mixture of subunits controlled by different alleles, whereas durum wheat quality depends on specific LMW subunits encoded at the Glu-A3, Glu-B3 and Glu-B2 loci (Ruiz and Carrillo, 1995b; Nieto-Taladriz et al., 1997). It also has been reported that the superior gluten strength of LMW-2 types may be at least partially a quantitative effect, as LMW-2 proteins are expressed in greater amounts than LMW-1 proteins (Autran et al., 1987; D’Ovidio et al., 1999). LMW-2 types exhibit a strength range from moderate to very strong, yet the relationship between cooking quality and gluten strength is not clear for LMW-2 varieties with widely differing strength characteristics (Rao et al., 2001; Schlichting et al., 1998). Furthermore, the improvement in cooking quality associated with increasing drying temperature varies among LMW-2 varieties. In particular, the strongest gluten varieties do not show as large an improvement as some weaker varieties (Schlichting et al., 1998). When blended with weak gluten durum wheat, however, very strong gluten LMW-2 durum wheat types will enhance pasta cooking quality more than moderate strength LMW-2 types (Fig. 20.4) (Schlichting et al., unpublished results). Semolina millstreams exhibit a wide range in protein content which, as discussed earlier, impacts directly on pasta texture (Matsuo and Dexter, 1980). In particular, wheat flour produced as a by-product of semolina milling has a much higher protein content than semolina, imparting superior pasta firmness (Houliaropoulis et al., 1981). However, durum wheat flour and low-grade semolina streams are usually excluded from premium pasta products because of their negative impact on pasta colour due to a higher concentration of oxidative enzymes. Egg Other optional ingredients that can have an influence on the textural characteristics of the cooked product may be added to the primary semolinawater dough. One of the more common or traditional of these ingredients is egg. The egg may be added completely frozen and shelled, fresh, powdered or as egg white (Giese, 1992). Addition of the egg albumin helps to maintain a firm texture and decrease stickiness by strengthening the protein network formed by the gluten proteins (Matsuo et al., 1972; Milatovic and Mondelli, 1991). In countries where common wheat or farina is used to make pasta, the addition of egg can significantly improve texture as well as nutritional characteristics and colour. Sanitation and allergen issues have led to the use

488

Texture in food 1200

40 °C

1000

800

Firmness (g-cm)

1200

70 °C

1000

800

1200

90 °C

1000

800

600 0

20

40 60 80 Blend level (%)

Kyle AC Melita AC pathfinder

100

120

AC navigator Plenty

Fig. 20.4 Regression lines (predicted firmness of cooked pasta vs blend level) showing the effect of blending semolina from a weak-gluten (LMW-1) variety Stewart 63 with semolina from five strong-gluten (LMW-2) varieties on cooked spaghetti firmness for spaghetti dried at 40° C, 70° C and 90° C.

of other ingredients to replace eggs (Kobs, 2000). For example, glycerol monostearate complexes with starch and decreases solubilization and migration of amylose out of the starch granule onto the surface of the pasta during cooking (Matsuo et al., 1986). This process decreases stickiness in products such as retortable pasta. It also helps to maintain the texture and mouthfeel of the pasta during many heating cycles (Giese, 1992; Kobs, 2000). Addition of glycerol monostearate alone can result in a decrease in cutting stress but,


489

in combination with the addition of gluten protein and high-temperature drying, it can improve the cooking texture of farina spaghetti (Kim et al., 1989). The addition of L-ascorbic acid has also been reported to have a positive influence on pasta texture by improving the protein network (Milatovic and Mondelli G, 1991). This vitamin is reported to be used widely in many European countries (particularly eastern Europe) where plain white flour is utilized in pasta processing (Anonymous, 2003a). Other additions Other means of improving texture include increasing protein content by the addition of protein from other sources that can form complexes similar to gluten. Research has shown that not all protein improves pasta texture to the same degree. For example, Matsuo et al. (1972) showed that while egg albumin and wheat gluten improved cooked pasta texture, wheat gliadin and high-protein rapeseed meal and soybean flour did not show an improvement. Thus, vital wheat gluten or whey-protein concentrate (Kobs, 2000) can be added to semolina- or farina-based pasta to reduce stickiness and increase firmness. However, addition of soybean flour, which has a role in increasing the body’s antioxidant status (Milo Ohr, 2003), must be made carefully since the soy protein impacts on pasta texture making it less firm and less resilient (Kim et al., 1989; Kobs, 2000). Various gums have been added to canned or frozen pasta products to enhance pasta texture (Teague, 1988) as well as being used to contribute to product fibre content (Andon, 1987). Edwards et al. (1995) showed that the addition of xanthan gums improved pasta firmness. Kobs (2000) reported, however, that while gums improve firmness the bite becomes rubbery. Other sources of dietary fibre obtained by the addition of oat or pea fibre have resulted in a deterioration of the cooked pasta texture (Dougherty et al., 1988; Edwards et al., 1995). Whole-wheat pasta is another source of dietary fibre, but the presence of the bran and germ particles interferes with the continuity of the gluten matrix causing a decrease in firmness (Edwards et al. 1995; Manthey and Schorno, 2002). Traditional coloured pasta made by the addition of tomato or spinach, for example, has been joined by more exotic flavoured pasta that contain herbs and spices such as basil, lemon pepper, garlic, parsley and red pepper (Giese, 1992). Kobs (2000), quoting industry experts, indicates that these ingredients will not influence the texture of cooked pasta as long as 5% or less is present. Finally, it is possible to make unconventional pasta using flours from sources other than wheat including rice, starch, potato, maize, peas, lentils, etc. The protein present in these products is not able to form the gluten network characteristic of pasta made from wheat, and fortification with gluten protein or the use of different processing methods is required to give a reasonable product (Giese, 1992). By and large, the textural properties of these products are poor and they do not exhibit the firm bite characteristic of durum or common wheat pasta.

490

Texture in food

20.4 Influence of processing Durum wheat semolina milling conditions and millstream selection have some effect on pasta texture. Extrusion presses are being developed with greater capacity, increased mixing speed and less mixing time. For these presses, to assure dough homogeneity, semolina granulation is made finer for more uniform and rapid absorption of water (Marchylo and Dexter, 2001). Reducing semolina particle size by grinding increases damage to starch because durum wheat is very hard (Resmini et al., 1996). As damaged starch increases, there is greater loss of solids during cooking as the starch is gelatinized (Matsuo and Dexter, 1980) and a greater tendency to surface stickiness (Grant et al., 1993). The negative effects of finer semolina on pasta texture are negated to some extent when pasta is dried at high temperature or ultra-high temperature (Dexter and Marchylo, 2001). 20.4.1 Extrusion The two key stages in the pasta process determining the cooking quality of pasta are extrusion and drying. Extrusion conditions determine the physical properties and internal structure of pasta dough. Over-heated, over-worked dough produces poor quality, sticky, slimy cooked pasta (DeFrancisci, 2003). Correct filling of the extrusion auger is essential to achieve maximum output of extruded product without excessive heating of dough (Dalbon et al., 1996). Mechanical energy is dissipated as heat. Heat, pressure and shear during extrusion make the gluten network within the dough continuous, and the dough becomes plastic and translucent (Matsuo et al., 1978). The continuity of the gluten protein network and how well it is preserved during cooking are primary determinates of pasta texture (Dexter et al., 1978; Donnelly, 1982; Zweifel et al., 2003). Accordingly, extrusion cylinders are water-jacketed with cooling water to control dough temperature and protect gluten protein functionality. If dough temperature in the extruder exceeds 50 ºC, gluten protein denaturation will occur, and the physical properties of pasta dough and texture of cooked pasta will be adversely affected (Abecassis et al., 1994). Extrusion pressure also affects pasta cooking quality. It must be sufficient to create a sufficiently compact structure to stand up to cooking (Ingelbrecht et al., 2001; Pagani et al., 1989). Excessive pressure causes shearing and tearing of the dough inside the extruder which can cause damage to the structural organization of the protein and to pasta cooking quality (Dalbon et al., 1996). Dalbon et al. (1996) recommended an extrusion pressure of 9– 12.5 MPa. Debbouz and Doetkott (1996) reported that pasta firmness is best at water absorption near 31%, barrel temperature between 35 and 45 °C, and screw speed around 25 RPM. The type and condition of the die will affect extrusion pressure, the dimensions of the pasta and the condition of the surface (Maldari and Maldari, 1993). Dies are made from bronze, and usually have a Teflon insert. The advantages of Teflon coating are longer die lifetime, smoother pasta surface,


491

better appearance and superior cooking quality (Donnelly, 1982). Presses with Teflon dies are more efficient than those with bronze dies. Teflon coating allows optimal filling of the extrusion worm resulting in higher pressure, higher extrusion speed and higher output per hour (Dalbon et al., 1996). Donnelly (1982) showed by scanning electron microscopy that the superior cooking quality of pasta made from Teflon dies is attributable to a more continuous protein network surrounding starch granules within the pasta, and also covering the pasta surface, compared to pasta extruded through bronze dies.

20.4.2 Drying Drying conditions profoundly influence pasta cooking quality. Temperature, humidity and airflow must be carefully controlled. If pasta dries too quickly the surface will harden and the pasta may fracture due to stress as moisture trapped within attempts to migrate to the surface. This fracturing, known as ‘checking’, results in weak structure and inferior cooking quality (Feillet and Dexter, 1996). There have been enormous advances in drying technology, beginning with the introduction of (HT) drying (> 60 ºC) in the 1970s (Pollini, 1996). The original attraction of HT drying was better control of bacteria and shorter drying time allowing more compact lines for a given capacity. It soon became apparent that HT drying also improved cooked pasta texture (Dexter et al., 1981b, 1983a, 1984; Grant et al., 1993; Malcolmson et al., 1993; Novaro et al., 1993). The benefits of HT drying to pasta texture include lower loss of solids to cooking water, less surface stickiness, better firmness and better tolerance to over-cooking (Table 20.3). It is believed that the benefit of HT drying is due to the combined effects of protein and starch modification. HT drying increases the extent of protein denaturation as evident from reduced solubility in acetic acid (Atkin and Khan, 1992; Dexter et al., 1981b). In contrast to the extrusion stage, protein denaturation is beneficial during drying because it strengthens a network that has already been established. During the initial drying steps, starch granules become less extractable, consistent with increased physical inclusion of starch or interaction between starch and gluten components (Vansteelandt and Delcour, 1998). Thermal stabilization of the protein network allows it to maintain its integrity better during cooking, resulting in greater resistance to structural breakdown during cooking (Zweifel et al., 2003). Perhaps of greater importance, HT drying modifies starch-pasting properties. Dalbon et al. (1985) showed that reconstituted pasta prepared from gluten and from starch that had been extracted from HT dried spaghetti exhibited superior cooking quality compared to reconstituted pasta containing starch extracted from low temperature (LT) dried spaghetti. Dexter et al. (1985) observed that the concentration of amylose on the surface of cooked spaghetti, a cause of surface stickiness, is reduced by HT drying. These observations

492

Texture in food

Table 20.3 Effect of pasta drying temperature on texture of cooked pasta. (Data taken from Dexter et al., 1984) Drying temperature

Cooking loss (%)

Stickiness (N/m2)

Cooking score (sec/m × 10–6)

6.9 5.9 7.1 6.6

630 560 510 440

11.5 17.3 20.0 23.6

10.1 8.6 8.7 8.4

855 570 530 455

8.3 11.7 13.8 14.4

Cooked to optimum time (12 min) 39 70 80 85

°C °C °C °C

Over-cooked 10 min (22 min) 39 70 80 85

°C °C °C °C

can be explained by heat moisture treatment (annealing at low moisture content) of starch during HT drying (Cunin et al., 1995; Vansteelandt and Delcour, 1998; Yue et al., 1999; Zweifel et al., 2000). Changes in starch during HT drying result in higher gelatinization temperature, increased paste viscosity, less starch granule swelling and less amylose exudation. Improvement in pasta texture by HT drying is enhanced by increasing drying temperature from 80 ºC to 100 ºC, and by shifting the HT phase to a later stage of drying when pasta moisture is lower (Zweifel et al., 2003).

20.4.3 Regrinds Another processing factor influencing pasta texture is ‘regrinds’. Regrinds are dried pasta that is ground to fine particle size and recycled to the press. Regrinds comprise waste produced by the cutting of long goods, and product that cannot be marketed due to checking or cracking. Donnelly (1980) estimated that about 5–10% of production ends up as regrinds, and reported that levels above 15–20% are not advisable in spaghetti dried by LT because of deleterious effect on cooking loss. Fang and Khan (1996) reported that regrinds produced from spaghetti dried at HT or UHT are more deleterious to pasta firmness and cooking loss than regrinds from spaghetti dried at LT.

20.5 Trends in consumer preference When considering consumer preference for pasta textural characteristics the Italian tradition of ‘al dente’ eating properties, characterized by high degrees of firmness and elasticity, generally first comes to mind. However, it should


493

be realized that this consumer preference is not universal and this must be taken into account when evaluating trends. For example, in countries such as Brazil and Colombia, most but not all consumers prefer pasta with softer textural attributes. This preference can be traced to the practice of producing pasta using common wheat farina or patent flour in lieu of durum wheat semolina (Kim et al., 1989). As noted earlier, common wheat pasta typically has softer textural attributes so in these countries consumers have become accustomed to soft pasta. Pasta cooked ‘al dente’ is thought to be under-cooked by many consumers and is not acceptable. In Japan, on the other hand, consumers are more familiar with the textural attributes of oriental style udon noodles, which are also prepared using common wheat. These noodles also would be characterized as having softer or less firm textural attributes. Not surprisingly, many Japanese consumers look for similar textual properties in cooked pasta. In all these countries, however, the Italian cooking tradition is making inroads. In Brazil importation of Italian pasta, where it is served in restaurants, has started to promote the Italian ‘al dente’ tradition. There also is a move by the national pasta industries to promote consumer buying of durum pasta produced nationally. In Japan, demographics also seems to be playing a role in consumer textural preference, with younger Japanese indicating a preference for the firmer textural properties of pasta cooked in the Italian tradition, while older generation consumers are content with softer pasta. Although the dried pasta sector continues to dominate sales (Anonymous, 2000; Harrison, 1999), consumers in urban centres, especially in the industrialized world, are also showing a trend towards buying convenience foods that are ready to serve and easy to prepare. For example, in the USA, it is reported that ready-to-eat and frozen main dishes will surpass homemade as the most often served main dinner item (Sloan, 2003). This trend has implications related to pasta texture since the production of frozen pasta involves heating and freezing which can affect the firmness and other textural attributes of the cooked product. An appropriate choice of shape and thickness of the cut, along with the addition of optional ingredients as noted above, can help in maintaining acceptable textural properties in the frozen pasta (Kobs, 2000). In Japan, production of cooked frozen pasta is of increasing importance because of consumer purchasing of meals in convenience stores. This trend may also influence durum wheat quality requirements, particularly in relation to gluten strength, since final cooked pasta must exhibit appropriate textural characteristics even though it has gone through a freezing and subsequent cooking process. Stronger gluten durum wheat should produce pasta that will better maintain its textural characteristics through the freezing-cooking cycle. Similar issues are encountered with microwavable and retortable pasta, which undergo a variety of heating processes before serving. Additives are used with such products to maintain good textural properties (Giese, 1992). Another trend in the pasta market is the increasing health consciousness of consumers. Manufacturers have been taking advantage of this by promoting

494

Texture in food

chilled fresh pasta as representing a healthier and convenient meal source (Anonymous, 2000). Textural quality of these products remains important but can be influenced by the type of treatment used to prevent bacterial growth and maintain shelf life (Anonymous, 2003a,b). The trend to healthy diet has also led many consumers to look for new sources of dietary fibre. Soluble dietary fibre and β-glucan are reported to lower serum cholesterol levels and reduce the risk of cardiovascular diseases (Betschart, 1988; Bhatty 1993; Newman and Newman, 1991; Yokoyama et al., 1997). Research has shown that pasta can be prepared using mixtures of pearled barley fractions (Marconi et al., 2000), barley flour enriched with β-glucan (Knuckles et al., 1997) and naked oat flour (Sgrulletta et al., 2001). Barley has neutral flavour and texture properties (Pszczola, 2003) and consequently pasta with acceptable sensory properties can be produced. Textural properties are negatively influenced by the addition of barley to the semolina but the addition of egg (Knuckles et al., 1997) or the use of HT drying with the addition of vital wheat gluten improves overall cooking quality (Marconi et al., 2000). Addition of naked oat flour at 50% and 30% levels also produces pasta with acceptable organoleptic characteristics (Sgrulletta et al., 2001). Although the impact of oat flour on pasta textural properties was not presented by Sgrulletta et al. (2001) it was noted that cooking time increased, which suggests that the texture of the cooked product is affected. Potential exists for the manipulation of the textural properties of pasta by changing the durum wheat starch characteristics. Durum wheat comprises about 70% starch, which suggests that changes in this major semolina component, particularly its amylose to amylopectin ratio, could impact on cooking quality. Reconstitution studies using starch from non-durum sources (Dexter and Matsuo, 1979) have shown that cooked pasta firmness and resilience are influenced by starch amylose content. Further studies using partially waxy durum lines, which exhibited amylose contents of about 21– 22% as compared to normal lines of 26–27%, however, have not shown changes in textural properties along with the decrease in amylose content (Sharma et al., 2002). It may be possible that a more substantial decrease in amylose content is needed before textural properties of cooked pasta will be affected, which has been shown for Asian salted noodles (Guo et al., 2003).

20.6

References

ABECASSIS J, ABBOU R, CHAURAND M, MOREL M-H

and VERNOUX P (1994) Influence of extrusion conditions on extrusion speed, temperature, and pressure in the extruder on pasta quality, Cereal Chem, 71(3), 247–53. AGNESI E (1996) The history of pasta. In Pasta and Noodle Technology. Eds J E Kruger, R R Matsuo and J W Dick, St Paul, MN, AACC, 1–12. ALARY R, ABECASSIS J, KROBREHEL K, and FEILLET P (1979) Influence de l’eau de cuisson, et notamment de son pH, sur les characteristiques des pâtes alimentarires cuites, Bull ENSMIC, 293, 255–62.


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and MALDARI C (1993) Design and performance of pasta dies, Cereal Foods World, 38(11), 807–10. MANTHEY F A and SCHORNO A L (2002) Physical and cooking quality of spaghetti made from whole wheat durum, Cereal Chem, 79(4), 504–10. st MARCHYLO B A and DEXTER J E (1996) Durum wheat now and into 21 century. In C W Wrigley, Conference Proceedings of the 46th Australian Cereal Chemistry Conference– Cereals’96, Sydney, Australia, September, 1996, Royal Australian Chemical Institute, Australia, 345–52. MARCHYLO B A and DEXTER J E (2001) Pasta production. In Cereal Processing. Ed. G Owens, Cambridge, Woodhead, 109–30. MARCHYLO B A, DEXTER J E, CLARKE J M and AMES N (1998) Effects of protein content on CWAD quality. In Wheat Protein – Production and Marketing. Eds D B Fowler, W E Geddes, A M Johnston and K Preston, University Extension Press, University of Saskatchewan, Saskatoon, SK, 53–62. MARCONI E, GRAZIANO M and CUBADDA R (2000) Composition and utilisation of barley pearling by-products for making functional pastas rich in dietary fibre and β-glucans, Cereal Chem, 77(2), 133–9. MATSUO R R and DEXTER J E (1980) Comparison of experimentally milled durum wheat semolina to semolina produced by some Canadian commercial mills, Cereal Chem, 57(2), 117–22. MATSUO R R and IRVINE G N (1969) Spaghetti tenderness testing apparatus, Cereal Chem, 46(1), 1–6. MATSUO R R and IRVINE G N (1971) Note on an improved apparatus for testing spaghetti tenderness, Cereal Chem, 48(5), 554–8. MATSUO R R and IRVINE G N (1974) Relationship between the GRL spaghetti tenderness tester and sensory testing of cooked spaghetti, Can Inst Food Sci Technol J, 7(2), 155– 6. MATSUO R R, BRADLEY J W and IRVINE G N (1972) Effect of protein content on the cooking quality of spaghetti, Cereal Chem, 49(6), 707–11. MATSUO R R, DEXTER J E and DRONZEK B L (1978) Scanning electron microscopy study of spaghetti processing, Cereal Chem, 55(5), 744–53. MATSUO R R, DEXTER J E, KOSMOLAK F G and LEISLE D (1982) Statistical evaluation of tests for assessing spaghetti-making quality of durum wheat, Cereal Chem, 59(3), 222–8. MATSUO R R, DEXTER J E, BOUDREAU A and DAUN J K (1986) The role of lipids in determining spaghetti cooking quality of durum wheat, Cereal Chem, 63(6), 484–9. MATSUO R R, MALCOLMSON L J, EDWARDS N M and DEXTER J E (1992) A colorimetric method for estimating spaghetti cooking loss, Cereal Chem, 69(1), 27–9. MENGER A (1979) Crucial points of view concerning the execution of pasta cooking tests and their evaluation, Comp, Rendus Symposium Inter sur les matieres premieres et pâtes alimentaires (Rome), 53–60. MENGER A (1982) Influenza dell’acqua di cottura sulle paste alimentari di diversa qualità, Tech Molitoria, 33(1), 23–32. MENGER A (1985) Entwicklung eines 5-punkte-prueschemas zur sensorischen Beurteilung von Teigwaren, Getreide Mehl Brot, 39(2), 61. MILATOVIC L and MONDELLI G (1991) Pasta Technology Today, Pinerolo, Italy, Chirotti. MILO OHR L (2003) More for the sport, Food Tech, 57(2), 63–8. MISKELLY D M (1998) Modern noodle-based foods – raw material needs. In Eds A B Blakeney and L O’Brien, Pacific People and Their Food, St Paul, MN, AACC, 123– 42. NEWMAN R K and NEWMAN C W (1991) Barley as a food grain, Cereal Foods World, 36(9), 800–805. NIETO-TALADRIZ M T, RUIZ M, MARTINEZ M C, VAZQUEZ J F and CARRILLO J M (1997) Variation and classification of B-low molecular weight glutenin subunit alleles in durum wheat, Theor Appd Genetics, 95(7), 1155–60. MALDARI D

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and NARDI S (1993) Combined effect of protein content and high-temperature drying systems on pasta quality, Cereal Chem, 70(6), 716–19. OH N H, SEIB P A, DEYOE C W and WARD A B (1983) Noodles I. Measuring the textural characteristics of cooked noodles, Cereal Chem, 60(6), 433–8. PAGANI M A, RESMINI P and DALBON G (1989) Influence of extrusion process on characteristics and structure of pasta, Food Microstructure, 8(7), 173–82. PAYNE P I, JACKSON E A and HOLT L M (1984) The association between gamma gliadin 45 and gluten strength in durum wheat varieties: a direct causal effect or the result of genetic linkage?, J Cereal Sci, 2(2), 73–81. POGNA N, LAFIANDRA D, FEILLET P and AUTRAN J-C (1988) Evidence for a direct causal effect of low molecular weight subunits of glutenins on gluten viscoelasticity in durum wheats, J Cereal Sci, 7(3), 211–14. POLLINI C M (1996) THT technology in the modern industrial pasta drying process. In J E Kruger, R R Matsuo and J W Dick, Pasta and Noodle Technology, St Paul, MN, AACC, 59–74. PSZCZOLA D E (2003) New ingredient developments are going with the grain, Food Technol, 57(2), 46–61. RAO V K, MULVANEY S J, DEXTER J E, EDWARDS N M and PERESSINI D (2001) Stress-relaxation properties of mixograph semolina-water doughs from durum wheat cultivars of varying strength in relation to mixing characteristics and bread-making and pasta-making performance, J Cereal Sci, 34(2), 215–32. RESMINI P, PAGANI M A and PELLIGRINO L (1996) Effect of semolina quality and processing conditions on nonenzymatic browning in dried pasta, Food Australia, 48(8), 362–7. RUIZ M and CARRILLO J M (1995a) Relationship between different prolamin proteins and some quality properties in durum wheat, Plant Breeding, 114(1), 40–44. RUIZ M and CARRILLO J M (1995b) Separate effects on gluten strength of Gli-1 and Glu-3 prolamin genes on chromosomes 1 A and I B in durum wheat, J Cereal Sci, 21(2), 137–44. SCHLICHTING L M, MARCHYLO B A, DEXTER J E and AARTS W (1998) Inter-relationships among gluten strength, drying temperature and spaghetti colour, abstract 122, Cereal Foods World, 43(7), 526–7. SCHLICHTING L M, MARCHYLO B A, DEXTER J E and CLARKE J M (unpublished results) Effect of blending extra strong gluten durum varieties on cooking quality of spaghetti dried at different temperatures, presented at AACC annual meeting Seattle, WA, Nov 3, 1999. SGRULLETTA D, DESTEFANIS E and POLLINI C M (2001) Durum wheat and oat: a good association for innovative alimentary pasta at high nutritional value, Italian Food and Beverage Technology, 23(March), 31–3. SHARMA R, SISSONS M J, RATHJEN A J and JENNER C F (2002) The null-4A allele at the waxy locus in durum wheat affects pasta cooking quality, J Cereal Sci, 35(3), 287–97. SLOAN A E (2003) Top 10 trends to watch and work on: 2003, Food Tech, 57(4), 30–50. SMEWING J (1997) Analyzing the texture of pasta for quality control, Cereal Foods World, 42(1), 8–12. SZCZESNIAK A S (1963) Classification of textural characteristics, J Food Sci, 28(4), 385–9. TEAGUE G D (1988) Xanthan gum and alginates in fresh and dried pasta for texture improvement, abstract 11, Cereal Foods World, 33(8), 662. VANSTEELANDT J and DELCOUR J A (1998) Physical behavior of durum wheat starch (Triticum durum) during industrial pasta processing, J Agric Food Chem, 46(7), 2499–503. VOISEY P W and LARMOND E (1973) Exploratory evaluation of instrumental techniques for measuring some textural characteristics of cooked spaghetti, Cereal Science Today, 18(3), 127–33, 142–3. VOISEY P W, WASIK R J and LOUGHHEED T C (1978a) Measuring the texture of cooked spaghetti, 2. Exploratory work on instrumental assessment of stickiness and its relationship to microstructure, Can Inst Food Sci Technol J, 11(4), 180–88.

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and WASIK R J (1978b) Measuring the texture of cooked spaghetti, 1. Sensory and instrumental evaluation of firmness, Can Inst Food Sci Technol J, 11(3), 142–48. WALSH D E (1971) Measuring spaghetti firmness, Cereal Science Today, 16(7), 202–5. WOOD J A, BATEY I L, HARE R A and SISSONS M J (2001) A comparison of Australian and imported spaghetti, Food Australia, 53(8), 349–54. YOKOYAMA W H, HUDSON C A, KNUCKLES B E, CHIU M-C M, SAYRE R N, TURNLUND J R and SCHNEEMAN B O (1997) Effect of barley β-glucan in durum wheat pasta on human glycemic response, Cereal Chem, 74(3), 293–6. YUE P, RAYAS-DUARTE P and ELIAS E (1999) Effect of drying temperature on physicochemical properties of starch isolated from pasta, Cereal Chem, 76(4), 541–7. ZWEIFEL C, CONDE-PETIT B and ESCHER F (2000) Thermal modifications of starch during high temperature drying, Cereal Chem, 77(5), 645–51. ZWEIFEL C, HANDSCHIN S, ESCHER F and CONDE-PETIT B (2003) Influence of high temperature drying on structural and textural properties of durum wheat pasta, Cereal Chem, 80(2), 159–67.

21 Improving the texture of fried food C-J. Shieh and C-Y. Chang, Da-Yeh University and C-S. Chen, Chao-Yang University of Technology, Taiwan

21.1

Introduction

Texture is, in addition to flavor, one of the most important attributes of fried food and is always an issue in the manufacturing of fried food products. In many cases a crunchy fried product indicates freshness, while lack of crunchiness often implies prolonged storage. For fried foods like potato chips, this is generally true. For fried chicken, however, a juicy interior may be a higher priority, and a crispy exterior crust would be a plus. In either case, texture, whether interior or exterior, is key to consumer acceptance upon the first bite. Before a systematic approach to improving the texture of fried food can be implemented, it is important to be able to define, or quantify, texture and screen the factors that determine the texture of the final product. This chapter discusses common methods adopted in the measurement of texture (Section 21.2), factors that affect the texture of the product (Section 21.3). Based on these, a systematic approach, the Response Surface Methodology (RSM), to improving texture will be described (Section 21.4). This is followed by a case study that illustrates the application of RSM (Section 21.5).

21.2 Measuring texture Texture determines the rheological property (the relationship between applied physical stress, forces applied per unit area, and deformation of materials), or the mechanical property of solid foods which, in turn, determines how people feel during mastication. Although mouthfeel is a complex function of

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many variables, for analytical purposes, it is possible to simplify the complex physical interactions which take place during mastication to three basic types of physical forces: compressive, tensile and shear. Compressive force refers to the force acting on the solid food specimen in the direction pointing to the center. Compression testing equipment records the resisting force of the solid specimen to deformation (or strain, commonly expressed as the relative distance traveled by the compression probe, or a plunger, in the direction of the compression), and the deformation during the process of compression. The design of the testing equipment usually involves a mobile probe driven by a motor moving at a preset speed towards the food specimen sitting on a stationary platform. An alternative arrangement uses a stationary sample probe and a mobile platform. Take a piece of battered fried fish fillet as an example. Frequently, it is desirable to have the fish fillet wrapped in a crispy crust, of a certain thickness, formed from batter during frying. Looking at the cross-sectional area of a piece of the crust, one can see that it consists of many small empty chambers separated by thin walls. When this piece of crust is subject to compression force, the resistance to deformation initially increases linearly with distance. On a stress–strain (resistance–deformation) curve, this initial slope is referred to as the ‘elastic modulus’ (Szczesniak, 1983) or the ‘modulus of deformability’ (Mohsenin and Mittal, 1977). This modulus, measured before any degree of structural failure has taken place, is related to the firmness of the sample. As this piece of crust is further compressed, the rate of increase of resistance soon becomes non-linear up to a point at which some structural failure begins to take place (some chambers collapse). This point of initial structural failure is called the ‘bioyield point’. During this stage of compression, the structural failure is local and the resistance may either decline or remain virtually constant. The rupture of these chambers is normally accompanied by the pleasing sound of crunchiness during mastication. Some researchers have studied this acoustical effect of crisp foods and related the sound to the sensation of crunchiness (Christenson and Vickers, 1981; Drake and Halldin, 1974; Mohamed et al., 1982). When local structural failure propagates and reaches a point of massive failure, the resistance force will decline rapidly. The whole stress–strain curve provides quantitative information about the food specimen. Tensile force is the force that pulls the specimen apart, and it is an important tool in evaluating the resistance of a material to elongation. Voisey and deMan (1976) pointed out that during mastication, the wedging action of teeth imposes tensile stress on foods. Tensile measurement is important in areas such as quantifying the strengths of fiber, dough, membrane and muscle. The measurement has been used by a number of researchers to study the mechanical properties of raw and cooked muscle fibers (Bouton and Harris, 1972; Bouton et al., 1975; Stanley, 1976). Tensile measurement provides more rheological information about elastic foods and has limited applications

Improving the texture of fried food

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in crisp fried foods. However, in cases like pre-fried instant noodles, the tensile strength of the noodle after rehydration is an important factor in its chewy attributes and is an index of quality that should not be overlooked. Shear force refers to the force acting in the direction parallel to the plane of action. Early shear testing equipment was designed to measure the mechanical properties of muscle and, with some improvement in sensitivity, is still widely used in meat research. The shear measurement on a Warner-Bratzler shear apparatus is carried out by applying force across muscle fibers via a blunt edge. Although shear is not the only type of force that could cause the resulting deformation, it is the major contributor. Voisey and Larmond (1974) pointed out that, besides shear, compressive and tensile stresses are also involved in causing the deformation in a shear measurement. Other researchers (Szczesniak, 1983; Peleg, 1987) have reached a similar conclusion, i.e. it is almost impossible to study food texture using shear forces alone. In light of the fact that the shear-deformation pattern varies among different foods, as well as foods that are only slightly different in terms of texture, it is widely accepted as a means of quantitative study of food texture. Szczesniak and coworkers (1970) used a Kramer shear press to study the textures of a variety of foods and published their corresponding characteristic shear-deformation patterns. With the aid of modern microcomputers, rheometers have become more versatile and accurate. Many manufacturers offer inexpensive multi-functional rheometers for various types of stress–strain study. By combining the following four elements – a probe, a driving system, a sensing unit and a readout system – a rheometer can provide valuable information about food texture. Typical types of probe are: flat plunger, plate, piercing rod, penetrating cone, cutting blade, shearing jaws and cutting wires (Szczesniak, 1983). The driving mechanism usually involves an electronically- or computer-controlled motor that can run at a pre-set speed and drive the probe (or the sample platform) to a designated distance with high accuracy. A weight/pulley arrangement or hydraulic system can also be used as the driving mechanism. A sensing unit is able to quantify any resistant force it finds by using a simple spring, strain gauge, loadcell or force transducer. The signal from the sensing unit is usually analog (voltage or current), and it can be read or processed by a recorder that draws the stress–strain curve. Currently, it is common practice to transform an analog signal into a digital signal (AD) through an interface card plugged into a personal computer. Digital data can be stored for later analysis. In more advanced models, for improved efficiency and to minimize human error, operation of the machine can be automated through an AD/DA interface card controlled by a program residing in the computer. Software commands, in the form of digital signals, issued by the program are transformed, by the DA (digital to analog) part of the interface card, into analog signals that control the testing machine.

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Texture in food

21.3 Factors influencing texture More than five factors which determine the texture of the fried product can be easily identified. Before frying, characteristics of the food, such as water content, size, types and amount of protein and starch, composition of coating materials (batter, breadcrumbs, etc.) and additives, are important factors which establish the necessary chemical and physical environment which leads to the desired texture after frying. Processing variables, such as frying time and temperature, cooling condition, moisture control, etc., can be used to fine-tune the texture of products. This section briefly discusses the influence of these variables and picks out a few which might form a suitable basis for a systematic approach to improving texture using techniques such as RSM. 21.3.1 Water Water content is important in almost all kinds of foods, with only a very few exceptions. Frying differs from other cooking methods in that it uses temperatures higher than the normal boiling point of water under atmospheric pressure. During frying, water evaporates at a rate higher than with boiling at 100 °C. As a result, the temperature of the interior portion rises faster than normal boiling. Furthermore, in cases such as battered or breaded frying, high temperature at the food/oil interface leads to the development of a barrier that reduces water loss from the interior portion and keeps the inside tender. The oil temperature can be as high as 160–180 °C during water evaporation, leading to a high heat transfer rate (due to the large temperature difference of 60–80 °C), but some of the heat transferred is carried off as latent heat of vaporization. This physical phenomenon of latent heat removal of energy keeps the temperature at the oil/food interface, during the early stage of frying, virtually constant at normal boiling point (Blumenthal, 1991), thus preventing the food surface from premature charring or burning (in some cases, slight charring or burning is welcomed), and this is the beauty of frying. As the water content decreases, the rate of energy removal in the form of latent heat of vaporization starts to decline and this leads to an increase in the temperature at the interface, and some degree of browning which is desirable during this stage. Water is also one of the factors responsible for forming porous, crunchy networks in many fried foods. Battered frying is a good example to explain the mechanism involved in forming such a texture. Starting from regions close to the interface, high-rate water evaporation begins upon contact with high-temperature frying oil. A barrier or crust-like layer soon develops and gradually becomes tough. As long as the interior water that becomes vapor and rushes to the interface keeps supplying this barrier layer, the temperature of the frying object at the interface will not exceed normal boiling point. The viscosity of the pasty batter, together with the barrier layer, leads to resistance to bubbles formed from the rapid increase in the molecular volume of water.


505

When the water vapor does not have a clear passage to the interface, pressure builds up and the interior temperature may be higher than normal boiling point which results in faster cooking. Another effect of the resistance to vapor release is the volume expansion of the fried object, leaving small chambers produced by rapid boiling of interior water. The size of the chambers is determined by the rate of dehydration, and the relative ease of migration of water through the surface matrix, which is a function of the composition of the battering material. The former is a function of frying temperature and the latter depends on the strength of the walls (or membranes) separating the chambers which, in turn, is set by the physical properties (density, viscosity, surface tension, etc.) of the battering paste as decided by its chemical composition. While the quest for a crispy exterior crust is of great concern in the manufacture of fried foods, a juicy interior, in the case of foods like fried fish or chicken, is just as important. The initial water content of the raw material, the water-holding capacity, and the remaining water after frying are all of great importance in controlling both the interior and the exterior texture. Too little interior water results in loss of tenderness and too much exterior water results in loss of crispness. Water, as well as the rate of water evaporation, is the key to fine-tuning texture.

21.3.2 Size The size of the frying object determines the time required for the temperature at the center to reach the desired level. If the size is too large, the surface will be charred or burned while the interior portion will still be undercooked. For instance, for French fries, proper time and temperature are required for the interior starch gelatinization to proceed to the right degree and texture. If the potato pieces are too large, there may not be enough time for the temperature at the center to reach the right level for starch gelatinization to proceed before charring and burning at the surface takes place. On the other hand, if the size is too small, the time for developing an acceptable crisp skin will be slightly shorter than normal, due to less water supply, the interior temperature will rise much faster, water may start to be lost and this will result in unacceptable interior texture. The balance between size and energy supply is therefore important.

21.3.3 Protein and starch Protein and starch are the major constituents in foods that are responsible for forming the various characteristic types of texture. At the elevated temperature reached during frying, reactions of both a physical (phase change, volume expansion, solute concentration, etc.) and a chemical (destruction and formation of chemical bonds) nature are taking place. Protein denaturation and starch gelatinization are typical examples of the combined effect, physical as well

506

Texture in food

as multiple-order chemical, reactions. These reactions may involve breaking of hydrogen bonds, formation (or breaking) of covalent bonds between amino acids, breaking of glycosidic bonds, rearrangements of three-dimensional structure, hydration and dehydration, and fragmentation of large molecules. The resulting protein-starch network is important in determining the rheological properties either of the exterior coating or the interior food body. Protein and starch content can frequently be manipulated to affect water-holding capacity, and consequently influence the texture. Kadan and coworkers (1997) reported that the protein/starch ratio is an important factor which can be adjusted in order to optimize the texture of both the interior food body and the crust. A variety of proteins have been used in formulating modern batter recipes: cheese powder, egg albumen, whey protein, gluten, soy protein. Some of them are prehydrolysed in order to serve different purposes. Protein is a versatile compound that can function as an emulsifying agent, a film forming agent, a structural material, and many others (Cheftel et al., 1985). Soy protein is sometimes added to meat products to improve water-holding capacity, flavour, and cohesiveness (Brewer et al., 1992; Kotula, 1976). Fibrous muscle protein tends to lose water and form aggregates that are tough and dry after prolonged cooking or frying. Mechanical fine cutting of meat to manufacture meatball (fish, pork or beef) is a good example of protein manipulation to improve water-holding capacity. After fine cutting, fibrous muscle protein molecules are smaller and less organized and thus less likely to form aggregates during cooking or frying and more capable of retaining water. Starch is the major component in many commercial premixed battering powders and is responsible for the body of the crust of battered fried products. Starch gelatinization is crucial in frying, since it enables water-retention and provides volume expansion. Carbohydrates, serving various functions and in different forms, are also used in many new formulations of batters: gums, pregelatinized starch, modified starch, high amylose starch (starch with less branched structure) and dietary fibers. Kadan and co-workers (1997) studied the effect of amylose and protein on the texture of rice-based fries. They reported that in high protein content rice-based fries, protein molecules tended to form a barrier around starch granules and this retarded water uptake during starch gelatinization. Consequently, the water-holding capacity was reduced, which led to loss of more water during extrusion at 90 °C, and the resulting texture was hard and tough. Their experience demonstrated that the types, states and interactions and the ratio of the two macromolecules (protein and starch, originally present or added) play important roles in setting the textures of fried foods. In addition to structural contributions, chemical reactions between proteins and carbohydrates, through browning reactions, are also known to develop special flavour and pigments in fried foods. 21.3.4 Additives Additives like salts often induce subtle changes in functionality of the proteins The addition of phosphates has been shown to improve a protein’s water-


507

holding capacity in meat (Moore et al., 1976; Neer and Mandigo, 1977; Whiting 1984). Lin and Kuo (1994) studied low-temperature storage (0 °C and –20 °C) of battered and breaded chicken breast, and compared the effect of injected phosphate solution, soy protein and oil emulsions on its texture and palatability when fried. They concluded, by panel studies, that for 0 °C storage, injection of phosphate significantly improved tenderness and juiciness of the meat. Storage at –20 °C, together with a slow freeze–thaw process to separate water from the fibrous muscle protein, and injection of olive oil emulsion, was found to improve the fried product. Other additives, like sodium chloride, chemical leavening agent and stabilizers, although minor in quantity, are also significant. Leavening agents, added to batters, provide volume expansion during frying, and affect the texture of the crust. The presence of salt such as sodium chloride increases the water boiling point and consequently influences heating rate. Fat is particularly important in the mouthfeel of a fried product, for instance, in improving the tenderness of turkey breast (Larmond and Moran, 1983; Moran 1992; Moran and Larmond, 1981). Some compounds are not added for a specific purpose, but are generated during frying. One example is oil degradation at high temperature which releases long chain free fatty acids. An important theory which is related to this phenomenon is the ‘Surfactant Theory of Frying’ (Blumenthal and Stockler, 1986; Blumenthal, 1991;). The theory states that as the frying oil degrades, more surfactants (i.e. metal salts of fatty acids) are formed, which increase contact between the frying oil and water-based foods. Consequently, the heat transfer rate to the food surface is increased, leading to enhancement of local darkening and drying. The quality of the frying oil, especially in terms of free fatty acid released, is therefore a crucial factor in the texture of fried foods.

21.3.5 Processing variables Processing variables like frying time and temperature are factors that can be easily adjusted without too much trouble in order to improve texture. As mentioned above, high oil temperature means high temperature gradient and consequently high heat penetration rate and faster increase of temperature near the center. However, before the operator boosts up the oil temperature, it should be noted that, at high temperature, the effect of increased water evaporation rate is more profound near the surface than in the interior portion. The exterior is likely to become dry and on the point of becoming burned or charred while the interior still remains undercooked. Often, the cut size could be adjusted to counterbalance the increased heat transfer rate. However, this is commonly limited by customer preferences or economic criteria; moreover, adjusting size may involve other effects on, for example, oil absorption, flavor development, or even production cost.

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Texture in food

21.4 The use of response surface methodology (RSM) An awareness of the influences of various factors on the texture of the product will give a clear picture of the possible variables which can be manipulated (the independent variables) in order to improve or optimize the texture (the dependent variable). Among dozens of possible candidates, use of some is limited by consumer habit or unavailability of the required tool, while others may involve long-term research and development. The experienced manager may advise giving a higher priority to those variables that are convenient to adjust, and leaving those that demand more time and effort for later study. After screening, the remaining few may thus form the basis for a focused study. However, optimization is not easy even with only two or three variables. RSM has been developed particularly for the optimization of sophisticated multivariable systems where the quantitative relationship between key variables is not always clear, as is the case with a complex operation such as frying. RSM allows simultaneous consideration of more than one variable at many different levels, and of the corresponding interactions between these variables, on the basis of a relatively small number of experiments.

21.4.1 Principles of RSM The word ‘response’ refers to how a function (to be optimized) reacts to change(s) in its independent variable(s). Take a quadratic function of the form: f (x) = 10 – x2, for illustration. Changing the independent variable ‘x’ from 1 to 2, the ‘response’ of ‘f ’ would be ‘changing from 9 to 6’. The curve of f (x) with ‘x’ (the independent variable) being the abscissa is a concave down parabola. The highest point of the curve can easily be located graphically at x = 0, where f (x) = 10. Another way of looking at the question of locating the highest point (or optimum point) is to use calculus: equating the first derivative of ‘f ’ (with respect to x) to zero, and finding the solution. In this example the equation would be f ′ = 2x = 0, and the solution is x = 0 which is the same answer as was obtained by the graphical method. Graphical and analytical methods are two powerful tools, if not the only two, in optimization. It is much more complex when the function ‘f ’ involves two independent variables: z = f (x, y). The graphical representation of the function will be a surface in three-dimensional space, on which z, the dependent variable, changes as x and y vary according to the relationship defined by f. Standing on the highest point of the curved surface, as is the case with a one variable function, calculus tells us that fx and fy (the first partial derivatives of f with respect to x and y) are zero. However, it necessary to be aware that the reverse is not necessarily true. An analytical solution can be obtained by equating both ( fx and fy) to zero and solving the simultaneous equations. Since an optimum point is merely one form of critical point – the solution gained may represent a saddle point – its solution should be verified by further mathematical testing. Numerical tools may be required for the solution of the above


509

simultaneous equations when they are non-linear, and it will be even more difficult if non-linear differential equations are involved. However, these are beyond the scope of this chapter. Should this be the case, readers are encouraged to turn to references about numerical methods, or computer software packages for numerical solutions. In spite of all the trouble and efforts which may be needed in order to obtain an analytical or numerical solution, the graphical method may be considered to be the friendlier option. In practical applications, it is quite likely that quantitative description of the relationship between the dependent variable (e.g. texture indices, quality, production cost) and the independent variable(s) (e.g. water content, frying temperature, surface to volume ratio) is impossible, and the optimum point cannot be found analytically. On such occasions, the graphical method, although less precise, is a practical and straightforward option. It provides the graphical version of the description of the function without knowing how the function is mathematically defined. Should the graphical method be chosen to study the behavior (the response) of the unknown function, the next question would be: whether the independent variables are to be changed one at a time, or simultaneously? There are times when one can find the optimum point by adjusting one variable at a time while holding other variable(s) constant: the one-variable-at-a-time technique. Consider an example where the amount of a new type of starch in a batter mix (x %) and the oil temperature (y °C) are the two variables under investigation. Assume that at present, an oil temperature (y) of 175 °C is being used. Therefore, while holding y constant at 175 °C, a series of experiments involving changing x from 0 to 50% is carried out and volume expansion (%) is chosen as the dependent variable (z). From the graph of z vs x the optimum xopt, can be decided. In the second run, x is held constant at xopt, and y is changed. A z vs y graph is thus obtained, and yopt can be decided. This procedure is, given the mathematical definition of f, equivalent to solving fx = 0 for x while holding y constant, and solving fy = 0 for y while holding x constant, instead of solving simultaneous equations. Although the method is strategically simple, the potential interaction between independent variables is not accounted for. In cases of significant interaction, the optimum point located by the one-variable-at-a-time technique could be significantly different from the true optimum point. All experimentation is prone to human error and, in addition, sampling reproducibility in non-homogeneous systems such as fried foods is poor; hence fluctuations of data are not surprising. To counter these inherent errors, scattered data points on a graph need to be smoothed by finding a curve (or surface) that best represents the pattern of obtained experimental data. Regression is a mathematical tool that helps us to decide which line, or curve, or surface best fits (represents) given experimental data by minimizing an object function called ‘sum of squares’, which is the sum of the square of the distances between the data points and the line, curve or surface. Most graphical software packages are capable of performing versatile regressions.

510

Texture in food

Statistically, a larger sampling size (more experiments) will bring us closer to the true solution. However, ‘how many is enough?, is the question we tend to ask under pressure of increased time and effort. In addition to regression, sampling size is another question which needs to be addressed. As stated above, regression involves minimizing the ‘sum of squares’, which is calculated based on the distances (or deviations) between data points and a ‘curve’ (or surface) which best represents scattered data. Therefore, in order to have a target for measuring distances, we need to assume a function (e.g. a polynomial) whose characteristic shape is similar to the pattern of the data presented. Mathematical theories can then be applied to find the minimum of ‘sum of squares’ by adjusting parameters (e.g. coefficients of a polynomial). Typical mathematical models used in such cases are polynomial, sigmoidal, Gaussian functions, etc. The chosen mathematical functions (or models) for regression do not necessarily by themselves imply any physical or chemical significance. These functions could be adopted solely for the purpose of finding a curve or surface that best represents the data. A mathematical model that has a sound theoretical background would provide far more insight to the problem under investigation; however, this is rarely the case. In RSM, it is assumed that in the neighborhood of an optimum point, concavity (whether concave up or down, or even twisted) of an arbitrary surface makes it reasonable to use a quadratic function as an approximation of the surface. The assumption may still hold even if the region of concern is not near an optimum point, but is small enough for the approximation to be valid. Most of the time, this simplification is good enough for practical purposes. Box and Wilson first proposed the concept of RSM in 1951 (Box and Wilson, 1951). RSM uses a quadratic function of the form: Y = a0 + a1 X1 + a2X2 + a3X1X2 + a4 X 12 + a5 X 22 to approximate the surface in the neighborhood of an optimum point. Where Y is the dependent variable (one of the texture indices in this case) a0 through a5 are the coefficients (to be determined by regression) and X1, X2 are the factors (or operation variables which can be manipulated in the production process) that influence texture, the dependent variable. The shape of the surface representing the quadratic model is determined by the coefficients. The six adjustable coefficients (a0 through a5) together with its quadratic nature make the model sufficient for most practical applications. Theoretically, manipulating six parameters should enable the shape of the surface to be adjusted to fit any set of data that are smooth and quadratic in nature, given that the data fall either in the vicinity of an optimum point, or in a small enough region. However, in practice, it is quite likely that one or more of the following situations might occur. • Experimental data tend to fluctuate, and sometimes the true object function is not quadratic even in the neighborhood of the optimum point (poor degree of fitness for quadratic model).


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• Independent variables do not fall in the vicinity of the optimum point. • One or more of the chosen factors might not affect the dependent variable to a sufficiently significant level to be included in the model. While understanding and differentiating amongst the above listed possibilities is crucial, it is just as important to answer the following: is the experimental error within tolerable limits or is the response of the dependent variable quadratic in nature; is the region covered small enough to be represented by the model; and, are the chosen factors sufficently significant to be included in the model? Statistical analysis is therefore necessary in order to verify the data, and to reach a conclusion which has a scientific basis. In order to minimize the time and effort involved in carrying out numerous experiments and, at the same time, have a large enough sample size for statistical validity, careful design of the experiment is important. Experiment design is a technique developed to establish an optimal number of experiments (Mason et al., 1989; Montgomery, 1984; Thompson, 1982) which can simultaneously satisfy the criteria of minimum number of experimental runs and large enough sample size to claim statistical significance. The next section discusses the basics of experiment design. 21.4.2 Experiment design As mentioned before, people tend intuitively to turn to the one-variable-ata-time technique for its conceptual simplicity and ignore the possible interaction between independent variables. A good example of the interaction between factors is that between water content (W ) and frying temperature (T ). Assuming W and T are the chosen factors for optimization, one possible interaction will be that T tends to influence the way W affects the texture and vice versa. Since water molecules are removed at a higher rate at high temperature than at low temperature, the rate of water evaporation is affected by temperature, and this will inevitably affect texture of the product. Should the interaction be minor or negligible, a one-factor-at-a-time search will give a satisfactory result. If it is thought that interaction may be taking place then a more complex experiment design should be considered. The price paid will be a minor one in comparison with the benefits in terms of more convincing results and more statistical information. An experiment design for one-variable-at-a-time optimization is shown in Table 21.1. The experiment involves five levels for each factor. The levels are expressed in coded form, which can be linearly transformed back to their corresponding true values, so that the arrangement could be applied to other systems. In the coded form, one unit could represent 10 °C difference in oil temperature, or 5% water content. From run numbers 1 to 5, X2 was held constant at 0 (the center), and it was assumed that X1 = –1 was found to produce the highest response (Y2). Then, from run numbers 6 to 9 (the combination of X1 = –1 and X2 = 0 was carried out in run number 2), X1 was fixed at –1, while X2 varied from –2 to +2, and it was assumed that X2 = +1

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Table 21.1

Experiment design for one-factor a time optimization (two-factor five-level)

Run number

Independent variables

Dependent variable

X1

X2

Y

1 2 3 4 5 6 7 8 9

–2 –1 0 +1 +2 –1 –1 –1 –1

0 0 0 0 0 –2 –1 +1 +2

Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9

Optimum point

–1

+1

Yoptimum = Y8

was found to be optimum (Y8). X1 = –1 and X2 = +1 which produced the highest response (Y8) was therefore identified as the optimum point. It cannot be over-emphasized here that the true optimum point could be elsewhere, should interaction effects be significant. Central Composite Design (Mason et al., 1989; Montgomery, 1984; Thompson, 1982) is commonly employed for systems with potential interactions effect(s) between factors. For a n-factor-five-level design, five coded levels (–d, –1, 0, +1, +d ) are assigned to each factor, where d is called the extended level and d = (2)n/4. An example of two-factor-five-level design is given in Table 21.2. For two-factor design d = (2)2/4 = 1.414, for three-factor design d = (2)3/4 = 1.682, and so on. In Table 21.2, run numbers 1 to 4 correspond to a two-level factorial design, and run numbers 9 and 10 are duplicate experiments so that statistical diagnosis can estimate experimental error. For more detailed explanation of the theories involved in the experiment design, Table 21.2 Run number

1 2 3 4 5 6 7 8 9 10

Central composite design (two-factor five-level) Independent variables

Dependent variable

X1

X2

Y

1 1 –1 –1 0 0 1.414 –1.414 0 0

1 –1 1 –1 –1.414 1.414 0 0 0 0

Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10


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readers are encouraged to go to the cited references. Comparing Tables 21.1 and 21.2, it is clear that Central Composite Design can provide more information, and the price is as little as a 10% increase in time and effort. Considering the benefit from verification of the potential interactions between factors, this small price should be worth paying.

21.4.3 Statistical analysis After doing experiments according to Table 21.2, a group of ten observations (Y1 through Y10) can be used to carry out regression using the quadratic model as stated above. The obtained model, with the six coefficients determined by regression, represents a surface in the Y – X1 – X2 space. Using a computer graphic tool, one can visually pinpoint the critical point, be it a stationary point (where varying factors will not significantly change the dependent variable) or extrema (maxima or minima). While getting the response surface, the questions about error, interactions, or even whether or not the factor(s) should be included in the model in the first place, remain to be answered. Statistical analysis could provide answers to these questions. Statistical analysis can be done by ‘analysis of variance’ (ANOVA, Box et al., 1978). If it is thought that interaction may be taking place then a more complex experiment design should be considered. The price paid will be a minor one in comparison with the benefits in terms of more convincing results and more statistical information. One of the most important ANOVA terms in the application of RSM is R2 (R-square), which shows the goodness of fit of the mathematical model. R2 is determined by calculating the ratio of regression sum of square (SSR) to the total sum of square (SST): R2 = SSR/SST. Since SST is the sum of SSR (which comes from lack of fit) and SSE (the error sum of squares, which comes from pure experimental error), the closer R2 is to 1 (or 100 on a percentage basis) the smaller SSE is and, therefore, the more intimate the relationship between the model prediction and the true response. Another quantity, the P-value, indicates how significant is each term in the model (X1, X2, X1X2, X 12 and X 22 ). The P-value for i-th term in the model reveals that for the term Xi in the model, the probability that Xi is not significant to the response is Pi. For example, P2 = 0.05 means that the probability that X2 is not significant to the response is 0.05. In common language, there is 95% of the chance that X2 is significant. It can also be said that X2 is significant at 5% level. In practice, depending on the desired level of accuracy, a P-value lower than 0.1 (10% level) is commonly considered acceptable. Recently, advances in engineering and the sciences have provoked the development of all-in-one computer software packages that merge computer graphics, experiment design, regression, statistical analysis, worksheet and documentation. SAS (SAS 1989), the Statistical Analysis System, is widely used. Design-Expert (1996) by Stat-Ease is very user-friendly. Similar software packages like STATISTICA (2000) and SPSS (2000) are also popular in the scientific software market. These software packages normally have a

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powerful tutorial system that will guide new users through the steps of RSM optimization. RSM optimization has wide applications in various fields such as food technology, chemical technology, material engineering and the list goes on. Many successful examples can be found in the literature. Junqua et al. (1997) maximized the production of microbial transglutaminase, an important enzyme in protein-related foods, using RSM. They narrowed down the search domain by the one-factor-at-a-time technique and used the obtained optimum point as the center of the ‘central composite design’ in subsequent RSM optimization. A three-fold increase of the enzyme was achieved using RSM. Mahoney and co-workers (1974) optimized lactase production by the conventional onefactor-at-a-time procedure. Building on their work, Chen et al. (1992) applied RSM to further increase the enzyme production by 60%.

21.5 A case study: fried gluten balls Fried gluten ball is a popular fried food in the Chinese community. Added to soups or stir-fried dishes, it absorbs mixed flavors and becomes rehydrated, tasty and very chewy. Tonnes of fried gluten ball is sold each year on the food market in Taiwan. The manufacturing process is as follows: 1 2 3 4

wheat flour is washed with water to separate gluten from the starch; the wet gluten is immersed in water for 30 minutes; it is then collected, cut and shaped into wet gluten balls; wet gluten balls are then deep fried using three or four deep frying pans controlled at different temperatures; 5 in the first and second frying pans, water evaporation expands the gluten balls, establishing their basic volume, shape, texture and colour; 6 frying in the final pan(s) completes the ageing of the balls. During the stage of gluten extraction, as the flour is washed with water, the networked structure of gluten is gradually forming (Bietz and Wall, 1980; Huebner, 1977). Upon hydration, glutenin becomes swollen and, at the same time, absorbs gliadin together with some of the albumin and globulin. The network structure of gluten is co-stabilized by disulfide bonds and hydrogen bonds, as well as hydrophobic interactions (Huebner, 1977). This kind of network is the key factor in the development of the texture of fried gluten ball during frying. Initially, the water content of the wet gluten balls is high, and fast evaporation of water leads to major volume expansion (as much as 15–20 times the initial size) during the first stage of frying (the first and the second frying pan). This produces highly porous, dried and crispy gluten balls. Fryings in the first and the second pans are the most important steps in setting the final quality of the gluten balls. Oil temperatures of the first and the second frying


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pans are therefore chosen to be the two factors for RSM optimization of the texture (Chen et al., 1998). The flour used was untreated, milled commercially from mixed grist of hard red wheat imported from America. The protein content, on 13.5% moisture basis, was 13.96%, and ash content was 0.53%. Wet gluten balls weighing 100 g each were fried continuously in three consecutive frying pans. Each frying pan, with identical dimensions, contained 10 l of soybean oil. The frying time in each pan was 120, 90 and 70 seconds respectively. For the reasons stated above and from previous experiences, the texture and color of the fried gluten balls were nearly fixed after the first and second frying pans, and the oil temperature of the third pan was controlled at 195 ± 3 °C. 21.5.1 Experiment design The rotatable central composite design (Mason et al., 1989) consisting of a two-factor-five-level arrangement with 10 observations (eight combinations with two replications at the center point) was adopted. The two factors (oil temperatures of the first and second frying pans, T1 and T2), and the coded values of the five levels of each factor are listed in Table 21.3. In order to cover various ways of looking at the quality of the product, it is better to encompass as many quality indices as possible; four instrumental texture indices, three subjective panel test scores and spectrophotometer measured color were selected to be the dependent variables which were studied individually and cross-referenced. These are discussed in the remaining part of this section. Sensory evaluations, although subjective, directly reflect consumer preferences. However, mouthfeel is a complex, abstract and entangled overall sensation that is hard, if not impossible, to trace back to individual physical properties. Instrumental measurements can provide a quantified basis that is best used as a complement to sensory analysis; and instrumental results can be seen as reliable only if they are validated against sensory measurements. 21.5.2 Measurements A group of 30 experienced panelists was organized for sensory evaluation. Panelists scored the samples in three ways: appearance score (AS), texture Table 21.3 Coded values and corresponding real values of independent variables. (Chen et al., reproduced with permission from John Wiley & Sons) Independent variables

Coded levels –1.414

–1

0

1

1.414

X1(T1 °C) X2(T2 °C)

126 151

130 155

140 165

150 175

154 179

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Texture in food

score (TS) and total acceptance score (TAS). AS and TAS were analyzed by a hedonistic test, and TS was analyzed by a comparison test using three samples. Volume expansion is the characteristic in fried products which is responsible for the porous network structure. For the same mass of gluten ball, larger size indicates larger void volume and thinner membranes dividing these void cells. Consequently, it results in a texture which is not too tough and yet crispy, and which in the end is to be judged by consumer preferences. The expansion volume (EV) and expansion ratio (ER) were therefore considered relevant to the overall quality as dependent variables for this study. It is expected that higher EV/ER would correlate to higher AS and TAS. Compression testing was applied, using a rheometer, to obtain a force– distance curve, and form the curve, peak force (PF) and brittleness breakdown (BB) that are important texture indices in this case. PF is a measure of the hardness of the specimen and is defined as the maximum force at 75% compression during the first bite. BB is a measure of crispiness and is defined as the first major peak (or force) at failure before the maximum force is obtained during the first bite. A Sun rheometer (Sun CR200D, Sun Scientific Co. Ltd, Japan), mounted with a plunger (adapter No.14) was used to measure PF and BB. The specimen was put on a sample platform, which traveled upward against the plunger at a constant speed of 60 mm/min, and the compression distance was set to be 12 mm. The measured values of 30 grains of fried gluten balls, produced using the same T1 and T2 were averaged. Typical force–distance curves are shown in Fig. 21.1. A colorimeter (Color Analyzer, Color Mate OEM, Milton Roy Co., USA) was used to measure 30 grains of the fried gluten balls and averaged. Of the three parameters in the Hunter color system, the Hunter b value (HB) was found to correlate best with AS, TS and TAS. Panelists suggested that a light yellow color implies fresh frying oil and a product which has not been over fried. As will be seen next, HB is negatively correlated to AS, TS and TAS.

21.5.3 Analysis of the results The quadratic regression for the eight selected quality indices, with T1 and T2 being the independent variables, was carried out using SAS’s RSREG (response surface regression) procedure (SAS, 1989). Obtained coefficients and R2 are listed in Table 21.4. From the satisfactory values of R2 (the ones for EV and ER are slightly lower) it can be said that the quadratic model relating quality indices and T1, T2 fits the experimental data fairly well. Obtained models (from regression) were used to generate response surfaces for each dependent variable, as shown in Fig. 21.2. First, look at the EV and ER surfaces. As T2 is low (~150 °C), optimum T1 is around 135 °C. But at low T1, increasing T2 would significantly improve EV as well as ER. Meanwhile, as we shift to higher T2 (say, 180 °C), optimum T1 shifts to a lower temperature (126 °C). Curvature of the EV and ER surfaces and the shift of optimum T1

Improving the texture of fried food 2000

517

(a)

1000 0 (b) 2000 1000 0 (c)

Force (g)

2000 1000 0 (d) 2000 1000 0 (e) 2000 1000 0

0

3

6 9 Distance (mm)

12

15

Fig. 21.1 An example of force–distance curves used to determine peak force (PF) and brittleness breakdown (BB) of fried gluten balls.

upon changing T2, imply that the assumption of quadratic behavior should stand, and that the interaction between the two temperatures should be significant. However, this conclusion would be too hasty without examining the ANOVA result (Table 21.5). The P-values of the five terms in the model for EV and ER listed in Table 21.5 tell us that only T1 is significant at 5% (P < 0.05) level while the rest of the five terms are not significant (P > 0.05). What ANOVA is suggesting here is that the proposed quadratic model (for EV or ER) is not suitable for representing experimental data, and that reconsideration is in order. Nevertheless, an important piece of information that the surface indicated might be considered to be true is that at low T1 we need to increase T2, while at high T1, the major portion of water evaporation would be carried out in the first frying pan and increasing T2 will not further improve EV and ER. The reason for inadequate fitness between model and

–222.32 3.39 0.058 –0.0079 0.0034 –0.0079 82.57

a0 a1 a2 a3 a4 a5 R2 –17666 271.22 4.38 –0.64 0.28 –0.63 82.55

ER 1543.74 –236.28 183.79 0.83 –0.62 0.13 92.89

PF 1737.92 –243.53 187.35 0.85 –0.64 0.14 93.29

BB 157.88 –0.36 –1.57 –0.0018 0.0025 0.0056 91.02

HB –189.95 2.98 –0.082 –0.009 0.0018 –0.0035 89.74

AS

–73.94 1.78 –0.46 –0.0065 0.0017 –0.0006 93.72

TS

–145.57 2.92 –0.57 –0.0092 0.0029 –0.0028 90.22

TAS

EV, expansion volume; ER, expansion ratio; PF, peak force; BB, brittleness breakdown; HB, Hunter-b value; AS, appearance score; TS, texture score; TAS, total acceptance score b Y = a0 + a1X1 + a2X2 + a3 X 12 + a4 X 22 + a5X1X2, where Y represents the quality indices, X1 is T1; X2 is T2

a

EV

Coeffb

Table 21.4 Quadratic model coefficients and R2 values for the response surfaces of different quality indices. (Chen et al., 1998, reproduced with permission from John Wiley and Sons)

518 Texture in food


2000

17 13 126 140 T1(°C)

180 165 154

ER(%)

EV(cm3)

21

150 T2(°C)

1310 910 126 140 T1(°C)

180 165

154 150 T2(°C)

1120 720 126 140 T1(°C)

154 150 T2(°C)

7.9

180 165 154 150 T2(°C)

TS

6

4.9 1.9 126

140

T1(°C)

180 165 154 150 T2(°C)

4.5 2.0 –0.5 126

180 165 140

T1(°C)

3 0 126

154 150 T2(°C)

180 165 140

T1(°C)

Hunter b value

AS

180 165

1520

BB(g)

PF(g)

1600 1200 126 140 T1(°C)

1710

TAS

519

154 150 T2(°C)

13.2 11.2 9.2 126

180 165 140

T1(°C)

154 150 T2(°C)

Fig. 21.2 The response surfaces of frying temperatures and quality indices of fried gluten balls. T1, the temperature of the first deep frying pan; T2, the temperature of the second deep frying pan; EV, expansion volume; ER, exapansion ratio; PF peak force; BB, brittleness breakdown; AS, appearance score; TS, texture score; TAS, total acceptance score. (Chen et al., 1998, reproduced with permission from John Wiley & Sons).

data could be the inherent experimental error, and increasing sample number might help. PF and BB surfaces revealed that, knowing that a significant increase in PF and BB may result in a texture that is tough, it is advisable to operate in the region of low T1 and moderate T2. Although variation of T2 did not dramatically affect PF and BB (the curvature due to variation of T2 is not substantial), the effect of slight change in PF and BB might be magnified

520

Texture in food

Table 21.5 ANOVA for the frying temperatures vs the quality indices of the fried gluten balls. (Chen et al., reproduced with permission from John Wiley & Sons) Source

Responsea (P-values) EV

ER

PF

BB

HB

AS

TS

TAS

Model T1 T2 T 12

0.110 0.032 0.122 0.159

0.111 0.032 0.123 0.159

0.020 0.004 0.298 0.032

0.018 0.003 0.300 0.030

0.073 0.016 0.110 0.242

0.042 0.010 0.457 0.030

0.017 0.002 0.574 0.030

0.038 0.009 0.417 0.026

T 22 T1T2

0.601 0.291

0.600 0.292

0.176 0.772

0.167 0.741

0.462 0.228

0.618 0.380

0.515 0.820

0.427 0.478

a

EV, expansion volume; ER, expansion ratio; PF, peak force; BB, brittleness breakdown; HB, Hunter b value; AS, appearance score; TS, txture score; TAS, total acceptance score.

when measuring TS and TAS. Therefore, the implication from PF and BB surfaces should be cross-checked with TS and TAS surfaces. However, in this case, TS and TAS are not very sensitive to variation in T2 either. The Pvalues of the model and the five terms for PF and BB (Table 21.5) are telling the same story: model, T1 and T12 are significant (P < 0.05), while the terms involving T2 (T2, T22 and T1T2) are not significant. Similar conclusions could be reached for TS and TAS. The lack of significance of T2 to these dependent variables discussed indicated that serious precision control of T2 is not required. Although texture is the main concern in this chapter, the response surfaces of HB and AS would be a good example of elaborating a negative correlation between the two dependent variables, and the choice of an optimum operating condition. The P-values for HB (Table 21.5) indicate significance of the model at 10% level, and T1 at 5% level, and T2 at 11% level. This is in line with what was shown on the response surface of HB, since all the curves of constant T1 on the surfaces are relatively flat with respect to T2 (Fig. 21.2). Consumers prefer a light brown color (low HB) of fried gluten ball to a dark one (high HB), therefore HB should be negatively correlated to AS, as is shown in Fig. 21.2. The critical values of the frying temperatures and the characteristics of the stationary points (where the dependent variable is not very sensitive to variations of the independent variables) of the response surfaces for the quality indices studied are shown in Table 21.6. The optimum temperatures were chosen on the basis of merging RSM search and engineering principles not included in the RSM procedure. Frying at lower temperature implies less energy consumption and a lower rate of oxidative oil degradation. The figure shows that below T1~130 °C, for constant T2, PF and BB remained virtually constant. AS and TAS were at their maximum when T1~135 °C. Although AS, TS and TAS showed further increases beyond T2~155 °C, the increases were minor. Besides, HB would increase (darkening) with T2 > 155 °C (in the vicinity of T1~140 °C). T1 of 130–143 °C and T2 of 155–161 °C were


521

Table 21.6 The critical values of the frying temperature and the characteristics of stationary points for the quality indices of fried gluten balls obtained using RSM. (Chen et al., reproduced with permission from John Wiley & Sons) Process variables

PF

BB

HB

AS

TS

TAS

T1(°C) T2(°C) Stationary point

130 161 Saddle

130 161 Saddle

143 156 Saddle

136 155 Saddle

131 160 Saddle

134 159 Saddle

a

PF, peak force; BB, brittleness breakdown; HB, Hunter b value; AS, appearance score; TS, texture score; TAS, total acceptance score.

therefore chosen as the optimum frying temperatures for the first and second frying pans. For verification, while fixing temperature of the third frying pan at 195 ± 3 °C, the optimum T1 and T2 (130 ± 3 °C and 155 ± 3 °C) were adopted for a trial production of gluten balls. The result was compared to products made before RSM optimization. The improved texture was soft and elastic rather than brittle and rigid. Sensory evaluation scores (AS, TS and TAS) were all higher than the commercial standard scores of 4–5. The result of RSM optimization is therefore justified.

21.6

Conclusions

A systematic search for optimum conditions requires a quantified basis (by instrumentation or panels) and efficient experiment design. RSM optimization is a powerful tool which is particularly useful in areas such as improving the texture of fried food where the function correlating texture indices and manufacturing factors is frequently not clear. Among dozens of potential factors, careful screening, based on professional judgment, must be performed, in order to reduce the number of independent variables, and focus on a few key factors. The window of search should be kept within realistic limits if the quadratic model is to be applicable. Finally, statistical analysis can help to justify a regression result and provide a basis for decision-making. In mathematical optimization for many applications, it is commonly expected that the response surface will have a bell shape for which the point located at the top of the surface can be picked as the optimum point without any debate. However, in the case study of this chapter, such a type of response surface was not obtained. Other engineering criteria were applied to compromise between pros and cons, according to implications revealed by the response surfaces. Cross-checking of the optimization result based on instrumental measured quantities with that from panel studies is important for validation. Although it is a powerful optimization tool with versatile applications, without professional knowledge and experience, the contribution of RSM is limited.

522

Texture in food

21.7

References

and WALL J S (1980) Identity of high molecular weight gliadin and ethanol-soluble glutenin subunits of wheat: relation to gluten structure, Cereal Chem, 57, 415–20. BLUMENTHAL M M (1991) A new look at the chemistry and physics of deep-fat frying, Food Technology, 45(2), 68–71. BLUMENTHAL M M and STOCKLER J R (1986) Isolation and detection of alkaline contaminant materials (ACM) in used frying oils, J Am Oil Chem Soc, 63(5), 687–92. BOUTON P E and HARRIS P V (1972) A comparison of some objective methods used to assess meat tenderness, J Food Sci, 37, 218–21. BOUTON P E, HARRIS P V and SHORTHOSE W R (1975) Possible relationships between shear, tensile and adhesion properties of meat and meat structure, J Texture Stud, 6, 297–314. BOX G E P, WILSON K B (1951) On the experimental attainment of optimum conditions, J Royal Statist Soc, B13, 1–45. BOX G E P, HUNTER W and HUNTER J S (1978) Statistics for Experimenters, New York, John Wiley & Sons. BREWER M S, MCKEITH F K and BRITT K (1992) Fat, soy and carrageenan effects on sensory and physical characteristics of ground beef patties, J Food Sci, 57(5), 1051–2. CHEFTEL J C, CUQ J L and LORIENT D (1985) Amino acids, peptides and proteins. In Food Chemistry. Ed. O R Fennema, New York, Marcel Dekker, Inc., 296–8. CHEN C S, CHEN J J, WU T P and CHANG C Y (1998) Optimising the frying temperature of gluten balls using RSM, J Sci Food Agric, 77(1), 64–70. CHEN K C, LEE T C and HOUNG J Y (1992) Search method for the optimal medium for the production of lactase by Kluyveromyces fragilis, Enzyme Microb Technol, 14(8), 659– 64. CHRISTENSON C M and VICKERS Z M (1981) Relationships of chewing sounds to judgments of food crispness, J Food Sci, 45, 574–8. DESIGN-EXPERT (1996) Stat-Ease Corp., 2021 East Hennepin Avenue, Suite 191, Minneapolis, MN 55413, USA. DRAKE B and HALLDIN L (1974) Food crushing sounds: an analytic approach, Rheol Acta, 13, 608–13. HUEBNER F R (1977) Wheat flour proteins and their functionality in baking, Baker’s Dig, 51(25), 154. JUNQUA M, DURAN R, GANCET C and GOULAS P (1997) Optimization of microbial transglutaminase production using experimental designs, Appl Microbiol Biotechnol, 48, 730–34. KADAN R S, CHAMPAGNE E T, ZIEGLER G M and RICHARD O A (1997) Amylose and protein contents of rice cultivars as related to texture of rice-based fries, J Food Sci, 62(4), 701–3. KOTULA A W (1976) Evaluation of beef patties containing soy protein during 12-month frozen storage, J Food Sci, 41, 1142–6. LARMOND E and MORAN E T JR (1983) Effect of finish grade and internal basting of the breast with oil on sensory evalution of small white toms, Poultry Sci, 62, 1110–16. LIN Y H and KUO J C C (1994) Palatability and storage stability of breaded chicken breast, J Food Sci, (Taiwan), 21(3), 216–27. MAHONEY R R, NICHERSON T A and WHITAKER J R (1974) J Dairy Sci, 58, 1620–25. MASON R L, GUNST R F and HESS J L (1989) Statistical Design and Analysis of Experiments – With Application to Engineering and Science, New York, John Wiley & Sons. MOHAMED A A A, JOWITT R and BRENNAN J G (1982) Instrumental and sensory evaluation of crispness: I. In friable foods, J Food Eng, 1, 55–60. MOHSENIN N N and MITTAL J P (1977) Use of rheological terms and correlation of compatible measurements in food texture research, J Texture Stud, 8, 365–70. MONTGOMERY D C (1984) Design and Analysis of Experiments, New York, John Wiley & Sons. BIETZ J A

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and SCHMIDT G R (1976) Effect of salt, phosphate and non-meat protein on binding strengths and cook yields of beef rolls, J Food Sci, 41, 424–9. MORAN E T JR and LARMOND E (1981) Carcass finish and breast internal oil basting effects on oven and microwave prepared small toms: cooking characteristics yields and compositional changes, Poultry Sci, 60, 1229–36. MORAN E T JR (1992) Injecting fats into breast meat of turkey carcasses differing in finish and retention after cooking, J Food Sci, 57(5), 1071–6. NEER K L and MANDIGO R W (1977) Effect of salt, sodium tripolyphosphate and frozen storage time on properties of a flaked cured pork product, J Food Sci, 42, 738–44. PELEG M (1987) The basics of solid foods rheology. In Food Texture, Instrumental and Sensory Measurement. Ed. H R Moskowitz, New York, Marcel Dekker Inc., pp 3–17. SAS (1989) SAS/STAT User’s Guide, Version 6 (4th edn, Vol 2). SAS Institute Inc., Cary, NC, USA. STANLEY D W (1976) The texture of meat and its measurement. In Rheology and Texture in Food Quality. Eds J M deMan, P W Voisey, V F Rasper and D W Stanley, Westport, CT, AVI Publishing Co., pp 28–42. SPSS (2000) SPSS Inc., 233 S. Wacker Drive, 11th floor, Chicago, Illinois 60606, USA. STATISTICA (2000) StatSoft Inc., 2300 E. 14th Street, Tulsa, Oklahoma 74104, USA. SZCZESNIAK A S (1983) Physical properties of foods: what they are and their relation to other food properties. In Physical Properties of Foods. Eds M Peleg and E B Bagley, Westport, CT, AVI Publishing Co., pp 28–37. SZCZESNIAK A S, HUMBAUGH P R and BLOCK H W (1970) Behavior of different foods in the standard shear compression cell of the shear and the effect of sample weight on peak area and maximum force, J Texture Stud, 1, 356–78. THOMPSON D (1982) Response surface experimentation, J Food Proc Preserv, 6, 155–88. VOISEY P W amd DEMAN J M (1976) Application of instruments for measuring food texture. In Rheology and Texture in Food Quality. Eds J M deMan, P W Voisey, V F Rasper and D W Stanley, Westport, CT, AVI Publishing Co. VOISEY P W and LARMOND E (1974) Examination of factors affecting performance of the Warner-Bratzler meat shear test, Can Inst Food Sci Technol J, 7, 243–9. WHITING R C (1984) Addition of phosphate, proteins and gums to reduced salt Frankfurter batters, J Food Sci, 49, 1355–62.

Index

acoustic emissions see sound input tests acoustic firmness sensors 266 acoustic myography (AMG) 59 acoustic signatures 151, 155 air channels and texture 213 air drying 394–5 air-conducted sound 147–8 air-coupled transducers 103 airflow planimeter 69 alcohol insoluble solids 274–5 Alternative Forced Choice (3-AFC) tests 10 AMG (acoustic myography) 59 amplitude-time plots 22, 152, 154 amylose content of rice 452–3, 455, 470 analytical tests 8 antisense technology 307, 324, 329 appearance definition 4 visual flavour 4 apples air channels and texture 213 calcium treatments 374–6 force/deformation tests 118, 133 freezing 374, 381–2 harvest maturity 226–7 mastication 75–6 mealiness 197–8 near infrared (NIR) tests 176 sound input tests 160, 161 transport and storage 196, 222–7 vacuum infusion 372, 374–6, 378–9 and freezing 381–2 apricots 377

aqueous freezant (AF) 398 aqueous preservative solutions 374–5 Arabidopsis genes 322 artificial neural networks (ANN) 103 Asco Firmness Meter 131 Asian noodles 475, 476 Asian pears see Japanese pears asparagus 356–7 attribute ratings 39, 42–3 attribute-property relationship 206–7 avocados 175 ß-galactosidase 330–1 bacteria 323 baguette-type bread products 435–6, 443 baked products see bread bananas 334 batters 84–5, 504–5, 506 beef see meat beetroot 250, 357 bending tests 125–6 BerryBounce 137 beverages 42 biopolymers 216–18, 234 bioyield point 115, 129 biscuits 76 bite forces 61 bite tenderometer 15 bite test 481 black olives in brine 422–4 black oxidised olives 419–22 blanching 391, 393–4 Blanching Response Amplification Model (BRAM) 227–31

526

Index

bolus formation 7, 55, 71 bone-conducted sounds 147–8, 150–1 Brabender viscoamylograph 453–4 bread 432–9 baguette-type products 435–6, 443 cellular structure 441 Chorleywood Bread Process (CBP) 442–3, 448 cohesiveness 437 Compressimeter 438–9 compression tests 438–40 Cone Indenter 438–9 crispness 215 crumb and crust 434, 447 deformation tests 438 density 441, 446 dough mixing 442–3, 448 firmness 436, 439 flour properties 441–2 freezing 445 gas retention 441–2 gluten formation 432–4, 441 hardness 436, 440 improvers 442 moistness 436, 440–1, 444 mould growth 444 no-time dough mixing 442–3 nuclear magnetic resonance tests 191–2 part-baking 447 Pressure-Vacuum mixer 442–3, 448 processing 442–3 puncture test 440 raw material quality 441–2 refrigerating 445 retrogradation process 445 sandwich-type products 435–6 sensory assessment 437–8 softness 437, 438, 439 springiness 437 squeeze test 438, 439 staleness 437, 444–5, 446–7 storage 443–5 texture improvement 445–7 texture measurements 437–42 Texture Profile Analysis (TPA) 439– 40 bread coatings 84–5, 93–4, 96–7 Bread Quality Imaging System 25–6 brittleness 86–7, 213

bruise volume/threshold measurement 285–7 C-Cell 441 calcium treatments 374–7 of apples 374–6 of apricots 377 of carrots 376 and cell wall strengthening 374 of eggplants 376 of lemons 376 of mushrooms 376 of olives 425–6 of strawberries 376, 377 of tomatoes 351, 352 calibration equations 281–2 canned apricots 377 canned tomatoes 352 canonical variate analysis (CVA) 75 capsaicin 5 carrots 75, 358, 376 cassava 347 category scales 36 cell adhesion 251–4 cell turgor 213, 244–7, 266, 275–6 cell walls breakage 246–7 and cell adhesion 252–4 degradation and enzymes 321–2 formation 297–8 of olives 412–14 polysaccharides in 271, 349–54 ripening changes in 272–4 strengthening with calcium 374 structural components 244, 271 cellular basis of crispness 244–7 cellular solids 215 cellular stability 249–51 cellular structure 244–55 of bread 441 cellulose degradation 222 cereals 124, 170–3, 484, 494 chalkiness 44–5 cheese 75, 192, 198 chemical irritants 5 chemical structure see plant structure Chen models 263 chewing 55, 66–7, 206 see also mastication; oral processing chicken nuggets 94, 96–7

Index chimeric gene constructs 325–6 Chinese water chestnut 250, 357 chocolate 16, 17, 178–9 chocolate beverages 42 Chorleywood Bread Process (CBP) 442– 3, 448 chufa 250, 357 citrus fruits 280, 376 coatings batters 84–5, 504–5, 506 breadings 84–5, 93–4, 96–7 freezing and glazing compounds 393 and osmotic dehydration 396 cohesiveness 92, 437 colorimeter 516 colour formation 4, 297 Compressimeter 14, 438–9 Compression Tester 483 compression tests 14, 119, 122–3, 127 of bread 438–40 of fried food 502, 503, 516 multiple compression tests 439–40 of olives 421–2 of pasta 481–4 of pears 260, 261 shear tests 87, 119, 123–4, 503 Cone Indenter 438–9 confectionery 16, 17, 178–9 consumer perceptions data analysis 73–5 description problems 33–4, 44 of food enjoyment 5–6 importance of texture 34, 39 linear relationships 40 multivariate analysis techniques 41–4 non-linear relationships 40–1 of pasta 492–4 of quality 206–9, 451, 452–5 single attribute analysis 41 testing 36–9 understanding 3, 39–48 see also in vivo measurement; instrumental measurement; sensory evaluation corn tortilla chips 47–8 correlation coefficient 19 crackliness 159–60 crispness and crunchiness 82–103 and batters 84–5, 504–5, 506 of bread 215

527

and breadings 84–5, 93–4, 96–7 cellular basis 244–7 data evaluation and analysis 91–4 definition 83, 84, 92 geometrical properties 85–6 measuring 21–3 mechanical tests 86–7, 88, 96, 99–100 panellist training 91–3 and plant structure 244–7 sound tests 83–4, 87–91, 158–60, 162, 243 storage conditions 91 structural properties 85–6 ultrasonic property measurements 95–6, 98, 100–2 cross-linking of proteins 304–6 cultivar effects 345, 349–50 cultivation conditions 275–8, 358 of apples 226–7 of olives 410 soil treatments 345, 359 of tomatoes 350–1 of wheat 484–5 curing 346 cutting devices 14 data analysis 73–5, 91–4 force deformation tests 126–7 instrumental measurements 18–20 magnetic resonance imaging (MRI) 188–9 nuclear magnetic resonance (NMR) 188–9 sound input tests 149–50, 152–5, 157–8, 163 deformation of the food matrix 367 deformation tests see force deformation tests Delwiche model 263 density of bread 441, 446 destructive tests 109–10, 118–27, 146, 148–55 difference from control tests 10 difference tests 9–10 dipping 392–3 discrimination tests 9 dough mixing 442–3 drop tests 136–7, 138 drupes 410 dry-salted olives 424–5

528

Index

drying conditions 491–2 duo-trio tests 9 durum wheat 484, 494 dynamic tests 117–18, 133–4 ears 147–8 edograms 62 eggplants 376 eggs 487–9 elastic properties 24–5, 74, 112–15 of pasta 482 theory of linear elasticity 113–15 time-dependent 113 time-independent 112–13 viscoelasticity 113, 116 electromyography (EMG) 20–1, 57–9, 60, 75–6 electropalatography (EPG) 21 empirical tests 14, 73, 109, 118, 243 endo-glucanase 331–3 engineering techniques 335 enjoyment of food 5–6 environmental conditions see cultivation conditions enzymes 218–21, 228–9, 234 ß-galactosidase 330–1 and cell wall degradation 321–2 endo-glucanase 331–3 and genetic modification of tomatoes 327–33 pectate lyase (PEL) 334 pectinesterase 329–30, 392 polygalacturonase 327–9 and ripening-related softening 248 and vacuum infusion 377–80 see also pectin; peroxidases (PODs); polyphenoloxidases (PPOs) EPG (electropalatography) 21 ethylene production 224 European pears 259–61 exhaled-breath sampling 72 expansin 333, 352 extensins 358–9 external preference mapping 45–8 extruder sounds 162 extrusion 476, 490–1 Falling Number Values 484 farina 484 Fast Fourier Transform 19

ferulic acid 355, 357 fibre 216, 411–12, 494 finite element modelling 268–9 firmness 128–34 acoustic sensors 266 Asco Firmness Meter 131 and biopolymers 216–18 of bread 436, 439 and calcium infusion 374–7 chemical compounds affecting 270–8 impact force responses 135–8 laser air-puff tester 133 Magness-Taylor (MT) test 119–20, 128–31, 266, 268 of pears 262–6 and polyamines infusion 374–7 ratio of force to velocity 133 stiffness coefficient 161–2 fish 190, 196, 198, 502 Fito model 365–71 flavour 206 definition 4 exhaled-breath sampling 72 release of 6, 7, 71–2 visual flavour 4 flour 171, 441–2, 484 flow devices 14 food bolus 7, 55, 71 food breakdown 68–70, 112 see also mastication; oral processing food enjoyment 5–6 force deformation tests 19–20, 24–5, 28, 109–39, 438 bending tests 125–6 bioyield point 115, 129 data analysis 126–7 destructive methods 109–10, 118–27 dynamic tests 117–18, 133–4 elastic properties 112–15 empirical methods 109, 118 fundamental methods 109, 118 impact force responses 135–8, 262–3 impact probes 138 loading rates 110 meat 115 mechanical properties 110–18 non-destructive methods 110, 128– 38 puncture tests 14, 119–20, 127, 267, 284, 375, 440

Index quasi-static tests 117–18, 129–33 and strain 111, 260–1 and stress 110–11, 260–1 structural properties 110–18 tension tests 125, 482, 502–3 torsion/twisting tests 124–5 vibration tests 134–5 viscoelastic properties 113, 116 viscoplastic properties 113 see also compression tests force measurement 61 force to velocity ratio 133 Fourier transformation 19, 74, 103 fractal analysis 103, 154–5 fracture mechanics 18, 92, 244 freezing 381, 388–405 apples 374, 381–2 and blanching 391, 393–4 bread 445 and coatings 393, 396 dipping 392–3 glazing compounds 393 high-pressure treatments 388, 399– 400, 404, 405 immersion chilling and freezing 398– 9 jams 400–5 juice loss 390 osmotic dehydration 395–8 partial air drying 394–5 pre-freezing treatments 390–400 for jams 401–4 problems with 390–1 rate of freezing 388 strawberries 381, 388, 389 tomatoes 350–1 frequencies 147, 148, 155 fried food 82, 501–21 additives 506–7 battered frying 504–5, 506 compression tests 502, 503, 516 fish fillet 502 gluten balls 514–21 measuring texture 501–3 oil degradation 507 oil temperature 504, 507 processing variables 507 and protein 505–6 response surface methodology (RSM) 508–14, 521

529

shrimp 84–5 size of food 505, 507 and starch 505–6 storage 507 Surfactant Theory of Frying 507 tensile tests 502–3 volume expansion 516 water content 504–5 fructose 270 fruit pastilles 75 fruit and vegetables crosslink forming reactions 357–8 drop tests 136–7, 138 flavour release 71 and gelatinisation 342–3 grading costs 176 heat processing 249–51, 359 impact force responses 135–8 impact probe tests 138 lignin formation 355–7 magnetic resonance imaging (MRI) 196, 198–9 near infrared (NIR) diffuse reflectance 173–6, 179, 279–83 organically grown 359 pasteurised fruit 382 phenolic reactions 354–8 post-harvest shelf life 374–5 processing effects 213–14, 249–51, 359 resonance frequencies 157 sorting devices 134–5, 136–7, 138 sound input tests 160–2 sources of texture 211–13 starch and texture of 275–6, 342–8 storage 259–60, 275–8 torsion/twisting tests 124–5 turgor 213, 244–7, 266, 275–6 see also cultivation conditions; plant structure fundamental tests 15–16, 73, 109, 118, 209, 210–11, 243 gas absorption isotherm (BET) 69 gas retention of bread 441–2 gelatin 26, 69, 342–3 gelling agents 380–2 genetic transformation 307, 321–36, 358–9 ß-galactosidase 330–1

530

Index

chimeric gene constructs 325–6 endo-glucanase 331–3 expansin 333 extensins 358–9 pectate lyase (PEL) 334 pectinesterase activity 329–30 polygalacturonase activity 327–9 quantitative trait loci (QTL) 326–7 Ti plasmid 323–6 of tomato puree 358 of tomatoes 327–33 geometrical properties and crispness 85–6 of pasta 478 glazing compounds 393 glucose 270 gluten balls 514–21 gluten formation 432–4, 441 gluten strength 486–7 glycerol monostearate addition 488–9 gnathosonics 64 grading costs 176 Grain Research Laboratory (GRL) 483 green beans 353–4 green pickled olives 418–19 gum addition 489 hardcore 357 hardness 34, 86–7, 92 of bread 436, 440 of rice 459, 460, 465–7 of wheat 124, 171, 173 harvest conditions see cultivation conditions heat treatments 218–19, 249–51, 359 high temperature drying 491–2 mechanical behaviour of tissues 373 oven-drying 426–7 pre-freezing 392 thermal softening 250–1 vacuum infusion 376–7 hedonic scales 36–7 hedonic tests 8 high temperature drying 491–2 high-pressure treatments 388, 399–400, 404, 405 hot water blanching 391 human senses see sensory evaluation hydrodynamic mass transfers 364, 365– 71, 395

hydrophobic compounds 309 image analysis 25–6, 441 see also magnetic resonance imaging (MRI) image histograms 189 imitative tests 15, 73, 243 immersion chilling and freezing 398–9 impact force responses 135–8, 262–3 impact probe tests 138 importance of texture 34, 39 in vitro measurement 304–6 in vivo measurement 20–3, 53 acoustic myography (AMG) 59 electromyography (EMG) 20–1, 57– 9, 60, 75–6 electropalatography (EPG) 21 vibromyography (VMG) 59–60 see also sound input tests instrumental measurement 13–20, 54–5 analysis and validation 18–20 compared to sensory evaluation 243– 4 development of methods 16–18 empirical tests 14, 73, 109, 118, 243 force deformation tests 19–20, 24–5, 28, 109–39 fracture mechanics 18, 244 fundamental tests 15–16, 73, 109, 118, 209, 210–11, 243 imitative tests 15, 73, 243 magnetic resonance imaging (MRI) 187, 188, 195–9 near infrared (NIR) diffuse reflectance 167–80 nuclear magnetic resonance (NMR) 184–95 of pasta 481–4 of rice 468–9 see also sound input tests ionic strength 307–8 jams 400–5 Japanese pears 259, 261–2, 270–8 Jasmine rice 45–6 jaw movement 56, 61–4 juiciness juice loss when freezing 390 and plant structure 247 just-about-right (JAR) scales 37–8, 49

Index kinesiology 58, 61–4 kinesthesis 5, 7 kinetic modelling 210 kiwifruits 134 Kramer Shear Process 123 L-ascorbic acid addition 489 labelled affective magnitude (LAM) scales 48 laser air-puff firmness tester 133 leavening agents 507 legume beans 355–6 legume seeds 251 lemons 376 lignin formation 298–9, 355–7 line scales 36 linear regression 19, 48–9 linear relationships 40 linear voltage displacement transducer (LVDT) 63–4 loading rates 110 low-field systems 187, 189–90, 192–5 lye treatment 418–19, 421–2 Magness-Taylor (MT) test 119–20, 128– 31, 266, 268 magnetic resonance imaging (MRI) 187, 188, 195–9 and cheese 198 data analysis 188–9 and fish 196, 198 and fruit and vegetables 196, 198–9 image histograms 189 proton-density 187 qualitative evaluation 195–6 quantitative evaluation 196–9 small-bore scanners 188 wide-bore scanners 188 malocclusion 64 MARS (multivariate adaptive regression splines) 49–50 Martin Tenderometer 175 mass transfer 364, 365–71, 395 mastication 7, 20–1, 53–66, 75–6 chewing 55, 66–7, 206 and food breakdown 68–70, 112 jaw movement 56, 61–4 measuring 56–66 muscle activity 56, 58–9 oral sensitivity 56

531

teeth movement 56, 72, 246–7 bite forces 61 see also oral processing mealiness 197–8, 247, 344, 350 measurement of bread texture 437–42 of crispness 21–3 of fried food texture 501–3 of mastication 56–66 of pasta texture 478–84 see also instrumental measurement; sensory evaluation meat chicken nuggets 94, 96–7 force deformation behaviour 115 near infrared (NIR) diffuse reflectance 176–8, 179 nuclear magnetic resonance (NMR) 190, 194 poultry breast meat 40 storage conditions 178 tenderness 33, 75 Mechanical models 116 mechanical properties and crispness 86–7, 88, 96, 99–100 and force deformation tests 110–18 measuring mechanical texture 242–3 of olives 411, 414–15, 422, 424–5 of pasta 478 and sound input tests 151–2 of strawberries 390 and thermal treatments 373 microphones 149, 150, 162–3 microwave blanching 391, 393–4 milk-based products 178 milling see cereals mixing devices 14 modelling food texture 205–35 apple transport and storage 222–7 attribute-property relationship 206–7 biopolymers 216–18, 234 Blanching Response Amplification Model (BRAM) 227–31 cellular solids 215 consistency 234 dedicated models 206 disciplines 210–11 and enzymes 218–21, 228–9, 234 finite element modelling 268–9 fundamental models 209, 210–11

532

Index

goals of 209, 233 kinetic modelling 210 man-made products 214–16 notation 234–5 pears 268–70, 287–8 process-oriented modelling 211 quality assignment 206–9 reversed engineering 231–3 sources of texture 211–13 moistness of bread 436, 440–1, 444 monosodium glutamate 5 motility effect 55 mould growth 444 mouthfeel 7, 501–2 MRI see magnetic resonance imaging (MRI) multi-tasking 60 multiple compression tests 439–40 multiple linear regression (MLR) 19 multivariate adaptive regression splines (MARS) 49–50 multivariate modelling 41–4, 74 muscle activity 56, 58–9 mushrooms 354, 376, 380–1 near infrared (NIR) diffuse reflectance 167–80 calibration equations 281–2 and cereals 170–3, 179 and chocolate 178–9 and fruit and vegetables 173–6, 179, 279–83 and meat 176–8, 179 and milk-based products 178 and pulses 178 and rice 467 spectra types 280–1 V-method 173 nectarines 176 NMR see nuclear magnetic resonance (NMR) no-time dough mixing 442–3 non-destructive tests 110, 128–38, 146, 147, 155–8 non-linear relationships 40–1 noodles 475, 476 nuclear magnetic resonance (NMR) 184– 95 bread products 191–2 and cheese 192 data analysis 188–9

and fish 190 low-field systems 187, 189–90, 192–5 and meat 190, 194 and potatoes 192 starch-based products 191–2 and water distribution 184–5, 189– 90 and water protons 185 and water-holding capacity 192–5 odour responses 5 oil degradation 507 oil temperature 504, 507 olive oil content 417 olives 410–30 black olives in brine 422–4 black oxidised olives 419–22 calcium treatment 425–6 cell walls 412–14 chemical composition 410 compression test 421–2 cultivation 410 dry-salted olives 424–5 fibre content 411–12 green pickled olives 418–19 lye treatment 418–19, 421–2 mechanical properties 411, 414–15, 422, 424–5 olive oil content 417 oven-drying processing 426–7 processing 411, 418–25, 426–7 ripening 412–16 structural properties 410–11 oral processing 6–7, 55, 214 bolus formation 7, 55, 71 food breakdown 68–70, 112 motility effect 55 salivation 7, 55, 68 swallowing 7, 55, 60–1, 62, 67–8 see also mastication oral sensitivity 56 ordinary least squares (OLS) regression 48–9 organically grown fruit and vegetables 359 osmotic dehydration (OD) 372, 383, 395–8 improving mass transfer rate 395 pulsed vacuum osmotic dehydration 397–8

Index salt concentrations 395–6 vacuum osmotic dehydration 396–7 Ottawa Texture Measuring System 123 oven-drying processing 426–7 oxalate soluble pectin 275, 283–5 pain responses 5 paired comparison tests 9 panellist selection 24 panellist training 91–3 part-baking bread 447 partial air drying 394–5 partial least squares analysis (PLS) 19, 43–4 pasta 475–94 additions to 487–9 bite test 481 breaking strength 482 coloured pasta 489 compression tests 481–4 consumer preference trends 492–4 cooked frozen pasta 493 cooking times 479 drying conditions 491–2 egg addition 487–9 elasticity 482 extrusion 476, 490–1 geometric properties 478 gluten strength 486–7 glycerol monostearate addition 488–9 gum addition 489 instrumental measurement 481–4 L-ascorbic acid addition 489 manufacturing process 476–7 measurement of texture 478–84 mechanical properties 478 origins 476 processing effects 490–2 protein content 485–9 raw materials quality 475, 484–9 regrinds 492 residue in cooking water 484 scoring systems 480 sensory evaluation 479–80 spaghetti 479–80, 481–2 stickiness 483 tenderness index 481 tension tests 482 water to pasta ratio 479 pasteurised fruit 382

533

peaches 136–7, 197 pears 259–90 bruise volume/threshold measurement 285–7 compression tests 260, 261 European pears 259–61 firmness testers 262–6 Japanese pears 259, 261–2, 270–8 model of texture 268–70, 287–8 sensitivity of measurement methods 266–7 storage 259–60, 275–8 turgidity 276 Pearson correlation coefficient 19 peas 175, 343–4 pectate lyase (PEL) 334 pectin methyl-esterase (PME) 377–80, 392 pectinesterase 329–30, 392 pectins 222, 224–5, 227–8, 271–2, 274– 5, 276–8, 321–2, 328 from citrus fruits 280 and green beans 354 oxalate soluble pectin 275, 283–5 and potato texture 353 peroxidases (PODs) 295–312 biochemical and physiological role 296–9 and cell wall formation 297–8 controlling activity of 307–11 cross-linking of proteins 304–6 and the lignification process 298–9 location, biosynthesis and transport 296 modification through processing 310–11 molecular structure 300–1 occurrence of 295–6 reactions catalysed by 301–4 regeneration of activity 310–11 role in food texture 311 and stress/pathogen defences 299 thermal inactivation 310 phenolic reactions 354–8 crosslink forming 357–8 lignin forming 355–7 phosphates 506–7 piezoelectric sensors 67 pineapples 283 plant structure 244–55

534

Index

alcohol insoluble solids 274–5 cell adhesion 251–4 cell turgor 213, 244–7, 266, 275–6 cellular stability 249–51 and crispness 244–7 and juiciness 247 and mealiness 197–8, 247 of olives 410 pre-cooking effects 249–50 and processing 249–51 of rice 452–3 ripening-related softening 247–9, 252, 272–3 of strawberries 389–90 thermal softening 250–1 and thermal treatments 249–51 water content 270 see also cell walls PODs see peroxidases (PODs) Poisson’s ratio 113–14 polyamines infusion 374–7 polygalacturonase activity 327–9 polyphenoloxidases (PPOs) 295–312 biochemical and physiological role 296–9 and colour formation 297 controlling activity of 307–11 cross-linking of proteins 304, 306 and hydrophobic compounds 309 and ionic strength 307–8 modification through processing 307–10 molecular structure 300–1 occurrence of 295 and pressure 308 proteolytic activation 309–10 reactions catalysed by 301–4 role in food texture 311 and temperature 308 and viscosity 308 and water activity 308 polysaccharides 271, 304–6, 349–54, 412–16 porosity values 367 post-harvest shelf life 374–5 potato chips 159, 501 potatoes 344–7 cultivar effects 345 curing 346 environmental influences 345

magnetic resonance imaging (MRI) 198–9 mealy behaviour 344 near infrared (NIR) analysis 174–5 nuclear magnetic resonance (NMR) 192 pectins and texture of 353 peroxidases activity 299 soggy texture 344–5 starch breakdown 346–7 storage 346–7 poultry breast meat 40 PPOs see polyphenoloxidases (PPOs) pre-cooking effects 249–50 pre-freezing treatments 390–400 for jams 401–4 preference mapping 41, 48–9 external preference mapping 45–8 pressure waves sound input tests 147 pressure-shift freezing see high-pressure treatments Pressure-Vacuum mixer 442–3, 448 principal component analysis (PCA) 19 process-oriented modelling 211 processing effects on bread 442–3 on fried food 507 on fruit and vegetables 213–14, 249– 51, 359 on olives 411, 418–25, 426–7 on pasta 490–2 and plant structure 249–51 see also heat treatments product design 6 proportional odds models (POM) 49 proprioception 5, 7 proteins cross-linking 304–6 engineering techniques 335 expansin 333 flour protein 441–2 in fried food 505–6 and fruit texture 283 in pasta 485–9 proteolytic activation 309–10 proton-density 187 pulsed vacuum osmotic dehydration 397–8 pulses 178

Index puncture tests 14, 119–20, 127, 267, 284, 375, 440 quality evaluation 206–9 of rice 451, 452–5 Quantitative Descriptive Analysis (QDA) 11–13, 75 quantitative trait loci (QTL) 326–7 quasi-static tests 117–18, 129–33 quinones 301 R-index tests 10 Rapid Visco Analyser (RVA) 453–5 raspberries 378 raw materials quality 441–2, 475, 484–9 recording of sounds 150–2 refrigerating bread 445 regression analysis 19, 48–9 regrinds 492 release of flavour 6, 7, 71–2 Repertory Grid methods 22 resonance frequencies 25, 147, 155, 157 response surface methodology (RSM) 508–14, 521 retrogradation process 445 reversed engineering 231–3 rheometers 503, 516 rice 35, 191, 451–70 ageing 451–2 amylose content 452–3, 455, 470 chemical composition 452–3 cooked rice 451, 452, 455, 459–63 moisture profile during boiling 462–3 cooking tests 453–5 hardness 459, 460, 465–7 hydration 455–9, 461 water diffusion 458, 462 water uptake rate 456–8 instrumental measurement 468–9 Jasmine rice 45–6 milling effects 461 near infrared (NIR) analysis 467 physiochemical properties 452–3 quality evaluation 451, 452–5 sensory evaluation 464–5, 468–9 stickiness 464, 465–7 storage 451–2, 465, 467–8 viscosity 453–5 Rice Taster 468

535

ripening 247–9, 252, 272–4 of olives 412–16 of tomatoes 330, 351–2 Roano Surface Tensiometer 466 RSM (response surface methodology) 508–14, 521 salivation 7, 55, 68 salt concentrations 395–6 sandwich-type bread products 435–6 Satake Neuro Fuzzy Rice Taster 468 scales category scales 36 hedonic scales 36–7 just-about-right (JAR) scales 37–8, 49 labelled affective magnitude (LAM) scales 48 line scales 36 scoring systems 480 selection of panellists 24 semolina 484, 487 sensitivity of measurement methods 266–7 sensory evaluation 3–13, 54–5, 242–4 analytical tests 8 of bread 437–8 classification of procedures 8–9 colour 4 compared to instrumental measurement 243–4 difference tests 9–10 discrimination tests 9 hedonic tests 8 multi-tasking 60 odour 5 pain responses 5 of pasta 479–80 quantitative descriptive tests 11–13 of rice 464–5, 468–9 of strawberries 389 taste sensations 4–5 time-intensity (T-I) tests 60–1 touch stimulii 5 see also mastication; oral processing shear compression tests 14, 87, 119, 123–4, 503 shrimp 84–5 single attribute analysis 41 Sirognathograph 63

536

Index

size of fried food 505, 507 small-bore scanners 188 smoothness 44 snack foods 82 sodium chloride 507 softness of bread 437, 438, 439 SoftSense 137 SoftSort 137 soggy texture in potatoes 344–5 soil treatments see cultivation conditions sonic resonance tests 25 sonic transmission tests 157 sonotubometry 68 sorbitol 270 sorting devices 134–5, 136–7, 138 sound input tests 21–3, 25, 64, 146–63 acoustic signatures 151, 155 air-conducted sound 147–8 amplification of signal 149 amplitude-time plots 152, 154 bone-conducted sounds 147–8, 150–1 and crackliness 159–60 and crispness and crunchiness 83–4, 87–91, 158–60, 162, 243 data analysis and storage 149–50, 152–5, 157–8, 163 destructive tests 146, 148–55 detection of sound 147–8 extruder sounds 162 fractal analysis 154–5 frequencies 147, 148, 155 and fruit quality 160–2 mechanical testing 151–2 microphones 149, 150, 162–3 non-destructive tests 146, 147, 155–8 pressure waves 147 recording of sounds 150–2 resonance 147, 155 sonic transmission tests 157 sound pressure levels 152 sound wave analysis 152–5, 163 sources of texture 211–13 spaghetti see pasta spectroscopy 167–8 see also near infrared (NIR) diffuse reflectance Spectrum Method 12–13 springiness of bread 437 squeeze test 438 Squeezometers 439 staleness in bread 437, 444–5, 446–7

starch degradation in fruit 275–6 in fried food 505–6 and near infrared analysis 171–2 nuclear magnetic resonance (NMR) 191–2 in potatoes 346–7 retrogradation process 445 and soil treatments 345 synthesis 336 and vegetable texture 342–8 statistical analysis and validation 18–19, 513–14, 516–21 see also regression analysis steam blanching 391 stickiness 17–18, 464, 465–7, 483 stiffness 161–2, 213 storage of apples 196, 222–7 of bread 443–5 and crispness 91 of fried food 507 of fruit 259–60, 275–8 of meat 178 of mushrooms 354 of pears 259–60, 275–8 post-harvest shelf life 374–5 of potatoes 346–7 of rice 451–2, 465, 467–8 of sweet potato 348 temperatures 225–6 of tomatoes 350–1 strain 111, 260–1 strawberries calcium treatments 376, 377 firmness 277 freezing 381, 388, 389 genetic transformation 334 and jams 400–5 mechanical properties 390 microstructure 389–90 pre-freezing treatments 393 sensory quality 389 thawing 400–1 water binding properties 390 stress 110–11, 260–1 stress/pathogen defences 299 structural properties and crispness 85–6 and force deformation tests 110–18 of olives 410–11

Index and vacuum infusion 371 sugarbeet 250, 357 Surfactant Theory of Frying 507 swallowing 7, 55, 60–1, 62, 67–8 sweet potato 347–8, 348, 357 sweet rolls 196 symmetrised dot-pattern (SDP) displays 20 taste buds 5 taste sensations 4–5 teeth movement 56, 72, 246–7 bite forces 61 malocclusion 64 Teflon dies 491 tenderness 33, 75, 481 tenderometers 344 tension tests 125, 482, 502–3 Tensipressure 467 texture, definition 4, 33, 241–2 Texture Profile Analysis (TPA) 11, 56, 126–7, 439–40 Texturometer 15, 466–7, 469 thawing strawberries 400–1 theory of linear elasticity 113–15 thermal processing see heat treatments Ti plasmid 323–6 time-dependent elasticity 113 time-independent elasticity 112–13 time-intensity (T-I) tests 13, 60–1 tissue tension 213 tomato puree 358 tomatoes calcium treatments 351, 352 canned tomatoes 352 cell wall polysaccharides 349–52 cultivar and maturity 349–50 environmental conditions 350–1 force/deformation tests 131, 135 freezing 350–1 genetic modification 327–33 imaging analysis 196 mealiness 350 ripening related softening 330, 351–2 and storage 350–1 tongue 5, 7, 21, 55 torsion/twisting tests 124–5 tortilla chips 47–8 touch stimuli 5 training panellists 91–3 triangle tests 9–10

537

turgor 213, 244–7, 266, 275–6 ultrasonic tests 25, 95–6, 98, 100–2 ultrasonic velocity 96, 98 uniaxial compression test 122 V-method 173 vacuum infusion 364–84 and apples 372, 376, 378–9, 381–2 calcium infusion 374–7 deformation of the food matrix 367 enzyme addition 377–80 and freezing 381–2 gelling agents 380–2 mass transfer phenomena 364, 365– 71 polyamines infusion 374–7 porosity values 367 structural modifications 371 thermal treatments 376–7 viscosity effects 370–1 vacuum osmotic dehydration 396–7 vegetables see fruit and vegetables vibration tests 134–5 vibromyography (VMG) 59–60 viruses 323 viscoelastic properties 113, 116 viscoelastograph 482–3 viscoplastic properties 113 viscosity 308, 370–1 of rice 453–5 visual flavour 4 vitamin C 297, 299 Volodkevich bite tenderometer 15 volume expansion 516 Warner-Bratzler (WB) test 123–4 water content and distribution 184–5, 189–90, 222, 270, 308 binding properties 390 fried food 504–5 holding capacity 192–5 water protons 185 water to pasta ratio 479 wheat 124, 170–3, 484, 494 white corn tortilla chips 47–8 wide-bore scanners 188 xanthan impregnation 380–1 yams 348, 355 Young’s modulus 113, 115

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