Fishery Products: Quality, Safety and Authenticity

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FISHERY PRODUCTS Quality, safety and authenticity

Edited by Hartmut Rehbein Jörg Oehlenschläger

A John Wiley & Sons, Ltd., Publication

FISHERY PRODUCTS

FISHERY PRODUCTS Quality, safety and authenticity

Edited by Hartmut Rehbein Jörg Oehlenschläger

A John Wiley & Sons, Ltd., Publication

This edition first published 2009 © 2009 Blackwell Publishing Ltd Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwell’s publishing programme has been merged with Wiley’s global Scientific, Technical, and Medical business to form Wiley-Blackwell. Registered office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom Editorial offices 9600 Garsington Road, Oxford, OX4 2DQ, United Kingdom 2121 State Avenue, Ames, Iowa 50014-8300, USA For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wileyblackwell. The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloging-in-Publication Data Fishery products : quality, safety and authenticity / edited by Hartmut Rehbein, Jörg Oehlenschläger. p. cm. Includes bibliographical references and index. ISBN 978-1-4051-4162-8 (hardback: alk. paper) 1. Fishery products–Quality control. 2. Fishery processing–Quality control. I. Rehbein, Hartmut. II. Oehlenschläger, Jörg. SH335.5.Q35F58 2009 664′.94–dc22 2008039852 A catalogue record for this book is available from the British Library. Set in 10 on 12 pt Times by SNP Best-set Typesetter Ltd., Hong Kong Printed in Singapore 1

2009

Contents

List of contributors Preface Introduction

Chapter 1

Basic facts and figures Jörg Oehlenschläger and Hartmut Rehbein 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9

Chapter 2

Chapter 3

xi xiii xv

Introduction World fishery production Categories of fish species Fish muscle Nutritional composition Vitamins Minerals Post mortem changes in fish muscle References and further reading

1 1 1 3 4 4 10 15 15 17

Traditional methods Peter Howgate

19

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11

19 20 23 29 30 31 32 33 33 34 35

Introduction TVB-N Methylamines Volatile acids Volatile reducing substances Indole Proteolysis and amino acids pH Refractive index of eye fluids Discussion and summary References

Biogenic amines Rogério Mendes

42

3.1 3.2 3.3 3.4

42 44 47 49

Introduction Factors affecting amine decarboxylase activity Safety aspects Quality assessment

v

vi

Contents

3.5 3.6 3.7 Chapter 4

Chapter 5

Regulatory issues Methods of biogenic amine quantification References

54 55 59

ATP-derived products and K-value determination Margarita Tejada

68

4.1 4.2 4.3 4.4 4.5

68 69 79 81 81

VIS/NIR spectroscopy Heidi Anita Nilsen and Karsten Heia 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9

Chapter 6

Introduction Analytical principles and measurements Constituents: assessment of chemical composition Freshness and storage time Authentication Safety Other quality parameters Summary and future perspectives References

89 89 89 92 96 98 98 99 100 101

Electronic nose and electronic tongue Corrado Di Natale and Gudrun Ólafsdóttir

105

6.1 6.2 6.3

105 106

6.4 6.5 6.6 6.7 6.8 6.9 Chapter 7

In vivo role of nucleotides Post mortem changes Methodology for evaluating the K-value or related compounds Conclusions References

Introduction to the electronic nose and olfaction Application of the electronic nose and electronic tongue Colorimetric techniques, optical equipment and consumer electronics Classification of fish odours Quality indicators in fish during chilled storage: gas chromatography analysis of volatile compounds Application of the electronic nose for evaluation of fish freshness Combined electronic noses for estimating fish freshness Conclusions and future outlook References

108 109 111 114 116 119 120

Colour measurement Reinhard Schubring

127

7.1 7.2 7.3

127 128 130

Introduction Instrumentation Novel methods of colour evaluation

Contents

7.4 7.5 7.6 Chapter 8

Chapter 10

Chapter 11

131 159 159

Differential scanning calorimetry Reinhard Schubring

173

8.1 8.2 8.3 8.4

173 174 178

8.5 8.6 Chapter 9

Colour measurement on fish and fishery products Summary References

vii

Introduction Principle of function of the instruments First applications of DSC on fish muscle and other seafood Recent applications of DSC for investigating quality and safety Summary References

181 204 204

Instrumental texture measurement Mercedes Careche and Marta Barroso

214

9.1 9.2 9.3 9.4 9.5

214 216 229 231 231

Introduction Instrumental texture Texture measurement for quality classification or prediction Conclusions References

Image processing Michael Kroeger

240

10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10

240 241 243 244 244 245 246 246 249 249

Introduction Quality characteristics from images Spectral signature of images Elastic properties from images Analysis of image data Results and discussion Freshness determination from images Firmness information from images Conclusions References

Nuclear magnetic resonance Marit Aursand, Emil Veliyulin, Inger B. Standal, Eva Falch, Ida G. Aursand and Ulf Erikson

252

11.1 11.2 11.3 11.4 11.5 11.6

252 253 257 259 265 266

Introduction Magnetic resonance imaging Low-field NMR High-resolution NMR The future of NMR in seafood References

viii

Contents

Chapter 12 Time domain spectroscopy Michael Kent and Frank Daschner 12.1 12.2 12.3 12.4 12.5 Chapter 13

Chapter 14

Introduction Measurement system Time domain reflectometry measurements Conclusions References

273 275 278 283 285

Measuring electrical properties Michael Kent and Jörg Oehlenschläger

286

13.1 13.2 13.3 13.4 13.5 13.6

Introduction Fischtester Torrymeter Use of the Fischtester Summary References

286 286 287 294 296 297

Two-dimensional gel electrophoresis Flemming Jessen

301

14.1 14.2 14.3 14.4 14.5

301 302 305 310 312

Introduction Two-dimensional gel electrophoresis (2DE) 2DE applications in seafood science 2DE-based seafood science in the future References

Chapter 15 Microbiological methods Ulrike Lyhs 15.1 15.2 15.3 15.4 15.5 Chapter 16

273

Microorganisms in fish and fish products General aspects of microbiological methods Most probable number method Molecular methods References

318 318 320 336 336 338

Protein-based methods Hartmut Rehbein

349

16.1 16.2 16.3 16.4 16.5 16.6 16.7 16.8

349 349 351 356 357 357 359 359

Introduction Fish muscle proteins Electrophoretic methods for fish species identification High-performance liquid chromatography Immunological methods and detection of allergenic proteins Determination of heating temperature Differentiation of fresh and frozen/thawed fish fillets References

Contents

Chapter 17

Chapter 18

DNA-based methods Hartmut Rehbein

363

17.1 17.2 17.3 17.4 17.5 17.6

363 364 366 368 379 380 388

18.1 18.2

388

Chapter 21

Introduction Species and breeding stock identification by lipid analysis Verification of the production method Identification of the geographic origin Future prospects References

389 394 398 403 404

Sensory evaluation of seafood: general principles and guidelines Emilia Martinsdóttir, Rian Schelvis, Grethe Hyldig and Kolbrun Sveinsdóttir

411

19.1 19.2

411

19.3 Chapter 20

Introduction DNA in fishery products Genes used for species identification Methods Conclusions and outlook References

Other principles: analysis of lipids, stable isotopes and trace elements Iciar Martinez

18.3 18.4 18.5 18.6 Chapter 19

ix

General principles for sensory analysis Application of sensory evaluation to fish and other seafood References

417 422

Sensory evaluation of seafood: methods Emilia Martinsdóttir, Rian Schelvis, Grethe Hyldig and Kolbrun Sveinsdóttir

425

20.1 20.2 20.3 20.4 20.5 20.6 20.7

Introduction Difference tests Grading schemes Quality index method Descriptive sensory analysis Consumer tests (hedonic) References

425 425 427 430 438 440 440

Data handling by multivariate data analysis Bo M. Jørgensen

444

21.1 21.2 21.3 21.4

444 444 446 447

Introduction What is multivariate data analysis? Arrangement of data for bi-linear modelling The outcome of bi-linear modelling

x

Contents

21.5 21.6 21.7 Chapter 22

451 453 453

Traceability as a tool Erling P. Larsen and Begoña Pérez Villarreal

458

22.1 22.2 22.3

458 460

22.4 22.5 22.6 Index

Validation and prediction Real examples and further reading References

Introduction Traceability from older times to the present Traceability research in the seafood sector and other EU-funded food traceability projects Validation of traceability data Traceability in a global perspective References

465 466 468 470 472

List of Contributors

Ida G. Aursand, SINTEF Fisheries and Aquaculture, N-7465 Trondheim, Norway; and Department of Biotechnology, NTNU, N-7491, Trondheim, Norway Marit Aursand, SINTEF Fisheries and Aquaculture, N-7465 Trondheim, Norway Marta Barroso, Instituto del Frío CSIC, c/José Antonio Novais 10, 28040 Madrid, Spain Mercedes Careche, Instituto del Frío CSIC, c/José Antonio Novais 10, 28040 Madrid, Spain Frank Daschner, Technische Fakultät der Christian-Albrecht-Universität, Institut für Elektrotechnik und Informationstechnik, Kaiserstrasse 2, D-24143 Kiel, Germany Corrado Di Natale, Department of Electronic Engineering, University of Rome ‘Tor Vergata’, Via del Politecnico 1; 00 133 Roma, Italy Ulf Erikson, SINTEF Fisheries and Aquaculture, N-7465 Trondheim, Norway Eva Falch, SINTEF Fisheries and Aquaculture, N-7465 Trondheim, Norway Karsten Heia, Nofima, Marine, N-9291 Tromsø, Norway Peter Howgate, 26 Lavender Row, Stedham, Midhurst, West Sussex GU29 0NS, UK Grethe Hyldig, DTU Aqua, National Institute of Aquatic Resources, Technical University of Denmark, Søltofts Plads, Bygning 221, DK-2800 Kongens Lyngby, Denmark Flemming Jessen, DTU Aqua, National Institute of Aquatic Resources, Technical University of Denmark, Søltofts Plads, Building 221, DK-2800 Kongens Lyngby, Denmark Bo M. Jørgensen, DTU Aqua, National Institute of Aquatic Resources, Technical University of Denmark, Søltofts Plads, Building 221, DK-2800 Kongens Lyngby, Denmark Michael Kent, The White House, Greystone, Carmyllie, by Arbroath, Angus DD11 2RJ, UK Michael Kroeger, technet GmbH, Pestalozzistrasse 8, D-70563 Stuttgart, Germany Erling P. Larsen, DTU Aqua, National Institute of Aquatic Resources, Technical University of Denmark, Søltofts Plads, DTU, Bygning 221, DK-2800 Kongens Lyngby, Denmark Ulrike Lyhs, Ruralia-Institute, Seinäjoki Unit, University of Helsinki, Kampusranta 9C, 60320 Seinäjoki, Finland Iciar Martinez, SINTEF Fisheries and Aquaculture Ltd, 7465 Trondheim, Norway xi

xii

List of Contributors

Emilia Martinsdóttir, Matís (Food research, Innovation and safety), Skulagata 4, IS-101 Reykjavík, Iceland Rogério Mendes, Department of Technological Innovation and Upgrading of Fishery Products, INRB/IPIMAR, Av. De Brasilia, 1449-006 Lisboa, Portugal Heidi Anita Nilsen, NOFIMA, Marine, N-9291 Tromsø, Norway Jörg Oehlenschläger, Max Rubner Institute, Federal Research Institute for Nutrition and Food, Unit for Seafood Quality, Palmaille 9, D-22767 Hamburg, Germany Gudrun Ólafsdóttir, Department of Food Science and Nutrition, Faculty of Science, University of Iceland, Hjardarhagi 2-6, 107 Reykjavík, Iceland; and Syni Laboratory Service, Lyngháls 3, 110 Reykjavík, Iceland Begoña Pérez Villarreal, Food Research Division, Txatxarramendi Ugartea z/g, 48395 Sukarrieta (Bizkaia), Spain Hartmut Rehbein, Max Rubner Institute, Federal Research Institute for Nutrition and Food, Unit for Seafood Quality, Palmaille 9, D-22767 Hamburg, Germany Rian Schelvis, Wageningen IMARES, P.O. Box 68, NL-1970 AB IJmuiden, The Netherlands Reinhard Schubring, Max Rubner Institute, Federal Research Institute for Nutrition and Food, Unit for Seafood Quality, Palmaille 9, D-22767 Hamburg, Germany Inger B. Standal, SINTEF Fisheries and Aquaculture, N-7465 Trondheim, Norway; and Department of Biotechnology, NTNU, N-7491, Trondheim, Norway Kolbrun Sveinsdóttir, Matís (Food research, Innovation and safety), Skulagata 4, IS-101 Reykjavík, Iceland Margarita Tejada, Instituto del Frío (CSIC), José Antonio Novais, 10, 28040 Madrid, Spain Emil Veliyulin, SINTEF Fisheries and Aquaculture, N-7465 Trondheim, Norway

Preface

The contribution of fisheries and aquaculture to the human food supply has increased very significantly in recent decades. What is remarkable for this part of the food sector is the large share of fish that enters international trade, with some 37% of all fish caught and cultured being traded across national borders. So it can be argued that fish and fishery products are in the forefront of globalization, as products from all corners of the world can be found on the international market. There are many interesting facets to how this came about, in particular how well developing countries have adapted to the strict trading regimes of the modern marketplace for fish and fishery products. As food retailers consolidate in ever-larger units, the competition for customers intensifies. This has direct effects through the whole supply chain, not least primary producers. Besides, large retailers now have so much reputation at stake that they spend large sums of money to minimize the risk of ‘food scandals’ ever being attributable to the products they sell. This translates into ever more and stricter food safety and quality criteria with which all the actors in the food chain have to comply. This is one of the reasons for a rise in private standards of various sorts that are stricter than the standards set by governments. This rise in private standards is seen by many as a potential new form of protectionism. The objective of the World Trade Organization (WTO) is to facilitate free trade between nations to ‘improve the welfare of the peoples of the Member Countries’. The WTO Agreements, particularly the Sanitary and Phytosanitary Agreement (SPS) and the Technical Barriers to Trade Agreement (TBT), were set as the framework within which technical standards would be operated. In 1995 it was decided that the food standards of the Codex Alimentarius would be the standards used to resolve safety and quality questions in international trade disputes. Free trade is a very important issue on the international agenda. The international system created through the WTO is meant to create a ‘level playing field’ so that all can participate in international trade and to allow ‘trade to flow smoothly, freely, fairly and predictably.’ Thus, the importance of food standards to ascertain if they comply with agreed minimum criteria. The SPS Agreement stipulates that food standards should be based on sound science and be risk based. There is also a call for harmonization of standards and equivalence of different national standards relating to food safety management systems as long as they adhere to the same level of protection. That is a brief description of the framework, but all food standards are linked to specific methods by which compliance with them is measured. This book deals with the methods commonly used to measure the quality of fish and fishery products. Going through it is truly a story attesting to the great progress that has been made in this area in recent decades. It is interesting to see how the science has moved forward to increasing automation and online, non-destructive methods to ascertain characteristics of the products. It is also interesting to see how sensory evaluation, which not so long ago was considered subjective and thus unscientific, has been turned into an objective scientific tool in its own right. Competition in the food market makes it imperative for retailers not only to present products that are safe to eat and taste good, but also nutritionally balanced. Increasingly they xiii

xiv

Preface

also have to comply with environmental criteria such as not originating from a fish stock that is overfished or from vessels fishing illegally. In such a competitive environment, it is tempting for producers to cut corners. Water can be added to increase weight, expensive fish species can be substituted by cheaper ones or chemicals can be added to remove smells, to name only a few examples. At the same time, any wrongdoing or fraud can inflict huge damage in lost reputation for the producing countries and retailers. Therefore, objective analytical methods to verify and check compliance are of the utmost importance at all levels in today’s food chains. I therefore congratulate the authors for this important contribution to a seafood sector that aspires to deliver safe, tasty and wholesome food to the demanding modern consumer. The seafood sector has been in the forefront of globalization and this book will contribute to ensuring that quality and safety standards will be implemented by processors both in developed and developing countries. Grimur Valdimarsson Director Fish Products and Industry Division Fisheries and Aquaculture Department Food and Agriculture Organization of the United Nations Rome Italy

Introduction

When we started our careers as analytical chemists and biochemists in a seafood-dedicated institution more than 30 years ago, there were only a few analytical methods known to analyse safety and quality of seafood and which were used as well in research as in industry. The important methods were few: electrical devices as the Fischtester and Torrymeter; total volatile basic nitrogen (TVBN) for the determination of spoilage (often wrongly referred to as a freshness determination method); colony-forming units (cfu) in microbiology; and some methods for the determination of proximate composition, additives and non-desirable components. Textbooks, especially about analytical methods applied in seafood research and seafood-related industry, were missing; and monographs in this field were rare. Worthy of mention are the books written by Ludorff and Meyer (1973, in German), Connell (1995, 4th edition) and Botta (1995). More recently (2003), a book was edited by Luten, Oehlenschläger and Olafsdottir containing some chapters with information about instrumental and sensory measurement techniques. During the past 30 years the situation has changed dramatically. The changes were mainly initiated by two major causes. Firstly, there was a rapid development in the field of analytical instrumental methods, both in further development or application of existing and novel methods. Secondly, development in the research framework programmes of the European Commission enabled scientists not only to travel to congresses to meet and talk to their peers but also to conduct integrated research projects together. The latter has been very effective in the past 15–20 years. The results of these research projects and Concerted Actions led to a jump forward in seafood-related instrumental techniques in Europe. We ourselves have participated in several research projects, of which a few that were very productive and future-orientated are worth mentioning. The Concerted Actions ‘Evaluation of fish freshness’ and ‘Fish quality labelling and monitoring’, and the Research Projects ‘Multi-sensor techniques for monitoring the quality of fish (MUSTEC)’, ‘Seafood quality identification (SEQUID)’, and ‘Identification of species in processed seafood products using DNA-based diagnostic techniques’. Many of the authors in this book have also been partners in such research projects and have been selected to be contributors based on their skills and experience provided there. Unfortunately, many results obtained in the research projects have been hidden in confidential reports and never published. One of us (J.O.) has been chairman of the ‘WEFTA (Western European Fish Technologists’ Association) Working Group on Analytical Methods in Fish and Fishery Products’ since 1988. During this time,we increasingly desired to collect and publish the knowledge about modern analytical methods from all the different disciplines in a comprehensive book to make it available in a convenient form for the reader. From the first idea to realisation took many years, but the results are now in your hands. To our knowledge, this is the first book that concentrates on instrumental methods used in the seafood world. It is not a textbook about analytical methods but a guidebook for applied methodologies in the seafood area. It was our aim to present chapters that could be used by a wide range of interested readers, from students or beginners in the field who want xv

xvi

Introduction

to get a first overlook about the topic and who can be guided further by the numerous references, to advanced readers who will certainly find some new information that cannot be obtained elsewhere in such a concentrated and condensed form. The book contains chapters about traditional, instrumental, microbiological, sensory and authenticity methods. Finally, it has two chapters about multivariate data analysis and traceability. The book does not include chapters about determination of organic and inorganic pollutants for two reasons: (1) there are excellent books already available on analytical chemistry; (2) the importance of the topic and the multitude of analytical methods and pollutants would need a separate book. The chapters have been written by scientists who are all intensively working in their respective areas and who are highly specialised. Nevertheless, it is our hope that the chapters and the whole book are easily readable and understandable. We are very grateful to all authors who have contributed to this book; we thank them deeply for their patience and willingness to consider our wishes for changes, amendments and additions to the chapters during the preparation of the book. We especially thank Dr Ute Ostermeyer (Max Rubner-Institute, Hamburg, Germany), who has considerably contributed to the section about vitamins in the introductory chapter. We further thank the anonymous reviewers who have read the chapters and given valuable advice. May this book be a reference for seafood-related analytical techniques for years to come! Hartmut Rehbein Jörg Oehlenschläger

Chapter 1

Basic facts and figures Jörg Oehlenschläger and Hartmut Rehbein

1.1

Introduction

With more than 30,000 known species, fish form the biggest group in the animal kingdom that is used for the production of animal-based foods. Only about 700 of these species are commercially fished and used for food production. Further, some 100 crustacean and 100 molluscan species (for example mussels, snails and cephalopods) are used as food for humans. The amount captured worldwide is registered annually by the Food and Agriculture Organization of the United Nations (FAO). Fish and other seafood are very important in covering a part of the protein demand for humans. In 2000, food fish contributed 15.9% to the human diet on a worldwide basis (fish as a percentage of total animal protein intake). There are, however, great differences between continents and countries. In low-income, food-deficient countries (LIFDC) fish contributes 20.6%, in Asia 23.3%, in China 21.1%, whereas in South America the contribution amounts only to 5.7%, in North and Central America to 7.1% and in Europe to 10.3%. The average contribution in developed countries is 12% whereas it is 18.8% in developing countries (FAO).

1.2

World fishery production

World fishery production has been developing rapidly since 1950 (Table 1.1). In 1948 only 22 million metric tonnes of fish were captured, whereas in 2004 the world production amounted to more than 140 million tonnes. The dramatic increase of captured fish from 1950 to 1975 was followed by a somewhat more moderate increase between 1975 and 1990, and stagnation since then. Today, most fish stocks are fully exploited and a few are even overexploited. The growth in world fishery production in the past 10–15 years is based on a steadily growing aquaculture. The proportion of species farmed by aquaculture of the total world fishery production amounts today to more than 40%. The countries contributing most to total world fishery production of 140,457 million tonnes in 2004 are listed in Table 1.2. In Table 1.3 the major captured fish species are listed, and in Table 1.4 the major species farmed by aquaculture. Of this world fishery production of 140,457 million tonnes, 105,632 million tonnes (75.2%) were used for human consumption. Of these 75%, 39% were marketed fresh, 19% frozen, 8% cured and 9% canned. 1

2

Fishery Products: Quality, safety and authenticity

Table 1.1 Development of world fish production (catch and aquaculture) since 1900. Year Million tonnes

1900 4

1948 22

1958 40

1968 67

1978 73

1988 99

1995 117

1998 118

2004 140

Table 1.2 World fishery production: top 10 countries in 2004. Country China Peru India Indonesia Chile USA Japan Thailand Norway Vietnam

Capture

Aquaculture

Total (tonnes)

16,892,793 9,613,180 3,615,724 4,811,320 4,935,376 4,959,826 4,401,341 2,845,088 2,522,225 1,879,488

30,614,968 22,199 2,472,335 1,045,051 674,979 606,549 776,421 1,172,866 637,993 1,198,617

47,507,761 9,635,379 6,088,059 5,856,371 5,610,355 5,566,375 5,177,762 4,017,954 3,160,218 3,078,105

Table 1.3 World fishery production in 2004: fish species captured (greater than 1 million metric tons). Species Peruvian anchovy Alaska pollock Blue whiting Skipjack tuna Atlantic herring Chub mackerel Japanese anchovy Chilean jack mackerel Largehead hairtail Yellowfin tuna European pilchard

Taxonomic name

Amount captured (tonnes)

Engraulis ringens Theragra chalcogramma Micromesistius poutassou Katsuwonus pelamis Clupea harengus Scomber japonicus Engraulis japonicus Trachurus murphyi Trichiurus lepturus Thunnus albacares Sardina pilchardus

10,679,338 2,691,939 2,427,862 2,092,356 2,019,933 2,017,276 1,795,844 1,778,777 1,587,452 1,384,358 1,062,432

Table 1.4 World aquaculture production of fish, crustaceans and molluscs in 2004 (greater than 1 million metric tonnes). Species Pacific oyster Silver carp Grass carp Carp Japanese carpet shell Bighead carp Crucian carp Nile tilapia Whiteleg shrimp Atlantic salmon Japanese scallop

Taxonomic name

Quantity (tonnes)

Crassostrea gigas Hypophthalmichthys molitrix Ctenopharyngodon idellus Cyprinus carpio Ruditapes philippinarum Hypophthalmichthys nobilis Carassius carassius Oreochromis niloticus Penaeus vannamei Salmo salar Patinopecten yessoensis

4,429,337 3,979,292 3,876,868 3,387,918 2,860,152 2,101,688 1,949,758 1,495,744 1,386,382 1,244,637 1,126,159

Basic facts and figures

3

Part of the world fishery catch is processed into fish meal, which is used as a fertiliser or as animal (mainly fish) feed. For this, some target fish species such as sand eel and anchoveta (so-called ‘industry fish’) are caught. The fish oil recovered during the fish meal process is to some extent also used for human nutrition. The stagnation of the world fish catch has led to an intensive discussion about better use and management of the resources. Also, quality aspects that have been neglected for many years are back on the agenda (careful handling of the the catch, prolonged shelf life of ice- and frozen-stored fish, optimisation of yield in fish processing machines, etc.).

1.3 Categories of fish species Fish species can be divided into categories, for example according to their habitat as marine and freshwater species. Some species such as European eel and most salmon can live in both Table 1.5 Categories of marine and freshwater fish species according to their total fat content in edible tissue (fillet).

Category

Total fat content (%)

Common names

Taxonomic names

1–5

White halibut, wolffish, plaice, hake, dab, grey mullet, red mullet, redfish, whitch, sole, turbot, brill, trout, tench, whitefish

Medium fatty fish species

>5–10

Redfish, sardine, swordfish, Bream, catfish, albacore, dogfish, tuna, conger, salmon

>10

Sprat, black halibut, mackerel, herring, eel

Gadus morhua, Pollachius pollachius, Pollachius virens, Melanogrammus aeglefinus, Molva molva, Brosme brosme, Coryphaenoides rupestris, Macrourus berglax, Micromesistius poutassou, Molva dypterygia, Esox lucius, Perca fluviatilis, Stizostedion lucioperca, Lophius piscatorius, Microstomus kitt, Theragra chalcogramma Hippoglossus hippoglossus, Anarhichas lupus, Anarhichas minor, Pleuronectes platessa, Merluccius merluccius, Limanda limanda, Mugil cephalus, Mullus surmuletus, Sebastes marinus, Sebastes mentella, Glyptocephalus cynoglossus, Solea solea, Psetta maxima, Scophtalmus rhombus, Trutta trutta, Tinca tinca, Coregonus sp. Sebastes marinus, Sebastes mentella, Sardina pilchardus, Xiphias gladius, Abramis brama, Silurus glanis, Squalus acanthias, Thunnus thynnus, Conger conger, Salmo salar Sprattus sprattus, Hippoglossoides platessoides, Scomber scombrus, Clupea harengus, Anguilla anguilla

Lean fish species

Fatty fish species

4


environments. Typical representatives of freshwater fish are: carp, pike, perch, pikeperch, tench; examples of marine fish are: cod, saithe, redfish, mackerel and herring. Often, their anatomical shape is also used for categorisation: roundfish such as saithe, cod and hake; flat fish species such as plaice, dab and flounder; and snake-shaped fish such as eel, lamprey and moray. Also, the categories groundfish and swarmfish are used. Groundfish are those species that search for their prey close to the bottom of the sea (cod, flat fish); swarm fish are those that gather in big schools, mainly the small, pelagic, fatty species such as herring, sprat and sardine. Most species belong to the group of bony fish (Osteichthyes), which means that they have a fully developed bony skeleton. Some fish species have so-called real bones which are not attached to the backbone or to other bones but are located free in the muscle tissue. These bones are formed by hardened connective tissue. Sharks, rays and chimaeras belong to the group of cartilagenous fish (Chondrichthyes). They have no bones, but a cartilaginous tissue which is enforced by calcium carbonate. Fish species can also be divided into four classes (Table 1.5) based on their nutritional properties, for example their fat content, which can vary in some species depending on their state of maturity from 1% to 30%.

1.4

Fish muscle

Fish flesh, fish muscle or fish fillet is the name for the body musculature of fish reaching from head to tail: this muscle forms the major part of the edible portion of fish. This side muscle consists of segments (myomers) lying between connective tissue layers (myocommata). The muscle fibres within the myomers are longitudinally orientated. The proportion of fish flesh to total body weight varies between 40% and 65%, depending of species, shape, age and the physiological status of the fish. Fish with more elliptical cross sections (tuna, herring and salmon) exhibit a much higher proportion of the edible part than flatfish species or species with very big heads such as monkfish. Fish flesh consists of light and dark musculature. Both types can be differentiated by chemical composition, physiological importance and nutritional value. Most species have more light than dark muscle. Herring and mackerel have approximately equal amounts of light and dark muscle. The dark muscle occurs just below the skin in the area of the lateral line, and continues as a wedge shape to the backbone. The light musculature is used for rapid, sudden movements and obtains energy mainly from anaerobic glycolysis. For continuous swimming, fish use their dark musculature. This type of muscle is therefore well developed in pelagic species (herring, mackerel, tuna), well supplied with blood and rich in myoglobin. The metabolism of dark muscle is aerobic; energy is provided by lipids and carbohydrates.

1.5

Nutritional composition

The nutritional composition of fish (Tables 1.6 and 1.7) is comparable to that of warmblooded animals; in relation to essential elements such as selenium and iodine, it is superior.

Table 1.6 Compositional data of edible part (fillet) of marine and freshwater fish species. Data are average values calculated or estimated and rounded from several food composition tables as well as from our analyses and can be subject to great variations depending on intrinsic fish parameters such as state of maturity, sex, age, season, nutritional status, etc. Fish species

Components

22 : 6 (n − 3) (g/100 g) Cholesterol (mg/100 g)

Sea bass

Bluefish

Burbot

Common carp

Channel catfish

Cod

Pacific cod

Cusk

Eel

Engraulis encrasicolus

Morone saxiatilis

Pomatomus saltatrix

Lota lota

Cyprinus carpio

Ictalurus punctatus

Gadus morhua

Gadus macrocephalus

Brosme brosme

Anguilla anguilla

73 20 5 1.4 131 548 147 41 174 383 104 1.7 0.2 0.07 37

79 18 2 1 97 406 15 40 198 256 69 0.4 0.03 0.12 375

71 20 4 1 124 519 7 33 227 327 60 0.08 0.05 0.2 375

67 18 6 1.5 127 531 41 29 415 333 49 1.5 0.06 0.04 13

80 16 3 1 95 397 14 23 209 358 43 0.5 0.03 0.03 13

81 18 0.4 1.2 82 343 7 24 174 403 71 0.4 0.03 0.01 37

76 19 0.7 1.3 87 364 10 31 204 392 31 0.3 0.02 0.02 37

68 18 12 1.4 184 770 20 20 216 272 51 1.6 0.02 0.04 7

0.08

Total PUFA 0.28 41

79 19 1 1.2 90 377 50 32 200 404 97 0.8 0.2 0.7 136

0.538

0.169

0.252

0.07

0.238

0.13

81 18 0.5 1.2 82 343 16 32 203 413 54 0.5 0.03 0.02 33 187 0.064

0.911 30

0.585 80

0.519 59

0.096 60

0.114 66

0.234 58

0.12 39

0.135 37

0.084 0.063 51


Moisture (g/100 g) Raw protein (g/100 g) Total lipids (g/100 g) Ash (g/100 g) Energy (kcal/100 g) Energy (kJ/100 g) Calcium (mg/100 g) Magnesium (mg/100 g) Phosphorus (mg/100 g) Potassium (mg/100 g) Sodium (mg/100 g) Zinc (mg/100 g) Copper (mg/100 g) Manganese (mg/100 g) Selenium (μg/100 g) Iodine (μg/100 g) 20 : 5 (n − 3) (g/100 g)

Anchovy

5

6

Continued Fish species

Components Moisture (g/100 g) Raw protein (g/100 g) Total lipids (g/100 g) Ash (g/100 g) Energy (kcal/100 g) Energy (kJ/100 g) Calcium (mg/100 g) Magnesium (mg/100 g) Phosphorus (mg/100 g) Potassium (mg/100 g) Sodium (mg/100 g) Zinc (mg/100 g) Copper (mg/100 g) Manganese (mg/100 g) Selenium (μg/100 g) Iodine (μg/100 g) 20 : 5 (n − 3) (g/100 g) 22 : 6 (n − 3) (g/100 g) Cholesterol (mg/100 g)

Haddock

White halibut

Greenland halibut

Herring

Ling

Mackerel

Monkfish

Mullet

Redfish

Melanogrammus aeglefinus

Hippoglossus hippoglossus

Rheinhardtius hippoglossoides

Clupea harengus

Molva molva

Scomber scombrus

Lophius piscatorius

Mugil cephalus

Sebastes marinus

78 19 0.7 1.2 87 364 33 39 188 311 68 0.3 0.06 0.03 30 186 0.059 0.126

78 21 2.3 1.4 110 460 47 83 222 450 54 0.4 0.03 0.02 37 22 0.071 0.292

70 14 14 1 186 778 3 26 164 268 80 0.4 0.03 0.01 37 74 0.526 0.393

72 18 9 1.5 158 661 57 32 236 327 90 1 0.09 0.04 36 41 0.709 0.862

80 19 0.6 1.4 87 364 34 63 198 379 135 0.3 0.1 0.03 36 175 Total PUFA 0.22 31

64 19 14 1.4 205 858 12 76 217 314 90 0.6 0.07 0.02 44 109 0.898 1.401

83 15 1.5 1.2 76 318 8 21 200 400 18 0.3 0.03 0.02 36 27 Total PUFA 0.61 33

77 19 3.8 1.2 117 490 41 29 221 357 65 0.5 0.05 0.02 36

79 19 1.6 1.2 94 393 107 30 216 273 75 0.5 0.03 0.02 43 70 0.08 0.211

36

45

42

31

33

0.217 0.108 49

42


Table 1.6

Fish species

Components


Saithe

Alaska Pollack

Orange roughy

Bluefin tuna

Skipjack

Yellowfin tuna

Turbot

Wolffish

Esox lucius

Pollachius virens

Theragra chalcogramma

Hoplostetus atlanticus

Thunnus thynnus

Euthynnus pelamis

Thunnus albacares

Psetta maxima

Anarhichas lupus

79 19 0.7 1.2 88 368 57 31 220 259 39 0.67 0.05 0.2 12

78 19 1 1.4 92 385 60 67 221 356 86 0.37 0.05 0.02 36 121 0.071

82 17 0.8 1.2 81 339 5 57 376 326 99 0.44 0.04 0.02 22 46 0.15

76 15 0.7 0.9 69 289 30 30 200 300 63 0.75 0.1 0.02 36

68 23 5 1.2 144 602 8 50 254 252 39 0.6 0.09 0.02 36

71 22 1 1.3 103 431 29 34 222 407 37 0.82 0.09 0.02 36

71 23 15 1.3 168 452 16 50 191 444 37 0.52 0.06 0.02 36

77 16 3 2 95 397 18 51 129 238 150 0.22 0.04 0.02 36 180 Total PUFA 0.88

80 18 2 1.2 96 402 6 30 200 300 85 0.78 0.03 0.02 36

0.033 0.074 39

0.35 31

0.283

0.22 71

0.890 20

38

0.071 0.185 47

0.037 0.181 45

39

0.307 0.316 43 Continued


Moisture (g/100 g) Raw protein (g/100 g) Total lipids (g/100 g) Ash (g/100 g) Energy (kcal/100 g) Energy (kJ/100 g) Calcium (mg/100 g) Magnesium (mg/100 g) Phosphorus (mg/100 g) Potassium (mg/100 g) Sodium (mg/100 g) Zinc (mg/100 g) Copper (mg/100 g) Manganese (mg/100 g) Selenium (μg/100 g) Iodine (μg/100 g) 20 : 5 (n − 3) (g/100 g)

Pike

7

8

Continued Fish species

Components Moisture (g/100 g) Raw protein (g/100 g) Total lipids (g/100 g) Ash (g/100 g) Energy (kcal/100 g) Energy (kJ/100 g) Calcium (mg/100 g) Magnesium (mg/100 g) Phosphorus (mg/100 g) Potassium (mg/100 g) Sodium (mg/100 g) Zinc (mg/100 g) Copper (mg/100 g) Manganese (mg/100 g) Selenium (μg/100 g) Iodine (μg/100 g) 20 : 5 (n − 3) (g/100 g) 22 : 6 (n − 3) (g/100 g) Cholesterol (mg/100 g)

Salmon

Chinook

Keta

Coho

Swordfish

Salmo salar

Oncorhynchus tschawytscha

Oncorhynchus keta

Oncorhynchus kisutch

Xiphias gladius

75 20 4 1.2 120 502 11 22 283 429 50 0.5 0.06 0.02 35

73 22 6 1.2 146 611 36 31 262 423 46 0.4 0.05 0.01 36

76 20 4 1.5 121 506 4 27 263 288 90 1.2 0.1 0.02 48

69 20 6 2.5 142 594 12 29 200 490 44 0.6 0.3 0.02 37 45 0.32 1.12 26

73 20 10 1.4 180 753 22 95 289 394 47 0.4 0.04 0.02 36 0.788 0.567 66

0.233 0.394 74

0.429 0.656 45

0.108 0.531 39


Table 1.6

Table 1.7 Compositional data of edible part (fillet) of marine crustacean and molluscan shellfish species. Data are average values calculated or estimated and rounded from several food composition tables as well as from our analyses and can be subject to great variations depending on intrinsic fish parameters such as state of maturity, sex, age, season, nutritional status, etc. Crustacean and molluscan shellfish species

Components


American oyster

Common octopus

Blue mussle

American lobster

Snow crab

Dungeness crab

Blue crab

King crab

Crassostrea gigas

Crassostrea virginica

Octopus vulgaris

Mytilus edulis

Homarus americanus

Chionoectes opilio

Cancer magister

Callinectes sapidus

Paralithodes camtschatica

81 12 2 1.6 86 360 26 4 34 197 320 286 1.6 0.1 3.4 45 99 0.188

77 19 1 2.2 90 377 48 0.3 27 144 275 296 3 1.7 0.06 41

81 19 1 2 90 377 26 2.5 49 133 173 539 2.8 0.6 0.03 35

79 17 1 1.7 86 360 46 0.4 45 182 354 295 4.3 0.7 0.08 37

79 18 1 1.8 87 364 89 0.7 34 229 329 293 3.5 0.7 0.2 37

80 18 0.6 1.8 84 351 46 0.6 49 219 204 836 6 0.9 0.04 36

0.268

80 15 1 1.6 82 343 53 5.3 30 186 350 230 1.7 0.4 0.03 45 25 0.076

0.292 53

0.081 48

0.253 28

82 9.5 2 1.2 81 339 8 5 22 162 168 106 17 1.6 0.6 77 0.438 0.25 50

85 7 2 1.4 68 285 45 6.7 47 135 156 211 91 4.5 0.4 64

Total PUFA 0.15 95

0.259

0.219

0.17

0.113 55

0.088 59

0.15 78

Total PUFA 0.13 42


Moisture (g/100 g) Raw protein (g/100 g) Total lipids (g/100 g) Ash (g/100 g) Energy (kcal/100 g) Energy (kJ/100 g) Calcium (mg/100 g) Iron (mg/100 g) Magnesium (mg/100 g) Phosphorus (mg/100 g) Potassium (mg/100 g) Sodium (mg/100 g) Zinc (mg/100 g) Copper (mg/100 g) Manganese (mg/100 g) Selenium (μg/100 g) Iodine (μg/100 g) 20 : 5 (n − 3) (g/100 g)

Pacific oyster

9

10


The protein of fish muscle is rich in essential amino acids, has a high biological value and can be digested easily. The amount of connective tissue is low (1–2%) compared with warm-blooded animals (10–13%). The content of non-protein nitrogen (NPN) components in fish flesh is high. The main components are creatine (200–700 mg/100 g), trimethylamine oxide (100–1000 mg/100 g), adenosine nucleotides (200–400 mg/100 g), free amino acids and dipeptides. Chondrichthyes contain high amounts of urea. The average sum of NPN amounts to 420 mg/100 g and contributes to 15% of raw protein content (nitrogen content × 6.25). The fat content of fish varies greatly in quantity and fatty acid composition. The protein content is almost constant. The fat content is mainly dependent on biological state of maturity, but also on nutritional status, age, catching ground and season. The fat is not homogeneously distributed in the body. In lean fish species, it is located in the liver as an energy reservoir; in fatty species, it is deposited in the muscle tissue, as a subcutaneous layer under the skin or in the intestines. In many fatty fish species, a linear correlation exists between the fat and water content of muscle tissue. Lean fish species have a higher proportion of polar lipids (phosphatidylcholine and phosphatidylethanolamine) than fatty fish species, in which the fat consists mainly of neutral lipids (triacylglycerols). The polar lipids are mainly located in the lipid bilayer of the cell membranes, whereas the neutral lipids are located in the fat cells of the energy reservoirs (liver, muscle). The cholesterol content of fish muscle is generally low (35 mg/100 g). Fish lipids differ from those of terrestrial animals mainly in their high content of longchain, highly unsaturated fatty acids of the n–3 series (eicosapentaenoic acid, 20 : 5 and docosahexaenoic acid, 22 : 6), often referred to as polyunsaturated fatty acids (PUFAs). The content of these PUFAs in fatty fish species can be high: dogfish 3 g/100 g, herring 2.3 g/100 g, mackerel 4.6 g/100 g, salmon 2.3 g/100 g and tuna 2.1 g/100 g. The highly unsaturated character of these fatty acids is the reason why they are susceptible to lipid oxidation and oxidative degradation. Fatty fish species therefore have a tendency to exhibit rancid tastes and odours after limited storage time.

1.6

Vitamins

The vitamin contents in fish are species specific. They can vary considerably within one species with age, size, sex, season, diet, state of health and geographic location. In fish farmed by aquaculture, the contents of vitamins reflect the composition of the corresponding components in the fish feed. Therefore, the vitamin content of wild and farmed fish can be different. From different food composition tables, the mean vitamin contents in raw muscle are summarised in Table 1.8a, b for several marine fish and in Table 1.9a, b for freshwater fish.

1.6.1

Fat-soluble vitamins

The liver of fish is a rich source of fat-soluble vitamins (A, D, E and K). In fish flesh, dark muscle contains more fat-soluble vitamins than white muscle because of its higher fat content.

Table 1.8a Fat-soluble vitamins in marine fish species (μg/100 g edible portion). Species Anchovy Anglerfish Atlantic halibut Catfish (wolffish) Cod Dab Flounder Greenland halibut Haddock Hake Herring (Atlantic) Herring (Baltic Sea) Horse mackerel Ling Mackerel Mullet Plaice Redfish (ocean perch) Saithe

n.d., No data.

Vitamin A

Vitamin D

Vitamin E

2.3 0.1–1.5 1.6–10.4 2.0–2.5 0.3–0.7 1.0–1.6 0.7–3.2 9.8–14.1 0.2–1.0 0.4–2.5 14.0–17.8 9.2 3.9–5.6 0.6–2.8 3–30 4.2–4.3 1.4–1.9 0.9–3.6 0.2–0.9 0.3–0.9 4.5–5.2 1.26 1.7–1.8 1.1–7.1 10.5–17.6 4.0–4.4 15.1–15.5 1.1–4.9 10.0 1.7–3.0 0.2–0.5 0.6–0.7 1.1

19 8–80 PO43− treatment. Myosin treated with Cl− and PO43− showed lower ΔH values on heating than the control, which is likely due to denaturation and unfolding of myosin by acid treatments. Among the two anions, PO43− treatment showed lower ΔH than Cl− treatment, indicative of greater denaturation of myosin by PO43− treatment (Raghavan and Kristinsson 2007). Richards et al. (2007) compared thermal stability of haemoglobin from tilapia (Oreochromis niloticus) with that from trout (Onchorhynchus mykiss) by microDSC. Tilapia haemoglobin had a single peak at 61 °C, whereas trout haemoglobin had one peak at 52 °C and another at 60 °C. Scans were conducted only at pH 7.4, as reliable results could not be obtained at pH 6.3 owing to extensive aggregation.

8.4.2

DSC measurements taken on invertebrates

The Argentine group of Crupkin specialises in this field. During the past 10 years, numerous papers have been published dealing with DSC measurements on mussels (Aulacomya ater ater), squid (Illex argentinus) and scallop (Zygochlamys patagonica). Whole adductor muscle of mussel free of connective tissue showed two transitions (Tmax 50.5 and 72.5 °C) and ΔH of 2.5 cal/g. Sarcoplasmic proteins contributed to both denaturation peaks. The DSC curves of actomyosin were similar to those of whole muscle. Two endothermic peaks (36 and 50.5 °C) were observed in the DSC curve of myosin. DSC curves corresponding to

184


actomyosin, paramyosin and sarcoplasmic proteins indicated that myosin and paramyosin contribute mainly to the first transition and that actin is responsible for most of the second transition in whole muscle. Thermal stability of whole muscle decreased with increasing pH and ionic strength. Total denaturation enthalpy significantly decreased with an increase of the ionic strength (Paredi et al. 1994). Post mortem thermal behaviour of the striated adductor muscles of mussel stored at 2–4 °C was characterised as follows. The exothermal peak present in pre rigor muscles from other species, such as fish or mammalian muscles, was not evident in DSC curves of either fresh or cold-stored adductor muscles. In the first 8 h of storage, the greatest increases in both total ΔH and ΔH related to the first endothermal transition were observed. It was suggested that onset of rigor mortis in the mussel can be determined by enthalpy measurements (Paredi et al. 1995). Mussels (Perna canicula), which were thermally treated during processing to facilitate an easier removal of shells, were investigated on their degree of doneness by DSC. DSC curves of differently treated samples revealed by the number of peaks identified as well as by their ΔH that trade samples in question were only blanched and not completely well cooked (Rehbein and Schubring 1996). The DSC curves of whole muscle of female squid showed four endothermic transitions, with Tmax equal to 45.9, 56.8, 67.2 and 79.2 °C, respectively. DSC curves of whole muscle of male squid showed three transitions, with Tmax equal to 47.9, 56.8, and 79.2 °C, respectively. Tmax of 67.2 °C, present only in female squid muscle, was related to sarcoplasmic proteins. Myosin and paramyosin contributed to the first transition, connective tissues to the second transition, and actin to the last transition. No major differences were observed in Tmax values that were related to the sex and sexual maturation stage of specimens. The lowest ΔH was found in muscle from immature females. Independent of sex and sexual maturation stage of specimens, no major changes were observed in either Tmax or ΔH during frozen storage of squid (Paredi et al. 1996). DSC curves of both striated and smooth muscles free of connective tissue derived from scallop showed two transitions, Tmax of 53.2 and 79 °C, and Tmax of 52.7 and 78 °C, respectively. These results indicate that the different paramyosin content of the muscles did not influence the thermal stability of their proteins. DSC curves of myofibrils and actomyosin were similar to those corresponding to respective whole muscles. Myosin from striated muscles showed a cooperative single peak, with Tmax equal to 48.8 °C. Similar Tmax values were observed in DSC curves of myosin from smooth muscle. As pH and ionic strength increased, thermal stability of whole muscle decreased. Smooth muscles were more affected than striated muscles. The pH increment significantly affected ΔH of whole smooth muscles. ΔH significantly decreased when ionic strength increased to 0.5 in both types of muscle (Paredi et al. 1998). DSC curves of both striated and smooth whole muscles of scallop showed two transitions, Tmax 55.0, 79.2 °C and Tmax 54.7, 78.7 °C, respectively. The pH increase (5.0 to 8.0) significantly decreased ΔH of whole striated muscles. Significant decreases in ΔH were also observed in DSC curves of smooth muscles at pH 8.0. ΔH significantly decreased when ionic strength increased from 0.05 to 0.5 in both types of muscle. Striated muscles were affected more than smooth muscles by changes in the chemical environment (Paredi et al. 2002). Three major endothermic peaks at 50, 57 and 74 °C were found in the DSC curve of the mantle of cuttlefish (Sepia esculenta). The first and second peaks mainly corresponded with the denaturation of myosin and collagen. The third peak was that of actin, which was almost native up to 63 °C, whereas the other proteins had

Differential scanning calorimetry

185

been completely denatured below 63 °C (Mochizuki et al. 1995a, b). Comparable results were reported for Sepia pharaonis (Thanonkaew et al. 2006). They found three endothermic transitions, with Tmax values of 49.8, 59.8 and 74.7 °C in the head portion, and three in the mantle portion with Tmax values of 50.3, 60.3 and 78.8 °C, corresponding to the thermal denaturation of myosin and paramyosin, connective tissues and actin, respectively. It was concluded that the thermal stability of an individual portion varied, depending on cuttlefish portions. In frozen and thawed freshwater prawns, the onset and peak melting temperatures corresponding to myosin denaturation, as well as ΔH of prawn muscle, decreased after freezing– thawing treatments (Srinivasan et al. 1997a). There were no significant differences in the thermal properties of prawns with changes in the rate of freezing. However, the thermal properties were influenced by the rate of thawing. Rapid thawing resulted in a lower thermal stability of prawn proteins compared with slow or moderately fast thawing methods. Keeping prawn shells intact or not intact during freezing–thawing did not alter the thermal properties of the prawn proteins. Fresh prawns were subjected to five freeze–thaw cycles (−29 °C to 22 °C). ΔH decreased from 16.6 J/g (fresh) to 13.5 J/g after one freeze–thaw cycle with minor changes thereafter (Srinivasan et al. 1997b). The influence of protein content (25, 35 and 40%) in the feed given to blue shrimp on their thermal stability was investigated (Rivas-Vega et al. 2001). DSC curves showed three transition peaks, with Tmax values of 52, 72 and 86 °C. An influence of feed was not obvious. When fish (blue marlin, saury and skipjack) and shellfish (scallop and prawn) meat were compared for their thermal properties, three main endothermic peaks were displayed in the DSC curves (Uddin et al. 2001). Values of Tmax for myosin and sarcoplasmic proteins were approximately similar in both fish and shellfish meats, whereas that for actin was significantly higher in shellfish compared with fish meat. From our recently performed DSC measurements on deepwater rose shrimps (Parapenaeus longirostris) caught in Turkey, it became obvious that frozen/thawed muscle of shrimp exhibited three peaks at 33.4, 50.4 and 57.9 °C, respectively. Pre-heating of samples to different temperatures in the range 30–70 °C caused the following changes in the DSC curves: at 40 °C the first peak disappeared, at 50 °C the second peak disappeared additionally, and at 70 °C no peak was detectable. However, in the range 50–65 °C, an additional peak at 73 °C became visible. A comparison of DSC curves taken from deepwater rose shrimp with those of black tiger shrimps (Penaeus monodon) farmed in Vietnam and white shrimps (P. vannamei) farmed in Ecuador revealed that the values of Tmax of the first peak were different, at 40 and 45 °C, respectively. This can be seen as an indication that species are possibly more responsible than environmental temperature for thermal stability of muscle proteins of shrimp. Thermal stability of muscle proteins from black tiger shrimp and from white shrimp were compared by DSC (Sriket et al. 2007). Two major peaks were obtained, corresponding to myosin and actin peaks. The two shrimps had similar Tmax and ΔH values of the first peak, suggesting that myosins of both shrimp muscles had similar temperatures and energies required for denaturation. Tmax of the second peak, representing actin of black tiger shrimp, was lower than that of white shrimp. However, no differences in ΔH of actin were found between the two species. This result revealed that the actin of white shrimp meat was more likely to resist thermal denaturation than that of black tiger shrimp meat. In shark muscle during heating, the following changes measured by DSC were assumed (Chen 1995): (1) changes in conformation of myosin molecules including solubilisation and

186


denaturation of myosin tails might occur at a low temperature (30–43 °C), corresponding to the low endothermic peaks; (2) at higher temperatures (47–57 °C), the head portion of the myosin molecule might project from the filament to enable more interactions among head portions; and (3) further interactions between myosin and actin could then form the actomyosin complex. In DSC analysis of small hammerhead shark (Sphyrna levini) muscle during cold storage at 5 °C, an exothermic peak within 25–45 °C and a low-temperature endothermic peak (LTEP) around 35 °C were observed after 8 and 56 hours, respectively (Chen et al. 1996a). The disappearance of LTEP accompanied the increase in the peak area at 61 °C, which was considered attributable to the denaturation of actomyosin complex. The thermal stability of thresher shark (Alopias pelagicus) muscle decreased during frozen storage at −18 °C (Chen et al. 1996b). The exothermic peak between 30 and 40 °C observed for the unfrozen muscle and the LTEP disappeared in the DSC pattern of muscle stored for 150 days. The longer the fish chunks were stored, the sooner the LTEP or endothermic peak around 50 °C disappeared and the endothermic peak at around 62 °C appeared. During frozen storage of silvertip shark (Carcharhinus albimarginatus), it was observed that the exothermic peak at around 45 °C visible for both fresh and 7-day frozenstored muscle disappeared in the muscle stored longer than 14 days. Tmax of the endothermic peak of around 57 °C shifted to a lower temperature, and ΔH of muscle diminished with storage time. This revealed that the thermal stability of shark muscle decreased for the duration of storage time (Chen 1996). The DSC pattern of different shark meat (catsharks, Scyliorhinus spp., smoothhounds, Mustelus spp., liveroil sharks, Galeorhinus spp.) showed two main endothermic peaks and additionally a smaller one at lower temperatures. Skin was characterised by a pronounced peak that can be attributed to collagen. Additionally, DSC measurements were taken on previously heat-treated shark meat and skin (smoothhounds). Peaks disappeared gradually in the DSC curves taken on meat with increasing temperature. In skin samples the collagen peak at around 48 °C disappeared completely after heating to 45–50 °C, whereby after heat treatment to 50 °C a new small peak appeared at a lower temperature (around 28 °C), which resulted probably from denaturation of collagen to gelatine (Schubring 2007). Figure 8.4 displays the DSC curves of several invertebrates and shows the species-specific pattern.

8.4.3

DSC measurements on connective tissue and collagen

In Japan, a great amount of fish scales is produced in sardine-processing factories and has potential as an important collagen source. The DSC curve of soluble collagen obtained from sardine scales indicated one peak, with Tmax at about 44 °C. This denaturation temperature was about 10 °C lower than that of pig collagen gel and is therefore interesting for food application (Nomura et al. 1996). Isinglass, a substance used to clarify alcoholic beverages, is derived from the swim bladder of certain tropical fish and consists predominantly of collagen. It exists as a rod-like triple helical molecule and is thermally labile. A Tmax of 29.8 °C and ΔH of 58.1 J/g were measured by DSC (Hickman et al. 2000). Collagens of skin and bone from bigeye snapper were classified as type I collagen. An endothermic peak, with Tmax at 31.0 and 31.5 °C, was observed for collagens from the skin and bone rehydrated in water, respectively. For collagens rehydrated in acetic acid, Tmax shifted to lower temperatures, 28.7 and 30.8 °C for collagens from skin and bone, respectively (Kittiphattanabawon et al. 2005).


187

–5.8 Scallop –6.0

–6.2

–6.4

Shrimp

Squid

–6.6

–6.8 ↑ Exo 25

30

35

40

45

50

55

60

65

Furnace temperature (°C) Figure 8.4 DSC curves of several invertebrates (scallop, Patinopecten spp.; shrimp, Parapenaeus longirostris; squid, Todarodes sagittatus).

Skin collagen from bigeye snapper has high thermal stability compared with those reported for some cold-water fish species (Jongjareonrak et al. 2005). The effect of frozen storage (−10 and −30 °C), formaldehyde and fish oil on collagen, isolated from cod muscle, was investigated (Badii and Howell 2003). DSC showed a highly cooperative transition at 28.2 °C for isolated collagen. Changes in the thermodynamic properties of collagen were observed on frozen storage at −10 °C compared with the control at −30 °C because of changes in structure. In the presence of formaldehyde, there were no changes in the DSC collagen transition; however, in the presence of fish oil, there was an increase in enthalpy and an extra peak was observed at 44.6 °C, indicating collagen–fish oil interaction. Interaction of gelatine obtained from the skin of North Sea horse mackerel (Trachurus trachurus) with egg albumin proteins was investigated (Badii and Howell 2006). Horse mackerel gelatine solutions of 3, 5, 7 and 10% w/w in distilled water denatured at Tmax 14.7 °C, 14.7, 15.1 and 15.0 °C, which were not significantly different; however, ΔH values were. The samples were heat–reversible, with minor changes in Tmax and ΔH on a second scan of each gelatine sample confirming that gelatine undergoes a helix to coil transition on heating and on cooling refolds, recovering most of the helical structure. DSC curves of egg albumin showed three transitions, which were not reversible. In the mixture of 3% gelatine and 10% egg albumin, gelatine showed one cooperative transition, with Tmax 15.0 °C and ΔH 1.5 J/g. Even in the presence of egg albumin, this transition was reversible.

188


Gelatines were prepared from the skins of the tropical fish, sin croaker (Johnius dussumeiri) and shortfin scad (Decapterus macrosoma), and compared for their thermal stability with bovine gelatine. The values of Tmax of gelatine gels were 28.9, 24.6 and 18.5 °C respectively for bovine, shortfin scad and sin croaker gelatines. The melting point of bovine gelatine was significantly higher than that of the other gelatines. These melting points were far higher than those reported for cod skin (Cheow et al. 2007).

8.4.4

Heat-induced gelation process

Heat-induced gelation of surimi, an intermediate product produced by repeated washing of minced fish and mixing with cryoprotectants to extend its frozen shelf life, is an important step in the manufacture of a variety of surimi-based seafoods such as kamaboko, fish meat gel, and crab and other shellfish analogues. Heat-induced gelation of surimi is a complex physicochemical process involving structural and functional changes of myofibrillar proteins. DSC measurements were an obvious application for investigating these processes. Arrowtooth flounder myosin was found to undergo a multistage denaturation process, as characterised by an endothermic trough and peaks. Tonset was observed at 25 °C whereas Tmax was seen at 36 °C with one transition peak detected at 30 °C. This indicates that myosin of arrowtooth flounder is highly unstable to heat (Visessanguan et al. 2000). To investigate how proteolysis affects heat-induced gelation, papain was added to arrowtooth flounder myosin. This significantly decreased the enthalpy required to induce myosin denaturation without significant changes in Tonset and Tmax. Thermal denaturation kinetics indicated decreases in both activation energy and the rate of myosin denaturation (Visessanguan and An 2000). DSC curves of silver hake and mackerel surimi showed three endothermic peaks. When starch was added to surimi samples, an endothermic peak having a large area at a temperature of about 70 °C was observed, which overlapped the third actin transition. No significant shifts in the endothermic peaks of myofibrillar proteins were detected with increasing starch content. Transition temperatures for the starch–surimi system were higher than those for the starch–water system. There were deviations in the apparent heat capacity function calculated from DSC measurements in surimi samples containing starch. These are attributed to gelatinisation of starch and modification of water structure (Belibagli et al. 2003). Protein structural changes during preparation and storage of surimi investigated by DSC revealed a loss of myofibrillar proteins from surimi after three washing cycles and indicated greater protein stability in surimi compared with minced fish (Moosavi-Nasab et al. 2005). The use of polysaccharide gum such as hydroxypropylmethylcellulose in horse mackerel surimi as a possibility to reduce fat content resulted in variation of thermal stability (Chen et al. 2005). Medium-grade Alaska pollock surimi was used to investigate the effects of functional protein additives on calorimetric properties. The myosin peak temperature was shifted to higher values with addition of protein additives. These protein additives appeared to delay denaturation (unfolding) of fish proteins. Protein additives reduced the enthalpy of endothermic peaks. The reduction in enthalpy was possibly due to increased protein aggregation enhanced by protein additives (Park 1994). The thermal denaturation of tilapia (Oreochromis nilotica) surimi was shown to occur through three independent processes, and the temperatures at which these processes took place were 57.1 °C, 61.6 °C and 65.7 °C.


189

Addition of 0.2% of carrageenan raised the gelatinisation temperatures of tapioca and modified waxy maize starches by 8 °C and 4 °C, respectively. However, it decreased the reaction enthalpy of waxy maize from 101.1 to 10.4 cal/g, and tapioca from 184.1 to 4.4 cal/g. The addition of starch and carrageenan to surimi was investigated by DSC. It was observed that for the surimi/tapioca and surimi/tapioca/carrageenan, the transition temperature increase was 20 °C compared with pure surimi (Barreto et al. 2000). Mixtures of κcarrageenan plus other hydrocolloids (locust bean, guar, xanthan, í-carrageenan, sodium carboxymethylcellulose, and sodium alginate) were examined for their effects on the thermal behaviour of heat-induced gels made from washed blue whiting mince. DSC revealed faint interactions for the mixtures of κ-carrageenan with locust bean and with xanthan, and weakly synergistic gelling effects between the last two hydrocolloids. The blend of κ-carrageenan with sodium alginate exhibited thermally strong synergistic interactions, but no particular effects were induced on corresponding functional properties (Perez-Mateos et al. 2001). Changes in thermal properties of ribbonfish (Trichiurus spp.) meat during different periods of ice storage were investigated (Dileep et al. 2005). The DSC profile of fresh ribbonfish meat revealed transitions at 33.2 °C, 48.9 °C and 61.0 °C, indicating the denaturation temperature of different protein fractions. The gelatinisation temperature of tapioca starch solution was found to be in the range 60–65 °C, and for cornstarch 67–70 °C. The viscoelastic properties of ribbonfish meat were altered significantly, both due to the addition of starch and ice-storage period (Dileep et al. 2005). DSC was used to study the effect of fish protein, salt, sugar and monosodium glutamate (MSG) on gelatinisation of tapioca and sage starches in fish cracker mixtures. One endothermic transition was observed for fishstarch mixtures (10–90% wet fish) if the moisture content was more than 61%. The effect of the salt on the starch gelatinisation was greater than sugar and MSG. Sugar and MSG addition to the mixture had little effect on gelatinisation of starch in the system. Two per cent salt increased the gelatinisation temperature by 4–5 °C. Tonset and Tmax increased with increases in fish content in fish-starch mixtures but the conclusion temperature (the temperature at which the DSC signal ceases to deviate from the baseline) remained relatively constant. Increases in fish content also narrowed the gelatinisation temperature ranges (Cheow and Yu 1997).

8.4.5

Antifreeze activities

Antifreeze glycoproteins originating from fish were analysed for their activities by DSC. These proteins prevent the growth of ice crystals in the supercooled organisms. In polar fish, antifreeze glycoprotein consists of eight glycoproteins, sequentially numbered based on the mobility in polyacrylamide gel electrophoresis. DSC revealed that the low molecular mass glycoprotein 8 was sensitive to cooling rate, whereas the high molecular mass glycoproteins 1–5 were not. DSC curves revealed an initial shoulder in the exotherm direction upon cooling, which correlates with observed c-axis ice growth. DSC further revealed that glycoprotein antifreezes have a linear increase in thermal hysteresis or antifreeze activity with a decrease in sample ice content (Hansen et al. 1991). Recently, it was shown that the smaller antifreeze glycoproteins 7 and 8 from Antarctic fish showed reduced levels of inhibition of ice growth, as indicated by the absence of an initial exotherm and the absence of a lag at the start of each run (Ramløv et al. 2005).

190


The cryoprotective properties of proline in cod muscle were studied during freezing to − 60 °C and subsequent heating to 10 °C. No exothermic or endothermic transitions due to the crystallisation of proline were observed, suggesting that proline has the ability to stay in solution when the freezable water fraction of cod muscle was converted to ice. Therefore, proline may represent a promising ingredient as a cryoprotective in fish products (Rasmussen et al. 1997). Protein denaturation during frozen storage at −18 °C for 16 weeks was studied in jack mackerel (Trachurus murphyi) actomyosin. The cryoprotective effect of different additives was evaluated at a level of 8% (w/w): sucrose/sorbitol (1 : 1), maltodextrin 25 DE, milk whey and sodium lactate. DSC curves showed two endothermic transitions with Tmax at 46.8 °C and 68.9 °C assigned to myosin and actin, respectively. The best cryoprotective effect was achieved with sucrose/sorbitol and maltodextrin 25 DE. These additives showed Tmax and ΔH values for myosin and actin significantly higher than the control (Dondero et al. 1996). The antifreeze activities of saccharides that consisted of glucose measured by DSC were higher than those of the other food components. In salts, those that possessed high ionic charge had high antifreeze activities. In water-soluble amino acids, a few amino acids (threonine, arginine and proline) that formed no eutectic mixture above −20 °C had especially high antifreeze activities (Mizuno et al. 1997). A DSC heat denaturation study on the effects of various maltodextrins and sucrose on protein changes in minced blue whiting muscle during frozen storage at −10 and −20 °C revealed that all maltodextrins slowed the decreases in ΔH ascribed to myosin and actin, making evident a noticeable effectiveness against protein denaturation, especially at −20 °C. Sucrose was as effective as maltodextrins at −20 °C, but was the least effective treatment at −10 °C. Significant correlations between both ΔH and either protein solubility or formaldehyde production were found at each storage temperature (Herrera et al. 2001a, b). Unlike conventional surimi processing, a novel method of fish protein isolate chemically induces denaturation by altering pH during the process. The new process for fish protein isolate, where protein structures are intentionally unfolded/refolded by pH-shift, is almost ready for commercialisation. However, it is unknown whether cryoprotectant will be required or not. This process has been investigated to improve gelation properties and yield using acid- or alkali-aided treatment. The aims were to determine the effect of pH on fish protein isolate during frozen storage (pH 5.5 and pH 7.0) and the effect of cryoprotectant on the functional properties of the fish protein isolate. The DSC curve of Pacific whiting conventional surimi contained four endothermic transitions, with Tmax of 35.4 °C, 41.2 °C, 51.1 °C, and 68.8 °C, respectively. All alkali-treated protein isolates, with cryoprotectants with and without freeze/thaw treatments, showed three endothermic peaks, with Tmax about 33.5 to 34.7, 46.2 to 47.8 and 66.5 °C to 68 °C, respectively. Alkali-treated protein isolates kept frozen at pH 5.5 were relatively less stable than those stored at pH 7.0. Actin was highly sensitive to the pH-shift method, particularly samples without cryoprotectants. DSC curves of actin appeared at Tmax of 66–68 °C with very small endothermic transition (10–15 °C) Fresh chilled fish (0–5 °C)

Packaging atmosphere Aerobic Aerobic

Dominating microflora

Typical SSO

Shewanella putrefaciens, Pseudomonas spp., Shewanella putrefaciens, Pseudomonas spp. Moraxella, Acinetobacter Shewanella putrefaciens, Vibrionaceae Aeromonas spp., Shewanella putrefaciens

Semi-preserved Lactic acid bacteria marinated fish Lightly salted and Modified Data not available fermented fish atmosphere Sources: Huss 1995; Gram and Huss 1996

Shewanella putrefaciens, Photobacterium phosphoreum, Lactic acid bacteria Photobacterium phosphoreum, Lactic acid bacteria Lactic acid bacteria (Lactobacillus, Lactococcus, Carnobacterium), Enterobacteriaceae (Serratia spp., Hafnia alvei, Enterobacter spp.), Photobacterium, Brochothrix thermospacta Lactic acid bacteria (Carnobacterium)

Lactic acid bacteria (Lactobacillus, Leuconostoc, Weissella, Carnobacterium) Lactic acid bacteria (Lactobacillus) Lactic acid bacteria (Lactobacillus, Enterococcus, Lactococcus)

Microbiological methods

Shewanella putrefaciens, Vibrionaceae, Photobacterium Photobacterium, Shewanella putrefaciens, Modified Pseudomonas spp. atmosphere Cold–smoked fish Vacuum Pseudomonas spp., Enterobacteriaceae, Acinetobacter, Staphylococcus spp., Shewanella putrefaciens, Vibrionaceae, Photobacterium Pseudomonas spp., Hot-smoked fish Vacuum Enterobacteriaceae, Staphylococcus spp., Lactic acid bacteria ‘Gravad’ fish Vacuum Data not available Vacuum

319

320


the fish (brining or salting by injection) and smoking at temperatures not higher than 28°C. Among the countries producing cold-smoked salmon, France is the leading country followed by Denmark, Germany and the United Kingdom (Cardinal et al. 2004).

15.2

General aspects of microbiological methods

The aims of microbiological examination of fish and fish products are to evaluate the hygienic quality of fish and to detect possible pathogenic microorganisms. They consist of the measurement of total aerobic bacteria, spoilage bacteria and pathogenic bacteria (Huss 1995). The food microbiologist can choose from a wide variety of conventional microbiological and new molecular biological methods to analyse fish and fish products. Traditional microbiological methods require much manual labour, are time-consuming and costly. They require skills in the execution and interpretation of the results (Huss 1995). Furthermore, these methods are slow and the results can only be obtained after 1–5 days. However, incubation and agar-based detection methods are still the main methods used for determination of bacterial counts. They include qualitative tests (absence or presence), often used for detection of pathogenic bacteria, and quantitative methods, used for enumeration of viable cells of all kinds of microorganism (Holbrook 2000). Table 15.2 presents recommendations for agar types, incubation temperatures and times for different general bacterial counts in different products. As for all kinds of food product, there are two basic classical approaches to determine the microorganisms in a fish product: (1) direct plating of specimens of the fish product onto a selective agar; and (2) incubation of the extract of a fish product in an enrichment medium followed by plating onto a selective agar. Direct plating from the food includes the standard plate count (pour plating) and the surface count technique (spread plating), which are both used for different bacterial counts. The standard plate count method is the most widely used method for determination of numbers of viable cells or colony-forming units (cfu) (Jay 2000). Exposure of the inocula to the molten agar, which has a temperature of 45–49°C, is generally acceptable for determining of mesophilic and thermophilic bacteria (Holbrook 2000). Disadvantages are, for example, the long incubation time and limitations due to a specific culture medium and incubation conditions like atmosphere and temperature. Furthermore, both viable single cells and cell clumps are counted, which might lead to wrong numbers of bacteria (Jay 2000; Robinson 2002). In the determination of the heat-sensitive psychrotrophic bacteria, the surface count technique has been used. This has the advantage that the bacteria do not come into contact with the melted hot agar (Holbrook 2000). Strict aerobes are obviously favoured by this technique, whereas microaerophilic bacteria grow slower (Jay 2000). There are several disadvantages to the surface count technique (Jay 2000; Robinson 2002): It is time-consuming. Only 0.1 ml of dilution can be plated per conventional Petri dish. Undetected contaminant colonies occurring on the prepared plate can spread over the whole plate during the spreading procedure. A resulting film of growth (also when the agar surface is not dry enough before plating) can mask the development of colonies to be counted and make enumeration difficult. Incubation of the food product in an enrichment medium is often used for opportunistic pathogenic and pathogenic microorganisms, for example Listeria, Clostridia and Salmonella.

Table 15.2 Detection media, incubation temperature and incubation time used for determination of total number of bacteria including mesophilic, psychrotrophic and hydrogen-sulphide-producing bacteria in fish and fishery products.

Microorganisms Total viable count (including mesophilic bacteria)

Recommended incubation temperature (°C)

Plate count agar (PCA), pour plating

20 °C/30 °C

Iron agar (IA), pour platinga

Tryptic soy agar (TSA), pour plating Plate count agar (PCA), pour plating

Recommended incubation time (days)

Fish and fishery product

References

Remarks

1–3 days

Fresh fish stored in ice, vacuum-packaged coldand hot-smoked fish, modified-atmospherepackaged hot-smoked fish, fish stored in sous-vide

Addition of 0.5% NaCl (Leroi et al. 1998)

21 °C

3 days

21 °C/25 °C

3 days

Vacuum-packaged ‘gravad’ fish, salted and fermented fish packaged in modified atmosphere and in air Vacuum-packaged coldsmoked fish

Civera et al. 1995; Leroi et al. 1998; Lyhs et al. 1998a; Paarup et al. 2002; Taliadourou et al. 2003; Chytiri et al. 2004; González-Fandos et al. 2005; Cakli et al. 2006; Hozbor et al. 2006; Nordic Committee on Food Analysis 2006 Lyhs et al. 2002, 2007

5 °C/7 °C

10–14 days

Fresh fish stored in ice, fish stored in sous-vide, vacuum- and modifiedatmosphere-packaged hot-smoked fish

Addition of 1% NaCl (Magnússon et al. 2006)

Truelstrup Hansen et al. 1995, 1998 Cakli et al. 2006; GonzálezFandos et al. 2005; Huss et al. 2006

Continued


Total viable count (including psychrotrophic bacteria)

Medium (pour plating/spread plating)

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322

Table 15.2

Continued

Hydrogensulphide (H2S)producing bacteria

a


Recommended incubation time (days)

Long and Hammer’s agar (LH), spread plating

10 °C/15 °C

3–7 days

Tryptic soy agar (TSA), spread plating Iron agar (IA), pour plating

10 °C

5 days

20 °C/21 °C/ 22 °C/25 °C

2–4 days

Counting white and black colonies

Fish and fishery product

References

Dalgaard et al. 1997b, 2003; Fresh fish stored in air, vacuum-packaged coldKoutsoumanis et al. 1998; smoked fish, salted and Jørgensen et al. 2000; fermented fish packaged in Leroi et al. 2001; Emborg modified atmosphere and in et al. 2002, 2005; Pournis air, modified- atmosphereet al. 2005; Nordic packaged fish, cooked and Committee on Food brined modifiedAnalysis 2006; Lyhs et al. atmosphere-packaged 2007 shrimps Truelstrup Hansen et al. Vacuum-packaged ‘gravad’ and cold-smoked fish 1995, 1998; Lyhs et al. 2001a Fresh fish stored in air and in ice, vacuum-packaged ‘gravad’ and cold-smoked fish, salted and fermented fish packaged in modified atmosphere and in air, modified-atmospherepackaged fish

Gram et al. 1987; Rosnes et al. 1997; Koutsoumanis et al. 1998; Leroi et al. 1998; Emborg et al. 2002; Paarup et al. 2002; Sveinsdóttir et al. 2002; Taliadourou et al. 2003; Chytiri et al. 2004; Lyhs et al. 2001a, 2007; Pournis et al. 2005; Nordic Committee on Food Analysis 2006

Remarks Addition of 1% NaCl (Dalgaard 2000)


Microorganisms

Medium (pour plating/spread plating)


323

When microorganisms have been through stress by freezing, drying, smoking, heating, and so on, they made have been killed or injured. Some might have survived but require optimal conditions to grow again. Therefore they should be grown in enrichment first and then plated on a medium selective for the target microorganism. To substitute for the enrichment step, techniques such as filtration, centrifugation or magnetic separation might be applied. Enrichment is a critical step in enhancing the growth of certain bacterial species while inhibiting the development of unwanted microorganisms. After the culturing step, identification of isolated and purified colonies using morphological, biochemical and immunological tests is still widely used. However, owing to the possibility of misidentifications, difficulties and unreliability of the results, DNA-based methods are increasingly used for these phenotypic tests.

15.2.1

Total viable count or aerobic plate count

The total number of microorganisms – called the total viable count (TVC) or aerobic plate count (APC) if performed by traditional methods – is the number of bacteria (cfu/g) in a food product that are capable of forming visible colonies on a culture medium at a given temperature (Huss 1995). TVC is not the measure of the ‘total’ bacterial population, only a measure of the fraction of the microflora that is able to produce colonies under the conditions of the growth medium and the incubation. It has long been known that the incubation temperature greatly influences the number of colonies developing from the same sample. Many marine microorganisms in seafood are psychrotolerant and heat labile. It has been shown that bacterial counts in fish products stored at chilled temperatures determined at 20–25°C are much higher than counts determined at 37°C (Liston 1957; Huss 1995). Therefore, to determine TVC including psychrotrophic microorganisms in seafood, pour plating with agar at about 45°C or incubating the plates at 30–37°C must be avoided. To determine TVC including mesophilic microorganisms in fish and fish products, common plate count agars (PCAs) on pour plates have been the substrates mostly used. Nowadays, iron agar (IA), a non-selective and indicative medium, is used to an increasing degree. It was developed to enumerate hydrogen sulphide (H2S)-producing microorganisms, which are important spoilage organisms of chilled and aerobically stored fresh fish (Gram et al. 1987). For aerobic bacterial counts including (mesophilic) H2S-producing and other non-heat-labile bacteria, pour plating on IA and 3–4 days’ incubation at 25°C is relevant. IA gives significantly higher counts than PCA (Gram 1990). H2S-producing microorganisms appear as black colonies owing to precipitation of iron sulphide (FeS), which is formed by decomposition of thiosulphate and/or l-cysteine (Gram et al. 1987; Huss et al. 1997; NCFA 2006). Both thiosulphate and l-cysteine are constituents of the IA and facilitate H2S production. NonH2S-producing microorganisms appear as white colonies. An overlay is usual poured on top of the IA to pretend fading of the black colonies appearing when FeS is oxidised. Lauzon (1997), studying shelf life and spoilage of American plaice (Hippoglossoides platessoides), concluded that the addition of the overlay was not necessary as the number of black colonies was generally greater for spread-plated than pour/overlaid plates. Spread-plating of IA usually gave higher total counts. However, the use of IA has disadvantages like any other agar plate method: long detection time and a relatively high detection level (50–100 cfu/g).

324


Thus, it is not possibile to get an early warning of high numbers of H2S-producing microorganisms in the fish product (Skjerdal et al. 2004). However, for qualitative determination of these microorganisms this is not relevant. Skjerdal et al. (2004) describe a simple, rapid and quantitative method based on a patent of Lorentzen et al. (2003) for estimating the sulphideproducing bacteria in aerobically and cold-stored fish from Arctic and temperate areas. It detects sulphide-producing bacteria at levels above 105.5–106 cfu/g fish within 8 hours, and has a detection level of 16 cfu/g fish. The method is based on iron sulphide formation leading to fluorescence reduction, which is measured as changes in background medium fluorescence in a liquid medium incubated at 30°C during the growth of sulphide-producing bacteria. The sulphide production in fish of Arctic and temperate waters is dominated by Shewanella putrefaciens, which has an optimum growth temperature of 30–32°C and is known as the specific spoilage organism in marine temperate-water fish. It is possible to predict the remaining shelf life of fish of unknown time–temperature history with this method. Many marine microorganisms in seafood require sodium chloride (NaCl) for growth. Therefore, van Spreekens (1974) modified Long and Hammer’s agar (LH) (Long and Hammer 1941) by the addition of 0.5% NaCl to the medium. According to Dalgaard (2000), spread plating on pre-chilled plates of LH with 1% NaCl and subsequent incubation at 15°C for 5–7 days is a useful procedure for enumerating TVC of heat-labile and psychotolerant marine bacteria (dominating the spoilage microflora for fresh marine fish, particularly when vacuum – and modified-atmosphere-packed). Cardinal et al. (2004), characterising the quality of vacuum-packaged cold-smoked salmon products from supermarkets in different European countries, also recommended the use of spread plates of LH instead of pour PCA for determinating total psychrotrophic counts. Counts on LH have been generally one logarithm higher than counts on PCA (Joffraud and Leroi 2000). Koutsoumonis and Nychas (1999) noted that there was no significant difference between bacterial counts on LH and IA when all colonies (black and white) were counted on IA. Furthermore, on LH, luminous colonies can be counted after 4 days (NCFA 2006). Another agar used for determining TVC including mesophilic and psychrotrophic bacteria, for example in vacuum-packaged coldsmoked fish, is tryptic soy agar (TSA), sometimes containing 0.5% NaCl (Truelstrup et al. 1995 1998; Lyhs et al. 2001a).

15.2.2

Spoilage bacteria

Fresh, untreated seafood harbours a quite heterogeneous microflora. The flesh of healthy, live fish is generally thought to be sterile as the immune system prevents bacteria from growing in the flesh (Huss et al. 1997; Gram and Huss 2000). When the fish dies, the immune system stops functioning and bacteria can proliferate freely. The microflora of a fish product is composed of the natural, indigenous flora and the microflora typical of the processing environment. Depending on the type of preservation during storage, few bacteria of this microflora outgrow the others, depending on which is capable of rapid growth under the imposed conditions (temperature, atmosphere, microbial interactions, and so on). Spoilage is defined as the sensory changes resulting in a fish product being unacceptable for human consumption. It is caused by autolytic and chemical changes or off-odours and off-flavours due to bacterial metabolism. When a fish product is rejected based on sensory assessment, the microflora present, the so-called spoilage microflora, can still consist of


325

different species (Huss et al. 1997). Typically, one or two species of the group of bacteria called specific spoilage organisms (SSO) will cause the production of metabolites associated with the off-flavours and off-odours (Huss et al. 1997; Gram and Huss 2000). SSOs are typically present in low numbers and constitute only a very small fraction of the microflora on newly processed seafood (Gram and Dalgaard 2002). Table 15.3 shows recommendations for agar types, incubation temperatures and times for SSO counts in fish and fishery products. Pseudomonas spp. Pseudomonas spp. have been reported to be the SSO in ice-stored tropical freshwater fish (Gram et al. 1990). Together with S. putrefaciens, they are also spoilers in tropical marine fish (Gram 1992) and in fish obtained from temperate waters in the Mediterranean Sea (Koutsoumanis and Nychas 1999; Tryfinopoulou et al. 2000) both stored in ice aerobically. Gennari et al. (1999) used tryptone–peptone-extract (TPE) agar for determinating Pseudomonas spp. in fresh and spoiled sardines stored in ice. Pseudomonas agar base supplemented with three antibiotics, cetrimide, fusidin and cephaloridine (CFC), has been used by more researchers during studies of fresh fish stored aerobically in ice (Koutsoumanis and Nychas 1999; Talidourou et al. 2003; Chytiri et al. 2004) and packaged under modified atmosphere (Pournis et al. 2005). The antibiotics added to the CFC agar have been considered as good selective agents for most Pseudomonas spp. (Mead and Adams 1977). Many manufacturers state that cetrimide, a quaternary ammonium compound, is inhibitory to most bacterial species, except for Pseudomonas aeruginosa. However, the CFC agar has been found to be the most appropriate medium supporting the growth of Pseudomonas. Tryfinopoulou et al. (2001) studied the selectivity of the CFC agar during the enumeration of fish samples stored aerobically and under modified atmosphere at 0, 10 and 20°C. The selectivity of the agar was affected by storage temperature of the fish and type of packaging. In samples stored in air at low temperatures (0 and 10°C), most isolates were identified as Pseudomonas whereas isolates identified as Enterobacteriaceae were negligible. In fish stored at 20°C, the proportion of Enterobacteriaceae was higher than those of Pseudomonas and S. putrefaciens regardless of the storage atmosphere condition. The selectivity of the agar decreased with an increase in the interfering microorganisms (for example S. putrefaciens and Enterobacteriaceae), and had almost disappeared when the background flora was more robust than the organisms to be detected (Mossel et al. 1995). The differentiation of Enterobacteriaceae from Pseudomonas can be achieved by the oxidase test, but there is no effective and rapid test to differentiate Pseudomonas from Shewanella. Therefore, the enumeration of Pseudomonas directly from CFC agar is not adequate with fish samples. H2S-producing bacteria H2S-producing microorganisms are found to be important spoilage organisms of chilled and aerobically stored fresh fish (Gram et al. 1987; Koutsoumanis and Nychas 1999; Taliadourou et al. 2003; Chytiri et al. 2004; Hozbor et al. 2006) and in chilled, packaged, fresh and preserved fish (Truelstrup Hansen et al. 1998; Lyhs et al. 2001a; Emborg et al. 2002; Paarup et al. 2002; Magnusson et al. 2006). Different bacteria such as S. putrefaciens,

326


Recommended incubation time (days) Fish and Fishery product

Microorganisms

Medium

Photobacterium phosphoreum

Iron agar (IA), spread plating

15 °C

5 days

Shewanella putrefaciens Pseudomonas spp.

Iron agar (IA), spread plating Cetrimide fusidin cephaloridine (CFC) agar

25 °C

3–4 days

20 °C

2 days

Fresh fish stored in ice, modified-atmosphere packaged fish

Pseudomonas isolation agar PseudomonasAeromonas selective agar

30 °C

2 days

25 °C

3–4 days

Vacuum-packaged coldsmoked fish Fresh fish stored in ice

Fresh fish stored in ice, vacuum- and modified-atmospherepackaged fish, vacuum-packaged cold-smoked fish Fresh fish stored in ice

References

Remarks

Emborg et al. 2002, 2005; Giménez. and Dalgaard 2004; Lopez-Caballero et al. 2002 Hozbor et al. 2006 Cato et al. 1986; Civera et al. 1995; Koutsoumanis et al. 1998; Taliadourou et al. 2003; Pournis et al. 2005 Civera et al. 1995 Hozbor et al. 2006

Addition of supplements (cetrimide, fusidin and cephaloridine)


Table 15.3 Detection media, incubation temperature and incubation time used for determination of counts of specific spoilage bacteria in fish and fishery products.

Lactic acid bacteria

3–7 days

Nitrate Actidion Polymyxin agar (NAP), pH 6.2, spread plating

20 °C/25 °C

3 days

Rogosa SL agar, spread plating

20 °C/25 °C

5 days

Slanetz and Bartley agar (SB), spread plating

44 °C

2 days

KanamycinEsculine Azide Agar

37 °C

2 days

Vacuum-packaged fish, vacuum-packaged cold- and hot-smoked fish, modifiedatmosphere-packaged hot-smoked fish, fish stored in sous-vide, acetic-acid preserved fish, salted and fermented fish packaged in modified atmosphere and in air Vacuum- and modifiedatmosphere-packaged fish, vacuumpackaged coldsmoked fish, cooked and brined modifiedatmosphere-packaged shrimps Acetic-acid fish preserve, vacuumpackaged coldsmoked fish Vacuum-packaged coldsmoked fish, cooked and brined modifiedatmosphere-packaged shrimps Vacuum-packaged coldsmoked fish

Nordic Committee on Only anaerobic incubation Food Analysis 1991; Civera et al. 1995; González-Fandos et al. 2005; Lyhs et al. 2001a, 2001b, 2004, 2007; Pournis et al. 2005; Cakli et al. 2006

Jørgensen et al. 2000; Leroi et al. 2001; Emborg et al. 2002, 2005; Dalgaard et al. 2003

Only anaerobic incubation

Leroi et al. 2000; Lyhs et al. 2001b

Only anaerobic incubation

Dalgaard et al. 2003; Giménez and Dalgaard 2004

Civera et al. 1995


Enterococci

20 °C/25 °C/30 °C

De Man, Rogosa and Sharpe agar (MRS), pour plating

Continued

327

Medium


Brochothrix thermospacta

Streptomycin thallium acetate actidion agar (STAA), spread plating Violet red bile glucose agar (VRBG), pour plating

20 °C

3 days

30 °C/37 °C

1–2 days

TSA, 1–2 h at room temperature, pour plating, overlaid by VRBG agar

25 °C/30 °C

2 days

Casein-peptone soymealpeptone (CASO) agar, pour plating overlaid by VRBG agar

30 °C

2 days

Enterobacteriaceae

Recommended Fish and Fishery product incubation time (days) Fresh fish stored in ice, fresh fish stored in air, Vacuum-packaged cold-smoked fish, modified-atmospherepackaged fish Fresh fish stored in ice, fresh fish stored in air, vacuum- and modified-atmospherepackaged hot-smoked fish, modifiedatmosphere-packaged fish, salted and fermented fish packaged in modified atmosphere and in air Vacuum-packaged coldsmoked fish, fish stored in sous-vide, vacuum- and modified-atmospherepackaged fish

Vacuum-packaged coldsmoked fish

References

Koutsoumanis et al. 1998; Leroi et al. 2001; Taliadourou et al. 2003; Chytiri et al. 2004; Pournis et al. 2005 Koutsoumanis et al. 1998; Lyhs et al. 2001a, 2007; Taliadourou et al. 2003; Chytiri et al. 2004; Pournis et al. 2005; Cakli et al. 2006

Civera et al. 1995; Jørgensen et al. 2000; Paarup et al. 2002; Giménez and Dalgaard 2004; Emborg et al. 2005; González-Fandos et al. 2005 Leroi et al. 2001

Remarks


Microorganisms

328

Table 15.3 Continued


329

types of Enterobacteriaceae, Aeromonaceae, Vibrionaceae and some lactobacilli such as Lactobacillus sakei are able to produce hydrogen sulphide (Gram et al. 1987; Truelstrup Hansen 1995; Gram and Huss 1996; Joffraud et al. 2001; Leroi et al. 2001; NCFA 2006). In contrast, neither Pseudomonas nor Photobacterium phosphoreum produce significant amounts of H2S (Gram and Huss 1996). Good results in enumerating H2S-producing microorganisms were obtained in fresh and spoiled fish, for example, stored aerobically in ice (Koutsoumanis and Nychas 1999; Sveinsdóttir et al. 2002; Taliadourou et al. 2003; Chytiril et al. 2004) and packaged under vacuum (Leroi et al. 1998; Lyhs et al. 2001a) or modified atmosphere (Emborg et al. 2002; Pournis et al. 2005). However, Lyhs et al. (2006) reported a total absence of H2S-producing microorganisms when studying maatjes herring stored in air and under MAP at 4°C and 10°C. It is known that H2S-producing bacteria are favoured by a low oxygen level, but inhibited in environments with high or increasing carbon dioxide levels as in modifiedatmosphere or vacuum packaging (Jørgensen et al. 1988; Rosnes et al. 1997; Lyhs et al. 2001a). Studying the effect of smoke and salt on the microflora of vacuum-packaged cold-smoked salmon, Leroi et al. (2000) reported that salt produced a strong linear inhibition on H2S-producing bacteria. The salt used in the processing of maatjes herring might have influenced these bacteria by suppressing their growth. Shewanella spp. S. putrefaciens is considered to be the SSO in marine temperate-water fresh fish stored in ice (Gram and Huss 1996; Debevere and Boskou 1996; Koutsoumanis and Nychas 1999). Gram et al. (1987) identified it as the major spoilage organism in vacuum-packaged cod fillets stored in ice. On the other hand, it has been found to be of little importance in spoiled, modified-atmosphere packaged cod fillets (Dalgaard et al. 1993; Dalgaard 1995; Debevere and Boskou 1996). S. baltica has been identified as the main H2S-producing organism growing on iced marine fish caught in the Baltic Sea (Fonnesbech Vogel et al. 2005). There is no selective medium for culturing of S. putrefaciens (Rudi et al. 2004). Shewanella spp. have usually been identified by standard phenotypical key tests (Stenstrøm and Molin 1990). However, phenotypic characteristics do not allow always for species differentiation, for example between S. putrefaciens and S. algae (Fonnesbech Vogel et al. 1997), or between some psychrophilic Shewanella (Ziemke et al. 1997). Phenotypic limits of and within the psychrotropic Shewanella group are currently being redefined (Fonnesbach Vogel et al. 2005; Satomi et al. 2006). Recently, combining phenotypic and genetic analyses has shown success in determining novel species within the genus Shewanella (Satomi et al. 2006). Enterobacteriaceae Mesophilic Enterobacteriaceae (Gram and Huss 1996) have been found from fish caught in polluted waters. A mixture of lactic acid bacteria and Enterobacteriaceae may develop at the end of the chilled storage of vacuum-packaged cold-smoked salmon and rainbow trout, respectively (Truelstrup Hansen 1995; Lyhs et al. 1998a; Leroi et al. 2001). During study of different fish products packaged under vacuum and modified atmosphere (Pournis et al. 2005; Cakli et al. 2006; Lyhs et al. 2006) and unpacked stored in ice

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(Koutsoumanis and Nychas 1999; Taliadourou et al. 2003; Chytiri et al. 2004) Enterobacteriaceae have been successfully determined on violet red bile glucose (VRBG) agar using the pour plate technique. Typical Enterobacteriaceae colonies are counted after 1–2 days at incubation temperatures between 25 and 37°C (NCFA 2005). Other authors (Jørgensen et al. 2000; Paarup et al. 2002; Gimenez and Dalgaard 2004; Gonzalez-Fandos et al. 2005; Mejlholm et al. 2005) used pour plates of TSA for determination of Enterobacteriaceae, which after 1–2 hours at room temperature was overlaid by VRBG agar. Leroi et al. (2001), studying different microbiological parameters in vacuum-packaged coldsmoked salmon, used pour plates of casein-peptone-soymeal-peptone agar (CASO) overlaid by VRBG agar. For phenotypic identification of Enterobacteriaceae, different tests, for example absence of cytochrome oxidase, fermentative metabolism of glucose, DNAase production and the ability to ferment sorbitol using Hugh and Leifson’s basal medium, have been used (Lyhs et al. 1998a; Paludan-Müller et al. 1998). The API 20E system is another reliable method of high accuracy used for identification of Enterobacteriaceae isolated from vacuumpackaged, cold-smoked fish (Truelstrup Hansen 1995; Lyhs et al. 1998a; Paludan-Müller et al. 1998). Lactic acid bacteria Lactic acid bacteria (LAB) comprise the major component of the spoilage flora, which develops on vacuum-packaged, lightly preserved fish products (Civera et al. 1995; Truelstrup Hansen 1995; Leroi et al. 1998; Lyhs et al. 1998a; Paludan-Müller et al. 1998; Lyhs et al. 2001a; Leroi et al. 2001; Jørgensen et al. 2001; Joffraud et al. 2001) and hot-smoked fish products (Jöckel et al. 1986; Paleari et al. 1990; Zorn et al. 1993) after a few weeks’ storage at chilled temperatures. Important members of spoilage LAB are, among others, the genera Lactobacillus, Carnobacterium and Enterococcus. Lactobacillus spp. have been identified as the SSO in marinated raw or cooked herring preserved in vinegar and salt (Meyer 1956; Kreuzer 1957; Lerche 1960; Reuter 1965; Erichsen 1967; Sharpe and Pettipher 1983; Lyhs et al. 2001b 2004). Smoked and charred Baltic herring with high levels of enterococci have been related to food poisoning outbreaks in Finland (Korkeala and Pakkala 1988). Enterococci together with carnobacteria were dominant members of spoilage associations of cooked and brined modified-atmosphere-packaged shrimps when stored at high and low storage temperatures, respectively (Dalgaard and Jørgensen 2000; Dalgaard et al. 2003). For determination of LAB counts in vacuum- and modified-atmosphere packaged fish and fish products (Civera et al. 1995; Lyhs et al. 2001a 2004 2007; Pournis et al. 2005; Gonzalez-Fandos et al. 2005), the De Man, Rogosa and Sharpe (MRS) agar, originally introduced by De Man et al. (1960), has been successfully used. This agar is also recommended by the NCFA (1991). Other authors (Leroi et al. 2001; Jørgensen et al. 2000; Emborg et al. 2002; Dalgaard et al. 2003) recommended nitrate actidion polymyxin (NAP) agar, which was originally developed by Davidson and Cronin (1973). For enumeration of lactobacilli in vacuum-packed cold-smoked salmon (Leroi et al. 2000) and in an acetic-acid herring preserve (Lyhs et al. 2001b), Rogosa SL agar (Rogosa et al. 1951) has been used. This agar is selective for lactobacilli and inhibits Carnobacterium spp. Enterococci were detected in several fish products on Slanetz–Bartley agar (Dalgaard et al. 2003; Gimenez


331

and Dalgaard 2004), forming colonies with a red–pink centre after 2 days’ incubation at 44°C. The identification of LAB has mainly been based on traditional biochemical and physiological tests, which often produce very controversial results. Moreover, there are certain species that cannot readily be distinguished by phenotypic characters (Schleifer et al. 1995; Gancel et al. 1997; Lyhs et al. 1998a). Several molecular methods have been used for strain typing or characterisation of LAB. Paludan-Müller et al. (1998), studying the role of Carnobacterium species in the spoilage of vacuum-packaged, cold-smoked salmon, identified carnobacteria by whole-cell-protein patterns. Ribotyping (Grimont and Grimont 1986), which is based on patterns generated from the ribosomal RNA genes only, has been applied with success for the identification of the main spoilage LAB in vacuum-packaged, cold-smoked and ‘gravad’ rainbow trout (Lyhs et al. 1999 2001a) and marinated herring (Lyhs et al. 2001b 2004; Lyhs and Björkroth 2008). Photobacterium phosphoreum Members of Photobacteria can be luminous, occurring as light-organ symbionts in several fish (Dalgaard et al. 1997a). Photobacterium phosphoreum, a bioluminescent species, is the SSO in marine fish from temperate waters stored in modified-atmosphere (Dalgaard 1995; Gram and Huss 1996; Dalgaard et al. 1997b; Dalgaard et al. 1998; Emborg et al. 2002). In vacuum-packaged cold-smoked salmon at the end of chilled storage, P. phosphoreum has been found with LAB (Truelstrup Hansen 1995). P. phosphoreum is heat labile and can be killed in agar of 45°C. Thus pour plating and spread plating methods with incubation temperatures of 23–25°C are inappropriate for its detection (van Spreekens 1974; Dalgaard et al. 1993 1997b). There is a lack of selective media and methods for enumeration and specific detection of P. phosphoreum in fish products (Dalgaard et al. 1997b; Rudi et al. 2004). P. phosphoreum, belonging to the Vibrionaceae, does not grow on thiosulphate citrate bile sucrose (TCBS) agar, a vibrioselective agar (Dalgaard et al. 1997b). However, various nutritionally rich media have allowed the detection of this bacterium. Dalgaard et al. (1997b) compared different kind of agars for detecting TVC and luminous colonies in spoiled, chilled cod stored in air and under modified atmosphere: LH and IA (both with the addition of 1% NaCl) and a luminescence medium (LM) (Baumann and Baumann 1984). All media were incubated at 15°C for 7 days. LM gave significantly lower counts than LH and IA. Most P. phosphoreum strains from chilled fish products were non-luminous (van Spreekens 1974; Dalgaard 1995; Dalgaard et al. 1997b). Therefore, the number of luminous colonies cannot be used for enumerating P. phosphoreum. Lopez-Cababello et al. (2002) isolated P. phosphoreum from spoiled hake stored in ice plated on IA with 1% NaCl stored for 5 days at 15°C. Agars without NaCl are inappropriate for detecting P. phosphoreum (Dalgaard et al. 1997b). The NCFA (2006) recommends the addition of 1% NaCl to the LH to improve the detection of P. phosphoreum. Furthermore, spread plating and incubation at 15°C for 5–7 days should be used (Huss 1995a). However, several national and international authorities recommend pour plating on IA and PCA (NCFA 1994). Direct microscopic counting of spread plates incubated at 15°C allowed quantitative enumeration of P. phosphoreum in fish products (van Spreekens 1974; Dalgaard et al. 1996). With the conductance method, quantitative and selective detection of even low levels of P. phosphoreum in various fresh

332


fish products packaged in modified atmosphere has been possible (Dalgaard et al. 1996). To identify P. phosphoreum a scheme including tests, like growth in different NaCl concentrations and at different temperatures, gas production from glucose, ammonia (NH3) production from arginine, lysine and ornithine, reduction of trimethylamine oxide (TMAO) and nitrate (NO3−) and bioluminescence, have been used in different studies (Dalgaard 1995; Paludan-Müller et al. 1998). Brochothrix thermospacta Brochothrix thermospacta is the dominant spoilage bacterium of Mediterranean sea fish stored under modified atmosphere packaging (Drosinos and Nychas 1996; Koutsoumanis et al. 1998). It also may develop with LAB at the end of chilled storage in vacuum-packaged cold-smoked salmon (Leroi et al. 2001). With Carnobacterium maltaromaticum, it is responsible for spoilage in cooked and peeled modified-atmosphere packaged shrimps (Mejlholm et al. 2005). B. thermospacta is usual enumerated on streptomycin thallium acetate actidion (STAA) agar (Gardner 1966) using spread plates followed by identification using Gram staining, catalase and oxidase tests (Leroi et al. 2001; Mejlholm et al. 2005).

15.2.3

Pathogenic bacteria

Fish and fish products are known vehicles for transmission of foodborne diseases (Huss et al. 1997). Pathogenic bacteria associated with seafood can be categorised into three general groups: (1) bacteria (indigenous bacteria) that belong to the natural microflora of fish (Clostridium botulinum, pathogenic Vibrio spp., Aeromonas hydrophila); (2) enteric bacteria (non-indigenous bacteria) that are present due to faecal contamination (Salmonella spp., Shigella spp., pathogenic Escherichia coli, Staphylococcus aureus); and (3) bacterial contamination during processing, storage or preparation for consumption (Bacillus cereus, Listeria monocytogenes, Staphylococcus aureus, Clostridium perfringens) (Feldhusen 2000). However, indigenous pathogenic bacteria present in fresh cultured fish products are usually found at fairly low levels. When these products are adequately cooked before consumption and afterwards no recontamination takes place, food safety hazards are insignificant (Feldhusen 2000). Possible high levels of these bacteria are due to growth and might produce disease in humans (Huss 1997). For most RTE products like lightly preserved fish, the growth of pathogenic bacteria (especially L. monocytogenes and C. botulinum) is a serious safety concern. In cold-smoked and ‘gravad’ fish the amounts of smoke and salt, respectively, are not sufficient to prevent the growth of pathogenic bacteria (Lyhs et al. 1998b). Listeria spp. Owing to their processing method and extended shelf-lives at refrigeration temperatures, vacuum-packaged cold-smoked and ‘gravad’ fish products have to be considered as potential high-risk foods for L. monocytogenes (Civera et al. 1995; Loncarevic et al. 1996; Lyhs


333

et al. 1998b; Jorgensen and Huss 1998; Johannson et al. 1999). Few sporadic cases of listeriosis have been reported associated with the consumption of contaminated fish and shellfish (Facinelli et al. 1989; Ericsson et al. 1997; Miettinen et al. 1999; Brett et al. 1998). Traditional isolation methods of Listeria spp. and L. monocytogenes are based on selective enrichment followed by plating on selective media (Lunden 2004). During a two-stage enrichment procedure, the food samples are homogenised and incubated in a selective liquid enrichment medium with reduced concentration of selective agents (primary enrichment). After 24 hours a culture obtained from the first enrichment broth is transferred into a second selective liquid enrichment medium with full concentration of selective agents (secondary enrichment). The Nordic Committee on Food Analyses (NCFA) (2004) recommends the use of Listeria enrichment broth LBI and LBII or Fraser enrichment broth, and the International Organization for Standardisation (ISO) (Anonymous 1997) half-Fraser and Fraser enrichment broths. Plating out of cultures and identification from the enrichment broths is performed on Listeria-selective agar (polymyxin-acriflavine-LiCl-ceftazidime-aesculin-mannitol (PALCAM) or Oxford agar). The presence of Listeria spp. and/or L. monocytogenes is confirmed by appropriate morphological, physiological and biochemical tests performed on several presumptive colonies (Anonymous 1997; Scotter et al. 2001; NCFA 2004; Autio 2003; Lunden 2004). Because a differentiation of L. monocytogenes colonies from other non-pathogenic Listeria species is not possible on PALCAM or Oxford agar, other plating media like chromogenic culture media (Agar Listeria Ottaviani and Agosti (ALOA), RAPID’ L. mono) (Karpiskova et al. 2000; Vlaemynck et al. 2000; Polivka 2001; Leclerq 2004; Becker et al. 2006) or Listeria monocytogenes blood agar (LMBA) (Johansson 1998) have been developed and used in recent years. Clear advantages (rapid and specific differentiation of L. monocytogenes from other Listeria spp. by specific reactions directly on the agar plate, lower costs, time reduction) let most authors recommend the substitution of the standard media by these other selective media or to use a combination of both. In addition, molecular typing methods should be used as an alternative to the classical confirmation test (Scotter et al. 2001). Clostridium botulinum C. botulinum type E is the most common type that is mainly associated with fish and fish products. It is of particular concern because of its ability to grow at temperatures between 3 and 5°C (Feldhusen 2000). Vacuum-packaged hot-smoked, air-dried and salted fish products have been identified as causes of botulinum (Slater et al. 1989; Weber et al. 1993; Korkeala et al. 1998; Hyytiä et al. 1998; Lindström et al. 2004 2006). Furthermore, vacuumpackaged unprocessed, ‘gravad’ and cold-smoked fish and smoked river lampreys from the Baltic Sea have been shown to be a serious health risk for C. botulinum type E (Hyytiä et al. 1998). Conventional detection and isolation of C. botulinum are based on culturing in a liquid medium. The standard method for detection of the botulinum toxin in the culture supernatant is the mouse bioassay (Kautter and Salomon 1977). This method is not only time consuming and expensive, but it also raises ethical concerns about due to the use of experimental animals (Lindström et al. 2001). A broad variety of routine liquid media are available but they are all non-selective, allowing other bacteria to grow (Lindström and Korkeala 2006). As plating media, blood agar and egg yolk agar (EYA) are mostly used (Hausschild and Hilsheimer

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1977). EYA enables the lipase reaction which is typical for C. botulinum. However, other clostridia produce lipase as well and may therefore confuse the identification (Cato et al. 1986). The development of selective media is complicated by the fact that the species C. botulinum consists of four physiologically and genetically distinct groups of organisms (Lindström and Korkeala 2006). To identify Clostridium spp., commercial tests based on biochemical reactions have been developed. However, different tests are able to identify clostridia to species level (Head and Ratnam 1988; Marler et al. 1991), but other tests fail owing to the diverse physiology of C. botulinum (Brett 1998; Lindström et al. 1999). They are time-consuming, laborious and often susceptible to technical bias affecting the success of identification (Lindström and Korkeala 2006). Aeromonas spp. Aeromonas spp. can be found in most aquatic environments (Huss et al. 1997), but also in fish and fish products like cold- and hot-smoked fish (Gobat and Jemmi 1993), fresh fish, fish-eggs and shrimps (Hänninen et al. 1997) and pre-packaged fresh fish (GonzàlezRodriguez et al. 2002a; Herrera et al. 2006). They are thought to be part of the spoilage flora of chilled freshwater fish stored in air (ICMSF 1998) and might originate from contamination after processing (Gobat and Jemmi 1993; Hänninen et al. 1997). Palumbo et al. (1985) developed the starch ampicillin (SA) agar to allow a rapid quantitative recovery of A. hydrophila from foods in the presence of many competing bacteria. Havelaar et al. (1987) combined the advantages of two selective agars in a new medium, ampicillin–dextrin (AD) agar. Gobat and Jemmi (1995) studied seven selective agars and two enrichment broths for their suitability for isolating mesophilic Aeromonas spp. from meat, fish and shellfish samples. For qualitative isolation they recommended enrichment in alkaline peptone water (APW, pH 8.7) followed by consecutive plating onto ampicillin sheep blood agar (ASBA 30; 30 mg/l ampicillin) and bile salts-irgasan-brillant green (BIBG) agar. Hänninen et al. (1997) cultured different fish samples either directly on AD agar or enriched the samples first in tryptic soy broth containing 30 μg/ml ampicillin followed by culturing onto AD agar. It should be noted that plates must be incubated at temperatures of 28–30°C because many Aeromonas spp. do not grow above 35°C (Hänninen et al. 1997). Suspected Aeromonas spp. must be confirmed and can be subdivided by morphological, physiological and biochemical tests, for example acid production from l-arabinose and salicin in O/F-medium, esculin hydrolysis, Voges–Proskauer test, lysine decarboxylase in Falkow’s medium, motility and growth in BHI + 1% NaCl at 37°C (Paludan-Müller et al. 1998). Also, commercial tests as different API systems (20E, 20NE, NE) have been used to identify Aeromonas spp. (Hänninen et al. 1997). However, many commercial test kits do not always recognise all Aeromonas spp. correctly (Altwegg et al. 1990). Those species that may grow at temperatures higher than 35°C can also show untypical biochemical characteristics (Hänninen and Siitonen 1995). Vibrio spp. Several Vibrio spp. are human pathogens which may also be transmitted to humans by the consumption of undercooked seafood or shellfish (Feldhusen 2000). In Western countries seafood-related illness caused by pathogenic Vibrio spp. is commonly associated with


335

crustaceans or molluscan shellfish, whereas finfish are common vehicles for outbreaks in Japan or other Asian countries (ICMSF 1998). V. parahaemolyticus is an important cause of gastrointestinal disease, most often after the consumption of seafood (Dalgaard 1998). A qualitative enrichment procedure is normally performed for detection of foodborne Vibrio spp. when microbiological methods are used (Donovan and Netten 1995). For isolation of V. parahaemolyticus, APW, glucose teepol salt broth (GSTB), salt polymyxin broth (SPB) or salt colistin broth (SCB) have been recommended as enrichment media. For plating, Donovan and Netten (1995) recommended the use of thiosulphate citrate bile salt (TCSB) agar in parallel with polymyxin mannose tellurite (PMT) or sodium dodecylsulphate polymyxin sucrose (SPS) agar. Plates should be incubated for 24 hours at 37°C to prevent overgrowth of competitors. Su et al. (2005) examined the selectivity and specifity of a chromogenic medium, Bio-Chrome Vibrio medium (BCVM) which is based on the formation of purple colonies. It was possible to differentiate V. parahaemolyticus from other Vibrio spp. However, they concluded that further studies are needed. Alam et al. (2001) used a nutrient agar with 2% NaCl (V. parahaemolyticus is halophilic) as a primary culture medium and transferred thereafter colonies to TCBS agar. There is a possibility of failing in the detection of Vibrio spp. when TCBS or APW are used directly. Thus, non-selective media can be used because they have the advantage of recovering injured or weak cells which are very sensitive to selective agents in selective media (Alam et al. 2001). Hayashi et al. (2006) described the soft-agar-coated filter method for detecting pathogenic V. parahaemolyticus. It is possible to detect contamination levels of five viable cells per gram sample within 2 working days regardless of the background flora. In principle, only viable V. parahaemolyticus cells are able to penetrate through the soft-agar-coated paper owing to their mobility. DNA released from dead, virulent V. parahaemolyticus cells into an enrichment medium are eliminated by a DNAse pretreatment and can thus not interfere with the followed polymerase chain reaction (PCR). Two genes for two haemolysins (thermostable direct haemolysin, TDH, and TDH-related haemolysin, TRH) which are considered to be major virulence factors of V. parahaemolyticus are then detected by a multiplex PCR assay. This method is a great improvement in the detection of pathogenic V. parahaemolyticus in seafood and helps to decrease time and laboratory work. Salmonella spp. Generally, disease surveillance reports from public health authorities indicate that human salmonellosis associated with consumption of farmed freshwater or marine fish and crustaceans occurs very rarely compared, for example, with poultry meat (Feldhusen 2000). According to Huss (2003), when seafood is harvested in a clean environment and handled hygienically, cross-contamination is the most common cause of findings of Salmonella. In raw seafood products mainly from tropical climates, there is a high prevalence of Salmonella (Dalsgaard 1998; Reilly and Käferstein 1999), whereas low prevalence or absence can be common in temperate regions (González-Rodriguez et al. 2002). Several authors studied prevalence and growth of Salmonella in seafood, for example D’Aoust (2000), GonzálezRodriguez et al. (2002), Brands et al. (2005) and Herrera et al. (2006). Usually, for detecting Salmonella, a two-stage enrichment procedure including pre-enrichment and enrichment in Rappaport-Vassiliadis-soy-peptone broth is used (NCFA 1999). Plating out of cultures and identification from the enrichment broth is performed on different agars, for example,

336


xylose-lysine-decarboxylase (XLD) agar (Nychas and Tassou 1996; González-Rodriguez et al. 2002) or bismuth-sulphite agar (Brands et al. 2005). After incubation at 37°C for 24 hours, the presence of characteristic colonies must be checked and confirmed by biochemical and serological tests.

15.3

Most probable number method

The prevalence of pathogenic bacteria in samples can be very low and it is possible that there is a lack of proper selective microbiological media for their detection. Therefore, enrichment in liquid media is often needed. In such cases, serial dilution tests measuring the concentration of a target microbe in a sample with an estimate – the most probable-number (MPN) method – can be used (Lindström and Korkeala 2006). In principle, several 10-fold dilutions of the sample are prepared. Three serial aliquots or dilutions are inoculated in test tubes containing the suitable liquid medium. They are then incubated and read for positive reactions. The number of positive tubes in each dilution is registered and the most probable number of bacteria per mass volume unit is read from the MPN table (Guyer and Jemmi 1991; Jemmi and Keusch 1992; Jay 2000; NCFA 2003; Lindström and Korkeala 2006). This method is simple, and specific microorganisms can be determined by using appropriate selective media. However, the disadvantages are that the morphology of colonies cannot be observed and a large amount of glassware is needed. Furthermore, this method lacks of precision (Jay 2000).

15.4

Molecular methods

Standard methods for recovering microorganisms from foods may include enrichment culture, streaking out onto selective or differentiating media or direct plating onto these, and identification of colonies by morphological, biochemical and immunological tests (Hill and Jinneman 2000). This all requires a lot of manual labour, is costly and usually needs between 2 and 5 days. Pathogenic microorganisms may be present at very low levels and they can be difficult to detect. Their detection may also be interfered with by components of the food or other bacteria present (Hill and Jinneman 2000). Furthermore, identification of microorganisms using phenotypical tests can often give incorrect results due to the influence of cultivation conditions (morphology, colour, and so on). The medium and the conditions (temperature and atmosphere) for culturing may also not accurately reflect the conditions given in the food (Cambon-Bonavita et al. 2001), or certain bacteria might be unable to grow on the medium used (Rudi et al. 2004). Molecular methods allow a rapid detection and identification of specific bacterial strains and/or virulence genes without the need for pure cultures. They are mainly based on oligonucleotide probes, PCR or antibody techniques. The advantages of molecular methods are: high sensitivity and specificity in detection and identification of bacteria; providing results within one working day; detection of the target microorganism at a very low detection level; a combination of the indication of the presence of the bacterium and the confirmation of its identity in one step; and identification of bacteria to species level. Many individual articles


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in research journals and books have described the different methods, their mechanisms and application in fish and fish products. Within the scope of this chapter, only a few publications can be referred to as examples for the use of molecular methods in detecting and identifying pathogenic and spoilage bacteria in fish and fish products, without claim for completeness and without valuation. Species-specific macromolecular patterns, such as lipopolysaccharides, fatty acids, proteins or DNA, can be used for bacterial identification. Strains belonging to the same species share characteristic bands and can be compared with type or reference strains in a database. Computer-assisted large-scale grouping analysis (species-specific cluster analysis) involves, for example, the creation of dendrograms to reveal groups of related organisms. Patterns of an unknown isolate can be identified against libraries containing corresponding patterns of known species. Two methods have been applied with some success for species-level identification of microbial strains in different fish products. Sodium dodecylsulfate– polyacrylamide gel electrophoresis (SDS–PAGE) of whole-cell proteins or cell envelope proteins has been applied to the identification of Carnobacterium spp. in spoiled vacuumand modified-atmosphere-packed cold-smoked salmon (Paludan-Müller et al. 1998). Determination of 16 + 23S rRNA gene restriction patterns (ribotyping) is based on patterns generated from the ribosomal RNA genes only. It has been used for identification of spoilage LAB in vacuum-packaged cold-smoked and ‘gravad’ rainbow trout and in semi-preserved marinated herring (Lyhs et al. 1998 2001a b, 2004) and for Aeromonas spp. in different fish (herring, rainbow trout, vendace and others) and fishery (shrimps and fish-eggs) samples (Hänninen et al. 1997). The use of probes and PCR has increased dramatically in recent years. Gene probes and PCR primers for detecting and identifying almost every foodborne pathogenic bacterial species have been developed. A probe is a fragment of a single-stranded nucleic acid that will specifically bind (hybridise) to complementary regions of a target nucleic acid. There are three different approaches to the design of nucleic acid probes: randomly cloned probes; probes complementary to specific genes and gene fragments (16S or 23S rRNA targeted oligonucleotide probes); and rRNA target probes (Schleifer et al. 1995). In addition to DNA hybridisation techniques, specific areas of the genes encoding rRNA can be used as templates for primer design for detecting nucleic acid hybridisation (Van Belkum 1994). PCR is a method for the in vitro amplification of a given region of DNA. A pair of specific oligonucleotides bracketing the region serves as primers for polymerase to initiate the DNA replication (Hill and Jinneman 2000). For fish products, PCR assays have been used to detect L. monocytogenes, for example in cold-smoked salmon (Simon et al. 1996; Rodriguez-Lazaro et al. 2005) and in ‘gravad’ rainbow trout (Ericsson and Stalhandske 1997). Furthermore, using PCR-based techniques, pathogenic Aeromonas spp. have been detected in raw and coldsmoked trout and salmon (González-Rodríguez et al. 2002b) and C. botulinum in rainbow trout (Hielm et al. 1996), in modified-atmosphere packaged fish (Kimura et al. 2001) and in whitefish (Lindström and Korkeala 2006). Cambon-Bonavita et al. (2001) performed PCR amplification coupled with amplification ribosomal DNA restriction analyses (ARDRA) in vacuum-packed cold-smoked salmon, and Rudi et al. (2004) examined partial 16S rRNA gene sequences of bacteria from modified-atmosphere-packaged salmon and coalfish. Giacomazzi et al. (2004) found the nested-PCR/temporal temperature gradient gel electrophoresis (TTGE) technique on the rpoB gene on pure cultures of reference strains to be a promising way for describing the microbial diversity in cold-smoked salmon.

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The use of a culture-independent molecular biology approach as an alternative method to cultivation has been found more and more promising for evaluating microbial communities in foods (Rudi 2003). However, as a powerful tool in future research, the combination of both traditional and molecular methods for the direct description of microbial communities with traditional methods for enrichment and isolation of important strains has been recommended (Cambon-Bonavita et al. 2001; Rudi et al. 2002). The question whether bacterial cells are dead or alive is important in monitoring foods and water safety (Nocker and Camper 2006). With traditional culture methods it is not possible to differentiate between dead and living bacteria or their DNA, or to detect intermediate states like cell injury (Berney et al. 2007). The most commonly used strategy is to focus on the presence of rapidly degrading RNA (Nocker and Camper 2006). Another possibility is the use of dyes and microscopy for differentiation between dead and living cells. Nowadays the LIVE/DEAD® Bac Light™ kit has been increasingly used by researchers in different fields (Berney et al. 2007). In a study about the changes of bacterial composition in spoiled oysters during storage, Romero et al. (2002) used this kit for determinating viable bacteria. Other alternatives have been described by Nogva et al. (2003) and Rudi et al. (2005) using ethidium monoazide bromide (EMA)-PCR. This diagnostic DNA-based method combines live–dead staining dye and real-time PCR. However, Vaitilingo et al. (1998) showed that reverse transcriptase PCR (RT–PCR) offers the greatest potential for the detection/discrimination of live and dead cells. Many seafood-related infections are caused by norovirus and hepatitis A virus. Electron microscopy was the most used laboratory diagnostic method for direct detection of viruses (Butt et al. 2004). Nowadays, enteric viruses are detected in shellfish concentrates by cell-culture infectivity assays, but increasingly by molecular techniques like PCR (Shieh et al. 1999). Gene probes and PCR can detect fewer than 10 virus particles (Hill and Jinneman 2000). RT–PCR has become available commercially and show greater sensitivity and specificity than electron microscopy (Butt et al. 2004).

15.5

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Chapter 16

Protein-based methods Hartmut Rehbein

16.1

Introduction

Electrophoretic, immunological and enzymatic methods have a long tradition in seafood analysis. A comprehensive overview of the application of protein-based techniques for identifying the species in fishery products has been given by Rehbein (1990) and Mackie (1996). However, in the past two decades, the polymerase chain reaction (PCR) has extensively replaced isoelectric focusing (IEF) and sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS–PAGE) as tools for species identification. Nevertheless, IEF of sarcoplasmic proteins is still in use for authentication of raw products (Berrini et al. 2006), mainly fresh or frozen fish fillets, because it is a very fast and cost-effective method with a high power of differentiation. Recently, protein- and DNA-based techniques have been reviewed for their suitability for identifying gadoid fish species (Hubalkova et al. 2007). Dipstick immunoassays offer a simple and rapid possibility for species identification, which may be used by fish processors, traders and food control authorities ‘in the field’, for example at the auction. Several immunological test systems have been developed for identifying fish species (see below), but to the knowledge of the author no kit or stick is commercially available. Beside species identification, the following topics will be dealt with: detection of allergenic proteins, determination of the heating temperature of smoked fish and differentiation between fresh and frozen/thawed fish fillets. Authenticity assessment by two-dimensional electrophoresis (2DE) or differential scanning calorimetry (DSC) are described in Chapter 14 and Chapter 8 of this book, respectively. In the following, the term ‘fish’ also denotes ‘mollusc’ and ‘crustacean’, if not indicated otherwise.

16.2

Fish muscle proteins

In most cases ‘seafood’ means muscle food, exceptions being caviar, canned cod liver and mussels. The two main classes of fish muscle proteins (Sikorski et al. 1994), myofibrillar 349

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Table 16.1 Protein-based techniques for species identification in fishery products. Analytical method Native IEF (isoelectric focusing) Urea IEF Cellulose acetate electrophoresis SDS-PAGE (sodium dodecylsulphate polyacrylamide gel electrophoresis) 2-DE (2-dimensional electrophoresis) Native IEF combined with enzyme specific staining Capillary zone electrophoresis High-performance liquid chromatography (HPLC) Immunoassays

Fishery product Raw, cold-smoked, hot-smoked, pre-fried, cooked, canned, formed, high-pressure treated products Raw, cold-smoked, hot-smoked, pre-fried, cooked, formed, high-pressure treated, surimi-based products Raw products (Yearsley et al. 1999, 2003) Raw, cold-smoked, hot-smoked, pre-fried, cooked, formed, high-pressure treated, surimi-based products Raw, cold-smoked, hot-smoked, pre-fried, cooked, formed, high-pressure treated, surimi-based products Raw products (Pineiro et al. 2000; Jiang and Xiong, 2006) Raw products (Gallardo et al. 1995) Raw products (Knuutinen et al. 1998) Raw, smoked, canned surimi-based products

and sarcoplasmic proteins, have been used to develop methods to authenticate declaration of species or processing technology. Differences in electrical charge, size (molecular mass) and amino-acid sequence of proteins result in different electrophoresis patterns, highperformance liquid chromatography (HPLC) chromatograms or matrix-assisted laser desorption/ionization–time of flight (MALDI–TOF) mass spectra for different fish species. An overview of the application of analytical methods is given in Table 16.1. Two classes of proteins, parvalbumins and myosin light chains (MLCs), are of great significance for fish species identification. Parvalbumins are water-soluble, acidic, calciumbinding proteins of low molecular mass (10–12 kilodaltons (kDa)) (Gerday 1982). Light muscle of fish may contain high concentration of parvalbumins (up to 5 mg/g wet weight), consisting of several isoforms with low isoelectric point (pI) values (between pH 4 and 5) (Rehbein et al. 2000). Parvalbumins from various fish species have been identified as major food allergens, possessing a high allergenicity (Bugajska-Schretter et al. 2000). Owing to their great heat stability, parvalbumins can be used not only for identification of raw fish by IEF (Figure 16.1), but also in the case of cooked or even canned fish products (Rehbein et al. 1990). The MLCs, which belong to the myofibrillar proteins, are useful for species identification for those fish products where the sarcoplasmic proteins have been removed during processing by washing steps, such as for surimi and surimi-based products, like kamaboko or imitation crab-meat. During fish processing and storage of products, fish muscle proteins are degraded to a varying extent. However, the statement that the proteins in canned fish or other fishery products have become irreversibly denatured, which is often found in papers about PCRbased species identification (Infante et al. 2004), is not quite correct. Products like canned salmon or herring are good examples for the stability of muscle proteins. To develop a method for identification of canned fish, canned salmon flesh had been treated with cyanogen bromide and the peptides obtained were separated by native IEF resulting in species-specific

Protein-based methods 8.45

7.35

6.55

5.85

5.20 4.55

3.50

351

pI value

pI calibration kit heated Turbot raw heated Norway pout raw heated Pollack raw heated Red mullet raw heated Anglerfish raw heated Brill raw heated Sprat raw <Parvalbumins>

Figure 16.1 Differentiation of fish species by IEF of sarcoplasmic proteins. Proteins were extracted from raw fillet (white muscle) and run on Servalyt Precote® 3-10; raw: original extract, heated: extracted heated to 70°C and clarified by centrifugation. Turbot, Psetta maxima; Norway pout, Trisopterus esmarki; pollack, Pollachius pollachius; red mullet, Mullus barbatus; anglerfish, Lophius piscatorius; brill, Scophthalmus rhombus; sprat, Sprattus sprattus.

peptide patterns (Mackie 1996). Differentiation between canned herring, sprat and sardine was achieved by native IEF of a protein fraction enriched in the heat-stable parvalbumins (Rehbein et al. 1990). On the other hand, DNA in canned fish is heavily degraded into fragments of 100–200 base pairs in length (Quinteiro et al. 1998). The main reason for preferring DNA over protein for fish species identification is the ease of selective amplification and characterisation (by sequencing, restriction-fragment length polymorphism (RFLP), single-strand conformation polymorphism (SSCP) and similar techniques of mutation detection) of DNA sequences using PCR-based techniques.

16.3

Electrophoretic methods for fish species identification

For more than two decades isolelectric focusing of sarcoplasmic proteins has been the most popular technique for fish species identification of raw products, like fresh, frozen or formed fillets. In IEF, proteins are separated according to the electrical charge of their surface,

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producing narrow bands within a preformed pH gradient. IEF of sarcoplasmic proteins has a high discriminating power and reproducibility. Occasionally, capillary electrophoresis methods have been described to analyse fish sarcoplasmic proteins (LeBlanc et al. 1994; Gallardo et al. 1995; Valenzuela et al. 1999).

16.3.1 Native isoelectric focusing Species identification by native isoelectric focusing (nIEF) consists of the following steps: z

z

z z z z

Extraction of water-soluble fish muscle proteins by mixing light muscle with three- to fourfold amounts of water, followed by a short-time centrifugation to remove nondissolved material. Loading of extract by means of sample application pieces or applicator strips on the gel, preferentially on the cathodic site for most fish species. Ready-to-use IEF gels containing ampholytes for different pH gradients are offered by several vendors. Run of the IEF until 3000–5000 volt hours have been reached. Ampholytes are removed and protein bands are fixed by shaking the gel in an acidic solution. Protein bands are visualised by staining with Coomassie dye. Comparison of protein profiles of unknown samples with patterns obtained from reference fish run on the same gel or from databases.

By using this protocol, 20–40 samples can be analysed within one working-day. Unknown samples may be identified either by comparison with the pattern of reference fish run on the same gel or by calculating the pI values of protein bands. Pattern and pI value for several commercially important fish species are available on the internet from databases like www. cfsan.fda.gov/~frf/rfe0.html (fish relevant for the North American market) or www.fischdb. de (fish relevant for the European market). Also, a library of IEF patterns of 43 commercial fish species belonging to the orders Pleuronectiformes and Gadiformes has been published (Tepedino et al. 2001). Protein patterns for scombroid, salmonid and gadoid fishes are shown in Figure 16.2. Each family exhibits a characteristic type of pattern: the red-fleshed scombroids have prominent bands mostly in the cathodic part of the gel, whereas salmonids give protein bands of high and medium pI value (pI 6–9), and gadoids possess strong bands in the anodic part of the IEF gel. Protein patterns are affected by the type of muscle used (Rehbein and Kündiger, 1984), the chemical composition of ampholytes, and the position of the sample within the pH gradient (see Figure 16.2). It is recommended placing the sample in a region devoid of strong protein bands. Numerous examples of application of nIEF for identification of fish species have been compiled previously (Rehbein 1990). The reliability of the technique was demonstrated by collaborative studies using one protocol (Lundstrom 1980) or several modifications of the technique. When each of eight European laboratories had applied its own method of IEF to identify 10 unknown samples of raw muscle by means of reference material, the

Protein-based methods Cathode ↓ sample application

353

Anode

P a r v

Oncorhynchus nerka O. gorbuscha Salmo salar O. mykiss Scomber scombrus Theragra chalcogramma Melanogrammus aeglefinus Pollachius virens Gadus morhua pI marker pI marker Onocrhynchus nerka O. gorbuscha Salmo salar O. mykiss Scomber scombrus Theragra chalcogramma Melanogrammus aeglefinus Pollachius virens Gadus morhua

sample application ↑

Figure 16.2 Characteristics of protein patterns of different fish species. Extract of fillet (light muscle) from salmonid species (Oncorhynchus spp., S. salar), mackerel (S. scombrus) and gadoid species (T. chalcogramma, M. aeglefinus, P. virens, G. morhua) was applied to the IEF gel either at the cathodic or anodic side. The position of parvalbumin bands is indicated, but with this type of gel (FocusGel 3-10, ETC) no sharp parvalbumin bands were obtained as with Servalyt Precotes®.

assignment between sample and reference was correct in 93% of cases (Rehbein et al. 1995). Recently nIEF has been found suitable to identify Aegean fish species (Ataman et al. 2006), freshwater fish commercially labelled ‘perch’, Perca fluviatilis, Lates niloticus, Stizostedion lucioperca, Morone chrysops x saxatilis, (Berrini et al. 2006), six species of puffer fish (Chen et al. 2003), and to distinguish steaks of blue marlin, Mediterranean spearfish and swordfish (Renon et al. 2005).

16.3.2

Urea IEF

Raw or denatured fish muscle proteins can be solubilised by mixing the fish flesh with 8 M buffered urea, and analysed by IEF with gels containing the same concentration of urea. The extract contains a mixture of sarcoplasmic and myofibrillar proteins. The method has been applied for identification of the fish species in surimi-based products, as well as in cooked, smoked or high-pressure treated fish. A standard operation procedure for urea IEF (uIEF) has been developed comprising the following steps (Etienne 1999; Rehbein et al. 1999):

354 z

z z z z z z z


Extraction of fish muscle protein by mixing light muscle with fourfold amount of 8 M urea/0.1 M 1,4-dithiothreitol (DTT)/20 mM sodium phosphate pH 6.5. The mixture is kept at room temperature for about half an hour to complete solubilisation of protein, then undissolved material is removed by high-speed centrifugation. CleanGels (obtainable from GE Healthcare or other vendors) are rehydrated with a solution containing 8 M urea and ampholytes. After prefocusing, extract is loaded on the gel by means of sample application pieces or applicator strips, which are placed at the cathodic site of the gel. Run of IEF until 5000 volt hours have been reached. Ampholytes and urea are removed and protein bands are fixed by shaking the gel in an acidic solution. Protein bands are visualised by staining with Coomassie dye. The gel is impregnated with glycerol and dried. Comparison of protein profiles of unknown samples with patterns obtained from reference fish run on the same gel or from databases. If pI values of proteins are calculated, the pI shift of proteins caused by urea has to be taken into account (Rehbein et al. 2000).

Instead of CleanGels, immobiline dry plates (Etienne et al. 1999) or home-made gels (An et al. 1989a, b) have been applied for uIEF with good results. From the protocol described above it is clear that uIEF is a technically demanding method, which needs experience to avoid pitfalls like crystallisation of urea during the run. Several applications of uIEF for species identification and analysis of protein changes during fish processing and storage are listed in Table 16.2. In several of these studies, the results obtained by uIEF had been compared with the results from nIEF and SDS–PAGE.

16.3.3

SDS–PAGE

Whereas differences in electrical charge are used for separation of proteins by IEF, all proteins carry a negative charge in SDS–PAGE. The negative charge is produced by loading proteins with the anionic detergent sodium dodecylsulphate; on average, 1.4 g of SDS is taken up by 1 g of protein. Secondary and tertiary structures are disrupted and protein-detergent-ellipsoids move through the polyacrylamide gel according to the molecular mass of the protein. The field of application of SDS–PAGE is nearly the same as it is for uIEF. A standard operation procedure for SDS–PAGE with ExcelGels has been developed previously, comprising the following steps (Pineiro et al. 1999): z

z z

Solubilisation of fish muscle by boiling in an SDS-containing solution (2% w/v SDS/ 0.1 M DTT, 60 mM Tris-HCl pH 7.5), and centrifugation to remove non-dissolved material. Flat-bed electrophoresis with ExcelGels (GE Healthcare). Silver staining of proteins.

This method has been applied to the identification of smoked salmon and eel (Mackie et al. 2000), cooked fish (Pineiro et al. 1999) and high-pressure treated products (Etienne

Protein-based methods

355

Table 16.2 Urea IEF as a tool for identification of fish and shellfish species and analysis of protein changes during processing. Objective of study Differentiation of shrimps Identification of fish species in surimi-based products Identification of sturgeon caviar Identification of blue crab (Callinectes sapidus); effect of season, location and processing on protein patterns Differentiation of raw and cooked bivalves (Meretrix lyrata, Ruditapes decussatus, Cerastoderma edule) Differentiation of cooked gadoids, flatfish and other species: collaborative study Differentiation of cooked salmonids and other species Identification of smoked salmonids, gravad salmonids and smoked eels Parvalbumins as marker proteins for uIEF Identification of formed and high pressuretreated fish Protein changes after irradiation and storage of horse mackerel (Trachurus trachurus)

Method

Reference

uIEF with home-made gel uIEF with home-made gel

An et al. 1989a An et al. 1989b

uIEF with home-made gel uIEF with home-made gel

Chen et al. 1996 Gangar et al. 1996

uIEF with CleanGel; extracts from adductor muscle uIEF with CleanGel and immobiline dry plate uIEF with CleanGel

Etienne et al. 2000b Rehbein et al. 1999

uIEF with CleanGel

Etienne et al. 2000a Mackie et al. 2000

uIEF with CleanGel uIEF with CleanGel

Rehbein et al. 2000 Etienne et al. 2001

uIEF with CleanGel

Silva et al. 2006

et al. 2001). The comparison of the efficiency of SDS–PAGE and uIEF for differentiation of closely related species demonstrated a good performance for both methods in flatfishes and hakes, but in salmonids and tunas several species could not be distinguished by uIEF (Etienne et al. 2000a). For differentiation of hot-smoked eel, uIEF was preferred to SDS–PAGE (Mackie et al. 2000). Many other gel types can be used for SDS–PAGE, either in horizontal or vertical electrophoresis chambers. The composition of the gel must be adapted to the size of proteins to be separated. As most of the proteins relevant for differentiation of fish species, like the MLCs and troponins, possess a molecular mass below 40 kDa, a total polyacrylamide concentration of 12–15% is recommended (Pineiro et al. 1999). SDS–PAGE has not only been applied to analysis of muscle food, but also to characterisation of fish roe and caviar. Figure 16.3 shows the protein patterns for gadoid roe and sturgeon caviar, which allow differentiation between these types of products. However, the technique was not suitable for differentiating caviar (Beluga, osietra, sevruga) of different sturgeon species (Chen et al. 1996). The main field of application of SDS–PAGE for the study of fish as food is analysis of protein changes during fish processing. Recently published examples are given by the analysis of protein changes after irradiation and ice storage of horse mackerel (Silva et al. 2006) and the study of denaturation of myofibrillar proteins during preparation of kamaboko gels (Shikha et al. 2006).

356


1

2

3

4

5

6 7

8

9

Figure 16.3 SDS–PAGE of roe and caviar. Lanes 1–5: sturgeon caviar; (1) Osietra, Acipenser gueldenstaedtii; (2 and 3) Beluga, Huso huso; (4 and 5) Osietra, A. baeri; lanes 6 and 7: cod roe, Gadus morhua; extract of lane 6 had twice the protein concentration as extract of lane 7; lanes 8 and 9: marker proteins of known of molecular mass (kilodaltons) are indicated.

16.4

High-performance liquid chromatography

In the last decades of the 20th century, several attempts were made to establish HPLC as an alternative to electrophoresis techniques for fish species identification (Ashoor and Knox 1985; Osman et al. 1987). Possible advantages of HPLC versus electrophoresis may be the short time required for sample preparation, and easier automation (for example by sample application systems). In most cases, reversed-phase HPLC has been used, but Rehbein (1990b) successfully tested anion-exchange chromatography as an alternative. For reversed-phase HPLC of sarcoplasmic proteins, preparation of samples is straightforward: light muscle of fish is extracted with water, extracts are acidified to pH 2 with trifluoracetic acid, then filtered through a 0.2 or 0.45 μm membrane filter and injected. In all cases, a gradient with an increasing concentration of acetonitril is applied, and the time of the HPLC run is varied between 30 and 60 minutes. This is much shorter than the time needed for IEF with slab-gels, but by HPLC one sample after the other has to be run whereas 20 samples can be applied simultaneously on an IEF gel. Identification of four gadoid fish species has been reported by Pineiro et al. (1997). Protein profiles did not change during storage of refrigerated fish for 10 days. In another study, 16 of the most common Finnish freshwater fish species were differentiated by species-specific HPLC chromatograms obtained using photodiode-array detection at 200–350 nm (Knuutinen and Harjula 1998).


16.5

357

Immunological methods and detection of allergenic proteins

Methods for identification of the fish or shellfish species are not only relevant for prevention of fraud, but are also important for detection of the presence of seafood as one of the most common food allergens. Several types of immunoassay have been developed for detection of seafood allergens, for process control and authentication of fish and shellfish. In most cases an enzyme-linked immunosorbent assay (ELISA) has been used owing to the sensitivity, short time of analysis, and specificity of the method. In the dip-stick format, ELISA can be applied in the ‘field’, for example by customers or food controllers. Recently an ELISA has been developed for identifying three clam species using polyclonal antibodies against soluble proteins extracted from whole clam. The polyclonal antibodies were made species-specific by blocking them with soluble proteins from heterologous clam species. Two formats of indirect ELISA, microtitre plates and immunostick tubes, were found to be suitable to differentiate between Ruditapes decussatus (grooved carpet shell), Venerupis rhomboides (yellow carpet shell) and V. pullastra (pullet carpet shell) (Fernandez et al. 2002). Work in the same laboratory on authentication of fish by immunoassay comprised production of polyclonal and monoclonal antibodies to be used in an indirect ELISA (Asensio et al. 2003a, b, c). Differentiation of grouper (Epinephelus guaza), wreck fish (Polyprion americanus) and Nile perch (Lates niloticus) was achieved using polyclonal antibodies as described above for clams (Asensio et al. 2003a). A monoclonal antibody specific to grouper and wreck fish (Asensio et al. 2003b) was developed and tested against raw, cooked and sterilised protein extracts of 15 fish species using indirect ELISA. The monoclonal antibody recognised only grouper and wreck fish samples and did not show any cross-reactivity with other fish species. Heated extracts performed just as well as raw extracts. The development of a specific monoclonal antibody for grouper allowed identification of this species by indirect ELISA with microtitre plates or immunostick tubes. Antisera against myosin alkali light chain 1 (MLC A1) have been used by Ochia et al. (2003) for identification of fish species in dried fish products (fushi and noboshi) by immunoblotting of MLC separated by SDS–PAGE. Owing to the heat stability of MLC, fish processed to fushi and noboshi could be identified by immunostaining with anti-mackerel-A1 antiserum. Earlier applications of immunological methods for species identification have been reviewed previously (Rehbein 2003). Fish and crustacean shellfish are common food allergens which have to appear on the ingredient list if present in the food. Control of correct labelling may be performed by immunoassays using antibodies against parvalbumin (for fish) or tropomyosin (for shellfish and crustaceans) (Swoboda et al. 2002; Fuller et al. 2006).

16.6

Determination of heating temperature

Fishery products are heated for several reasons: (1) to achieve the desired sensory properties by cooking, frying or smoking; (2) to produce a storable product; or (3) to destroy pathogenic

358


organisms like nematode larvae or bacteria. Information about the temperature the product has experienced during processing can be obtained by several methods of protein analysis. The following methods have been used, either alone or in combination: z z

z z z

Measurement of soluble protein. Determination of the temperature necessary to coagulate extracted protein. The coagulation test is performed by extracting the muscle sample with water or buffer, heating the extract and measuring the temperature at which protein coagulation takes place (Doesburg and Papendorf 1969). IEF of water-soluble protein. SDS–PAGE. Measurement of enzyme activity.

In search of suitable methods for determination of the heating temperature of products made from herring (Clupea harengus), mackerel (Scomber scombrus), saithe (Pollachius virens), haddock (Melanogrammus aeglefinus) and tuna (Thunnus alalunga), the coagulation test, IEF and measurement of water-soluble protein were compared (Rehbein 1992). It was found that most (about 80%) of the sarcoplasmic protein had become insoluble at the temperature of 60°C. IEF demonstrated that the heat stability of sarcoplasmic proteins varied considerably. Proteins of gadoid species were denatured sequentially from high to low pI value. In later studies, a similar result was obtained for rainbow trout (Oncorhynchus mykiss) (Figure 16.4). Three different methods, coagulation test, SDS–PAGE and measurement of activities of lactate dehydrogenase and glycerolaldehyde dehydrogenase, were applied to assess the endpoint temperature (EPT) of heated fish and shellfish meats (Uddin et al. 2002). Proteins had been extracted with NaCl solution. The coagulation test was able to determine EPT for meat

Cathode

Anode 55°C

60°C

65°C

70°C

Figure 16.4 Changes in the protein pattern of sarcoplasmic proteins caused by heating of extracts. Extract from light muscle of rainbow trout was heated to the temperatures indicated, clarified by centrifugation and run on Servalyt Precote 3-10®.


359

of skipjack tuna (Katsuwonus pelamis), red sea bream (Pagrus major), Kuruma prawn (Penaeus japonicus) and scallop (Patinopecten yessoensis) in the range 60–67°C. SDS–PAGE revealed the presence of a few thermostable proteins in extract of tuna meat (32–34 kDa, approximately14 kDa) and scallop adductor muscle (35 kDa, approximately14 kDa). As expected, enzyme activities decreased during heating of muscle tissue, but the extent of variability of enzyme activities for different specimens was not determined. The thermal inactivation of acid and alkaline phosphatases in fish and shellfish muscle was measured to determine the suitability of heating temperature (Kuda et al. 2004; Johnsen et al. 2007). The results suggest that measurement of residual phosphatase activity may be used to check the temperature seafood experienced during processing.

16.7

Differentiation of fresh and frozen/thawed fish fillets

Fresh fish and fillets have different sensory properties and spoilage characteristics compared with frozen/thawed products (Rehbein et al. 1978). The cells of fish muscle and their organelles are destroyed by freezing and thawing, and particle-bound enzymes are released into thaw drip and press-juice (Rehbein 1992b). Enhanced enzyme activity in these liquids has been measured, allowing differentiation between fresh and frozen/thawed fish fillets (Duflos et al. 2002).

16.8

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Rehbein, H., Kress, G. and Kündiger, R. (1990) Determination of species of fish in long-life canned products by isoelectric focusing. [In German.] Fleischwirtschaft 70: 706–709. Rehbein, H., Etienne, M., Jerome, M., Hattula, T., Knudsen, L.B., Jessen, F., Luten J.B., Bouquet, W., Mackie, I.M., Ritchie, A.H., Martin R., Mendes, R. (1995) Influence of variation in methodology on the reliability of the isoelectric focusing method of fish species identification. Food Chemistry 52: 193–197. Rehbein, H., Kündiger, R., Malmheden Yman, I., Ferm, M., Etienne, M., Jerome, M., Craig, A., Mackie, I., Jessen, F., Martinez, I., Mendes, R., Smelt, A., Luten, J., Pineiro, C. and Perez-Martin, R. (1999) Species identification of cooked fish by urea isoelectric focusing and sodium dodecylsulfate polyacrylamide gel electrophoresis: a collaborative study. Food Chemistry 67: 333–339. Rehbein, H., Kündiger, R., Pineiro, C. and Perez-Martin, R.I. (2000) Fish muscle parvalbumins as marker proteins for native and urea isoelectric focusing. Electrophoresis 21: 1458–1463. Renon, P., Bernardi, C., Malandra, R. and Biondi, P.A. (2005) Isoelectric focusing of sarcoplasmic proteins to distinguish swordfish, blue marlin and Mediterranean spearfish. Food Control 16: 473–477. Shikha, F.H., Hossain, M.I., Morioka, K. and Kubota, S. (2006) Effect of pH-shifting on the gel forming characteristics of salt-ground meat from walleye pollack. Fisheries Science 72: 870–876. Sikorski, Z.E., Sun Pan, B. and Shahidi, F. (1994) Seafood Proteins. Chapman and Hall, London. Silva, H.A., Mendes, R., Nunes, M.L. and Empis, J. (2006) Protein changes after irradiation and ice storage of horse mackerel (Trachurus trachurus). European Food Research Technology 224: 83–90. Swoboda, I., Bugajska-Schretter, A., Verdino, P., Keller, W., Sperr, W.R., Valent, P., Valenta, R. and Spitzauer, S. (2002) Recombinant carp parvalbumin, the major cross-reactive fish allergen: a tool for diagnosis and therapy of fish allergy. Journal of Immunology 168: 4576–4584. Tepedino, V., Berrini, A., Borromeo, V., Gaggioli, D., Cantoni, C., Manzoni, P. and Secchi, C. (2001) Identification of commercial fish species belonging to the orders Pleuronectiformes and Gadiformes: library of isoelectric focusing patterns. Journal of AOAC International 84: 1600–1607. Uddin, M., Ishizaki, S., Ishida, M. and Tanaka, M. (2002) Assessing the end–point temperatue of heated fish and shellfish meats. Fisheries Science 68, 768–775. Valenzuela, M.A., Gamarrra, N., Gomez, L., Kettlun, A.M., Sardon, M., Perez, L.M., Vinagre, J. and Guzman, N.A. (1999) A comparative study of fish species identification by gel isoelectric focusing, two-dimensional gel electrophoresis, and capillary zone electrophoresis. Journal of Capillary Electrophoresis and Microchip Technology 6: 85–91. Yearsley, G.K., Last, P.R. and Ward, R.D. (Eds) (1999) Australian Seafood Handbook: Domestic Species. CSIRO Marine Research, Hobart, Australia. Yearsley, G.K., Last, P.R. and Ward, R.D. (Eds) (2003). Australian Seafood Handbook: Imported Species. CSIRO Marine Research, Hobart, Australia.

Chapter 17

DNA-based methods Hartmut Rehbein

17.1

Introduction

In the course of globalisation of food production and distribution, trade with fishery products has steadily increased over recent decades. In many countries the situation on the market is characterised by shortage of traditional fish species, introduction of new species and products, and substitution of highly prized species by cheaper ones. As an example, on the German market cod (Gadus morhua) has been replaced with Alaska Pollock (Theragra chalcogramma) as raw material for deep-frozen products (fillets, fillet portions, fish sticks), and in Spain the North Atlantic hake (Merluccius merluccius) has been substituted by other hake species. It seems to become increasingly difficult to fulfil the consumer’s demand for high-quality fishery products, food safety and authenticity at a reasonable price. In September 2006 at the 28th Session of the Codex Committee on Fish and Fishery Products in Beijing, China, procedures for the inclusion of additional species in standards for fish and fishery products were discussed (Codex Committee on Fish and Fishery Products 2007). Cases of substitution and fraud have been uncovered in several countries. Food Standards Australia has published results of a pilot survey performed in 2003 on the identity of fish species as sold through outlets in Australia and New Zealand (http://www.foodstandards. gov.au/mediareleasespublications/publications/). Samples (n = 138) were analysed by DNA fingerprinting (PCR–RFLP: polymerase chain reaction–restriction fragment length polymorphism) to check products for correct labelling either as Australian barramundi (Lates calcarifer) or red emperor (Lutjanus sebae). Overall the survey found that 87% of all samples sold as barramundi had been labelled correctly. However, in the case of products sold as red emperor, about 41% of all samples were identified as being mislabelled. Recently, similar experiences were reported from the USA for groupers, red snapper and walleye. For example, DNA testing revealed that in 11 restaurants located in the Tampa Bay area, Florida, grouper was substituted by cheaper fish (hake, tilapia, catfish and other species) in more than 50% of cases (St. Petersburg Times, Tampa Bay 2006). The FDA list of examples of substituted seafood has 19 entries (http://www.cfsan.fda.gov/~frf/econ.html). In Europe, annual reports of food control laboratories regularly describe cases of mislabelled fish. 363

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Black caviar, a costly sturgeon product with a long tradition, represents another example of product shortage caused by over-exploitation of fish resources and destruction of natural habitats. Owing to reduction of sturgeon stocks in the Black Sea and Caspian Sea, caviar trade is restricted by Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) regulations and national rules (htpp://www.fws.gov). PCR-based methods have been developed for identification and differentiation of sturgeon caviar and successfully applied to detect fraud in the caviar trade (Birstein et al. 1997). The main objectives for authenticity assessment by DNA analysis are: z z z z z z z

To protect the consumer against fraud. To protect the consumer against health risks (for example allergic reaction against fish and shellfish). To safeguard fair trade. To prevent mislabelling of substitutive products. To ensure correct labelling of ethnic food. To protect endangered species. To support customs examinations.

The topic has been reviewed frequently over recent years (Bossier 1999; Civera 2003; Sotelo and Perez-Martin 2003; Martinez et al. 2005; Teletchea et al. 2005), and new publications about fish or shellfish identification are steadily appearing in scientific journals. The many aquatic animal species used for human consumption, several thousand species worldwide, which are often closely genetically related, is a considerable analytical challenge for food inspection laboratories. Currently, PCR-based methods deliver the best tools for species identification. The different techniques, which have been originally developed and applied for mutant detection in medical research in most cases (Taylor and Day 2005), are discussed in the following part of this chapter. Results obtained for several commercially important fish families (tunas, gadoids, hakes, flatfishes, salmonids, sturgeons, snapper, groupers and sharks) have been compiled in Tables 17.6–17.14. In the case of crustaceans and molluscs, the situation is less satisfactory, as for many species PCR-based techniques are lacking. A selection of publications dealing with shellfish authentication has been compiled in Tables 17.15 and 17.16. In the following part of this chapter the term ‘fish’ is used for fish and shellfish (crustaceans and molluscs), if not indicated otherwise.

17.2

DNA in fishery products

The edible part of most fishery products consists of fillet, which contains white and red muscle tissue. Other tissues used for human consumption are liver, roe, milt and skin. Mussel meat is an exception, as it is composed of different kinds of muscle and several other tissues (gills, digestive tract, and so on). The DNA content of whole fish was found to range from 140 to 900 μg/g wet weight, the mean value for 31 North Atlantic fish species being about 255 μg/g wet weight (Horstkotte and Rehbein 2006). The DNA content of fish fillet, fish eggs and other tissues is given in Table 17.1. The DNA content of a single grain of sturgeon or trout caviar was determined

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Table 17.1 DNA content of fishery products as determined with the fluorescence enhancement assay using the dye Hoechst 33258 (Rehbein unpublished results; Rehbein and Horstkotte 2003).

Fish species Carp, Cyprinus carpio

Whiting, Merlangius merlangus Rainbow trout, Oncorhynchus mykiss

Mackerel, Scomber species

Cod, Gadus morhua

Atlantic salmon, Salmo salar Eel, Anguilla anguilla North Atlantic hake, Merluccius merluccius Sevruga, Acipenser stellatus Osietra, Acipenser gueldenstaedtii Beluga, Huso huso Herring, Clupea harengus Skipjack, Katsuwonus pelamis

Type of tissue/product White muscle, raw Red muscle, raw Liver, raw Kidney, raw Spleen, raw White muscle, raw Red muscle, raw White Muscle, raw White muscle, cooked White muscle, sterilised Caviar (egg) White muscle, raw Red muscle, raw Kidney, raw Spleen, raw Fillet, hot smoked White muscle, raw Red muscle, raw Kidney, raw Spleen, raw White muscle, raw White muscle, hot smoked Roe, raw Caviar (egg) Caviar (egg) Caviar (egg) Bismarck herring Rollmops Light muscle, canned

DNA content (μg/g wet weight) 672 1177 4221 18,479 18,490 63 304 839 739 535 85 545 1705 8478 9062 177 478 870 4757 9040 261 180 1590 450 244 236 261 309 303

to be about 6 μg (Rehbein and Horstkotte 2003). Generally the DNA content increases from white to red muscle to innards (liver, spleen, kidneys). During processing (for example cooking, smoking, marinating) of fish, DNA may be degraded to some extent, but the reduction in DNA content and size does not hamper most of the PCR-based methods. However, in canned fish the DNA is fragmented into short pieces, making it necessary to use sequences of fewer than 200 base pairs (bp) for PCR (Quinteiro et al. 1998). Like other animals, fish and shellfish possess two different types of DNA in their tissues, belonging to nuclear and mitochondrial genomes. The mean haploid nuclear genome size for teleost fish is about 1.15 × 109 bp (www.genomesize.com) (for example 1.7 × 109 bp for zebra fish, Danio rerio), whereas the mitochondrial genome comprises about 1.65 × 104 bp (Meyer 1993). In muscle and other tissues of fish, each cell has a high number of mitochondrial genomes, but because of the greater size of the nuclear genome only 2 × 10−2% of total DNA belongs to mitochondrial DNA (Battersby and Moyes 1998). However, in fish

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Table 17.2 Comparison of copy numbers of nuclear and mitochondrial genomes in different cells.

Cell type

Mitochondria: nucleus

Mitochondrial DNA : total DNA

DNA content per cell (ng)

∼103 ∼107 – – 7 × 1010 3 × 1011

1% 99% – – >99.9% >99.9%

– – 150 18,250a 140 6500

Liver cell (rat) Egg (frog) Egg (Alaska Pollack) Egg (Alaska pollack) Egg (herring) Egg (rainbow trout) a

Sum of DNA

+ RNA (Aranishi 2006)

Table 17.3 Number of copies of nuclear single-copy genes in 1 gram of fish muscle.

Fish species Atlantic salmon, Salmo salar Rainbow trout, Oncorhynchus mykiss Carp, Cyprinus carpio Cod, Gadus morhua a

DNA content of white muscle of fish (μg/g wet weight)

C value (amount of DNA (pg) contained in a haploid nucleus)a

Calculated gene copy number per gram fish muscle

261 839

3.10 2.80

0.084 × 109 0.30 × 109

672 478

1.82 0.93

0.37 × 109 0.51 × 109

Data from Gregory 2008

eggs having only one haploid nuclear genome per egg, the share of nuclear DNA is very much less than 0.1%, as eggs contain very high amounts of mitochondrial DNA (Table 17.2). The number of single-copy nuclear genes in raw fish muscle can be calculated by dividing the DNA content of muscle through the DNA content of haploid genomes. The values given in Table 17.3 show that one gram of white muscle of fish contains 108–109 copies on average.

17.3

Genes used for species identification

Genes of fish and shellfish are classified as belonging to the nuclear or mitochondrial genome. The following types of nuclear genes have been used for species identification: z z z z

Introns and exons of protein coding genes. Ribosomal RNA genes (5S, 5.8S, 18S, 28S rRNA) (S: Svedberg units). ITS (internal transcribed spacer) of ribosomal genes. Microsatellites.

Protein coding genes are composed of DNA segments being either translated into proteins (exons) or removed during RNA processing (introns). Amplification of exon sequences has

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Table 17.4 Species identification of food fish and shellfish by PCR of nuclear genes. Species

a b

Gene

Hakes Tunas Cyprinids

ITS1-rRNA ITS1-rRNA ITS1-rRNA

Mackerels Sturgeons Sole and Greenland halibut

NTS-5S rRNAa Not specified 5S rRNA

Atlantic salmon and brown trout

5S rRNA

Trachurus spp.

5S rRNA

Mytilus spp. Shrimps 230 fish species Salmonids Salmonids

ITS, PLIIab Not specified Rhodopsine Parvalbumin Growth hormone

Method RFLP, sequencing Sequencing Species-specific primers RFLP RAPD Agarose gel electrophoresis Agarose gel electrophoresis Agarose gel electrophoresis RFLP RAPD Sequencing SSCP SSCP

Reference Perez et al. 2005 Chow et al. 2006 Wyatt et al. 2006 Aranishi 2005 Urbanyi et al. 2004 Cespedes et al. 1999 Pendas et al. 1995 Karaiskou et al. 2003 Heath et al. 1995 Phongdara et al. 1999 (http://fishtrace.org) Rehbein 2005 Rehbein 2005

NTS: non-transcribed spacer PLIIa: protamine-like sperm-specific protein

been rarely applied to species differentiation (Asensio et al. 2001) as exon sequences are considered to be relatively conservative. On the other hand, introns have been shown to yield substantial variability, resulting in polymorphism of sequence and length. This variability has been used to develop exon-primed intron-crossing (EPIC) PCR systems for population and species differentiation of fish and shellfish (Bierne et al. 2000; Chow and Nakadate 2004; Hubert et al. 2006). Ribosomes of eukaryotes have four types of RNA, named according to their size 5S, 5.8S, 18S and 28S rRNA. The genes for these RNAs are arranged in transcription units containing also internal transcribed spacer sequences (ITS). All cells contain multiple copies of the rRNA genes (Cooper and Hausman 2004), Microsatellites are short stretches of DNA arranged in tandem consisting of repeated units of one to six base pairs, mainly di-, tri- or tetra-nucleotides. They are mainly applied for characterisation of populations, parentage assignment, brood stock selection, constructing dense linkage maps and marker-assisted selection (Chistiakov et al. 2006). Moderately variable microsatellite markers may also be candidates for species identification, as recently demonstrated for differentiation between Anguilla species (Maes et al. 2006). By multiplexPCR of four microsatellite loci, identification of individuals belonging to A. anguilla, A. rostrata, A. japonica or A. marmorata was achieved using a Bayesian individual assignment technique. Examples of application of nuclear genes for species identification by various PCR-based techniques are given in Table 17.4. However, in most studies, mitochondrial genes have been preferred for identification of fish and shellfish. For several reasons mitochondrial genes are excellent tools for species identification of fishery products:

368 z

z z

z


The number of mitochondrial genomes exceeds the number of nuclear genomes by 10to 100-fold in muscle and other tissues. Thus PCR systems based on mitochondrial DNA have a very low detection limit. Mitochondrial DNA generally evolves much faster than single-copy nuclear genes, making it easier to differentiate between closely related species (Meyer 1993). GenBank and other databases contain many more sequences of mitochondrial genes than of nuclear genes of fishes and other animals. For example, the combination ‘cytochrome b and Teleostei’ gave 16,955 entries for core nucleotides, whereas ‘actin and teleostei’ resulted in 671 entries for core nucleotides (August 2006). In recent years, the whole mitochondrial genome of more than 240 teleost fish species has been sequenced and deposited in GenBank. However, usage of mitochondrial DNA has also some disadvantages:

z

z

Differentiation between hybrids and maternal species is not possible. Hybridisation is observed quite often for wild fish (Wyatt et al. 2006) and applied in aquaculture to improve traits (Urbanyi et al. 2004). Quantification of tissue amounts by real-time PCR is difficult if mitochondrial genes are used, as the copy number of mitochondrial genes per gram of muscle may vary.

17.4

Methods

At present, seafood authentication by DNA analysis is performed mainly by PCR-based methods, but DNA micro-arrays are under development (see section 17.4.4). The protocol consists of the following steps: z z z z

Isolation of DNA. Amplification of a selected sequence of mitochondrial or nuclear DNA by specific or universal primers. Characterisation of the amplicon. Interpretation of results.

17.4.1

DNA isolation

Extraction and isolation of DNA from raw or processed fish may be performed by standard methods used in molecular biology (Hoelzel 1992). As an alternative one of the numerous commercially available kits can be used. A short, simple and inexpensive DNA extraction procedure comprises solubilisation of tissue (muscle, roe) by urea and detergent followed by removal of PCR inhibitors by binding them to the Chelex resin (Aranishi et al et al. 2006; Chakraborty et al. 2006). A comparison of DNA extraction methods for food analysis has been published recently (Di Pinto et al. 2007). Many protocols do not include digestion of contaminating RNA by RNase treatment, resulting in a mixture of DNA and RNA as final product. In most cases, presence of residual RNA in DNA preparations will not pose problems for PCR analysis, but in quantitative PCR

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Table 17.5 Fish species identification by PCR using species-specific primers. Fish species analysed Sturgeons Tunas and bonitos Flatfishes

Frigate tunas Eels (A. anguilla, A. rostrata) Eels (A. anguilla, A. japonica) Grouper, wreck fish, Nile perch Mackerel, Scomber colias

Comments (Rehbein 2003) Specificity depended on PCR conditions Specificity depended on PCR conditions Primers for sole were specific, but primers for Pacific halibut reacted with other flatfishes

Species-specific TaqMan probes

Reference Birstein et al. 1997 Lockley and Bardsley 2000 Cespedes et al. 1999

Infante et al. 2004 Trautner 2006 Itoi et al. 2005 Asensio et al. 2001a Infante and Manchado 2006

RNA may act as a competitor to DNA in primer binding, thus lowering the efficiency of the PCR (Pikaart and Villeponteau 1993). Suitable methods for quantification of isolated DNA are measurement of the optical density at 260 nm (OD260 nm) (if RNA has been removed) or fluorescence enhancement assays (Downs and Wilfinger 1983; Gorokhova and Kyle 2002).

17.4.2

Polymerase chain reaction with species-specific primers

The fastest and most convenient PCR-based method for fish species identification is usage of species-specific primers or probes. However, owing to the large number of closely related species used for human consumption, for example more than 15 species of the family Scombridae, 12 species from the genus Merluccius or about 25 sturgeon species, it may be difficult to find primers of sufficient specificity. Examples for application of specific primers are given in Table 17.5. When testing the PCR system originally developed for differentiation of blue fin tuna (Thunnus thynnus) and bonito (Sarda sarda), it was found that the primers claimed to be specific for blue fin tuna reacted with another member of the genus Thunnus as well (Figure 17.1). Additional examples of limited specificity of fish PCR systems have been given previously (Rehbein 2003). These results demonstrate that many fish species have to be tested before the statement of specificity is justified.

17.4.3

Polymerase chain reaction with universal primers

The great number of PCR methods developed for identification of fish or shellfish species in seafood relies on so-called ‘universal’ primers. Universal primers may react with any fish species (Dalmasso et al. 2004), or only with members of certain families or genera if preselection is desired (Perez et al. 2005). Universal primers are selected from conservative regions of the nuclear (Perez et al. 2005) or mitochondrial genome (Wolf et al. 2000).

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T. thynnus T. orientalis T. albacares T. alalunga T. maccoyii T. obesus T. tonggol S. orientalis K. pelamis E. affinis A. thazard A. rochei 100 bp ladder T. thynnus T. orientalis T. albacares T. alalunga T. maccoyii T. obesus T. tonggol S. orientalis K. pelamis E. affinis A. thazard A. rochei

Figure 17.1 Specificity of PCR primers. Twelve tuna and bonito species of the genera Thunnus, Sarda, Katsuwonus, Euthunnus and Auxis were analysed by the PCR system described by Lockley and Bardsley (2000). In the upper part amplicons obtained by using the annealing temperature of 60°C are shown; in the lower part results of PCR with annealing at 69°C are presented. Amplicons were run on 10% polyacrylamide gel and visualised by silver staining. Sample application position and cathode are on the left side. The amplicon of the mitochondrial cytochrome b gene has a size of 207 bp.

The distance between primer binding sites should be a few hundred nucleotides for most types of fishery product, but less than 150 nucleotides in canned tuna (Quinteiro et al. 1998 ). If presence or absence of ‘fish’ as such should be proved in a given food, formation of PCR product can be detected by simple agarose gel electrophoresis or by more sophisticated real-time PCR analysis. The second method is used by the SureFood® Allergen Fish Real-Time PCR Kit (www.congen.de). The identification of a single genus, species or even population needs further characterisation of the amplicon by DNA sequencing or secondary methods of mutation detection like RFLP and single-strand conformation polymorphism (SSCP) analysis, which are more straightforward, faster and less costly than sequencing. Sequencing of amplicons The combination of PCR and DNA sequencing was introduced by Bartlett and Davidson (1992) to identify animal species including fish under the name of FINS (forensically informative nucleotide sequencing). In FINS analysis, an unknown sample sequence is compared by a genetic distance measurement method with a set of reference sequences. The sequence

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of the unknown sample will be clustered to those sequences from reference species to which the sample is phylogenetically most related (Quinteiro et al. 1998). A more convenient way of identifying an unknown sample is by aligning the obtained sequence to nucleotide sequences deposited in a database like GenBank using the program BLAST (basic local alignment search tool) (Parson et al. 2000; Brodmann et al. 2001). Several points have to be taken into consideration before starting BLAST or FINS analysis: (1) Does the sample consist of a single species or does it contain a mixture of species? In the second case, the PCR product has to be cloned before sequencing. Information about the number of species present in a product can be obtained by RFLP. (2) Selection of a sequence being as short as possible, but containing sufficient variation between species. (3) Selection of a gene having a large number of entries in databases (for example cytochrome b). The identity of sequences necessary to assign an unknown sample to a reference depends on the genus and the gene. Closely related fish species of the genus Sebastes or Acipenser have nearly identical cytochrome b sequences, whereas the differences in other genera and genes may be greater.

Restriction-fragment length polymorphism analysis RFLP analysis is currently the most popular method of species identification of fishery products. The technique comprises the following steps: PCR, cutting of the amplicon with one or more restriction endonucleases, separation of fragments by agarose or polyacrylamide gel electrophoresis, capillary electrophoresis (Dooley et al. 2005) or high-performance liquid chromatography (HPLC) (Horstkotte and Rehbein 2003). The fragment patterns can be compared with published results or with the patterns of references treated along with samples. A precondition for rational choice of suitable restriction endonucleases is knowledge of the sequence to be cut. The method has been successfully validated for several commercially important fish species (Dooley et al. 2005; Hold et al. 2001). The same 464 bp sequence of the mitochondrial cytochrome b gene was amplified in both studies, followed by restriction digestion and separation of fragments. Hold et al. (2001) used native polyacrylamide gel electrophoresis (PAGE) for resolving DNA restriction fragments of 36 species of the genera Merluccius, Oncorhynchus, Anguilla and others; DNA bands were visualised by silver staining. The advanced method applied by Dooley et al. (2005) was based on lab-on-a-chip capillary electrophoresis (CE) for fragment separation. Single species samples of 10 white fish species (gadoids and others) were correctly identified by five food control laboratories. By using a fast DNA extraction method, for example boiling of tissue with Chelex resin, a high-speed thermocycler and ready-to-run agarose gel (for example E-Gel, Invitrogen; Figure 17.2) or a CE system, results can be obtained within one working day. A multitude of restriction endonucleases is available on the market, which may be applied in most cases without the necessity of purifying the amplicon to be digested.

Single-strand conformation polymorphism analysis SSCP analysis is an inexpensive, convenient and sensitive method for detecting genetic variation by electrophoretic separation of single-stranded nucleic acids (Sunnucks et al.

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Fishery Products: Quality, safety and authenticity 4% Agarose (HR) 1

2

3

4

5

6

7

8

9

10 11 12

uncut amplicon 464bp

primer dimer

Figure 17.2 Identification of gadoid roe product by RFLP using an E-Gel (Invitrogen). A 464 bp sequence of the mitochondrial cytochrome b gene was amplified according to Hold et al. (2001), and digested with the restriction endonuclease Dde I. Lane 1: DNA marker; lanes 2–5: different brands of Tarama, a fish roe product; lane 6: Atlantic cod (Gadus morhua); lane 7: Pacific cod (Gadus macrocephalus); lane 8: North Atlantic hake (Merluccius merluccius); lanes 9 and 11: Greenland cod (Gadus ogac); lane 10: hoki (Macruronus novaezelandiae); lane 12: untreated amplicon. The products contained varying amounts of cod roe, together with roe from other fish species.

2000). The principle of the technique is shown in Figure 17.3. It comprises the following steps: z z z z

PCR to amplify DNA segments of 100–500 bp. Generation of single-stranded DNA (ssDNA) by heat, and alkali and/or organic solvents (ssDNA can be produced also by asymmetric PCR (Rehbein et al. 1998)). Separation of ssDNA strands by electrophoresis in slab-gels or capillaries (Binz et al. 2001) under non-denaturing conditions. The ssDNA strands in slab-gels can be visualised by staining with silver or fluorescence dyes; in the case of CE, PCR is performed with primers labelled with fluorescence dyes. Former usage of radioactive labelling for detection was less convenient.

The mobility of ssDNA in native gel electrophoresis is affected by the sequence-depending conformation of the nucleic acid strand, the type of gel matrix, the temperature and the composition of electrophoresis buffer (Hayashi et al. 1998). For better comparison of ssDNA patterns, it is recommended to run unknown samples and references on the same gel. The reliability of the method performed under controlled electrophoretic conditions has been demonstrated by collaborative study (Rehbein et al. 1999). Examples of seafood authentication by SSCP analysis are given in Tables 17.6–17.16. As prediction of the ssDNA pattern from the sequence is hardly possible, the development of a protocol needs several

Sequence 1: ATGCCAGTCA TACGGTCAGT

_____ _____

dsDNA

Sequence 2: ATGCAAGTCA TACG TTCAGT

_____ _____

Mix with formamide, heat & chill in iced water

Ø

Single-strand DNA

Native polyacrylamide gel electrophoresis and silver staining of DNA Lane 1: DNA marker, lane 2: PCR product; Lane 3–13: ssDNA patterns for different fish

ssDNA

dsDNA

Figure 17.3 Principle of SSCP analysis.

Table 17.6 Differentiation of tunas and bonitos. Species, product 8 species of the genus Thunnus and Katsuwonus pelamis, Sarda orientalis T. albacares, T. obesus, T. alalunga, K. pelamis Auxis thazard, A. rochei, canned fish Thunnus alalunga, T. albacares 17 species of tuna and bonito, canned fish T. albacares, T. thynnus T. obesus, T. alalunga, K. pelamis, canned fish 8 species of tuna and bonito, canned fish

Gene

Method

Reference

ITS1 (first internal transcribed spacer)

Sequencing of amplicons (∼600 bp)

Chow et al. 2006

Cytochrome b

SSCP

Cytochrome b, 12S rRNA, ATPase 6 16S rRNA

Sequencing, specific primers Specific TaqMan systems RFLP, SSCP

Colombo et al. 2005 Infante et al. 2004

Cytochrome b Cytochrome b

Nested PCR, sequencing, RFLP

Cytochrome b

SSCP

Lopez and Pardo 2005 Mackie et al. 1999 Pardo and BegoniaVillareal 2004 Rehbein et al. 1999

374


Table 17.7 Differentiation of gadoid fish species. Species, product

a

Gene

Method

Reference

8 species of the family Gadidae, 3 Lotidae, 3 Phycidae, Raniceps raninus, M. merluccius, Coryphaenoides rupestris, raw muscle 16 gadoids, raw or salted muscle

rRNA, cytochrome b

Sequencing

Bakke and Johansen 2005

Cytochrome b

Sequencing, RFLP

8 gadoids, raw muscle

Cytochrome b

3 gadoids

ATPase 6/8

25 species of the family Gadidae, 4 species of the family Merlucidae

Cytochrome b, cytochrome oxidase I

RFLP, SSCP, DGGEa TaqMan MGB probe Sequencing

Calo-Mata et al. 2003 Comi et al. 2005 Taylor et al. 2002 Teletchea et al. 2006

Denaturating gradient gel electrophoresis

Table 17.8 Differentiation of hakes (Merluciidae). Species, product

Gene

Method

Reference

9 species of hake 9 species of hake

Cytochrome b (a) 5S rRNA, (b) cytochrome b ITS1 rRNA Mitochondrial control region

RFLP (a) electrophoresis (b) RFLP Sequencing, RFLP Sequencing, RFLP

Hold et al. 2001 Perez and Garcia-Vazquez 2004 Perez et al. 2005 Quinteiro et al. 2001

Method

Reference

12 species of hake 11 species of hake

Table 17.9 Differentiation of flatfish. Species, product Solea solea, Pleuronectes platessa, Platichthys flesus, Reinhardtius hippoglossoides Solea solea, Pleuronectes platessa, Platichthys flesus 5 flatfish species 9 flatfish species 24 flatfish species 10 sole species 6 sole species

Gene Cytochrome b

SSCP

Cespedes et al. 1999a

Cytochrome b

RFLP

Cespedes et al. 1998

12S rRNA Cytochrome b

RFLP RFLP

Cytochrome b Cytochrome b, 12S rRNA, 16S rRNA U1 and U2 snRNA

Sequencing, RFLP Sequencing

Comesana et al. 2003 Sanjuan and Comesana 2002 Sotelo et al. 2001 Infante et al. 2004a

Electrophoresis

Manchado et al. 2006

DNA-based methods

375

Table 17.10 Differentiation of salmonids. Species, product 10 salmonid species 10 salmonid species 10 salmonid species, roe and cold-smoked fillet Oncorhynchus mykiss, Salmo salar, Brama raii, smoked fish Oncorhynchus mykiss, Salmo salar

Gene

Method

Reference

Cytochrome b Cytochrome b Cytochrome b, parvalbumin, growth hormone 5S rRNA

RFLP RFLP, validation study SSCP

Russell et al. 2000 Hold et al. 2001a Rehbein 2005

Electrophoresis

Carrera et al. 2000

Not identified

AFLP-derived primers specific for O. mykiss

Zhang and Cai 2006

Table 17.11 Differentiation of sturgeons. Species, product

Gene

Huso huso, Acipenser stellatus, A. gueldenstaedtii, caviar 22 sturgeon species 11 sturgeon species 22 sturgeon species 6 sturgeon species, caviar 12 sturgeon species

Method

Reference

Cytochrome b

Species-specific primers

Birstein et al. 1998

Cytochrome b, 12S rRNA, 16S rRNA Cytochrome b Cytochrome b Cytochrome b ND4 gene

Sequencing

Birstein and De Salle 1998 Wolf et al. 1999 Ludwig et al. 2002 Rehbein et al. 1999a Zhang et al. 2000

RFLP Sequencing, RFLP SSCP Sequencing

Table 17.12 Differentiation of snappers (Lutjanidae). Species, product

Gene

13 western Atlantic snapper species Red snapper, L. campechanus L. sanguineus, L. erythopterus, L. argentimaculatus, L. malabarius, Lethrinus leutjanus, Pinjalo pinjalo

Method

Cytochrome b, 12S rRNA Cytochrome b 12S rRNA

Reference

RFLP

Chow et al. 1993

Sequencing RFLP

Marko et al. 2004 Zhang et al. 2006

Table 17.13 Differentiation of groupers. Species, product

Gene

Method

Reference

Epinephelus guaza Epinephelus guaza

Not specified 5S rRNA

Asensio et al. 2002 Asensio et al. 2001a

6 grouper species

16S rRNA

RAPD Species-specific primers, electrophoresis Multiplex PCR, real-time PCR

Trotta et al. 2005

Table 17.14 Differentiation of sharks. Species, product Great white shark, Carcharodon carcharias 35 shark species from western North Atlantic Squalus acanthias, Scyliorhinus caniculus, Prionace glauca, Mustelus mustelus, Mustelus asterias Carcharhinus obscurus, C. plumbeus 16 Lamniform species

7 Carchariniform species 11 Carchariniform species

Gene

Method

ITS2, cytochrome b 12S RNA, 16S rRNA, valine tRNA 16S rRNA

ITS2 Cytochrome b, NADH2

Cytochrome b Cytochrome b

Reference

Species-specific primers Sequencing

Chapman et al. 2003 Greig et al. 2005

RFLP

Vicari et al. 2001

Species-specific primers Sequencing, universal shark primers, speciesspecific primers RFLP Sequencing, RFLP

Pank et al. 2001 Hoelzel, AE (2001)

Chan et al. 2003 Heist and Gold 1999

Table 17.15 Differentiation of molluscs. Species, product Bivalve species, raw Perna species, raw 15 mussel species of the genera Mytilus, Perna and others Mytilus spp., Pecten maximus and others Crassostrea and Ostrea oysters 8 Cephalopod species 10 Cephalopod species 5 species of the families Loliginidae and Ommastrephidae Pecten jacobaeus, P. maximus 4 scallop species, canned products

Gene

Method

Reference

18S rRNA COX I, NADH 4 18S rRNA, ITS 1, polyphenolic adhesive protein 18S rRNA, 16S rRNA 5S rRNA

SSCP. sequencing Specific primers Sequencing, RFLP

Livi et al. 2006 Blair et al. 2006 Santaclara et al. 2006

Specific primers

Bendezu et al. 2005

Specific primers

Cross et al. 2005

Cytochrome b 16S rRNA 16S rRNA

Sequencing, RFLP Sequencing RFLP

Chapela et al. 2003 Chapela et al. 2002 Colombo et al. 2002

16 S rRNA

Specific primers

Colombo et al. 2004

IST 1, ITS 2

RFLP

Lopez-Pinon et al. 2002

Table 17.16 Differentiation of shrimps. Species, product Shrimp, crab, lobster and crawfish species 5 Penaeid shrimps 7 shrimp species

Gene

Method

Reference

16S rRNA

RFLP

Brzezinski et al. 2005

16S rRNA COX I

SSCP, RFLP SSCP, RFLP

Khamnamtong et al. 2005 Rehbein 2001

DNA-based methods

Sample 1 Sample 2 Sample 3: Sample 4 Sample 5 Sample 6 Sample 7 Sample 8: Sample 9 Sample 10 Cathode ssDNA

377

Cod

Ling

dsDNA

Figure 17.4 Differentiation between five specimens of cod (Gadhus morhua) and ling (Molva molva) by RFLP–SSCP. A 464 bp sequence of the mitochondrial cytochrome b gene was amplified according to Hold et al. (2001), and digested with the restriction endonuclease Taq I yielding fragments of 243 (double-band) bp for cod and 215 and 251 bp for ling. The fragments obtained were further analysed by SSCP using CleanGel HP 15% as described by Rehbein (2005).PCR–SSCP strengthened the differences between cod and ling, and revealed some intra-species variability.

experiments to select a suitable amplicon and optimised electrophoretic conditions, which may be considered a disadvantage of the method. RFLP–SSCP analysis The combination of RFLP and SSCP was found to make it easier to detect sequence variations in large amplicons (Barros et al. 1997) and to enable the differentiation between species exhibiting the same restriction fragment pattern (Rehbein 2002). For fish species identification, the specificity of cutting sites of restriction endonucleases can be used in the first step to differentiate several species by distinct fragment pattern, whereas the enhanced sensitivity of SSCP in case of short sequences can be used to detect sequence differences between fragments (Figure 17.4). The number of restriction enzymes necessary for identification of a species may be reduced considerably by SSCP analysis of fragments. Random amplified polymorphic DNA analysis Random amplified polymorphic DNA (RAPD) analysis can be performed without prior knowledge of specific DNA sequences of the species under study. RAPD is a PCR-based method using one or two primers, in most cases 10 bp in length, binding at non-stringent annealing temperatures (35–40°C) at various sites of the genome resulting in DNA fingerprints. Elucidation of population structure or kinship analysis has been successfully performed in fish and shellfish research by RAPD (Ali et al. 2004). RAPD has been used in several studies for identification of fish species products. Partis and Wells (1996) investigated the potential of RAPD by analysing 116 specimens from eight species of fish important to the Australian market (barramundi, Nile perch, John Dory and other species). According to their results, the method was determined to be reasonably specific. In another study, products made from cod (Gadus morhua, raw and cooked fillet,

378


different types of roe), Atlantic salmon (Salmo salar, fresh and cold-smoked fillet) or Arctic charr (Salvelinus alpinus, fresh and hot-smoked fillet) gave species-specific fingerprints (Martinez et al. 2001). It is highly recommended to include suitable references (species, products) in the RAPD analysis, because the state of integrity of DNA, conditions of PCR and type of electrophoresis may influence the resulting DNA pattern.

17.4.4 Quantitative polymerase chain reaction A considerable number of fishery products contain muscle or other tissue of more than one species. Examples are fish cakes, pies, pastries, soups, baby food products and fish meal. Recently, PCR-based methods have been developed to determine the share of a certain fish species relative to the total amount of fish in the product. The copy numbers of nuclear genes (calmodulin, myostatin, parvalbumin) (Rehbein and Horstkotte 2003) have been determined for raw fish muscle (Rehbein and Horstkotte 2003) and baby food by real-time PCR. In the case of raw muscle, the three genes gave similar copy numbers (0.5–1.1 × 108 per gram wet weight) for several fish species. Hird et al. (2005) developed a method to quantify haddock (Melanogrammus aeglefinus) in commercial products using the nuclear transferrin gene for real-time PCR. The CT values (cycle number when fluorescence intensity crosses the set threshold) for haddock-specific primers and probes were related to CT values for a universal fish system. The relationship between haddock muscle weight and gene copy number (CT value) was found to remain relatively constant, throughout the year and across several fishing grounds. Applied to analysis of model samples consisting of mixtures of DNA of haddock with DNA of various other fish species, the PCR system turned out to be specific for haddock, being suited to quantifying the share of haddock within 7% of the true percentage. However, when processed fish was analysed, the CT value increased, owing to degradation of DNA by heat and pressure, and the relationship between the CT value and amount of tissue had changed. In another study, the mitochondrial 16S rRNA gene was used to construct fish speciesspecific primers and probes, as well as universal fish primers, for relative quantification. To quantify the relative amounts of albacore tuna (Thunnus alalunga) and yellow fin tuna (Thunnus albacares) in mixtures of raw muscle, two specific TaqMan systems were devised. Another system specific to scombroid species was taken as a consensus system. Good results were obtained for raw samples, whereas measurements of canned samples were highly inaccurate (Lopez and Pardo 2005). To summarise the experiences described above, it seems possible to determine by real-time PCR the share of a given fish species relative to the total amount of fish in raw products. The uncertainty of measurement by real-time PCR was similar to uncertainty of determination of nitrogen content (Hird et al. 2005).

17.4.5

Microarrays

DNA microarrays, also known as DNA chips, consist of a solid support with bound oligonucleotides (probes). Samples, for example PCR products labelled with fluorophores, are hybridised to the targets, and finally detected by scanning. The first high-density DNA

DNA-based methods

379

chip for the multi-detection of animal species in food and feed is obtainable from bioMerieux (www.biomerieux.com). The chip can be used to identify samples of several salmonid, scombroid and eel species, as well as a few other fish species. New DNA chips to identify marine organisms are under development (www.fish-and-chips.uni-bremen.de; www.tuat. ac.jp), but not yet available for routine food analysis. However, DNA microarray technology is already used in toxicology and ecotoxicology (Lettieri 2006), and has recently been applied to genetic stock identification of chum salmon (Oncorhynchus keta) (Sato et al. 2004).

17.5

Conclusions and outlook

Since the invention of PCR, numerous methods have been developed to identify all kinds of seafood, and the information is scattered in hundreds of publications. However, only a few methods have been validated by ring trials and have become official methods suitable for food control laboratories (Hold et al. 2001). What is urgently needed for making the work of seafood control laboratories more efficient is an international database covering fish and shellfish species traded worldwide, with open access to everybody engaged in seafood authentication. In recent years several differently structured databases have been created: z z z z z z

Fishtrace (http://www.fishtrace.org). Fishgen (http://fishgen.jrc.it/). FischDB (www.fischdb.de). Fish Barcode of Life Initiative (www.fishbol.org). AFLP database ([email protected]). Validation (www.seafoodplus.org; http://www.azti.es/dna_database/).

Fish Trace, initiated by a project funded by the European Union and now located at the Joint Research Centre in Ispra, Italy, has the following objectives: 1. To draw up a genetic catalogue of representative European marine fish species as indisputable evidence for the origin of the fish and fish products. 2. To pool reference biological materials for cross-referencing in fish traceability. 3. To establish a public accessible database compiling the new standardised data generated in the network. 4. To validate the information compiled in the database for its applicability for end-users in the analysis, characterisation and diagnosis of marine fish species. 5. To use the collection of standardised information to lend support to European policies for fishery stocks, food traceability and environmental protection. At the moment, the data (sequences for the mitochondrial cytochrome b gene and nuclear rhodopsine gene) of 230 fish species have been collected. Unfortunately, the database is restricted to European marine fish species. Fishgen is a small database, with data for 11 species available. The data have been collected from literature, and are not very useful for product analysis.

380


The aim of FischDB is to support food control laboratories by listing sequences of the cytochrome b gene, fragment sizes to be used in RFLP analysis, SSCP patterns and patterns and pI values obtained by isoelectric focusing of sarcoplasmic proteins. The explanations are in German, thus restricting the benefit of the database. The Fish Barcode of Life Initiative (FISH-BOL) is a global effort to establish a standardised reference sequence library for all fish species. An approximately 655 bp sequence of the mitochondrial cytochrome oxidase subunit I gene has been selected for amplification, and up November 2006 the database contained sequences of 2522 species (Ward et al. 2005). In the future, FISH-BOL can make PCR-based species identification much easier, but at the moment the practical application is limited by the nature of the gene (COX I). Most of the work previously performed in fish species identification is based on PCR of cytochrome b or the 12S or 16S rRNA gene respectively. Amplified restriction fragment length polymorphism (AFLP) is a PCR-based method widely used in population studies. Without prior knowledge of DNA sequences of the species under study, it is possible to obtain hundreds of genetic markers (Vos et al. 1995). AFLP gives more reproducible results than RAPD, but the limiting point is the necessity to use non- or slightly degraded DNA. Nevertheless, recently a database with AFLP patterns of 32 species of fish, molluscs and crustaceans (frozen and fresh products) has been generated (Maldini et al. 2006). Validation is a sub-project within the large European project SEAFOODplus with following objectives: z z

To validate the traceability systems developed and implemented in different fish production chains across Europe. To validate the traceability data coming from the chains testing different tools, such as PCR-based DNA analysis.

The database contains sequences of more than 50 fish species for several mitochondrial genes.

17.6

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Chapter 18

Other principles: analysis of lipids, stable isotopes and trace elements Iciar Martinez

18.1

Introduction

Dennis (1998) refers to food authentication as ‘the process by which a food is verified as complying with its label description’. Information on seafood compulsory in the European Union (EU) includes specification of the commercial designation and scientific name, method of production (‘caught’, ‘caught in freshwater’, ‘farmed’ or ‘cultivated’) and the area in which it was caught. For cultivated species, a reference should be made to the country in which the product undergoes the final developmental stage (EU Commission regulation No 2065/2001 of 22 October 2001 laying down detailed rules for the application of Council Regulation EC No 104/2000 as regards informing consumers about fishery and aquaculture products). Thus, issues that are relevant to authentication are the species, production method and geographic origin, in addition to new production methods for highly priced or exclusive products such as ‘organically produced’. Japan has legislation similar to Europe. The Law on Standardization and Proper Labeling of Agricultural and Forestry products (also known as Japanese Agricultural Standards – JAS – Law) of 1999 demands that the name and place of origin be indicated in fresh foods and some processed foods. This law was modified in October 2006 to make compulsory the inclusion on the label of information about the place of origin. In addition, and, for several processed foods such as boiled, dried or seasoned fish and surface roasted fillets, the main ingredients have to be listed. The USA applies the Federal Food, Drug, and Cosmetic Act and The Fair Packaging and Labeling Act Title 15 – Commerce and Trade, Chapter 39 – Fair Packaging and Labeling Program §145. This makes mandatory a statement of the identity of the commodity that shall be in terms of the name specified in, or required by, any applicable Federal law or regulation, or by using the common or usual name of the food. For the correct labeling of seafood, the US Food and Drug Administration (FDA) has elaborated, in cooperation with the National Marine Fisheries Service (NMFS), ‘The Seafood List’, which is a compilation of existing acceptable market names for imported and domestically available seafood. The list is an extensive, although not necessarily complete, listing of seafood commonly sold in the United States, which can be found at http://www.cfsan.fda.gov/~frf/seaintro.html. There does not seem to be a need to label products with the geographic origin or production method in the USA. This chapter will address the issues of authentication of the species, geographic origin and production 388

Other principles: analysis of lipids, stable isotopes and trace elements (a)

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Figure 18.1 (a) Triglyceride general structure. From http://en.wikipedia.org/wiki/Image: TriglycerideGeneralStructure.png. The copyright holder grants any entity the right to use this work for any purpose, without any conditions, unless such conditions are required by law. (b) Structural formula of the phospholipid phosphatidyl choline. From http://en.wikipedia.org/wiki/Image: Phosphatidyl-Choline. png. Both (a) and (b) have been released into the public domain by the copyright holder. This applies worldwide.

methods by targeting lipid composition, stable isotope distributions and trace element signatures.

18.2

Species and breeding stock identification by lipid analysis

Triacylglycerols are neutral lipids in which the glycerol is esterified with three fatty acids (Figure 18.1A). They are the main constituent of vegetable oil and animal fats, and constitute the main energy reserves in fish (Cowey and Sargent 1977; loc. cit.; Dalsgaard et al. 2003). Although the fatty-acid composition of triglycerides closely resembles that of the fish diet (Dalsgaard et al. 2003) and has been used to identify the production method (that is, farmed versus wild, discussed below), the positional distribution of the fatty acids in the carbons of the glycerol (α for the first and third carbons or β for the second carbon, in the middle position) has been used to identify species and adulterations in oils and oil mixtures. High-resolution 13C nuclear magnetic resonance (NMR) spectroscopy, described in detail in Chapter 11, provides information about the different types of carbon atom (their chemical shift value) and their relative amounts in the sample (as the relative intensities of the peaks). The technique has been applied to oils to elucidate their composition (triacylglycerol, oils, waxes, and so on), the fatty acids contained in the sample (which fatty acids and their relative amounts) and the position of the fatty acids in the triglyceride and phospholipid molecules (Aursand and Grasdalen 1992; Hidalgo and Zamora 2003, Siddiqui et al. 2003).

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Figure 18.2 High-resolution 13C NMR of mackerel, Scomber scombrus (top spectrum), and herring Clupea harengus (lower spectrum), oils. The peaks in the region 15–40 ppm are due to carbons in aliphatic chains (methylene region); the region 60–80 ppm to the carbons in the glycerol; 120–140 ppm to the olefinic region; and the peaks at 170–180 ppm are due to the carbonyl carbons in the oil. The figure was kindly provided by Inger Beate Standal.

Aursand and Grasdalen (1992) and Aursand et al. (2007) published the interpretation of the 13 C-NMR spectra of lipids extracted from Atlantic salmon, cod, trout, and several fish oil capsules; Siddiqui et al. (2003) published the spectra of encapsulated marine oil supplements. Figure 18.2 shows an example of a 13C NMR spectra of mackerel and herring oils. By using 13C NMR to examine the fatty-acid positional distribution in triacylglycerols, it has been possible to identify the composition of oils and the presence of contaminants (Aursand et al. 2007; Pfeffer et al. 1977; Ng 1985, Hidalgo and Zamora 2003). In plant oils, Zamora et al. (2001) were able to detect a 5% hazelnut oil contamination in virgin olive oil; Siddiqui et al. (2003) were able to distinguish natural fish oils from oils subjected to industrial refining as well as the addition of synthetic ethyl esters of docosahexaenoic acid (DHA, C22:6n–3) and eicosapentaenoic acid (EPA; C205n–3) to encapsulated oil supplements. Aursand et al. (2007) analyzed fish oils from different species (Atlantic salmon, cod and trout), different geographic origins of cod (Iceland, Barents Sea, Norway) and different degrees of processing, as well as several fish-oil health products, by 13C NMR and several multivariate data analyses techniques. They were able to classify correctly over 95% of the samples. In this work, the carbonyl region of the spectrum was shown to give information on whether natural oil had been chemically modified or oil from another species had been added (Aursand et al. 2007). Aursand et al. (1995a) identified the species of farmed Atlantic salmon, cod liver and seal oils by using differences in the positional distribution of n–3 fatty acids on the glycerol backbone of triacylglycerols by 13C NMR: 73–74% of the C22:6n–3 was found to be preferentially esterified at the β position of the triacylglycerols in depot fat of farmed Atlantic salmon and cod liver oil respectively. However, only about 38–40% of the C20:5n–3 was

Other principles: analysis of lipids, stable isotopes and trace elements

391

located in the β position in lipids of cod liver oil and farmed Atlantic salmon respectively, giving an almost random distribution of this fatty acid in triacylglycerols compared with C22:6n–3. C22:5n–3 was preferentially esterified (69%) at the β position of the triacylglycerols in muscle lipids of farmed Atlantic salmon. However, the amount of this fatty acid in cod liver oil was too low to allow analysis of its positional distribution. Those findings confirmed previous works by Brockerhoff et al. (1968) and Litchfield (1969) which demonstrated the general tendency of C20:5n–3, C22:6n–3 and C22:5n–3 to be preferentially esterified at the β position of fish and invertebrate triacylglycerols. Ando et al. (1992) showed that the positional distribution of C22:6n3 and C22:5n3 is related to the amount of C22:1 and C20:1 fatty acids in the triacylglycerols: in fish lipids with high contents of C20:1 and C22:1, nearly 70–80% of the C22:6n3 was in the β position of the glycerol moiety. The distribution in marine mammals was quite different: in harp seal oil, nearly 100, 97 and 95% of C22:5n3, C22:6n3 and C20:5n3, respectively, were esterified to the α (1 and 3) positions of the glycerol moiety, in accordance with previously obtained positional data for triacylglycerols of harp seal blubber (Brockerhoff et al. 1968; Litchfield 1969). The positional distribution of the fatty acids in triglycerides can be used as a speciesspecific marker in zooplankton as well. In the rotifer Brachionus plicatilis and in Artemia franciscana nauplii, whose feed had been enriched with fish oils, once the fatty acids were released from the dietary oils they were incorporated into both animals in a consistent manner, independent of their origin: although C22:6n–3 was preferentially esterified in the β position in the fish oils, it ended up in the α position in both Artemia and B. plicatilis (Ando et al. 2004a, b). That was not, however, the case in bovine adipose tissues (Smith et al. 1998). Long-term feeding of cattle with fatty acids of variable degrees of unsaturation induced significant alterations in the fatty-acid composition, consisting of changes both in the distribution and in the composition of the triacylglycerol species, which, in turn, accounted for marked differences in melting points among treatment groups. Phospholipids are made up of four components: fatty acids, a negatively charged phosphate group, a nitrogen-containing alcohol and a glycerol backbone (Figure 18.1b). With glycolipids and cholesterol, phospholipids are a major component of all biological membranes. Some work has shown the value of analyzing the fatty-acid type and their position in phospholipids for species identification. Medina et al. (1997) used multivariate data analysis on the phospholipid fraction for species identification in tuna products, comprising specimens of three different species, caught during different seasons, which had been either commercially processed or overprocessed. The three species were correctly classified by using a forward discriminant function analysis on 14 variables, including the phospholipid content, the proportions of each phospholipid class, and the content of the total phospholipid fatty acids (Figure 18.3). C16:0 and C18:1n–9 were the major saturated and mono-unsaturated fatty acids, respectively, in the three species, with a preponderance of C16:0 over C18:0, and differences in minor fatty acids, such as C17:0 and C20:1n–9. The major differences between species were on the amounts of polyunsaturated fatty acids, with important variations in the magnitudes of C20:4n–6, C20:5n–3, and C22:6n–3. The most relevant variables for the classification were the C20:4n–6 and C18:1n–7 contents for the first and second function respectively: all the samples were correctly classified according to the species.

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Fishery Products: Quality, safety and authenticity 10

Second discriminant function

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14

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First discriminant function Figure 18.3 Two discriminant function scores for canned tuna species: 䊏, albacore; 䊉, bonito; 䉱, big eye tuna. Reprinted with permission from Medina, I., Aubourg, S.P. and Martin, R.P. (1997). Species differentiation by multivariate analysis of phospholipids from canned Atlantic tuna. Reproduced with permission from the Journal of Agricultural and Food Chemistry 45: 2495–2499. Copyright (1997) American Chemical Society.

Fatty-acid profiling of several tissues has proved useful for differentiating among several species of Sebastes for which protein (Rehbein 1983; Nedreaas and Nævdal 1991a, b; Nedreaas et al. 1994;) and DNA analyses (Danielsdottir 1998; Sundt and Johansen 1998; Roques et al. 1999a, b) did not render clear results. Joensen and Grahl-Nielsen (2000, 2001) performed principal component analysis (PCA) and soft independent modeling of class analogies (SIMCA) on the fatty-acid content obtained by gas chromatography of oils extracted from the heart, gills, skull and otoliths of Sebastes viviparous, S. marinus and S. mentella (Figure 18.4). They found that the fatty-acid profiles appeared to be species-specific despite considerable individual variability and large differences in the tissues. Interestingly, the method could be used to identify species even when the specimens came from different locations, namely Norwegian or Faroese waters (Joensen and Grahl-Nielsen 2000), even though the fatty acids that contributed to the distinction among the species varied according to the geographic origin of the fish. This suggested that the composition of fatty acids may be population-dependent in some species. Therefore, as for genetic analysis, this may be useful for identifying the geographic origin when the populations discriminated by the analysis also segregated in space. Indeed, that proved to be the case for the Sebastes species (Joensen and Grahl-Nielsen 2004) and herring (Grahl-Nielsen and Ulvund 1990) in the Atlantic, striped bass stocks in American rivers (Grahl-Nielsen and Mjaavatten 1992) and between two stocks of cod reared under identical conditions on the Faroe Islands (Joensen et al. 2000). Fatty-acid profiling is now a method included in the ICES Stock Identification Methodology (Grahl-Nielsen 1997).

Other principles: analysis of lipids, stable isotopes and trace elements (b)

PC2 (14%)

PC2 (21%)

(a)

393

PCI (60%)

PCI (35%)

PC2 (28%)

(c)

PCI (54%) Figure 18.4 (a) PC-plot of the three redfish species, S. viviparus, S. marinus, and S. mentella, based on the fatty-acid composition in the heart tissues. Each symbol represents one individual fish. The percentage of the total variance along each of the principal components is given. (b) PC-plot of two redfish species, S. marinus and S. mentella, based on the fatty-acid composition in the heart tissues. Each symbol represents one individual fish. The percentage of the total variance along each of the principal components is given. (c) PLS-plot of two redfish species, S. marinus, and S. mentella, based on the fatty-acid composition in the heart tissues. Each symbol represents one individual fish. The percentage of the total variance along each of the principal components is given. Reprinted with permission from Joensen, H. and Grahl-Nielsen, O. (2000) Discrimination of Sebastes viviparus, S. marinus, and S. mentella from Faroe Islands by chemometry of the fatty acid profile in heart and gill tissues and in the skull oil. Comparative Biochemistry and Physiology 126B: 69–79. Copyright (2000), with permission from Elsevier.

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18.3

Verification of the production method

18.3.1

Verification of the production method by lipid analysis

The main energy requirements of marine fish are supplied by lipids and proteins (Shul’man 1960; loc. cit.; Dalsgaard et al. 2003). As already mentioned, fish store lipids in the neutral triglycerides (Figure 18.1) and many studies have shown that the fatty-acid composition reflects that of the diet in both wild and farmed fish (Hardy et al. 1987; Jobling et al. 2002; Bell et al. 2003a, b; Dalsgaard et al. 2003; Jobling 2004). A review of the verification of the production method of Atlantic salmon has recently been published by Martinez (2006). Owing to the high prices of fish oils and the danger of overexploitation of pelagic species, alternative oils, mostly vegetable but also from zooplankton, are being investigated in the formulations of fish feeds. Dietary inclusion of vegetable oils (50–100% substitutions: rapeseed or olive oil) does not seem to have any major impact on growth rate, feed efficacy or mortality in post-smolt salmon fed experimental diets for 42 weeks (Torstensen et al. 2004a, b). Moreover, these data accord with previous experiments with dietary rapeseed oil replacement (Bell et al. 2001, 2003a, b). However, the fatty-acid composition of tissue lipids is closely influenced by dietary fatty-acid composition in salmonids (Dosanjh et al. 1988, Lie et al. 1988, Torstensen et al. 2000). This is particularly the case for the triacylglycerols, which are the main lipid class found in salmon flesh. Ng et al. (2004) showed that there were no significant effects of the diet on growth, feed utilization efficiency, total lipid content in muscle or pigment concentration. The diets had been supplemented with 50 : 40 : 10 and 50 : 25 : 25 blends of fish oil : rapeseed oil : crude palm oil. It was notable in this work that, despite having high concentrations of saturated fatty acids, mainly C16:0, Atlantic salmon fed crude palm oil diets did not deposit a significant amount of this fatty acid in the muscle tissue, results that were corroborated by additional studies in salmon (Bell et al. 2002) and in tropical fish (Ng et al. 2001, 2003). However, specific fatty acids are selectively retained or utilized. This has been shown by studies demonstrating a selective deposition and retention of C22:6n–3, so that the concentrations in flesh were always higher than in the diet (Bell et al. 2001, 2003a). The Atlantic salmon analyzed by Nichols et al. (2002) had higher levels of C18:1n–9 and C22:6n–3 and less C20:5n–3 than the diets. It seems that C18:1n–9, C22:1n–11, C18:2n–6 and C18:3n–3 were selectively utilized in the flesh when present at high concentrations in the diet, so for these fatty acids, dietary concentrations were higher than in the fillet (Bell et al. 2001). Bell et al. (2003a) suggested that the latter fatty acids oxidize swiftly when present at high concentrations. The fatty acids C18:1n–9 and 18:2n–6 may act as markers for vegetable oils. In particular, the latter seems to be the most persistent (Bell et al. 2003a): after a dietary switch to fish oil and after the levels of C20:5n–3 and C22:6n–3 recuperate the original high levels, the ratio n3:n6 is not restored. Indeed, inclusion of the n3:n6 ratio as a variable on the data published by Aursand et al. (2000) for wild and farmed Atlantic salmon produced a statistical model in which the two production methods were clearly separated (Figure 18.5). Feeding pikeperch, Stizostedion lucioperca, diets formulated with linseed oil induced a decrease in saturated fatty acids and an increase in the C18:1n9 and the C18:2n–6 amounts in the fillet. However, the total polyunsaturated fatty-acid proportion remained constant (Molnar et al. 2006). Similarly, the fatty-acid composition analyses of juvenile Russian


PC2

395

Scores

10 W-1 F-1 F-1 F-1 F-1 F-1

5

W-1 W-1

F-2 F-2F-1 F-1 F-2 F-2 F-2 F-2 F-1

0

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F-1 F-1

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W-1 W-1

F-1 W-1

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–10 –5 Result1, X-expl: 73%, 17%

0.6

0

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15

20

C22:6n3

C22:1 n3/n6 0.3 C20:5n3 C20:1n9 C22:5n-3 C18:1n6 C25:1n9 0 C16:1

C18:0

C14:0 C16:0 C18:1n7

–0.3 –0.8 –0.6 Result1, X-expl: 73%, 17%

PC1 –0.4

–0.2

0

0.2

0.4

0.6

0.8

Figure 18.5 Principal component analysis on the fatty-acid composition of oil extracted from the white muscle of Norwegian wild Atlantic salmon (W-01) and of specimens farmed in Norway (F-01) and Scotland (F-02) estimated by gas chromatography. In addition to the fatty acids, the ratio n3/n6 [(C20:5n3 + C22:5n–3 + C22:6n3)/C18:2n6] was included as a variable. The model was fully cross-validated and each variable was given a weight of 1. The scores plot is shown on the top and the loadings plot on the bottom. The first and second principal components explained 73% and 17% of the total variability, respectively. The original data are from Aursand et al. (2000).

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sturgeon (Acipenser gueldenstaedtii) fed feeds including fish oil, soybean oil and sunflower oil showed that the total n–3 and n–6 in the whole body and the liver fatty acids varied significantly depending on the oil included in the feed. As expected, in the groups fed vegetable oil, the total amount of n–6 fatty acids was higher and the n–3/n–6 fatty-acid ratio was lower than in the sturgeon fed fish oil, both in muscle and liver fatty-acid composition (Sener et al. 2006). In several works where farmed shrimp species, including Penaeus monodon (Deering et al. 1997; Kumuraguru Vasagam et al. 2005) and Litopenaeus vannamei (Gonzalez-Felix et al. 2002), had been fed diets containing vegetable oils such as sunflower oil, peanut oil, palm oil, coconut oil, soybean oil, linseed oil, canole oil, animal (lard) or fish oils (sardine oil, menhaden oil or cod oil), the fatty-acid composition of the test diets was reflected to a certain extent in the fatty-acid composition of whole shrimp (Deering et al. 1997; GonzalezFelix et al. 2002; Kumuraguru Vasagam et al. 2005). However, as in the case of Atlantic salmon, certain fatty acids appeared to be actively synthesized and/or retained, because they were present in small amounts in some diets, but in relatively higher amounts in tissue of whole shrimp. These fatty acids were C20:4n–6; C20:5n–3 and C22:6n–3 (Kumuraguru Vasagam et al. 2005). Sparing or preferential retention of specific polyunsaturated fatty acids at the expense of saturated and monounsaturated fatty acids had also been demonstrated in previous works with shrimps (Deering et al. 1997; González-Félix et al. 2002). Thus, the fatty-acid profile, owing to the alteration it suffers in farmed fish to mirror that of the diet, can be considered a marker with clear potential for discriminating farmed from wild fish. However, care must be taken in particular to classify fish captured in areas were farming is practised, because it has been shown that feeding around farms affects the fattyacid composition and taste of fish. Skog et al. (2003) investigated claims by local fishermen that saithe captured in the vicinity of fish farms tasted worse than saithe from other areas. Their results showed that saithe collected near farms had a higher condition factor, that is, they were larger, than those from control sites of the same age, that they had been eating pellets and that the fatty-acid composition of their fillet was more similar to the composition of pellets than the composition of fish fillets from reference sites. In a sensory analysis, the test panel found that saithe from the fjord without fish farms tasted better than saithe collected near the cages, thus confirming the claims of the local fishermen (Skog et al. 2003). Fernandez-Jover et al. (2007) performed a similar study after previous observations (Dempster et al. 2002) of horse mackerel (Trachurus mediterraneus) feeding around sea-cages fish farms of sea bass and sea bream in the Mediterranean Sea. This study revealed that while the control fish not associated with fish farms had juvenile fish and small cephalopods in the stomachs, most horse mackerel associated with farms had pellets in their guts. They were also larger than the controls and had a markedly higher fat content and condition factor, although the total protein content did not differ between the two groups. The fatty-acid composition of the dorsal white muscle of horse mackerel associated with farms was similar to that of the feed pellets. The main differentiating fatty acid between the control horse mackerel and those around farms was C22:6n–3 present in higher amounts in the control fish, followed by C18:2n6 and C18:1n9 present in high levels in the commercial feed pellet and in the horse mackerel associated with farms. This work did not register a significant difference between the two groups in the amount of C20:5n–3, which may not be surprising because its level was very low in all groups (about 1.3%). Although in general n–3 and n–6 fatty acids were higher in control and in farm-associated fish respectively,


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C20:4n–6 was an exception because it was higher in the control fish. The study also revealed large individual variations in the farm-associated horse mackerel, which the authors attributed to differences in the residence times of individuals around the cages, and to different feeding habits during short migrations, whereas the fat levels in control fish were relatively stable. Therefore, one may expect intermediate fatty-acid profiles in wild fish feeding around farms and in escaped fish. Finally, as indicated by Refsgaard et al. (1998) and confirmed by Fernandez-Jover et al. (2007), one must always consider the very wide variation in the concentrations of lipid components that can be found in apparently homogeneous populations of farmed salmon – a statement that can easily be extended to other species – which brings in the necessity of analyzing many fish when an estimate of a population average is wanted. This large variability, and the fact that escaped farmed fish and wild fish eating around farms may display intermediate lipid profiles (Skog et al. 2003; Fernandez-Jover et al. 2007), may contribute to the difficulty of performing correct classifications as wild or farmed based solely on the fatty-acid composition. This claims the need to develop extensive databases with the profiles of known samples.

18.3.2

Use of the carotenoids in flesh to differentiate wild, farmed and organically produced salmon

Bacteria, algae, yeasts, molds and some plants can synthesize carotenoids, a group of natural fat-soluble pigments that have antioxidant properties. Most animals, on the other hand, except for copepods, which have been shown to be able to synthesize astaxanthin (Andersson et al. 2003), cannot synthesize carotenoids de novo, and must obtain these pigments from their diet (Prache et al. 2005). Astaxanthin is essential for the proper growth and survival of salmonids and it must be added to their feed to make up for the lack of a natural dietary source of the pigment in formulated feeds (Torrissen and Christiansen 1995). Natural astaxanthine is the main carotenoid found in the flesh of wild Atlantic (Salmo salar) and Pacific (Oncorhynchus spp.) salmon species. Astaxanthin is produced principally by plants, yeast and microalgae. It occurs in several different forms: stereoisomers, geometric isomers, and free or esterified forms. In its natural state, astaxanthin is usually associated with other molecules, it may be dissolved in the lipid fraction or it may be bound chemically to molecules such as fatty acids to form esters (Bernhard 1990). Free, unbound astaxanthin is less stable and its occurrence is rare. The most thermodynamically stable all-E (all-trans) isomer is also the most common geometric configuration in both synthetic and natural astaxanthin. However, although astaxanthin from natural sources tends to occur predominantly as either the 3S,3′S or 3R,3′R stereoisomers, the meso (3R,3′S) isomer is the most abundant in synthetic astaxanthin, produced as the free (unesterified) xanthophyll and as a 1 : 2 : 1 mixture of the three stereoisomers: 3S,3′S, 3R,3′S and 3R,3′R (Bernhard 1990). The major form currently being used in fish feeds is synthetic astaxanthin (McCoy 1999). The higher abundance of the isomer 3R,3′S fed to farmed fish is reflected in its composition: the proportion of the (3R, 3′S)-isomer is 10 times higher in farmed than in wild salmon, and it is higher in the eggs and alevins of farmed salmon fed synthetic astaxanthin, which also allows this method to be used to determine the spawning success of escaped farmed female

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Atlantic salmon (Lura and Sægrov 1991). Sægrov et al. (1997) used this criterion to identify whether Atlantic salmon populations were wild native or colonizing from escaped farmed fish in a Norwegian river. Astaxanthin deposition in Atlantic salmon tissues has also been shown to be influenced by the type of dietary oil used in the feed (Bjerkeng et al. 1999). Ostermeyer and Schmidt (2004) analyzed the astaxanthin and cantaxanthin contents in wild, farmed and organically farmed Atlantic salmon (currently being produced in Ireland, Scotland and Norway). These authors found the detection of canthaxantin to be a good indicator for conventionally farmed salmon. They could also distinguish salmon fed with synthetic astaxanthin from organically produced salmon fed the yeast Xanthophyllomyces dendrorhous, and these two from wild fish. However, when the astaxanthin used in the feed was produced from shrimp shells, it was not possible to differentiate conventionally from organically farmed salmon. They also noted that if, in the future, astaxanthin was to be produced from the alga Haematococcus pluvialis, it would no longer be possible to use the analytical method they describe in their work to differentiate farmed from wild salmon.

18.4

Identification of the geographic origin

European legislation establishes that the FAO area (Table 18.1) in which wild fish is caught should be part of the information available to consumers (EC regulation No 2065/2001 of 22 October 2001). The FAO areas are wide regions, and very often marketing is based on a narrower and even local area where the fish comes from, because consumers usually appreciate it more and are willing to pay higher prices for their local products. Similarly, Japan’s Law concerning Standardization and Proper Labelling of Agricultural and Forestry Products (JAS Law, Law No. 175 of 1950) requires for perishable, fresh fish the indication on the label (among other information) of the ‘product name’, ‘place of origin’ and, for marine products, whether it is ‘frozen-thawed’ or ‘cultured’. For processed foods, the label must Table 18.1 Catch area and identification of the area. (Commission Regulation (EC) No 2065/2001 of 22 October 2001 Laying down detailed rules for the application of Council Regulation (EC) No. 104/2000 as regards informing consumers about fishery and aquaculture products. Published in the Official Journal of the European Communities, 23 October 2001, pp. L278/6–L278/8)).

a b

Catch area

Identification of areaa

North-West Atlantic North-East Atlanticb Baltic Sea Central-Western Atlantic Central-Eastern Atlantic South-West Atlantic South-East Atlantic Mediterranean Sea Black Sea Indian Ocean Pacific Ocean Antarctic

FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO FAO

FAO Yearbook. Fishery Statistics. Catches. Volume 86 (1), 2000 Excluding the Baltic Sea

area area area area area area area area area area area area

21 27 27.IIId 31 34 41 47 37.1, 37.2 and 37.3 37.4 51 and 57 61, 67, 71, 77, 81 and 87 48, 58 and 88


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indicate the ‘product name’, ‘ingredients’ and, in many products, the ‘place of origin’ of the main ingredients. Additional reasons to establish methods to confirm the geographic origin lie in the perceived quality, or toxicity of the fish: some areas are considered to be ‘clean’ and others ‘polluted’, especially for the content of some metals (mercury, cadmium, arsenic, lead), elements (radioactive elements) and/or environmental pollutants (pesticides, PCBs, dioxine-like product, and so on) (Jacobs et al. 2002; Madeniian et al. 2002; Foran et al. 2004; Hites et al. 2004). Thus fish may be marketed according to how pristine and unpolluted the farming region is perceived by the customers, which in turn makes these customers more demanding about information of the geographic origin of the fish. Finally, customers tend to favor their own fish and ‘traditional’ fish, and are willing to pay higher prices for them. It is evident that as long as there are no methods to demonstrate the geographic origin of fish, false labels will be found in markets, offering fish from less attractive areas labeled as their more expensive counterpart.

18.4.1.

Geographic origin determined by the distribution of stable isotopes and trace elements

The determination of the geographic origin of fish was reviewed by Martinez et al. (2003). Techniques aimed at identifying the geographic origin of samples make use of the different distribution of isotopes in different geographic regions. The isotopes may be from the most common elements making up the organic material such as H, C, O, N, S, or isotopes of trace elements that nonetheless are either essential for normal functioning of organisms, such as zinc, selenium, magnesium, manganese, or contaminants picked up from the environment such as mercury, cadmium, lead, and so on. The ratios of stable isotopes are usually given as proportions (0/00) or as excess (delta (δ) values, which are also given as 0/00). These delta values (for example δ15N, δ13C) are the difference between the value of the sample and that of widely used natural standards which are considered to have a δ value of zero. The transformation of absolute (percentage) values into relative (to a certain standard) delta values is used because the absolute differences between samples and standard are quite small at natural abundance levels and might appear only in the third or fourth decimal place if the percentage values were reported. Examples of standards are: air for N, which has a percentage of 15N of 0.3663033% relative to the total (14N + 15N) and Pee Dee Belemnite for C, which has a content of 1.1112328% of 13C relative to the total (12C + 13C + 14C). An example of calculation of δ15N, would be (from http://www.uga.edu/~sisbl/stable.html#trace): δ15N 0/00 versus [std] = ((Rsample − Rstd)/Rstd) (1000 δ 0/00); where R = (At%15N/At%14N) The application of this approach is based on the fact that organisms accumulate in their tissues the elements present in their environment (water, air and soil) as well as those contained in their foods. Because there are differences in the isotope distribution of trace elements in different geographic locations (depending on the composition of soils, the weather conditions, fauna and flora, proximity to cities, industries or farms, and so on) these differences will be reflected in the composition of the fauna and flora of that particular location, and the fingerprint of the organisms can be used as a marker of their geographic provenance. For example, δ13C and δ15N increase from canopied to open forests or agriculture; δ13C and

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δD vary according to the photosynthetic pathway, and so on (Hobson 2005). The application of using isotope and trace-element analyses to determine the geographic origin of food, mainly meat, diary, beverages, and wines and cereals, has recently been reviewed by Kelly et al. (2005). It must be noted, however, that to make sensible comparisons, one should know and be aware of differences in the deposition and metabolic rates of the different elements in the different tissues of the species under consideration (Hobson 2005) The two main techniques used to determine the isotope ratios of natural products are isotope ratio mass spectrometry (IRMS) and site-specific natural isotope fractionation analyzed by nuclear magnetic resonance (SNIF–NMR). IRMS has the advantage over NMR that all except 12 elements can be analyzed by the technique; and SNIF–NMR has the advantage over IRMS that it allows the precise and accurate quantification of the natural abundance of 2H isotopomers (Martin and Martin 1991), whereas IRMS only gives a mean value of the deuterium content of a given chemical species. The SNIF–NMR® technology was developed to detect the adulteration of wines in the early 1980s by Gerard and Mar Yvonne Martin (Martin and Martin 1988, 1991; Martin et al. 1988). Today this method has been adopted as an official European method for authentication of wine (Official Journal of the European Community (1990) 33 L-272, 3 October 1990) and an AOAC approved technique for control of sugar addition in fruit juices (Martin et al. 1996). SNIF–NMR produces a unique and distinctive isotopic fingerprint for a variety of substances. The fingerprint is created on the basis of the isotopic composition of biomolecules because different isotopes of certain atoms (2H/1H, 13C/12C, 15N/14N, 18O/16O) occur at certain relative frequencies; these frequencies and the proportion of each isotope or the relative position of each isotope in a given molecule will vary depending on the geographic origin and the processing and production techniques applied to the sample. For example, it has been shown that it is possible to follow the primary photosynthetic metabolism of plant products by analyzing the 13C/12C ratio (O’Leary 1981), whereas the ratios of the stable isotopes of oxygen (16O/18O) and hydrogen (2H/1H) give a better indication of environmental conditions (Ziegler et al. 1976). One condition necessary to be able to apply SNIF–NMR satisfactorily for authentication of foodstuffs is the construction of large and representative databases containing the fingerprints of all the relevant products, preferably both authentic ones and those that may be used fraudulently. This is because identification is performed by multivariate data analysis classification techniques, which require the processing of the fingerprint obtained from the unknown sample with as many fingerprints as possible (usually in their thousands) of samples representative for all the possible species, tissues, origins, products, and production and processing conditions. As mentioned, SNIF–NMR is the official method to authenticate European wines, which can be done because the European Community has implemented the construction of a large databank containing approximately 12,000 isotopic profiles of Quality Wines Produced in Specific Regions (Martin 2005 and references therein). Every year, all wine-producing countries in the EU harvest several samples proportional to the area of their vineyards, which are analyzed by SNIF–NMR. The databank contains the SNIF–NMR parameters of 2H/1H of ethanol and the 13C and 18O determined by IRMS of ethanol and water, respectively. The method is also widely used to authenticate fruit juices, aromas and perfumes (Martin, 2005). Stable isotope variation was used by Boner and Förstel (2004) to differentiate meat from Europe and America by using isotopes of O and H, cattle from two German regions by using


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isotopes of N and S, and organically produced meat from conventional cattle by using 13 C/12C. However, these authors concluded that controlling the authenticity of cattle required additional data such as slaughtering day, region or local geographic origin and fodder base, so that the stable isotope analyses could be used to confirm or negate the declared information. Based on C and N isotope ratios, Schmidt et al. (2005) were able to differentiate American from European beef as well as organically produced from conventional beef, both Irish, by the isotopic composition of C, N and S, conventional beef having higher δ15N values than organic beef. In any case, it must be noted that meat from animals originating from different areas that have a similar climatology or geology may produce indistinguishable isotope signatures. Studies on fish have mostly been performed on oil extracts. The non-statistical distribution of 2H analysed by NMR was used to differentiate Atlantic salmon from different sources (Aparicio et al. 1998; Aursand and Axelson 2001; Aursand et al. 1995b, 2000, 2007). The ‘fingerprint’ that resulted from the chemical shift position and peak height of 13C NMR spectra of lipids was used to identify the species and origin of purified marine oils (Aursand et al. 1995b,c, 2007). Using three analytical techniques, namely gas chromatography, IRMS and high-resolution 2H SNIF–NMR spectroscopy on fish oils and lipids extracted from Atlantic salmon, Aursand et al. (2000) were able to classify correctly samples of wild and farmed salmon from Norway and Scotland according to both the geographic origin and the production method. Although the authors were not able to find significant differences between the (2H/1H)tot isotope ratios of feeds from Norway and Scotland, the feeds were correctly assigned to their corresponding fish group. It was therefore concluded that the observed differentiation between farmed salmon from Norway and Scotland may not be directly related to the composition of the feeds, but rather to the distribution of 2H in the fatty acids (Aursand et al. 2000). A combination of fatty-acid and δ15N composition was successfully used by Moltenkin et al. (2007) to differentiate organical from farmed salmon, and their geographic origin (Norway or Ireland). Currently, there are no standardized official methods to verify the geographic origin of fish. Establishment of such methods has been one of the aims of the European project COFAWS (EC, DG RTD, contract number G6RTD-CT-2001-00512). The five partners in the project tested the suitability of a series of analytical techniques including 1H-NMR, 2 H-NMR, 13C-NMR, 13C-IRMS, 18O-IRMS, GC and GC–IRMS, but only a few results have been published. Rezzi et al. (2003) were able to discriminate the geographic origin (Scotland, Iceland, Norway) of farmed Atlantic salmon by analyzing oil extracts from the white skeletal muscle by 1H-NMR. Specimens from six farms were separated into five clusters: three clusters, one for each farm from Scotland, one cluster comprising two Norwegian farms and one cluster for the only farm from Iceland. In addition to the C and H contained in the fatty acids, the water contained in the muscle tissue of fish reflects the aqueous environment in which the fish has been harvested. The two isotopic parameters that can be determined from this water, 2H and 18O NMR, also have potential as markers for the geographic origin. In a recent review, Franke et al. (2005) examined the suitability of stable isotope ratios and trace element signatures to identify the geographic origin of meat. Trace element signatures are usually analyzed by mass spectrometry, particularly IRMS or inductively coupled plasma mass spectrometry (ICP–MS). Franke et al. (2005) concluded that some elements are of particular interest to discriminate among products from different small-scale

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geographic regions, for example different regions in Germany, whereas others had the potential to differentiate meat from different continents (for instance Se is consistently higher in meat produced in America than in meat produced in Europe). However, the fact that most feeds are enriched with essential elements and minerals, that the animals may switch areas during fattening, and that customary changes in feeds are made during breeding (all of them concepts that with small variations apply to fish farming) did not allow use of a single element as a marker; rather, one should use those trace elements characteristic for the local water, soil and air as the most promising markers. As trace elements, stable isotopes are incorporated in local feeds and in the body of animals. Therefore the ratios of the rare to the abundant isotope may be specific for the given areas. The ratios 2H/1H and 16O/18O in the body tissues are primarily influenced by the (drinking) water, whereas the ratios of 12C/13C; 14 N/15N, 32S/34S and 86Sr/87Sr are usually markers for soil and feed (Franke et al. 2005 and references therein). Interestingly, animal products are usually enriched in 13C and 15N, depending on their diet, and the enrichments proceeds stepwise along the trophic chain from one level to the next. This helps to link the meat to its diet and if the diet is unique to a certain area, then to its geographic origin (Franke et al. 2005 and references therein). Hobson (2005) has recently published a review illustrating the use of stable isotopes, primarily δ13C; δ15N; δ34S; δ2H and δ87S, to trace nutritional origin and migration in terrestrial and aquatic animals, and in bats. Trace-element signatures in otoliths by inductively coupled plasma mass spectrometry (ICP-MS) have been used to distinguish between Atlantic and Mediterranean tuna (Secor et al. 2002) and different spawning aggregations of cod (Campana et al. 1994). Otolith microconstituent analysis has also been applied recently to study stock structure (Edmonds et al. 1989; Campana et al. 1994, 1995; Proctor et al. 1995) and migration rates (Secor 1992; Secor and Piccoli 1996) in a variety of fish species. The premise of this approach is that trace elements are incorporated into otoliths in direct proportion to their availability in surrounding water or food. Few laboratory experiments have been conducted to verify the assumption that otoliths can record environmental histories, but such studies have supported this assumption for uptake of strontium (Fowler et al. 1995; Limburg, 1995; Secor et al. 1995; Farrell and Campana 1996). Physiological factors, temperature and genetics may also affect the uptake of specific elements into otoliths (Kalish 1989; Thresher et al. 1994). In a study by Secor and Zdanowicz (1998) to differentiate bluefin tuna (Thunnus thynnus) from the Atlantic and the Mediterranean, larvae or young-of-the-year exposed to either the Gulf of Mexico or Mediterranean waters were expected to incorporate different mixtures of elements into their otoliths. Multivariate data analysis of the results obtained by ICP–MS of microconstituents measured in whole otoliths of juvenile northern bluefin tuna from the Mediterranean Sea and western Pacific Ocean showed a clear separation between the two groups, and that sodium, magnesium, manganese and zinc were most useful in differentiating Mediterranean and Pacific samples (Secor and Zdanowicz 1998). Most of the work on this subject has been based on the analyses of otholites and/or scales (Campana 1999; Campana and Thorrold 2001), but Born et al. (2003) and Yamashita et al. (2006) have shown that the approach is also valid when applied to soft tissues. Thus, Born et al. (2003) determined regional variations in long-term elemental diagnostics of stock differences among 159 minke whales harvested in west Greenland, the northeast Atlantic Ocean and the North Sea in 1998. The diagnostics tested included mercury, selenium and cadmium, 15N and 13C and stable lead-isotope ratios in muscle liver and kidney, and the trace and major element


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composition of baleen. Yamashita et al. (2006) applied trace-element analyses to identify the origin of eels. They analyzed the levels of six elements in eel muscle: selenium, mercury, zinc, copper, manganese and arsenic. They found that the first four of them were the main determinants of the first two factors in the multivariate data analysis that allowed identification of the origin of eels from three Japanese prefectures (Miyazaki, Kagawa and Shizuoka), Taiwan and China. The authors concluded that the use of techniques with high sensitivity, such as ICP–MS, would allow inclusion in the analysis of certain rare trace elements such as uranium, lead, cadmium and vanadium, which may be particularly useful for discriminating the geographic origin of fish. Interestingly, some of these very same elements are also highly relevant from the point of view of food safety, for example cadmium, mercury, lead or arsenic. If, in addition, the accumulation of some elements presents a species-specific pattern, as shown for mercury in the muscle of tuna and alfonsino (Yamashita et al. 2005), that would give the potential to identify the species, as well as determining the geographic origin and the potential detection of toxic levels of certain elements in seafood.

18.5

Future prospects

Analyses of the astaxanthin content and isomers and their pattern of deposition in different tissues has been useful in discriminating wild from farmed salmonids. However, the application is currently limited to species whose feed may contain these pigments, such as some crustaceans, salmonids and the breeding of some other species. If the addition of these compounds, which seem to have a positive effect on human health (Baker and Günther 2004), was to be allowed to all fish feeds, then these analytes could have a wider application in the identification of the production method. Incidentally, carotenoids have been mentioned as potential markers to verify traceability information on meat and milk of small ruminants (Prache et al. 2005). On the other hand, the use of alternative sources of carotenoids, indistinguishable from the natural sources, as mentioned by Ostermeyer and Schmidt (2004), may make the current analytical methods obsolete for differentiating these three production methods. To enforce legislation about species, production methods and geographic origin of seafood will need the collaboration of all relevant actors involved in the harvesting and production of seafood, to contribute the authentic samples necessary for the construction, updating and maintenance of databases of raw materials and of sampling methods and analyses. This has already been done for the authentication of wines in Europe, where all producers contribute samples of their annual production to the database. It is also the approach proposed by Hobson (2005) and Smith et al. (2003) to track migratory organisms and to link populations through their annual cycle – namely, to collect feathers from birds, exoskeletons from insects, and hairs from mammals in an archive – in order to carry out meaningful studies of longdistance dispersal in wild animals. The challenge in all cases is to identify the markers or combination of markers with discriminatory value and to have a database for each analysis comprehensive enough to allow the classification of as many samples as possible. International cooperation in the construction of the databases and the sharing of the results will be essential to cover the wide spectrum of samples and geographic origins that the current seafood trade comprises.

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Acknowledgments This work was performed with the financial support of the Research Council of Norway and of the European Union to the Integrated Research Project SEAFOODplus, contract No. FOOD-CT-2004-506359, and to the Specific Targeted Research Project SigmaChain, contract No. FP6-518451. Dr Marit Aursand and Inger Beate Standal (SINTEF Fisheries and Aquaculture Ltd, Trondheim, Norway) are gratefully acknowledged for allowing the use of their unpublished material and manuscript in press, and Dr Isabel Medina (Instituto de Investigaciones Marinas, CSIC, Vigo, Spain) for providing Figure 18.4. The author is also very grateful to Dr Michiaki Yamashita, Dr Yumiko Yamashita (National Research Institute of Fisheries Science, Yokohama, Japan) and MrYasuharu Takashima (IAA Center for Food Quality, Labeling and Consumer Services, CFQLCS, Tokyo, Japan) for information about legislation in Japan and results in the field of seafood authentication.

18.6

References

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Chapter 19

Sensory evaluation of seafood: general principles and guidelines Emilia Martinsdóttir, Rian Schelvis, Grethe Hyldig and Kolbrun Sveinsdóttir

19.1

General principles for sensory analysis

Sensory evaluation is the scientific discipline that evokes, measures, analyses and interprets human reactions to characteristics of food perceived through the senses of sight, smell, taste, touch and hearing. Scientific sensory evaluation methods must be performed under carefully controlled conditions in order to reduce the effects of test environment, personal bias, and so on. It is extremely important to define the problem to be solved or what is to be measured. Sensory evaluation is quantitative: numerical data are collected to establish relationship between product characteristics and human perception. It is critical to use proper analysis of the sensory data and interpret the results. The sampling system, methods and procedures for sensory evaluation must be very well defined to serve its purpose. In sensory evaluation, a sensory panel is established and panellists or inspectors trained to perform sensory analysis with clear and descriptive guidelines. The role of the panel leader is to select and train the panellists, manage product samples and references/standards, supervise preparation and presentations of the samples, maintain the technical skills and motivation of the panellists and compile the sensory data. Analysis and interpretation of the sensory data require understanding of the methods used and are performed by the panel leader or in cooperation by the panel leader/sensory staff and project leaders. The panellists must be monitored for their ability to perform the analysis by the panel leader. Textbooks on sensory evaluation of foods often describe the facilities required for sensory evaluation. The recommendations in these publications are intended for establishments or situations where sensory evaluation is a major activity, for example R&D laboratories of food companies and research institutes. Sensory evaluation can be practised at different levels in the fish processing industry. However, sensory evaluation for quality control must be performed no less accurately and conscientiously than in R&D laboratories, but the requirements need not be as elaborate. 411

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Specifically for fish and other seafood, these guidelines describe the tools for sensory evaluation: the senses, selection and training of panellists, facilities for sensory evaluation and sample preparation. In this chapter, application of sensory evaluation for different purposes is described, such as in research, quality control, product development, studies of shelf life and consumer studies. Referral to appropriate sensory methods is given, but each method is covered in chapter 20. We discuss the various application areas where sensory evaluation is used and different methods are chosen to serve the purpose of the measurement. Recommendations on which methods are practical in each situation are given.

19.1.1

The senses

Sensory evaluation is a systematic assessment of the appearance, odour, flavour and texture of food. During perception, most or all of the attributes overlap. However, with training, independent evaluation of each attribute is obtained (Meilgaard et al. 2006). In sensory evaluation of seafood, vision is very important. We see defects such as bloodstains, bones and parasites. The appearance of the fish, including the gills and eyes, gives information about the freshness of the fish. Odour of both raw and cooked fish is also important in sensory evaluation of freshness. People are very sensitive to various compounds produced in fish during storage, and especially spoilage, such as several sulphur and nitrogen compounds. Odours are described as reminding us of something else. Thus, the odour of fish can even be described as an odour of milk or cucumber, as can be seen in the sensory evaluation scheme for whole salmon (Table 20.3). There are four classical basic tastes: sour, salt, bitter and sweet. Various others have been proposed to belong to these basic tastes, such as metallic, astringent and umami. Flavour is the olfactory perception caused by volatile substances released from a product in the mouth as well as the basic tastes caused by soluble substances in the mouth. The chemical feeling factors stimulating the trigeminal nerve ends, for example those in mint and chilli peppers, are also a part of the flavour. In sensory evaluation of fish, the tactile sense is mainly used to evaluate the texture of fish flesh, for example by pressing a fingertip on the fish flesh to observe if the fish is still stiff or soft. The texture of seafood can also be sensed through chewing. References for further reading can be found in Meilgaard et al. (2006).

19.1.2 Selection and training of sensory panels Selection of participants in a sensory panel is based on their basic sensory acuity and ability to describe perceptions analytically. The panellist must know the procedures, improve their ability to identify sensory attributes and improve their sensitivity and memory to provide precise, consistent and standardised reproducible measurements. Personal characteristics are very important when selecting people for the sensory group, such as conscientiousness and accuracy. They should also be interested in sensory evaluation and food. Panellists must also be healthy and normally sensitive (taste and odour senses).

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They should know the nature and limits of the sense organs, and learn how to recognise and evaluate the sensory attributes of fish and seafood products. Basic selection tests and training guidelines can be found in Meilgaard et al. (2006), ISO 8586-1 (1993) and, more specifically for seafood, in Martinsdottir et al. (2001). The persons participating in a panel may be recruited outside the company or institute (external panels), as is more used in research institutes, or they may be recruited among the staff in the company or institute (internal panels), as is more common in industrial companies. Outside panellists maybe more focused, have a more neutral attitude to the samples and probably have more time to concentrate on the evaluation. The advantages of internal panels are that they are easy to reach at short notice and have product knowledge; however, they may be biased by having too much knowledge of the project or products. The training of the sensory panel should begin by describing the procedures of the sensory evaluation, what is expected of the panellists, and so on. The nature and limits of the sense organs are described, such as the importance of breathing deeply and resting between samples during odour evaluation. The schemes intended for use in the sensory evaluation must be carefully explained. Training results should be evaluated. The average and standard deviation of each sample should be calculated and a comparison made between the panellists, namely by performing statistical analysis (analysis of variance, for example). The ability of the panellists can be examined during repeated evaluation of the same samples. Repetition of the training will show the qualifications of the panellists. Regular training of the sensory panel should be done and performance of the panellists monitored. Psychological factors can influence the performance in sensory evaluation. Samples should be coded and randomly presented among the panellists, because given information with samples may lead to preconceived ideas as people may find what they expect to find. As an example, presenting a sample of fish fillet to a panellist with the information that the fish has been kept for a prolonged period in ice before filleting may influence that panellist not to use his or her senses adequately before judging the sample with negative attributes. An error can also occur if panellists associate different sensory attributes. An example is that a fish fillet from a species with a naturally darker muscle than fish fillet from a white fish species may be judged as not having fresh sensory characteristics because of the darker colour. It is also a source of error if the panellists let their hedonic personal judgement interfere with the evaluation. The panellists should be monitored to ensure they are adequately interested or whether they have become bored with or tired of the sensory evaluation. Some kind of encouragement (reward of some kind) and information about performance may be needed to keep the panellists interested. They are curious about their performance in sensory evaluation, and about what has been achieved by their work as a sensory panel. Sensory panels in research In research, both internal and external panels are used. For external panels, the recruitment can be done by announcement in a local newspaper. A first screening can be done by a telephone interview, where people for the test are picked out based on the requirements. All panel members must pass a test of their capability to use their senses and to express the response. According to ISO 5496 (2006), ISO 8586-1 (1993), ISO 8586-2 (1994), ISO 3972

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(1991), ISO/CD 13300 – 1 (2002) and ISO/CD 13300 – 2 (2002), the tests should contain training in detection and recognition of tastes and odours, sensitivity, ranking and/or triangle tests of basic tastes, odour, texture and appearance tests as well as scale- and product training. Based on the results from the test, the opportune candidates for the sensory panel are chosen. All assessors must be re-evaluated once a year during a similar procedure, but with some variation and with focus on the specific product, species, that the sensory panel are working with. The number of assessors depends on the sensory methods and the training of panellists. Depending on the sensitivity, the number of assessors for difference tests may vary from five to 60 or more (ISO 4120 (2004); ISO 5495 (2005)). For descriptive tests, a minimum of five to eight selected assessors or experts are required, specially trained in the method, though the usual number is 8–12 (ISO 6564 (1985); Meilgaard et al. 2006). Sensory panels in industry The method for sensory evaluation in the fish industry is to perform sensory analysis on the daily production. Therefore a sensory panel or trained inspectors should be established, usually within the compary. To avoid errors in the sensory evaluation in daily quality control, it is necessary to follow well-defined grading systems or guidelines and standards. The assessors must be selected, trained and have clear and descriptive guidelines to produce reliable results from sensory evaluation. The Codex guidelines for sensory evaluation of fish and shellfish in laboratories (Codex Standards 1999) can be regarded as helpful for selecting and training panellists in the industry. Generally, the selection and training methods for sensory panels in research (see previous section) apply for sensory panels in industry as well. Depending on the methods used in the industry, the selection criteria and norms for selection may not be as strict as for panels in research. Panellists for industry sensory panels are usually recruited within the company. This is logical but has some drawbacks as the staff may be too involved in the process or product, which might bias their evaluation. However, a company panel is more neutral to the sensory evaluation of the incoming raw material than the production manager. A panel member should not be pressed to make decisions about the product deposition. One of the advantages of an internal panel in a processing company is that the assessors are more likely to be motivated to perform the testing at inconvenient hours of the day (i.e. nightshift). The selection criteria for industry panels (Codex Standards 1999) are the ability to perceive and recognise odours in general and those relevant for the product, and ability to perceive colours and recognise tastes (if tasting is involved in one of the methods used). Another criterion is also to be able to rely on sensory perceptions and to report them appropriately, and to disconnect personal preferences and work-related preference. Training an industry sensory panel is similar to training a research panel. Most likely, the methods used during the training are grading and scaling instead of descriptive tests, and they are more species- or product specific. In any situation, the panel will eventually become an expert panel because of the limited variation in products and methods. This has the advantage that the results will become more precise and accurate; on the other hand, it has the disadvantage that experts tend to skip important steps within the evaluation process and become biased. In daily quality control there is a risk of repeating the same scores and the possibility of missing a developing trend or even defects in samples.


19.1.3

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Facilities for sensory evaluation of seafood

The physical settings should be designed to reduce disturbing factors. The facilities should be centrally located, temperature controlled, have adequate light and be free from odour and noise. Sensory facilities in research Textbooks on sensory evaluation of foods often describe the facilities required for sensory evaluation. There are international and national standards and guidelines for the design and construction of sensory assessment rooms (ISO 8589 (1988); NMKL Procedure No. 6 (1998); Meilgaard et al. 2006). The recommendations in these publications are intended for establishments or situations where sensory evaluation is a major activity, for example R&D laboratories of food companies and research institutes. Sensory facilities in industry Sensory evaluation can be practised at different levels in fish processing. For quality control, it must be performed no less accurately and conscientiously than in R&D laboratories, but the requirements need not be as elaborate. Sensory evaluation of whole fish is generally done by trained assessors (experienced experts or fish inspectors) in the reception or processing halls of fish factories or at auction sites. However, sensory evaluation of cooked fillets in quality control must be performed in rooms with special facilities. General requirements can be stated about the sensory facilities according to ISO 8589 (1988). A special room for sample preparation is preferred adjacent to, but separate from, the testing area. At least some separation should be provided so that the panellists cannot see the samples being prepared because this might enhance expectation error. The testing area should be located near the preparation room. In the testing area, noise level must be kept to a minimum and no unauthorised persons allowed to enter. Lighting is very important. Light must be adequate and appropriate: daylight (either real daylight or artificial daylight with a colour temperature of more than 5000 K). The ambient lighting in the testing area must be uniform, shadow free and controllable. The room should be free of any foreign odour. This can be achieved by installing an air conditioner with activated carbon filters. If necessary, a slight positive pressure may be created in the testing area to reduce the inflow of air from other areas. The testing area must be readily accessible to all panellists. A separation between panellists is necessary to avoid any distraction during the evaluation. No eating, drinking or smoking should be allowed in the testing area. The testing area must be easy to clean and disinfect. Regular cleaning and disinfecting must take place. The cleaning agents used must not leave odours in the testing area.

19.1.4

Seafood sample preparation

Preparation of seafood samples depends on the type or nature of the sample and the sensory method applied, for example whether the fish/seafood is evaluated whole, as raw fillets or cooked.

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Sampling for sensory analysis must be as representative as possible. Consideration must be given not only to how and where the samples are collected or cut, but also that the preparation must have a minimal influence on sensory characteristics of the samples. The sampling system, methods and procedures for sensory evaluation must be very well defined for sensory evaluation to serve its purpose in quality management. The sampling plan for pre-packaged food as described in the Codex sampling plans for pre-packaged foods (Codex Standards 1969) might be used as a basis for sampling plans. Lots and batches have to be defined before deciding the number of samples taken from each batch. A sample must be collected randomly from a defined homogeneous lot, and the number of fish/fillets or units within a sample will depend on the size of each unit. All samples should be individually blind-coded, with three-digit codes.

Whole fish and seafood, raw fillets For medium-sized or large fish (for example cod, red fish or salmon), at least three to five should be included within a sample, but 10 for small fish species, such as herring (Martinsdóttir et al. 2001). Each fish sample is blind coded and placed in random order on a clean table. The fish should be kept cool (2–7 °C) and constant during the evaluation.

Heat treated/cooked seafood samples For fat fish species, it is important to consider if the samples should be with or without skin. The dark muscle is immediately under the skin, so if the skin is removed from the fillets some of the dark muscle will be removed. This can influence intensity of rancid flavour because of the high lipid content in the dark muscle. Each sample serving should be 40–50 g for each assessor. For very small fish, the whole fillet might be required to obtain the right amount of sample. However, for large fish, to ensure that all assessors get their sample from the same part of the fish, samples are collected from the loin part of the fillets. Shrimp and prawns can be very different in size. Some may be big enough to be assessed as one sample (one ‘mouthful’), but others may be so small that several need to be included as one sample. The samples must be cooked or heated sufficiently, but not too much, and served at appropriate consumption temperatures. Cooking time and temperature needed should be tested using samples in appropriate containers and defined before the actual sensory evaluation. Fillets may be cooked in convection ovens at 100 °C in porcelain or heat-tolerant glass containers covered with lids, or aluminium boxes covered with aluminium foil, to a core temperature of 70 °C. The type of aluminium foil used must be controlled to ensure that it does not give a metal taste to the sample. If convection steam ovens are used, containers do not need to be covered during the heat treatment. Cooking in a water bath will require diffusiontight plastic bags under vacuum and heating at lower temperatures (70–85 °C) to a core temperature of 70 °C. Prawns and shrimps are cooked in fresh water for 3 minutes and served at normal consumption temperature. Samples can be cut into portions before or after heat treatment. If the texture and appearance is of no importance, it is possible to make minced samples to eliminate the differences between individuals.


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Sensory evaluation of samples is usually done in duplicate, and sometimes more replications are included. Different types of seafood product Different types of seafood product demand different sample preparation, depending on how the products are normally consumed. Some products might require heating before serving (see the section on heat treated/cooked samples), but other products are served cold or at room temperature. It is important to take into account the normal serving temperature and to ensure that the temperature remains constant during assessment. The usual serving size should be around 40–50 g, but the sample cut depends on the product. As an example, smoked fish samples may be prepared in different ways; cold smoked salmon may be collected as thin slices (0.5–1.0 cm) cut at the right angle to the fillet surface, or as fillets, served on a plate with lid or aluminium foil, at room temperature. Hot smoked rainbow trout may be collected as a fraction from the anterior, mid-dorsal part of the fillet and served at room temperature.

19.2

Application of sensory evaluation to fish and other seafood

Sensory evaluation is applied in research, in quality control, in product development and consumer studies. Seafood freshness is of special interest as seafood is a very perishable food with a limited shelf life.

19.2.1

Seafood research

In seafood research, sensory analysis is often used to monitor effects of different methods of handling, changes in processes or storage conditions on sensory quality and storage life of fish and seafood products. For wild-caught fish, this might be different fishing gear, new icing methods, and so on. For aquaculture, this might be differences in rearing conditions, feeding conditions, dietary modulation, (pre)slaughter handling, post-slaughter processing, and so on. Depending on the research questions, different methods can be used. As an example, for studying the effect of fishing gear and icing on quality and shelf life, the quality index method (QIM) (see Chapter 20.4.) is recommended. For other changes, descriptive methods can be used, such as quantitative descriptive analysis (QDA) (see Chapter 20.5.). Both methods will result in quantitative data and will form a basis for explanatory research, based on small differences. A difference test will be more simple but less useful for explanatory research. For some purposes an intensity scale or ranking test is more simple than QDA, but is sufficient to measure the effect of dietary modulation gradients.

19.2.2

Freshness of seafood

Bremner and Sakaguchi (2000) put forward an approach to the overall idea of freshness, as the total set of characteristics of a recently harvested product that bear on its ability to meet

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stated or implied requirements. Quality evaluated by the senses not only describes freshness sensory properties of the fish but also includes factors such as bleeding and storage that may normally be considered as good manufacturing practices. Those factors are described in section 19.2.4. Freshness makes a major contribution to the quality of fish and fish products (Oehlenschläger 1997). Sensory evaluation is the most important method and is widely used for freshness assessment in the fish sector. Many schemes have been developed for sensory evaluation of raw fish. Grading methods have been most common for buying and selling fish (Anonymous 1996). The former Torry Research Station (Shewan et al. 1953) developed the first modern and detailed method for freshness evaluation. In the article, score sheets for sensory evaluation of white raw fish are described and the sensory factors classified. The general raw appearance and odour, texture of the fish and the flesh, including belly flaps, are described as well as the odour, flavour and texture of cooked fish. The Torry scheme has been commonly used by producers and buyers of fish, especially in the UK. It is critical for a sensory system used in quality management that it reflects the different quality levels in a simple, reliable and documented way. Therefore improved seafood freshness systems that are both rapid and more objective have been developed for various fish species. The QIM is such system and has several unique characteristics. It is based upon a scheme originally developed by the Tasmanian Food Research Unit (Bremner 1985). QIM has to be adapted to each fish species. More information about QIM is described in Chapter 20.3.

19.2.3

Shelf-life studies

Sensory methods are a very important part of shelf-life studies. In these, the questions to be answered mainly deal with determining how long a product can be stored before the sensory quality changes make the product unacceptable and how the products change with storage time at a given temperature (Lyon et al. 1992). For the fish industry, shelf life is based on how long the company is prepared to accept the product in the marketplace before sale (Barbosa and Bremner 2002). For consumers, the end of shelf life is when the product no longer has acceptable sensory characteristics. The end of shelf life for whole fish is defined as the number of days that whole, fresh (gutted) fish can be stored in ice until it becomes unfit for human consumption. Shelf life of fish and fish products is thus the whole period of time in which it is regarded as being fit for human consumption. Spoilage due to microbial activity is the main limitation of the shelf life. Another cause of spoilage may be rancidity, especially in fat fish species. The QIM for whole fish can be used in shelf-life studies to predict storage time in ice and remaining shelf life. Estimated storage time in ice is defined as the number of days the fish has been stored in ice. From these results, a prediction can be calculated for the remaining shelf life (equal to the shelf–life estimated storage time). In the QIM reference manual (Martinsdóttir et al. 2001), an estimated shelf life for 12 fish species is given, assuming optimal storage conditions, namely storage in ice without fluctuations in temperature. The shelf life and the estimated storage time in ice are based upon the outcome of well-controlled storage experiments with whole, fresh fish (gutted) stored in ice under good manufacturing conditions on board the vessel, which implies proper gutting, washing and use of the appro-


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priate fish/ice ratio. The end of storage time is often defined when a trained sensory panel detects spoilage flavour in cooked samples of the fish. A linear relationship between the quality index and storage time in ice has been found, and the best fit of the regression lines calculated for each species are also shown in the manual. The regression lines are used to predict storage time in ice after evaluation of the quality index and estimate the remaining shelf life.

19.2.4

Quality control in fish industry

The application of sensory evaluation in the fish industry is mostly in quality control that considers several aspects like product variability, processing conditions and marketing objectives. The objective of using sensory evaluation in companies should be to develop an efficient way to understand the key attributes that affect consumer liking. Further, it should be to assess which raw-material and processing variables affect the final sensory properties and to use a system to measure and control the sensory attribute (Munoz et al. 1992). The methods best suited in quality control are those measuring variability like ratings and overall conformance to a product concept. Methods like overall difference tests and hedonic tests should be avoided. Sensory evaluation systems can very well be placed within the hazard assessment critical control point (HACCP) or within any quality control system as incoming control, process control and end-product control. They are most important when sensory analysis is used to define strict and exact procedures, standards (norms) and tolerances. Using the results of sensory evaluation as a part of a quality control program, analysis of the data might consist of comparing the results with a lower and upper limit that have previously been established by the management and the buyer. When QIM results are used, these limits refer to the total of demerit points or the quality index. When control charts are used to monitor seafood freshness of specific seafood over time, they permit immediate detection of trends and outof-control conditions, thus allowing appropriate handling and processing procedures to be corrected and the variability to be reduced (Martinsdóttir 2002). In quality control, standards for fish sampling are rarely available for specific fish products. This makes it difficult to set norms and tolerances. However, for use in training assessors for fish, freshness or other specifications of incoming goods (for example whole fish/fillets) with known history of handling, time and temperature from catch can be used. In specific process control and/or product control, detection of defects such as bruising, bones, scales, parasites, blood-spots, parts of skin and intestines, ruptured fillets, freezer burn, and so on is a part of the inspection. Defects can be detected by visual methods. All defects have to be defined and described in instructions and guidelines for the inspectors, like the number of areas damaged by guts and visible cod-worms. Defects like bruises and blood-spots can be measured and counted, and parasites (for example anisakis) can be counted and compared with standards. Colour cards are available for colour estimation of some fish products, such as for salmon grading and selection. Descriptions, eventually with photographs such as how a fillet should be placed in a consumer package, could be used. Taints like naturally occurring muddy, earthy off-odours can be detected in seafood. Sensory evaluation is an effective way of measuring such taints. Suitable methods for these

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types of quality control are described in Chapter 20.2 (difference tests) and 20.3 (grading methods).

19.2.5

Regulation

The EU regulation 2406/96 (Anonymous 1996) refers to only one sensory method for seafood: the EU-quality grading scheme for fresh fish. This method is to be used at the first point of sale and implies freshness and other quality items (parasites, pressure marks, injuries, blemishes and bad discolouration). There are different schemes for whitefish, bluefish, selachii, cephalopods and crustaceans. This method is to be used by experts or by the competent authority (inspection body). For HACCP, no specific sensory methods are described. For every critical control point, the best control method must be defined; sometimes sensory methods to estimate the freshness of, for example, incoming goods are very useful.

19.2.6

Product development

Sensory analysis can help in finding the answers to many of the questions in product development. In developing new products, modifying existing products or the production process, some of the questions are: does the product satisfy a consumer need? Will it be acceptable to consumers, wholesalers and retailers? Is it possible to change an ingredient? Is it unique? Will the production technology influence the sensory properties? What products does it replace or compete against? These questions are important for a food business to contribute to the product’s success. Sensory analysis may help to shorten development time and contribute to a better understanding of the product’s behaviour (Lyon et al. 1992). The differences test can be used for a fast sensory screening, the descriptive sensory analysis to make a sensory characterisation of the product, and the consumer test to evaluate the preferences and acceptability. Sensory analysis by a trained panel is often used to explain and predict consumer preferences, as described in this chapter (Sveinsdóttir et al. 2009).

19.2.7

Consumer studies

Eating-quality preference decisions are ultimately made during consumption. The structural and flavour-active components of the seafood are perceived by the sensory systems.This information is integrated with the consumer’s recollection of previous seafood-eating experiences, with the expectations created for the consumer by the retailer, and by the means of presenting the seafood as a product. Eating quality will vary from one species of seafood to another, and again by the choice of storage, handling, packaging, transportation, and so on

Sensory evaluation of seafood: general principles and guidelines Correlation Loadings (X and Y)

PC2

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• • O-Sour • • F-Frozen storage • F-Sour • O-Frozen storage

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•

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Cod products stored for 10 days at 0–2°C –1.0

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X-expl: 57%,17% Y-expl: 62%,22%

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• •

•

• • • • •

•

•

• • ••

•

•

• •

• •

•• •• • • •• •• • •• • • • • F-Fresh/sweet • • • • • • O-Fresh/sweet • • • ••••••• • •• • • •• Freshness (Torry Score) •• • • • T-Juicy • • • T-Tender • •• • • •

Cod products stored for 2 days at 0–2°C

0

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Figure 19.1 Multivariate analysis (PCR) of consumer liking (䊉) in relation to sensory characteristics of cod products; F, flavour; O, odour; T, texture.

made at each point in the chain from seafood catch, or slaughter, to consumption. Different consumers will have different experiences with seafood, related to availability and frequency of consumption, that will determine individual preferences. Consumer preferences or acceptability can be measured with hedonic methods (see Chapter 20.6). Consumer acceptance of fish is very much related to its freshness, even though consumer criteria for freshness have not yet been established. The demand for freshness or knowledge of the freshness stage has been from retailers. Retailers play a significant role in influencing consumer perception of quality (Bisogny et al. 1987). Several authors have studied the consumer awareness of quality factors related to fish. A high correlation was found between descriptions of trained panels and the opinions of consumers, even though the trained panel used a wider range of intensity scales (Sawyer et al. 1988; Bech et al. 1997). Further, these authors showed that it is possible to establish a link between consumers’ demand for taste quality and attributes from sensory profiling. Perception of taste determines a major part of overall attitudes to buying fish (Bredahl and Grunert 1997). Therefore consumer studies with tasting of products are very important. Sveinsdóttir et al. (2003) studied acceptability to Icelandic consumers and the sensory quality of fresh, thawed and modified-atmosphere-packed cod fillets of different storage time. Fish products with odours and flavours characteristic for very fresh fish were overall more accepted by consumers than products approaching the end of shelf life (Figure 19.1). However, consumers were also influenced by other sensory attributes such as texture. The

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reasons for the consumers’ acceptability are most reliably identified by sensory evaluation outperformed in parallel on the tested products, with methods such as QDA (chapter 20.5). Although different sensory attributes influence consumers’ acceptability of products, consumers often find it difficult to explain why they prefer one product to another, and the results may be difficult to interpret. However, descriptive sensory analysis performed by trained sensory panels provides accurate and detailed description of the products under study. The consumer acceptance or preference may then be related to the sensory characteristics of products by preference mapping (Greenhoff and MacFie 1994; McEwan 1996). Preference mapping has been used to study acceptability of various food products such as meat (Helgesen et al. 1997), beverages (Guinard et al. 2001; Geel et al. 2005), fruits (DaillantSpinnler et al. 1996; Thybo et al. 2003 ) and cheese (Murray and Delahunty 2000; Westad et al. 2004). A recent study on consumer acceptability in four European countries of different cod products (Sveinsdóttir et al. 2009) showed that consumers have very different preferences. Preference mapping showed that one group of consumers had particularly high preferences for sensory attributes characteristic for farmed cod, such as rubbery and meaty texture, meat odour and flavour, whereas another group preferred freshness characteristics of wild cod, such as sweet and metallic flavours. A third group preferred storage characteristics, for example hints of frozen storage and tablecloth odour, dark and discoloured appearance. The fourth group had generally low preferences, but had a tendency to prefer juicy, soft and tender cod. Further, a fifth group was identified, with generally high preferences, regardless of cod product.

19.3

References

Anonymous (1996) Council Regulation (EC) No 2406/96 of 26 November 1996 laying down common marketing standards for certain fishery products. Barbosa, A. and Bremner, A. (2002) The meaning of shelf-life. In: H.A. Bremner (Ed.) Safety and Quality Issues in Fish Processing. Woodhead Publishing Limited, UK, pp. 173–190. Bech, A.C., Kristensen, K., Juhla, H.J. and Poulsen, C.S. (1997) Development of farmed smoked eel in accordance with consumer demands. In: J. Luten, T. Børresen and J. Oehlenschläger (Eds) Seafood from Producer to Consumer, Integrated Approach to Quality. Proceedings of the International Seafood Conference on the occasion of the 25th anniversary of the WEFTA, Noordwijkerhout, The Netherlands, 13–16 November, 1995. Amsterdam, Elsevier Science B.V., pp. 21–30. Bisogny, C.A., Ryan, J. and Regenstein, J.M. (1987) What is fish quality? Can we incorporate consumer perceptions. In: D.E. Kramer and J. Liston (Eds) Seafood Quality Determination, Proceedings of the International Symposium on Seafood Quality Determination, Coordinated by the University of Alaska Sea Grant College Program, 10–14 November 1986, Anchorage, Alaska, U.S.A. New York, Elsevier Science B.V., pp. 547–573. Bjerkeng, B., Refstie, S., Fjalestad K.T., Storebakken, T., Røbotten, M. and Roem, A.J. 1997. Quality parameters of the flesh of Atlantic salmon (Salmo salar) as affected by dietary fat content and full-fat soybean meal as a partial substitute for fish meal in the diet. Aquaculture 157: 297–309. Bredahl, L. and Grunert, K.G. (1997) Determinants of the consumption of fish and shellfish in Denmark: an application of the theory of planned behaviour. In: J. Luten, T. Børresen and J. Oehlenschläger (Eds) Seafood from Producer to Consumer, Integrated Approach to Quality. Proceedings of the International Seafood Conference on the occasion of the 25th anniversary of the WEFTA,


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Noordwijkerhout, The Netherlands, 13–16 November, 1995. Amsterdam, Elsevier Science B.V., pp. 3–19. Bremner, H.A. (1985) A convenient easy to use system for estimating the quality of chilled seafood. Fish Processing Bulletin 7: 59–70. Bremner, A. and Sakaguchi, M. (2000) A critical look at whether ‘freshness’ can be determined. Journal of Aquatic Food Product Technology 9(3): 5–25. Codex Standards (1969) Codex standards for methods of analysis and sampling, ‘Sampling Plans for Prepackaged Foods (AQL 6.5)’, XPT 13-1969, Rome, FAO/WHO Codex Alimentarius. Codex Standards (1999) Codex standards for fish and fishery product, ‘Guidelines for the sensory evaluation of fish and shellfish in laboratories’, CAC-GL 31-1999, Rome, FAO/WHO Codex Alimentarius. Daillant-Spinnler, B., MacFie, H.J.H., Beyts, P.K. and Hedderley, D. (1996) Relationships between perceived sensory properties and major preference directions of 12 varieties of apples from the Southern Hemisphere. Food Quality and Preferences 7: 113–126. Geel, L., Kinnear, M. and de Kock, H.L. (2005) Relating consumer preferences to sensory attributes of instant coffee. Food Quality and Preferences 16: 237–244. Greenhoff, K. and MacFie, H.J.H. (1994) Preference mapping in practice. In: H.J.H. MacFie and D.M.H. Thomson (Eds) Measurements of Food Preferences. Academic and Professional, London, pp. 137–166. Guinard, J.X., Uotani, B. and Schlich, P. (2001) Internal and external mapping of preferences for commercial lager beers: comparison of hedonic ratings by consumers blind versus with knowledge of brand and price. Food Quality and Preferences 12: 243–255. Helgesen, H., Solheim R. and Næs, T. (1997) Consumer preference mapping of dry fermented lamb sausages. Food Quality and Preference 8: 97–109. ISO 3972 (1991) Sensory analysis – Methodology – Method of investigating sensitivity of taste. ISO 4120 (2004) Sensory analysis – Methodology – Triangle test. ISO 5495 (2005) Sensory analysis – Methodology – Paired comparison test. ISO 5496 (2006) Sensory analysis – Methodology – Initiation and training of assessors in the detection and recognition of odours. ISO 6658 (2005) Sensory analysis – Methodology – General guidance. ISO 8586-1 (1993) Sensory analysis – General guidance for selection, training and monitoring of assessors – Part 1: Selected assessors. ISO 8586-2 (1994) Sensory analysis – General guidance for selection, training and monitoring of assessors – Part 2: Experts. ISO 8589 (1988) Sensory analysis – General guidance for the design of test rooms. ISO 11035 (1994) Sensory analysis – Identification and selection of descriptors for establishing a sensory profile by a multidimensional approach. International Standard, 1st edition. ISO/CD 13300 – 1 (2002) Sensory analysis – General guidance for the staff of a sensory evaluation laboratory – Part 1: Staff responsibilities. ISO/CD 13300 – 2 (2002) Sensory analysis – General guidance for the staff of a sensory evaluation laboratory – Part 2: Recruitment and training of panel leaders. Lyon, D., Francombe, M.A., Hasdell, T.A. and Lawson, K. (1992) Guidelines for Sensory Analysis in Food Product Development and Quality Control. Chapman & Hall, London, UK, pp. 131. Martinsdóttir, E. (2002) Quality management of stored fish. In A. Bremner (Ed.) Safety and Quality Issues in Fish Processing. Woodhead Publishing Ltd, UK, pp. 360–378. Martinsdóttir, E., Sveinsdóttir, K., Luten, J., Schelvis-Smit, R. and Hyldig, G. (2001) Reference Manual for the Fish Sector: Sensory Evaluation of Fish Freshness. QIM-Eurofish, The Netherlands. McEwan, J.A. (1996) Preference mapping for product optimization. In: T Næs and E Risvik (Eds) Multivariate Analysis of Data in Sensory Science. Elsevier Science B.V., London, pp. 71–102.

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Meilgaard, G., Civille, V. and Carr, B.T. (2006) Sensory Evaluation Techniques, 4th edition. CRC Press, New York. Munoz, A.M., Civille, G.V. and Carr, B.T. (1992) Sensory Evaluation in Quality Control. Van Nostrand Reinhold, New York. Murray, J.M. and Delahunty, C.M. (2000) Mapping consumer preference for the sensory and packaging attributes of Cheddar cheese. Food Quality and Preferences 11: 419–435. NMKL Procedure No. 6 (1998) Generelle retningslinier for kvalitetssikring af sensorsike laboratorier. Norsk metodikkomitee for levnedsmidler. Oehlenschläger, J. (1997) Sensory evaluation in inspection. In: G. Ólafsdóttir, J. Luten, P. Dalgaard, M. Careche, V. Verrez-Bagnis, E. Martinsdóttir and K. Heia K (Eds) Methods to Determine the Freshness of Fish in Research and Industry. Proceedings of the Final Meeting of the Concerted Action ‘Evaluation of Fish Freshness, AIR3CT942283, Nantes Conference, November 12–14. International Institute of Refrigeration, Paris, pp. 339–344. Sawyer, F.M., Cardello, A.V. and Prell, P.A. (1988) Consumer evaluation of sensory properties of fish. Journal of Food Science 53: 12–24. Shewan, J.M., Macintosh, R.G., Tucker, C.G. and Ehrenberg, A.S.C. (1953) The development of a numerical scoring system for the sensory assessment of the spoilage of wet white fish stored in ice. Journal of the Science of Food and Agriculture 4: 283–298. Sveinsdóttir, K., Thorkelsdóttir, Á. and Martinsdóttir, E. (2003) Consumer survey: cod fillets packed in air and modified atmosphere (MAP). In Proceedings of the TAFT 2003 Conference, 10–14 June 2003, Reykjavik, Iceland. The Icelandic Fisheries Laboratories. Sveinsdóttir, K., Martinsdóttir, E., Green Petersen, D., Hyldig, G., Schelvis, R. and Delahunty, C. (2009) Sensory characteristics of different cod products and consumer preferences. Food Quality and Preference 20: 120–132. Thybo, A.K., Kühn, B.F., Martens, H. (2003) Explaining Danish children’s preferences for apples using instrumental, sensory and demographic/behavioural data. Food Quality and Preferences 15: 53–63. Westad, F., Hersleth, M. and Lea, P. (2004) Strategies for consumer segmentation with applications on preference data. Food Quality and Preferences 15: 681–687.

Chapter 20

Sensory evaluation of seafood: methods Emilia Martinsdóttir, Rian Schelvis, Grethe Hyldig and Kolbrun Sveinsdóttir

20.1

Introduction

Analytical objective sensory tests can be divided into two groups: discriminative tests and descriptive tests. Discriminative testing is used to determine if a difference exists between samples (triangle test, ranking test). Descriptive tests are used to determine the nature and intensity of the differences (profiling and quality tests). The subjective test is an affective test based on a measure of preference or acceptance of consumers (hedonic tests). Sensory responses can be variously measured and can be assigned to sensory impression in different ways: nominal data, ordinal data, interval data and ratio data. Nominal data are obtained if the samples are placed in groups that differ in name but do not have a quantitative relationship. The panellist places the samples in groups belonging to ordered series (namely slight, moderate and strong). Interval data are obtained if the samples are placed into numbered groups separated by an interval. In sensory evaluation of seafood, grading, ranking and scaling methods are the most frequently used methods. However, difference tests can be relevant to use in selected cases. Grading is a useful method of evaluation and is often used in commerce. It depends on one or two product experts. Graders usually learn the scale from other graders. Grading schemes usually have four or five steps. Grading suffers from the drawback that it is difficult or impossible to correlate other, measurable properties statistically. The EU-scheme (Chapter 20.3.1) is an example of a grading scheme. In ranking, three or more samples are arranged in order of intensity or degree of some specific attribute. For example, the colour of four different smoked salmon samples can be ranked in order of intensity. These methods are useful for research but less so for industry. A category scaling is a method where the panellists are asked to rate the intensity of a particular stimulus by assigning a value on a limited numerical scale.

20.2

Difference tests

Difference tests can be used to determine whether a difference exists in a single sensory attribute or in several. They determine whether there exists a perceptible difference in a given attribute, and the specification of the direction of difference. However, they do not 425

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give any indication of the extent of the difference. If the difference between the samples is very large and thus obvious, difference tests are not useful. The most frequently used difference tests are ranking (ISO 8587, 1988), the triangle test (ISO 4120, 2004) and the paired comparison test (ISO 5495, 2005).

20.2.1 Attribute difference tests: ranking In ranking, assessors receive three or more (two is a paired comparison test) samples. Their task is to arrange them in order according to the degree to which they exhibit some specified characteristics, for example four samples of herring ranked for the degree of rancidity. The number of assessors is chosen based on the sensitivity desired for the test. The rank numbers received by each sample are summed, and the resulting rank sums indicate the overall rank order of the samples. Rank orders cannot meaningfully be used as a measure of intensity, but they are amenable to significance tests such as the χ2-test and Friedman’s test. Note that the lack of a difference between samples in one attribute does not imply that no overall difference exists. Usually ranking can be done more quickly and with less training than evaluation by other methods. Thus ranking is often used for preliminary screening. The method gives no individual differences among samples and it is not suited for sessions where many criteria have to be judged simultaneously.

20.2.2 Overall difference test Triangle test The most frequently used difference test in sensory analysis of fish products is the triangle test. The number of assessors is chosen based on the sensitivity desired for the test. The assessors receive a set of three coded samples, are informed that two of the samples are identical in their sensory properties and that one is different. The assessors report which sample they believe to be different, even if the selection is based only on a guess. The triangle test is applicable even when the nature of the difference is unknown (that is, it determines neither the size nor the direction of difference between samples, nor is there any indication of the attribute(s) responsible for the difference). The method is applicable only if the products are fairly homogeneous. Analysis of results from the triangle test is done by comparing the number of correct identifications to the number expected by use of a statistical table. For example, if the number of responses is 12, there must be 9 correct responses to achieve a significant answer (1% level). Paired comparison This method can be used if the objective is to determine in which way a particular sensory attribute differs between two samples. In the paired comparison test, the assessors receive a set of two samples. They designate the sample that they consider the most intense for the sensory attribute under consideration, even if this choice is based only on a guess. The absence of difference for the attribute under study does not necessarily mean that no

Sensory evaluation of seafood: methods

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difference exists between the samples. As an example, dipping a fish fillet into a weak salty solution before freezing may influence the salty taste of the fillet. The assessors could be asked which sample is saltier than the other. The number of times that each sample is selected is counted, and the significance is determined by reference to a statistical table, taking into consideration the results obtained for the expected sample (one-sided test) or the highest number of responses obtained for either of the samples (two-sided test). For example, if the number of responses is 12, there must be 11 correct responses to achieve a significant answer (1% level).

20.2.3

Difference from control

Difference from control (degree of difference) was originally described by Aust et al. (1985). This is used when the objective is the combination of determining if a difference exists between samples and a reference or control sample, and estimating the size of such a difference. Quality assurance/quality control and storage studies are cases in which the relative size of the difference may affect the decision making. This difference test is appropriate if a simple difference cannot be used because of the normal heterogeneity of products like seafood products. Difficulties can occur in using the method for seafood because it requires a consistent reference sample or standard. Reference samples are usually very difficult to keep stable. The method described as a sensory quality control test may be categorised as a descriptive qualitative and quantitative method. This is described in the standard International IDF Standard 99B (1995) which applies to dairy products. This method has also been developed for drinking water (NMKL Method, 2005) and is very thoroughly described in the standard. Panellists are trained to recognise the different attributes occurring in the product during production or storage. In some cases, attributes can be rated in addition to the overall difference. When the panellists detect a difference between the reference samples, the deviation is indicated, eventually using a scale to indicate how much the sample deviates from the reference. Different forms of rating scales exist (Munoz 1992). It is useful to have a blind control within each session, which is a blind-labelled sample of the reference. This additional sample could be from a product of a different batch, which should be very similar. This would provide information about the test product’s variability. Terms from a nomenclature list describing deviations from appearance, odour, flavour and texture should be developed for each product. When the panellist detects a difference, he or she should choose a word from the nomenclature list as described. The method could be recommended for use with some seafood products like fish oil.

20.3

Grading schemes

Grading is the process of applying a categorical value to a lot or group of products. Although this may be based on sensory information, it is no longer generally considered an example of sensory evaluation methodology. Sensory grading most often involves a process of integration of perceptions by the grader. The grader is asked to give one overall rating of the combined effect of the presence of the positive attributes, the blend or balance of those

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attributes, the absence of negative characteristics and the comparison of the products being graded with some written or physical standard. Grading has the advantage that it offers the possibility of selecting products for different qualities. However, statistical correlation with measurable physical or chemical properties is difficult or impossible. Consequently, many of the time-honoured grading scales are being replaced by the methods described below. Examples of grading schemes for fish are the EU quality grading scheme (EU-scheme) and the Torry scheme.

20.3.1

EU-scheme

Specific for seafood, the EU regulation: ‘Council Regulation (EC) No 2406/96 of 26 November 1996 (Anonymous 1996), laying down common marketing standards for certain fishery products’ has only one sensory method in place: the EU-scheme for fresh fish. This method is to be used at the first point of sale and implies freshness and other quality items (parasites, pressure marks, injuries, blemishes and bad discolouration). There are different schemes for whitefish, bluefish, selachii, cephalopods and crustaceans. It is to be used by experts or by the competent authority (inspection body). A drawback of this method is that no training, practical procedures and sampling plan are available. Therefore it is hardly possible for outsiders to use this method in a reliable and reproducible way. Furthermore, the method only uses general parameters to describe freshness quality, it does not take different sensory characteristics of different species into account, nor does it provide useful information about the past or remaining storage time as it is too general, applying to many different species that spoil at different rates. In addition, the assessor is forced to grade the fish based on several quality parameters, which increases the risk of overemphasis on one single criterion. The method is not suitable for predicting the shelf life, nor for statistical analyses of reliability and reproducibility.

20.3.2

Torry scheme

For sensory evaluation of fish fillets, it is common to cook the fillets and evaluate their odour and flavour. The Torry scale is the first detailed scheme developed for evaluating the freshness of cod (Shewan et al. 1953). Many schemes used for sensory evaluation of cooked fillets by a trained panel are based on this original work with some modifications. The Torry scale (Table 20.1) is the most frequently used industry scale for evaluating the freshness of cooked fish. It is used both by producers and buyers. It is a descriptive 10-point scale that has been developed for lean, medium fat and fat fish species. Scores are given from 10 (very fresh in taste and odour) to 3 (spoiled). It is considered unnecessary to have descriptions below 3, as the fish is then not fit for human consumption. The average score of 5.5 has been used as the limit for ‘fit for consumption’ (Martinsdóttir et al. 2001). Members of the sensory panel detect evident spoilage characteristics, such as sour taste and hints of ‘off’ flavours. In Martinsdóttir et al. (2001), results from storage experiments on cod and haddock from two different seasons showed a high correlation between quality index and Torry scores (Figure 20.1). It could be of importance for the industry to compensate the sensory method


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Table 20.1 Torry scoresheet for freshness evaluation of cooked lean fish such as cod, haddock and pollock. Odour Initially weak odour of sweet, boiled milk, starchy, followed by strengthening of these odours Shellfish, seaweed, boiled meat Loss of odour, neutral odour Wood shavings, wood sap, vanillin Condensed milk, boiled potato Milk jug odours, reminiscent of boiled clothes Lactic acid, sour milk, TMA Lower fatty acids (for example acetic or butyric acids) decomposed grass, soapy, turnipy, tallowy

Flavour

Score

Watery, metallic, starchy. Initially no sweetness but meaty flavours with slight sweetness may develop Sweet, meaty, characteristic Sweet and characteristic flavours but reduced in intensity Neutral Insipid Slight sourness, trace of “off”-flavours Slight bitterness, sour, “off”-flavours, TMA Strong bitterness, rubber, slight sulphide

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Figure 20.1 Relationship between quality index (QI) of whole cod and Torry scores of cooked cod fillets versus storage time.

of cooked fillets with the use of a sensory evaluation of whole raw fish with the same result. This would be beneficial as evaluation of the whole raw fish is more rapid and performed much earlier in the production chain. It is also of great importance to have a method that can show a linear relationship with storage time in ice, as the results can be used for product management when remaining shelf life can be predicted.

20.3.3

Taint

Contamination and taint can be naturally occurring (such as the muddy, earthy off-flavour) or man-made (for example, petroleum or other processing effluents). Off flavours in seafood can occur as a result of many factors. The farm-raised catfish industry considers environmental off-flavours associated with blooms of blue–green algae and other microbes to be its

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Table 20.2 Intensity rating scheme for off-flavours. Rating 0 1 2 3 4

Definition No off-flavour can be detected in the sample Off-flavour at threshold level can be detected by tasting the cooked sample Distinct off-flavour can be detected by tasting the cooked sample Off-flavour can be detected in the odour of the cooked sample Off-flavour can be detected in the odour of raw fish

Sources: Bett (1997), modified from Johnsen and Bett (1996).

most significant problem. Algal blooms can produce geosmin and 2-methylisoborneol (MIB), which impart a muddy, musty flavour in seafood. Microbial production of the muddy metabolites geosmin and MIB can be a problem in freshwater fish such as trout, bream and pike. Based on the values presented in the literature (Howgate 2004), the estimated odour detection threshold in water at normal ambient temperature of natural geosmin is 0.015 μg 1−1 and 0.035 μg 1−1 for MIB. Because geosmin and MIB are much more soluble in lipid than in water, the concentration of the chemical in the lipid phase will be much greater than in water phase. This will influence the threshold in fish with different lipid content. Sensory evaluation is the accepted method for monitoring off-flavours in seafood where detection of unknown compounds or compounds at low concentrations is necessary (for example 1 part per billion (ppb) for geosmin and MIB) (Bett and Dionigi 1997). In research, the most appropriate method would be a descriptive method to determine the intensity of the off-flavour. However, in industry a more practical method is needed to discern off-flavoured fish from acceptable fish. Bett (1997) has developed a quality rating system for microwaved cooked fish (Table 20.2). This paper includes well-defined panel selection, training and sample preparation procedures. Extensive training is necessary for assessors to evaluate specific taints (Codex Standards 1999).

20.4 20.4.1

Quality index method Introduction

The quality index method (QIM) is a freshness grading system for seafood. It is based on a scheme originally developed by the Tasmanian Food Research Unit (TFRU) (Bremner 1985). QIM is widely accepted as a reference method in research. For further development it is important that the fish sector implements the method (Martinsdóttir et al. 2003). In quality management it is important to be able to apply a sensory system that reflects the different quality levels in a simple, reliable and documented way. QIM has those advantages, in addition to being rapid, cheap to use, non-destructive and objective compared with other sensory methods. Further, it is easy to work with as it includes instructions. It is a convenient method to teach inexperienced people to evaluate fish, and to train and monitor performance of panellists. As the quality index (the total sum of scores, referred to as the QI) is designed to increase linearly with storage time, the information may be used in production management (Figure 20.2).

Sensory evaluation of seafood: methods 25

Cod Salmon

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15 10 Remaining shelf life

Remaining shelf life

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Estimated maximum storage time

Figure 20.2 Use of QIM in estimating past and remaining storage time.

Several papers and book chapters describe the advantages of QIM and its use in research and industry, such as Hyldig and Nielsen (1997), Luten and Martinsdóttir (1997), Martinsdóttir et al. (2001), Schelvis-Smit and Luten (2003) and Martinsdóttir et al. (2003). Further, Hyldig et al. (2007) provide a detailed overview of QIM, such as the principle of QIM, development, training of QIM inspectors, application of the method and interpretation of results. The QIM-Eurofish Foundation (www.qim-eurofish.com) is an alliance between three fish research institutes in the Netherlands, Iceland and Denmark, which have been the most involved in research concerning QIM, development and allocation of the method. The aim of QIM-Eurofish is to promote and implement the use of QIM as a versatile quality tool within fisheries distribution or production chains in Europe. Manuals covering QIM schemes for 13 commercially important species have been published in 11 European languages (Danish, Dutch, English, French, German, Greek, Icelandic, Italian, Norwegian, Portuguese and Spanish; Martinsdóttir et al. 2001, 2004). The manuals include relevant high-quality photographs of attributes, and QIM calibration curves which offer the opportunity to estimate the remaining shelf life on the basis of a QIM assessment. Furthermore, guidelines for sensory evaluation, sample preparation, selection and training of panellists are given.

20.4.2

The principle of the quality index method

QIM is based on characteristic changes that occur in seafood with storage time in ice. A score from 0 to 3 points is given for changes of parameters in outer appearance (for example of eyes, skin and gills) and changes that occur in odour and texture (Tables 20.3–20.5). The colour of blood and fillets (or the cut surface at the flaps) is evaluated in gutted fish. For some fish species that are not gutted, such as redfish, dissolution of viscera is evaluated. The descriptions corresponding to each score for each parameter evaluated are listed in the QIM

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Table 20.3 QIM scheme for farmed salmon (Salmo salar). Quality parameter Skin

Colour/appearance

Mucus

Odour

Texture

Eyes

Pupils

Form

Gills

Colour

Mucus

Odour

Abdomen

Blood in abdomen Odour

Quality Index (QI)

Description

Score

Pearl-shiny all over the skin The skin is less pearl-shiny The fish is yellowish, mainly near the abdomen Clear, not clotted Milky, clotted Yellow and clotted Fresh seaweed, neutral Cucumber, metal, hay Sour, dish cloth Rotten In rigor Finger mark disappears rapidly Finger leaves mark over 3 seconds Clear and black, metal shiny Dark grey Matt, grey Convex Flat Sunken Red/dark brown Pale red, pink/light brown Grey-brown, brown, grey, green Transparent Milky, clotted Brown, clotted Fresh, seaweed Metal, cucumber Sour, mouldy Rotten Blood red/not present Blood more brown, yellowish Neutral Cucumber, melon Sour, fermenting Rotten/rotten cabbage

0 1 2 0 1 2 0 1 2 3 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 3 0 1 0 1 2 3 0–24

Source: Sveinsdóttir et al. 2002

scheme. Each parameter within the QIM scheme is evaluated independently. The description for score zero (0) should be characteristic for the parameter at the very beginning of the shelf life/storage time (for example the day of catch/slaughter), but higher scores are given for later stages of storage according to the changes occurring for that parameter. Using as an example the parameter eye-form for a fish species such as farmed salmon, three scores describe the changes of the form of the eyes during storage time in ice. At the very beginning of storage time the eye is convex (score = 0), then it becomes flat (score = 1), and at last concave (sunken) (score = 2). As the descriptions in the QIM scheme are species-dependent, the descriptions may be different for other species, as for brill, where at the beginning


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Table 20.4 QIM scheme for herring (Clupea harengus). Quality parameter General appearance

Skin

Bloodspot on gill cover

Stiffness

Belly

Smell

Eyes

Clarity Shape

Gills

Colour Smell

Quality index (QI)

Description

Score

Bright, shining Bright Dull None Small (10–30%) Big (30–50%) Very big (50–100%) Stiff, in rigor mortis Elastic Firm Soft Firm Soft Belly burst Fresh, seaweed/metallic Neutral Musty/sour Stale meat/rancid Clear Cloudy Normal Plain Sunken Characteristic, red Faded, discoloured Fresh, seaweed/metallic Neutral Sweaty/slightly rancid Sour stink/stale, rancid

0 1 2 0 1 2 3 0 1 2 3 0 1 2 0 1 2 3 0 1 0 1 2 0 1 0 1 2 3 0–20

Source: Jónsdóttir 1992

of storage, the eye form is flat (score = 0), but then it becomes slightly sunken (score = 1), and sunken (score = 2) with the storage time. Yet other species may require a different number of scores for the same parameter: for example, the QIM scheme for plaice has a fourth score (score = 3) for the form of eyes. Typically, the number of parameters in a QIM scheme is around 10, each with two to four score categories. Therefore, it is important that the description behind each score for a parameter is concise, simple and not too long. Otherwise, the assessor might get confused and the evaluation might require too much time. When all parameters in the scheme have been given a score, the scores for all the characteristics are summarised to give an overall sensory score, the so-called quality index (QI). The scientific development of QIM schemes for various species aims at having the QI to increase linearly with storage time in ice (Figure 20.2). In theory, the ideal curve or line starts at zero (zero scores at storage day zero); that is, the intersection is at (0,0) and its maximum (the maximum sum of scores) is reached at the

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Table 20.5 QIM scheme for plaice (Pleuronectes platessa). Quality parameter Appearance

Skin (both dark and white side)

Mucus

Eyes

Form

Brightness

Gills

Odour

Colour

Mucus

Flesh, fillets

Colour

Quality index (QI)

Description Fresh, bright, metallic, no discolouration Bright, but without shine Mat, rather dull, slight green/blue or purple discolouration Dull, green/blue, purple discolouration Clear, not clotted Slightly clotted and milky Clotted and slightly yellow Yellow and clotted Convex Convex but slightly sunken Flat or swollen (like a balloon) Flat, sunken in the middle Clear, black shining pupil Rather mat, black pupil Mat, opaque pupil Milky, grey pupil Fresh oil, seeweedy, metallic, peppery Neutral, oily, grassy, slightly musty Musty, bread, beer, malt, slightly rancid Rancid, sour, rotten, sulphurous Bright, light red Slightly discoloured, especially at the end of gill filaments Discoloured Yellowish, brown, grey No mucus Clear Yellowish, slightly clotted Yellow, brown, clotted Fresh, translucent, bluish Waxy, milky Dull, slightly discoloured, yellowish Opaque, discoloured, yellow, brown

Score 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0–24

Source: Martinsdóttir et al. 2001

day of sensory rejection. However, in practice the intersection is usually slightly above zero, as can been seen in the literature (see, for example, Martinsdóttir et al. 2001; Sveinsdottir et al. 2002, 2003; Vaz-Pires and Seixas 2006; Bonilla et al. 2007; ). Also, in many cases the maximum sum of QI scores does not correspond to the maximum shelf life (see, for example, Martinsdóttir et al. 2001; Sveinsdóttir et al. 2002; 2003; Bonilla et al. 2007). Despite coming from the same defined lot, for example the same storage time and same handling, individual fish may spoil at slightly different rates, owing to several factors such as different individual biological condition, treatment onboard the vessel and size. In addition, the fish continues to spoil, despite being past its shelf life.


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The definition of shelf life may also vary. Barbosa and Bremner (2002) discussed the various definitions of the term ‘shelf life’, and emphasised that the definition of the beginning and end of shelf life must be clear and well defined. It is important to consider how the product is presented to consumers. In some areas, such as Northern Europe, fillets are the most common product, whereas in Mediterranean countries consumers usually buy whole fish, which would be likely to result in different shelf life for the same fish. Usually, the end of shelf life is defined when the fish is not fit for human consumption. Spoilage due to microbial activity is the main limitation of the shelf life. Another cause of spoilage may be rancidity, especially in fat fish species. The flesh of newly caught fish is free of bacteria. However, considerable amounts of bacteria may be in the viscera, the gills and on the skin. When the fish is stored whole in ice, the deterioration caused by bacteria is minimal for the first days of storage. The flavour and odour compounds that characterise newly caught fish decrease and disappear in the first few days during storage in ice, and the fish flesh becomes almost flavourless and odourless. The number of bacteria increases rapidly in the flesh, using various compounds to grow, resulting in increasingly bad-smelling sulphurous and nitrogenous volatiles. The end of shelf life is most often determined by sensory evaluation (see the paragraphs on quantitative descriptive analysis (QDA; section 20.5) and the Torry scale (section 20.3.2). Trained sensory panellists evaluate cooked samples of the fish. Usually the end of shelf life is determined when the panel scores for those bad-smelling volatile off odours that have reached a certain limit. At these limits consumers are also likely to reject the fish. Estimated storage time in ice is defined as the number of days the fish has been stored in ice. If the end of shelf life has been defined, an estimate can be calculated for the remaining shelf life (=shelf life – predicted storage time). It is emphasised that the term ‘remaining shelf life’ should be used with some caution becasue of the uncertainty in the estimation. Various factors can affect the remaining shelf life. It depends on the handling of the fish. Rapid cooling after the catch and an uninterrupted cold storage, different fishing gear, bleeding and gutting methods are important. The season and catching ground can also have an effect. Before freshness is evaluated with QIM, a sample has to be taken from a homogenous lot. A homogenous lot could be a catching day or a batch. It should be kept in mind that individual fish spoil at different rates. Martinsdóttir et al. (2001) provide guidelines on how to collect samples for QIM evaluation, and recommend that three to ten fish, collected randomly from different places in fish boxes from a homogenous lot, are included in one sample. Controlled storage experiments suggest that increasing the amount of fish per sample increases the precision of the evaluation. Sveinsdottir et al. (2002) concluded that by assessing three fish in a sample, the storage time could be estimated with an accuracy of 2 days (at the 95% significance level), but examining more fish per sample might increase the precision.

20.4.3

Development of quality index method schemes

Different types of seafood have different characteristics and spoilage patterns, and QIM schemes must be to be adapted to each species incorporating their respective characteristics. QIM schemes have mainly been developed for whole raw fish, but several schemes have

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also been developed for other types of seafood, and products, such as raw, frozen/thawed fillets and cooked fillets. QIM has been developed for, or adapted to, several seafood species and products. Currently the literature offers publications on cod (Gadus morhua) (Larsen et al. 1992), herring (Clupea harengus) (Jónsdóttir 1992), Atlantic mackerel (Scomber scombrus), horse mackerel (Trachurus trachurus), European sardine (Sardina pilchardus) (Andrade et al. 1997), thawed whole cod, thawed cod fillets, thawed cooked cod fillets (Gadus morhua) (Warm et al. 1998), redfish (Sebastes mentella/marinus), deep water shrimp, fjord shrimp and peeled shrimp (Pandalus borealis), plaice (Pleuronectes platessa), brill (Rhombus laevis), dab (Limanda limanda), haddock (Melanogrammus aeglefinus), pollock (Pollachius virens), sole (Solea vulgaris), turbot (Scophthalmus maximus) (Martinsdottir et al. 2001), gilthead seabream (Sparus aurata) (Huidobro et al. 2000), farmed Atlantic salmon (Salmo salar) (Sveinsdottir et al. 2003), frozen hake (M. capensis and M. paradoxus) (Herrero et al. 2003), Mediterranean Hake (Merluccius merluccius) (Baixas-Nogueras et al. 2003), octopus (Octopus vulgaris) (Barbosa and Vaz-Pires 2004), cod fillets (Gadus morhua) (Bonilla et al. 2007), flounder (Paralichthys patagonicus) (Massa et al. 2005), air and modified-atmosphere-packed maatjes herring (Clupea harengus) (Lyhs and Schelvis-Smit 2005), Mediterranean anchovies (Engraulis encrasicholus) (Pons-Sánchez-Cascado et al. 2006), cuttlefish (Sepia officinalis), broadtail shortfin squid (Illex coindetii) (Vaz-Pires and Seixas 2006), farmed Atlantic halibut (Hippoglossus hippoglossus) (Guillerm-Regost et al. 2006) and tub gunard (Chelidonichthys lucernus) (Bekaert 2006). Additionally, schemes similar to QIM, based on the originally developed TFRU scheme, have been developed for other species, such as cultured sea bream (Sparus aurata) (Alasalvar et al. 2001) and cultured and wild sea bass (Dicentrarchus labrax) (Alasalvar et al. 2002). Several authors have suggested modifications to improve QIM schemes already developed. Sveinsdottir et al. (2002) introduced a slightly modified QIM scheme for farmed Atlantic salmon for more precise evaluation. Inácio et al. (2003) published a modified QIM scheme for horse mackerel (Trachurus trachurus). Nielsen and Hyldig (2004) introduced a modified QIM scheme for herring, and suggested further modification to improve its usability. They also demonstrated the different spoilage patterns of herring stored in ice or in a tank before landing, resulting in different QI scores. There are some concerns that the total QI may be influenced and reduced by washing the fish. This has been studied by Huidobro et al. (2001), who showed that the QI was significantly reduced at later storage stages by washing gilthead seabream (Sparus aurata). On the contrary, Inácio et al. (2003) found no such effects by washing horse mackerel (Trachurus trachurus). Meticulousness and thorough planning is needed to develop new QIM schemes. Useful information and guidelines on this are given in Hyldig et al. (2007), Martinsdóttir (2002) and Hyldig and Nielsen (1997). Whenever a QIM scheme is to be developed or adapted for new species, controlled ice storage studies must be conducted to ensure that the appropriate criteria and their corresponding defined characteristics are included in the QIM scheme. There are mainly three steps in the development of a new QIM scheme (Sveinsdottir et al. 2003). First, a pre-observation is conducted, where one or two experts in sensory evaluation of fish, preferably with some specific knowledge of the fish species, observe fish of different storage times, ranging from the beginning of storage time to past the expected end of shelf


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life. All changes occurring in appearance, odour and texture during storage are listed in a draft scheme. The next step is development of the QIM scheme and training of a QIM sensory panel, which should be done in several sessions. In each session, three or four different groups of fish that have been stored different periods within the expected shelf life are observed using the draft scheme. During the first session, the draft scheme is explained to the panellists while they evaluate fish identified with their storage time. The panellists evaluate each fish independently and are encouraged to comment on the scheme. Suggestions for changes are discussed and should be taken into consideration. In the following sessions, the panellists are trained by evaluating the samples without knowledge of the storage time before the sessions. During the development of the scheme, some parameters might be removed if the evaluation of those parameters is considered destructive to the sample. In addition, the descriptions should be checked to ensure that they are sufficiently clear and descriptive, and that the number of scores for each evaluated parameter span the changes occurring with storage time. In the final training session, the QIM scheme should be finalised. The results are analysed, such as observing the linear relationship between the storage time and the maximum QI scores. The performance of the QIM panellists is observed, by analysis scoring of individual panellists against the average scores and standard deviations to check the robustness of the QIM scheme. In parallel to the training of the QIM panellists, a second sensory panel could be trained in sensory evaluation of the cooked samples. In the third step, a full-scale shelf-life study is conducted. Throughout the storage trial, the fish must not be handled, so each time a new sample is taken. The QIM panel evaluates unknown coded samples in random order using the QIM scheme. The fish should be evaluated at least every third day during the shelf life of the fish. The day interval of course depends on the length of the expected shelf life, and if it is only a few days, samples should be evaluated each day or every other day. Preferably five fish should be evaluated on each of the storage days. During storage experiments, chemical and microbiological indices might also be measured to follow the spoilage pattern and may be used for comparison. In parallel, a trained sensory panel should conduct a sensory evaluation of cooked samples to estimate the reasonable maximum shelf life. The shelf life study should be repeated to observe if the same slope is obtained between the QI and the storage time. Analysis of the results from the shelf-life studies is an important part of the development. The linearity of the QI with storage time should be checked. The scores for each attribute in the QIM scheme should be studied against the storage time as well. The weight of scores might be changed to obtain a QI with a higher correlation to storage time.

20.4.4

Selection and training of panellists for evaluation of fish freshness by QIM

For the QIM for evaluation of whole fish kept in ice, a manual has been made that includes a section on panel selection and training. During training courses (standardised by QIMEurofish, www.qim-eurofish.com), candidates are appointed by the management of the company, selected on being conscientious and accurate, interested in sensory evaluation and readily available for testing on regular basis. The participants are chosen from the whole company, not necessarily persons from just the quality or incoming-goods departments.

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On the first day of the course these candidates are tested for colour blindness by Ishihara’s colour deficiency test (Ishihara 1998) and the smell identification test from Sensonics (http:// www.smelltest.com). The norms are that no colour deficiency is allowed. A minimum smell identification score of 29 out of 40 represents a ‘normal smeller’. For the selection of an industry panel dealing with specific off-flavours (that is, earthy off-flavour in farmed fish), participants should be able to identify these specific flavours as well. For QIM, the panel must be trained for three to six sessions (depending on previous experience with seafood sensory evaluation). For training, three or four samples of fish of different freshness with known storage time in ice and treatment are used. The storage time of the fish is introduced to the panellists before they start evaluation. They are asked if they can agree on the scores that should be given for each sample. A discussion is allowed between the panel leader and panellists. Next, the panellists separately evaluate blind coded samples of different freshness, and preferably the same samples three times. The samples are numbercoded. All panellists should become very familiar with fish of all freshness stages: that is, not only raw material that is on the borderline of production.

20.5

Descriptive sensory analysis

Sensory attributes, such as appearance, odour, flavour and texture, are highly species- and product-specific and can be measured in detail using descriptive sensory analysis. Descriptive sensory analysis is a very useful tool both for research and industry. It can be very simple and used to assess the single attributes of appearance, odour, flavour and texture, or many sensory attributes can be evaluated in each sample. The flavour profile is described in ISO 6564 (1985). QDA provides a detailed description of all or selected sensory characteristics in a qualitative and quantitative way (Meilgaard et al. 2006). Trained assessors should always be used in descriptive sensory analysis. The attribute to be assessed must be clearly defined and understood. Special care must be taken with several factors: some flavors are very special, like iodine (from bromophenols) and muddy (from geosmin or MIB), and must be known to the assessors; there might be considerable differences between the individual fish, and it can be a challenge to have homogeneous samples and even more complicated to get replicates for a panel of 12. In all cases the assessors require intensive training and a detailed briefing before each session. Continuous training during long-term projects, preferably using reference material, can reduce the risk of drifting. If the training is interrupted, the assessors might forget descriptor meanings and/or rating levels with time. The words used for describing different attributes must be cognitively clear. To make reference samples, it is good to have a definition of the sensory attribute. Reference samples for the different attributes can be found not only in seafood samples, but also in other foods like cucumber and boiled potato. Another example is the sensory descriptor ‘warm milk’. It is then important to know that milk should only be heated and not boiled, because when it boils a sulphurous odour develops (Hyldig 2007; Hyldig and Nielsen 2007). To see if these demands are fulfilled, the sensory data can be analysed for the signal-to-noise relation for each assessor and attribute. The results can then be evaluated with multivariate data analysis (Thybo and Martens 2000). Examples on how sensory attributes of fish can be described are given in Table 20.6.


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Table 20.6 Sensory descriptors of cooked salmon and cod products. Sensory attribute Appearance Discoloured Homogenous/ heterogeneous Light/dark colour White precipitation Odour Boiled milk Boiled potatoes Butter Frozen storage Meaty Putrid Rancid Sea/seaweed Sour Sourish Sulphur Sweet Table cloth TMA Vanilla Flavour Butter Cooked potatoes Fresh fish oil Frozen storage Meaty Metallic Mushroom Pungent Putrid Rancid Salt Sour taste Sourish Sweet TMA Texture Clammy Dry/juicy Firm/soft Flakiness Juicy Meaty Mushy Oily Rubbery Tough/tender

Description and Scaling

Brown or yellow spots, dark areas Left end: homogenous, even colour. Right end: discoloured, heterogeneous, stains Left end: light, white colour. Right end: dark, yellowish, brownish, grey White precipitation in the broth or on the fish Boiled milk, fruity/mushy odour Odour of boiled potatoes Butter odour, popcorn Reminds of odour found in refrigerator and/or freezing compartment Meaty odour, reminds of boiled meat Putrid odour Rancid fish, paint, varnish Fresh seaweed, fresh sea smell Sour dishcloth/sour sock, spoilage sour Acidic, fresh citric acid Sulphur, matchstick Sweet odour Reminds of a table cloth (damp cloth to clean kitchen table, left for 36 h) TMA odour, reminds of dried salted fish, amine Vanilla odour, sawdust, timber Butter flavour, popcorn Cooked peeled potatoes Fresh oil, fresh green hazelnut Reminds of food which has soaked in refrigerator/freezing odour Meaty flavour, reminds of boiled meat, meat sour, farmed fish Metallic flavour Mushroom flavour Pungent flavour, bitter Putrid flavour Rancid fish, paint, varnish Salt taste Sour taste, spoilage sour Acidic, fresh citric acid Sweet flavour, warm milk TMA flavour, reminds of dried salted fish, amine Clammy texture, tannin Evaluated after chewing several times: dry – pulls juice from the mouth Evaluate how firm or soft the fish is during the first bite. Force required to compress the sample between the molars The fish portion slides into flakes when pressed with the fork The samples ability to hold water after two or three chews Meaty texture, meaty mouth feel Mushy texture Amount of fat coating in the mouth Rubbery texture, chewing gum Evaluated after chewing several times

Sources: Green-Petersen et al. 2006; Sveinsdóttir et al. 2009

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The results from the descriptive sensory analysis can be presented as a spider plot for means of the different sensory attributes. For more detail, the sensory properties can be related to assessors, samples and trials by either principal component analysis (PCA) or partial least square regression (PLSR) using, for example, Unscrambler®, (Camo ASA, Norway) (Martens and Næs 1989). Both discriminant-PLSR (DPLSR) and ANOVA-PLSR (APLSR) models can be calculated (Martens and Martens 1999, 2001). Data from the sensory descriptive analysis can be corrected for the ‘level effect’ (that is, assessors using different parts of the line scale) by the method described by Thybo and Martens (2000).

20.6

Consumer tests (hedonic)

Acceptability of seafood, the degree of liking and disliking, is usually estimated using a scalar method, the most common being the nine-point structured hedonic scale. Validity of data generated using this method can be influenced by factors such as unequal size category intervals in the scale, and the tendency of consumers to avoid extreme values on the scale and to score close to the midpoint. Taking this into account, the method, or variations of it, is still recommendable for estimating hedonic quality of seafood. The most common hedonic scales are seven- or nine-point scales. They are simple to use and easy to implement. If the consumers are children, the scales are often more simple, fivepoint scales, and the hedonic scaling can also be achieved using face scales, such as simple ‘smiley’ faces, or more realistic pictures of adults. Hedonic scales have been widely studied and have been shown to be useful in the hedonic assessment of food. The hedonic scale assumes that consumer preferences exist on a continuum and that preference can be categorised by responses based on liking and disliking. The samples are served to the consumer in random order and the consumers are asked to indicate their hedonic response to the sample on the scale. The words chosen for each scale option are based on equal interval spacing. The categories on a nine-point scale are: dislike extremely, dislike very much, dislike moderately, dislike slightly, neither like nor dislike, like slightly, like moderately, like very much, like extremely. The result can be used for ranking the samples or for preference mapping (see Chapter 19.2.7). Another commonly used and very simple method is the paired comparison test, where the consumer is asked only to indicate which one of the two samples she or he prefers. This method does not give any information about the degree or intensity of the preference.

20.7

References

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Martinsdóttir, E., Sveinsdóttir, K., Luten, J., Schelvis-Smit, R. and Hyldig, G. (2001) Reference Manual for the Fish Sector: Sensory Evaluation of Fish Freshness. QIM-Eurofish, The Netherlands. Martinsdóttir, E., Sveinsdóttir, K., Luten, J., Schelvis-Smit, R. and Hyldig, G. (2004) Reference Manual for the Fish Sector: Sensory Evaluation of Fish Freshness. QIM-Eurofish, The Netherlands. Massa, A.E., Palacios, D.L., Paredi, M.E. and Crupkin, M. (2005) Postmortem changes in quality indices of ice-stored flounder (Paralichthys patagonicus). Journal of Food Biochemistry 29: 570–590. Meilgaard, M., Civille, G.V. and Carr, T. (2006) Descriptive analysis techniques. In: Sensory Evaluation Techniques, 4th edition. CRC Press, New York. Munoz, A.M., Civille, G.V. and Carr, B.T. (1992) Sensory Evaluation in Quality Control. Van Nostrand Reinhold, New York. Nielsen, D. and Hyldig, G. (2004) Influence of handling procedures and biological factors on the QIM evaluation of whole herring (Clupea harengus L.). Food Research International 37: 975–983. NMKL method, No. 183, (2005) Sensory quality control of drinking water. http: //www.nmkl.org/. Pons-Sánchez-Cascado, S., Vidal-Carou, M.C., Nunes, M.L. and Veciana-Nogués, M.T. (2006) Sensory analysis to assess the freshness of Mediterranean anchovies (Engraulis encrasicholus) stored in ice. Food Control 17: 564–569. Sanders, H.R. and Smith, G.L. (1976) The construction of grading schemes based on freshness assessment of fish. Journal of Food Technology 11: 365. Schelvis-Smit, A.A.M. and Luten, J.B. (2003) Catch index: development of a tool for measurement the quality of the catch handling at sea. In: J.B. Luten, J. Oehlenschläger and G. Ólafsdóttir (Eds) Quality of Fish from Catch to Consumer, Labelling, Monitoring and Traceability. Wageningen Academic Publishers, Wageningen, The Netherlands, pp. 137–144. Shewan, J.M., Macintosh, R.G., Tucker, C.G. and Ehrenberg, A.S.C. (1953) The development of a numerical scoring system for the sensory assessment of the spoilage of wet white fish stored in ice. Journal of the Science of Food and Agriculture 4: 283–298. Sveinsdóttir, K., Martinsdottir, E., Hyldig, G., Jorgensen, B. and Kristbergsson, K. (2002) Application of quality index method (QIM) scheme in shelf-life study of farmed Atlantic salmon (Salmo salar). Journal of Food Science 67: 1570–1579. Sveinsdóttir, K., Hyldig, G., Martinsdottir, E., Jorgensen, B. and Kristbergsson, K. (2003) Quality index method (QIM) scheme developed for farmed Atlantic salmon (Salmo salar). Food Quality and Preference 14: 237–245. Sveinsdóttir, K., Martinsdóttir, E., Green Petersen, D., Hyldig, G., Schelvis, R. and Delahunty, C. (2009) Sensory characteristics of different cod products and consumer preferences. Food Quality and Preference 20: 120–132. Thybo, A.K. and Martens, M. (2000) Analysis of sensory assessors in texture profiling of potatoes by multivariate modelling. Food Quality and Preference 11: 283–288. Vaz-Pires, P. and Seixas, P. (2006) Development of new quality index method (QIM) schemes for cuttlefish (Sepia officinalis) and broadtail shortfin squid (Illex coindetii) Food Control 17: 942–949. Warm, K., Boknæs, N. and Nielsen, J. (1998) Development of quality index methods for evaluation of frozen cod (Gadus morhua) and cod fillets. Journal of Aquatic Food Product Technology 7: 45–59.

Chapter 21

Data handling by multivariate data analysis Bo M. Jørgensen

21.1

Introduction

One of the main features of nature is the covariance structure linking various properties or phenomena. Not many entities exist or function independently of at least some others and exploration of physical or biochemical phenomena is incomplete without taking this into account. Recognition of, for example, which family, species or stock a certain individual belongs to is in general based on much more than one attribute, and concepts like quality or safety cannot themselves be quantified by a single figure although certain aspects may. Thus in most research, and in most routine supervision, several quantities are determined, providing the scientist or technician with a set of measurements on each entity. The primary purpose of collecting these data may be to describe a concept like quality, but an extra issue could be to gain new information on covariance structures. As valuable as classical univariate statistics may be, both of these outcomes will indisputably take advantage of a subsequent data analysis designed for data vectors with many or at least several measured quantities, namely for multivariate data.

21.2

What is multivariate data analysis?

The phrase ‘multivariate data analysis’ covers a wide range of mathematical methods for simultaneous treatment of several measurements on a sample. Some are developed and optimised to treat specific problems, and some are generic in nature. Several variants of what is named multi-linear modelling belong to the second type and have the further advantage of being understandable to laymen. Thereby the food scientist him- or herself is able to analyse their results without having to lean on experts in statistics and mathematics (see, for example, Martens and Martens (2001) for an argument of the advantage of that). Multivariate data analysis aims to take advantage of the correlation structure by substituting patterns of measurements for the single values. A sample is then characterised by an amount of each member of a usually small set of such patterns, and the difference between samples expressed as the difference in amounts. In that way, the patterns serve as fundamental characteristics and the amounts summarise even a high degree of variability in a few 444

Data handling by multivariate data analysis

445

figures. These may be considered as ‘generalised’ attributes by which, for example, quality or authenticity assessment can be based. When using the type of multivariate data analysis named multi-linear modelling, the patterns are also sometimes named latent structures because they are initially hidden behind the physical measurements but may be disclosed as a result of the data analysis. The latent structures provide information on the correlation of the measured quantities, which is often at least as interesting as the values of the quantities themselves. Multi-linear modelling, mostly bi-linear, but also frequently tri-linear and sometimes of even higher order, has turned out to be a very efficient generic approach. At first sight, this may be surprising as the world definitely is nonlinear in nature. However, locally, linear models are often sufficient (like Taylor series expansions), and if so, they are to be preferred at least because of their relative simplicity. This again leads to a high degree of interpretability, in contrast to nonlinear methods like artificial neural networks which are often applied to calibrations based on, for example, near-infrared (NIR) spectroscopy. These provide better fit but at the expense of lack in transparency (they act as ‘black boxes’). Also, in the multivariate frame, nonlinear variables may, if necessary, conveniently be introduced in linear modelling by just adding functions of the variables, for example squares (xi2) and cross-terms (xixj) as new columns in the data matrix. In many investigations, the main difference between measurements is related to the difference in the property of interest, for example, species or quality. It is therefore probably not surprising that one of the most useful bi-linear methods is principal component analysis (PCA). This method resolves the data matrix (see later) into latent structures named principal components of which the first sums up as much of the total sample variance as possible. The residual data may contain further structure, and the next principal component then catches as much as possible of that under the constraint that it must be independent of the former component(s). This data matrix decomposition continues until the residuals apparently contain nothing but noise. Because the main variation between samples is contained in the first few principal components, and that variation is likely to be the most interesting, these few components often suffice to describe the experimental results in an easily interpretable way. Occasionally, the main variation between samples is not the most interesting (relevant) in the actual context. In that case, another very useful and widely applicable method, PLS regression, may advantageously be substituted for PCA. The acronym PLS comes from partial least squares, focussing on the mathematics, but has also been explained as projection on latent structures, expressing relevance rather than main sample variance. Here, two blocks of data are in play. The second block is used as a means of rotating the bi-linear solution (model) away from the principal axes, to axes where the variation of interest is more evident. Apart from adding to the interpretability of results, the method has proven very successful in multivariate calibration where a certain quantity, for example fat or protein content, is determined from a fast instrumental measurement, for example an NIR spectrum (see, for example, the monograph by Næs et al. 2002). These two bi-linear methods, PCA and PLS, have proven useful in many fields of investigation and are generally applicable to almost all types of measurement, although specialised methods may give marginally better results. A good piece of advice to the fisheries scientist is therefore to get familiar with PCA and PLS and routinely apply the most relevant of those to the data at hand. Most often, they will turn out to be sufficient. In cases where the single

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Fishery Products: Quality, safety and authenticity Design variables

Sampled curves (data vectors) e.g. spectra and chromatograms

Single measurements

Sample 1 Sample 1 Sample 2 Sample 2

Sample N Sample N Figure 21.1 Schematic structure of the data matrix in bi-linear modelling. Each column contains the same type of variable, either a design variable indicating to what group in an experimental design a sample belongs, a single measurement (for example pH, salt percentage or an assessment of a sensory attribute) or a point of a sampled curve (for example a spectrum). Each row contains all variables belonging to a single sample. In the figure, duplicates are arranged as separate samples.

measurements produce higher-dimensional output, multi-linear extensions should be considered, though, because of their further advantages (see, for example, Smilde et al. 2004).

21.3

Arrangement of data for bi-linear modelling

The first step in bi-linear modelling is to construct a data matrix consisting of one row for each sample and one column for each measured quantity. A measured quantity may be an isolated value like pH, salt content or assessment of a sensory attribute. It may also be one of many sampling points of a curve (for example, a spectrum or a chromatogram). Figure 21.1 illustrates a data set containing both types of measured quantity supplemented by columns describing the experimental design. In that way, each row (excluding the design variables) is mathematically a vector containing all measurements on a single sample, and each column is a vector containing a single sort of measurement made on all the samples involved. The columns are traditionally named variables. Replicates are normally considered as, and therefore assigned to, separate samples (rows) in the data matrix rather than extending the variable space. This has several advantages: (a) it is easier to calculate averages over replicates if considered appropriate or advantageous; (b) too many variables with trivial correlations are avoided; (c) some error structure in the sample mode (direction) is immediately apparent. However, to make sense, this organisation demands that all measurements in a row are made on the same replicate. Otherwise, one should average over replicates before constructing the data matrix. Although it is straightforward to arrange measurements of a certain sample in one row in the data table, the other requirement, to put similar measurements in the same column, is not always that easy. This statement applies not to single measurements of, for example, pH


447

or salt concentration, but to sampled curves that may behave differently from measurement to measurement. Perhaps the most obvious example is if one wants to use a chromatogram directly without having to identify peaks and calculate peak areas. Owing to small but significant changes in the stationary phase during each run, the same compound present in several samples is eluted at different times, presenting its signal to different sampling points, i.e. in different variables. In that way, two identical samples, analysed for example first and last in an experimental series, will appear as different. This difference obviously blurs the data analysis and may prevent real differences of interest from being acknowledged. The existence of what may be named x-axis misalignment thus has to be taken care of, and recently developed methods (Malmquist and Danielsson 1994; Nielsen et al. 1998; Tomasi et al. 2004) seem to do this quite well. The problem is normally not that severe for spectroscopic measurements because of wavelength calibration. That is if the signal is sampled at the same nominal wavelengths from measurement to measurement, of course. This obvious requirement may pose a practical problem, for example when instruments have to be replaced with newer versions or software is upgraded. A misalignment may, however, be apparent and be caused by phenomena of interest. For example, unresolved overlapping peaks may change in relative magnitude between two samples, giving rise to a shift in the maximum of the composite peak. An attempt to align the signals would in this case remove relevant information. A situation like this should be recognised by replicate measurements showing equal behaviour, though. Only one data matrix is needed for PCA, and design variables are normally not included in calculations but may be useful for interpretation of results. To perform a PLS regression one must define two data matrices or two blocks in the large matrix and define which one is X and which is Y. For a calibration, the normal procedure is to define the sampled spectrum or equivalent as X and the variable to be determined from the model as Y (often a one-column block). For classification, the design variables in binary form (0 or 1) may function as either X or Y depending on the purpose of the calculations. If the model is to be used for classifying samples from multivariate measurements, these are put in the X block. If the effect of controlled experimental conditions on measured values is to be studied, the binary design variables make up the X block. The difference may seem a bit subtle and the results often reflect that in being rather similar.

21.4

The outcome of bi-linear modelling

The bi-linear modelling algorithms decompose the data matrix (X block) as outlined in Figure 21.2 into a product of two matrices, the scores matrix, T, and the transpose of the loadings matrix, P. The columns of the loadings matrix contain the patterns or generalised attributes as mentioned before, and the columns of the scores matrix contain the amounts of each pattern. For PCA, both matrices have orthogonal columns; that is, the patterns as well as their amounts are independent. For PLS regression, this applies to only one of the matrices depending of the algorithm used. The valid number of columns depends on the number of independent features. The maximal value is the number of rows or columns in the data matrix X, whichever is the smallest. However, often a few columns suffice to capture the systematic variation between samples making the residual matrix (Figure 21.2) containing mostly noise.

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Fishery Products: Quality, safety and authenticity p1′ X

= t1

p2′ + t2

+...+

Residual

X = TP¢ + Residual Figure 21.2 Bi-linear modelling. The data matrix X is decomposed into a sum of outer products of vectors ti whose elements are named scores and transposed vectors pi whose elements are named loadings. The decomposition may also be expressed as a matrix product where the scores vectors are columns in matrix T and the loadings vectors columns in matrix P. The model is linear in T as well as in P and thus bi-linear.

Samples are distinguishable by having different set of scores values (row elements in T) and variables by having different set of loadings values (row elements in P). Points representing samples that are similar for the measured values are thus relatively close to each other in scores plots of, for example, the second scores vector versus the first. Likewise, points representing measured values that co-vary through the sample set are close in the loadings plot of, for example, the second loadings vector versus the first. The two plots are linked in that the scores plot may reveal sample groupings and the loadings plot may show which of the measurements have mainly influenced the grouping. When investigating aspects of nature, it is a rule with only few exceptions that one cannot measure the whole population but usually a set of samples. These should be representative in the sense that the variability in measured quantities reflects the variability in the population. Likewise, the sample mean should be a good, or at least fair, estimate of the population mean. Bi-linear modelling focuses on the sample variability and rarely on the absolute values of the means. The clearest picture is therefore obtained by changing the zero points of the measured variables by subtracting their sample mean. As a rule of thumb, this means centring of the data matrix always ought to be done before bi-linear modelling. Another consideration is about scaling of the variables. Because the focus is on sample differences in the full pattern of measured variables, high absolute values tend to dominate. This is especially apparent when measurements are expressed in different units. And change in unit of a single variable, for example a relative fat content of 0.15 expressed in per cent as 15, will substantially change the loadings and thereby the emphasis of which variables are the most important for distinguishing between the various samples. It is therefore common practice to scale the measured values with the aim of balancing their contribution to the total variance. Unfortunately, the choice of scaling is not straightforward. The most popular choice is to divide each value in a data matrix column by the standard deviation of the values. This possibility, named ‘auto scaling’ in several multivariate data analysis software packages, makes sense when the main contribution to the standard deviation of the variables comes from genuine differences between samples. If, however, a variable is almost constant and its standard deviation thus is low and mainly caused by noise, it will be given a relatively high weight and may blur the result. A variable of that type is of course not very informative and should be excluded from the data matrix before model calculation. If only one type of measurement, for example sensory profiling data or sampled values of absorption spectra, is included in the data matrix, scaling is often omitted.


449

PC2

PC1

Figure 21.3 Example of a scores plot. The second scores vector from a PCA is graphed against the first. The data set contains eight samples from four treatments (codes) analysed in duplicate. Replicates are represented with equal symbols. Open and closed symbols of the same shape represent two individuals within the same code. Different shapes indicate different codes.

An example of a graphical representation of the outcome of a PCA is shown in Figures 21.3 and 21.4. It is a simulated case showing sensory profiles of four different types (codes) of fish sample. There were two individuals in each code, and each sample was analysed in duplicate. The sensory profile consisted of eight attributes: white and brown colour, sweet, marine, amine and rancid smell (or taste), and fibrous and flaky texture. The data matrix thus contained 4 × 2 × 2 = 16 rows and 8 columns. Data were mean centred, which explains why negative values of scores and loadings occur despite each attribute being assessed on a scale from zero to fifteen. The calculated model showed two principal components together explaining 93% of the total variance; the rest was considered as being due to measurement noise. The resultant scores matrix T thus contained 16 rows and 2 columns, and the loadings matrix P contained 8 rows and 2 columns. Figure 21.3 shows principal component 1 and 2 scores for the 16 determinations. The four codes are clearly separated into four groups; that is, the distance between points representing samples from the same code is much smaller than the distance between points representing samples from different codes. It is also seen that the duplicates are closer together than are the two individuals in the same code. From the distance between replicates one may estimate the error of determination, and the distance between replicate means within the same group informs about the size of the variation between individuals. To obtain a meaningful grouping, these two types of variance must of course be substantially smaller than the inter-code variance. This is a general requirement that applies to bi-linear modelling too. Outlying measurements may disclose themselves by, for example, replicates being far apart. In that case it is important to identify the outlier, correct (if caused by a data transfer

450

Fishery Products: Quality, safety and authenticity PC2 White

Sweet Marine

Fibrous Amine PC1

Flaky

Rancid Brown

Figure 21.4 Example of a Loadings plot. The second loadings vector from the same PCA as in Figure 21.3 is graphed against the first. The points are labelled according to the measured variables (a sensory profiling taken as an example).

error) or remove the ‘bad’ row from the data matrix and recalculate the model. This is because a large part of the variance is caused by the outlier which at least partly has defined the first principal component(s) and thereby made the model invalid. Having found a clear sample grouping, one may be interested in what variables caused this. Figure 21.4 shows principal component 1 and 2 loadings for the same model as in Figure 21.3. Points are shown as labels (variable names) according to the attributes assessed. Two almost independent phenomena are revealed having a main influence on one axis each. The first is related to microbiological activity (marine for fresh, amine for spoiled), and the second to lipid oxidation (white for fresh, rancid and brown for oxidised). Sweet and flaky are other characteristics of the unspoiled samples, whereas fibrous follows amine. Like in the scores plot where points that are close together have almost equal variable patterns, closely spaced variables in the loadings plot are correlated through the samples in the data set. If these samples are representative for the population from which they were drawn, ‘global’ conclusions on variable correlation may be drawn. That type of information is usually valuable, and in some situations it may even point out a possibility of avoiding difficult or expensive analyses in future experiments in favour of easier or cheaper ones providing an equally useful model. The scores plot and the loadings plot of the same model may be superimposed (with proper scaling of the axes). In the example in Figures 21.3 and 21.4, one may deduce that the group of samples marked with circles were of the highest quality, having higher values for sweet, marine and white than the average. The group of samples marked with squares were spoiled (high amine and fibrous), but not oxidised. The triangles pointing upwards represent samples that were rancid but not microbiologically spoiled, having high values of rancid and brown,


451

but not of amine. And the last group (triangles pointing downwards) contains samples that were of the lowest quality with respect to spoilage as well as to rancidity.

21.5

Validation and prediction

The bi-linear model X = TP, (Figure 21.2) may be rearranged to T = XP if the algorithm used scales P to containing orthonormal columns, which is often the case. That is, scores T2 from a new set of measurements may be placed in the scores plot by right-multiplying the new (mean-centred and possibly scaled) data matrix X2 with the loadings matrix of the original model. This provides two possibilities: the model validity may be evaluated by using new samples with known properties; or the properties of unknown samples may be estimated from their scores. The basic assumption when calculating the model in the first place, that the set of samples involved should be representative for the population from which they were drawn, may efficiently be tested by drawing a new, independent set of samples, a socalled test set. The samples must have known properties either measured by another, reliable method, or though their design. In the example in Figures 21.3 and 21.4, one might make another experiment with at least some of the same four treatments and apply exactly the same measurements on the new samples as were done on the calibration samples, that is the original samples on which the model is based. The scores of the new samples should place themselves in the right groups; otherwise, the model is of limited general applicability and the conclusions only valid for the original set of samples. A lack of representativeness may be due to a phenomenon that accidentally was not included in the original sample set. In our example, the total fat content might influence the importance of attributes connected to lipid oxidation. And the texture attributes would be affected by the pre-treatment conditions, for example if frozen-stored fish were frozen pre- or post-rigor. In such cases, one may recalculate the model including the new samples. Including more measurements, for example the fat content, in the data should also be considered. A new model should then be validated by a third sample set, a new test set. There are cases where drawing an independent test set is not practicable for some reason, for example a long storage experiment, fish caught under a rare event like an expedition, or the use of very expensive and tedious measurements. An internal validation, cross-validation (Stone 1974; see also Martens and Næs 1989), is then the best one can do and has to do. The samples are split into groups; one group is put aside as a test set and the (sub)-model calculated on the rest. The test set is then used as if it were an independent one and its scores matrix calculated. After the previously removed samples have been put back, another set is withdrawn as a test set, a new sub-model made and a new set of scores calculated. The procedure is continued until all samples have been used as a test sample once. Some consideration is necessary when segmenting the data matrix for the purpose of cross-validation. The one sample per segment choice, used for what is named full crossvalidation, is frequently met in the literature. It is not recommended though, unless only a few samples are available, because it costs many calculations without paying off compared with other options. Neither should one make sets consisting of one replicate each of all samples except when the measurement error is to be estimated, or in special cases where physically meaningful latent structures are to be validated (see, for example, Jensen et al. (2002) for an example). For experiments like the one illustrated in Figure 21.3, an obvious

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choice would be to divide the data matrix into segments containing the duplicate measurements on one fish, namely eight segments in total. In that way, all four codes are represented in all sub-models so that they span the inter-code variation. Also, the requirement that differences between individuals must be less than differences between codes in order for the result to be meaningful is probed. Cross-validation may also be used for estimating the valid number of principal components or latent structures (Wold 1978). Over-fitting, namely including too many components (columns in T and P), often shows up when ‘explained total validation variance’ is calculated as a function of the number of components included in the model. After a certain number, the explained variance stops to increase and may even decrease, because the over-fitted submodels fit the test samples less well than the model with the right number of components. Unless the full sample set is far from being representative, cross-validation seems to be efficient in estimating model size (number of components or latent structures), and the present author recommends it for that purpose and, if possible, collecting an independent set of samples as a test set for model validation. During the cross-validation procedure, each sub-model produces a set of scores and loadings. If these are stored, one may examine how much a given sample scores values or a given variable loadings value change from one sub-model to another. This provides an estimate of the uncertainty of the scores and loadings, namely how well their placement in the scores or loadings plot is defined. It is a very useful diagnostic, especially in cases where the grouping is not as pronounced as it is in Figure 21.3. The principle applies not only to the scores and loadings but also to all parameters of a model. In multivariate calibration using a PLS regression model, for example, the result may be expressed as y = Xb where the coefficient vector b has as many elements as there are variables (columns) in the data matrix X. The sub-model values of b can be used to estimate standard deviations of the coefficients, and to find by the well-known t-test which ones are significantly different from zero (Martens and Martens 2000). Only variables whose corresponding b-coefficients are significantly different from zero contribute to the determination of y. The other variables are not important to the model and may be left out, often with a decrease in prediction error as an extra benefit. The case presented in Figures 21.3 and 21.4 was chosen to illustrate some general points of bi-linear modelling. It is common to see plots of values of principal component 2 values versus principal component 1, either scores or loadings or both together in a so-called biplot. However, that is not the only way to illustrate results. For example, if the measured variables were sampled curves, for example spectra, there would be many points in the loadings plot, and it is often more illustrative to plot the loadings vectors against sample point number or its ‘natural’ quantity, for example wavelength, rather than to plot loadings vector 2 against vector 1. In calibration, it may be more informative to look at the coefficients, the b-vector as mentioned above, rather than the loadings. A third variant is to look at the so-called correlation loadings, which are defined as the correlation between the scores vectors and the measured variables (columns in the data matrix). This plot illustrates how much each measured quantity contributes to the scores and thereby to the parameters used to distinguish between samples. The quality of a calibration model for determining a quantity y from measurements on a set of samples X may also be judged by inspecting the plot of predicted values of the test set of samples against the known values determined by a standard method (or known


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Table 21.1 Some references to the use of multivariate data analysis in quality, safety or authenticity assessment of fish or seafood. Model use Mainly discrimination based on measurement of distinct quantities Mainly discrimination based on sampled signals Mainly calibration

References 2, 4, 10, 15, 19, 20, 22, 26, 30, 39, 40, 44, 47, 48, 51, 54, 58, 63 5, 6, 12, 14, 16, 25, 27, 28, 33, 37, 46, 55, 57, 59, 60 1, 7, 8, 9, 11, 13, 17, 18, 21, 24, 29, 28, 42, 45, 52, 61

from designed samples if possible). Points in that plot should spread around the line y = x, because, in effect, two methods determining the same quantity are compared. The sum of squared distances from the points to this line is an estimate of the sum of variances of the two methods if an independent test set were used. The plot may also reveal when the linear model is insufficient. The points are then often not randomly distributed around the line.

21.6

Real examples and further reading

It is not the purpose of this chapter to present a review of cases where multivariate data analysis has been used in fisheries research. However, for the reader who would like a more complete overview of the wide applicability of this analytical technique, some references are listed in Table 21.1. A recent review by Arvanitoyannis and van HouwelingenKoukaliaroglou (2003) covers a wide range of applications within food research, including a couple of fish-related examples. There are several excellent textbooks on the subject, some rather heavily founded on the mathematical aspects and some more easily readable by the layman. The first category includes Malinowski (1991), Vandeginste et al. (1998) and Smilde et al. (2004), whereas Martens and Martens (2001) and Næs et al. (2002) belong to the second. One of the classics, the monograph by Martens and Næs (1989), lies in between the two categories. Despite its mathematical language, Smilde et al. (2004) is highly recommended to readers interested in tri-linear or higher models.

21.7

References

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Arvanitoyannis, I.S., Tsitsika, E.V. and Panagiotaki, P. (2005) Implementation of quality control methods (physic-chemical, microbiological and sensory) in conjunction with multivariate analysis towards fish authenticity. International Journal of Food Science & Technology 40: 237–263. Aubourg, S.P., Sotelo, C.G. and Perez-Martin, R. (1998) Assessment of quality changes in frozen sardine (Sardina pilchardus) by fluorescence detection. Journal of the American Oil Chemists’ Society 75: 575–580. Barroso, M., Careche, M., Barrios, L. and Borderias, A.J. (1998) Frozen hake fillets quality as related to texture and viscosity by mechanical methods. Journal of Food Science 63: 793–796. Bassompierre, M., Munck, L., Bro, R. and Engelsen, S.B. (2004) Rapid dioxin assessment in fish products by fatty acid pattern recognition. Analyst 129: 553–558. Bechmann, I.E. and Jørgensen, B.M. (1998) Rapid assessment of quality parameters for frozen cod using near-infrared spectroscopy. Journal of Food Science & Technology 31: 648–652. Bechmann, I.E., Jensen, H.S., Bøknæs, N., Warm, K. and Nielsen, J. (1998) Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis. Journal of the Science of Food and Agriculture 78: 329–336. Boe, B. (1983) Quantitative separation of species in fish mixtures by multivariate analysis of electrofocused protein bands. Food Chemistry 11: 127–137. Bøknæs, N., Jensen, K.N., Andersen, C.M. and Martens, H. (2002) Freshness assessment of thawed and chilled cod fillets packed in modified atmosphere using near-infrared spectroscopy. Lebensmittel-Wissenschaft und -Technologie 35: 628–634. Cozzolino, D., Chree, A, Scaife, J.R. and Murray, I. (2005) Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species. Journal of Agricultural and Food Chemistry 53: 4459–4463. Cozzolino, D., Murray, I., Chree, A. and Scaife, J.R. (2005) Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy. Lebensmittel-Wissenschaft und -Technologie 38: 821–828. Cubadda, F., Raggi, A. and Coni, E. (2006) Element fingerprinting of marine organisms by dynamic reaction cell inductively coupled plasma mass spectrometry. Analytical and Bioanalytical Chemistry 384: 887–896. Duflos, G., Coin, V.M., Cornu, M., Antinelli, J. and Malle, P. (2006) Determination of volatile compounds to characterize fish spoilage using headspace/mass spectrometry and solid-phase microextraction/gas chromatography/mass spectrometry. Journal of the Science of Food and Agriculture 86: 600–611. Girard, B. and Nakai, S. (1993) Species differentiation by multivariate analysis of headspace volatile patterns from canned Pacific salmon. Journal of Aquatic Food Product Technology 2: 51–68. Hatae, K., Yoshimatsu, F. and Matsumoto, J.J. (1988) An integrated quantitative correlation of textural profiles of fish. Journal of Food Science 53: 679–683. Huang, Y., Cavinato, A.G., Mayes, D.M., Kangas, L.J., Bledsoe, G.E. and Rasco, B.A. (2003) Nondestructive determination of moisture and sodium chloride in cured Atlantic salmon (Salmo salar) (teijin) using short-wavelength near-infrared spectroscopy (SW/NIR). Journal of Food Science 68: 482–486. Ingemansson, T., Kaufmann, P. and Ekstrand, B. (1995) Multivariate evaluation of lipid hydrolysis and oxidation data from light and dark muscle of frozen stored rainbow trout (Oncorhynchus mykiss). Journal of Agricultural and Food Chemistry 43: 2046–2052. Ingemansson, T., Olsson, N.U. and Kaufmann, P. (1993) Lipid composition of light and dark muscle of rainbow trout (Oncorhynchus mykiss) after thermal acclimation: a multivariate approach. Aquaculture 113: 153–165. Isaksson, T., Togersen, G., Iversen, A. and Hildrum, K.I. (1995) Non-destructive determination of fat, moisture and protein in salmon fillets by use of near-infrared diffuse spectroscopy. Journal of the Science of Food and Agriculture 69: 95–100.


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Jensen, H.S. and Jørgensen, B.M. (1997) A sensometric approach to cod-quality measurement. Food Quality and Preference 8: 403–407. Jensen, K.N., Guldager, H.S. and Jørgensen, B.M. (2002) Three-way modelling of NMR relaxation profiles from thawed cod muscle. Journal of Aquatic Food Product Technology 11: 201–214. Jepsen, S.M., Pedersen, H.T. and Engelsen, S.B. (1999) Application of chemometrics to low-field 1H NMR relaxation data of intact fish flesh. Journal of the Science of Food and Agriculture 79: 1793–1802. Karoui, R., Thomas, E. and Dufour, E. (2006) Utilisation of a rapid technique based on front-face fluorescence spectroscopy for differentiating between fresh and frozen-thawed fish fillets. Food Research International 39: 349–355. Kent, M., MacKenzie, K., Berger, U.K., Knoechel, R. and Daschner, F. (2000) Determination of prior treatment of fish and fish products using microwave dielectric spectra. European Food Research and Technology 210: 427–433. Kent, M., Oehlenschlager, J., Mierke-Klemeyer, S., Manthey-Karl, M., Knoechel, R., Daschner, F. and Schimmer, O. (2004) A new multivariate approach to the problem of fish quality estimation. Food Chemistry 87: 531–535. Kent, M., Knoechel, R., Daschner, F., Schimmer, O., Tejada, M., Huidobro, A., Nunes, L., Batista, I. and Martins, A. (2005) Determination of the quality of frozen hake using its microwave dielectric properties. International Journal of Food Science & Technology 40: 55–65. LeBlanc, E.L., LeBlanc, R.J. and Blum, I.E. (1988) Prediction of quality in frozen cod (Gadus morhua) fillets. Journal of Food Science 53: 328–340. Li-Chan, E., Nakai, S. and Wood, D.F. (1987) Muscle protein structure-function relationships and discrimination of functionality by multivariate analysis. Journal of Food Science 52: 31–41. Malinowski, E. (1991) Factor Analysis in Chemistry. John Wiley & Sons Inc., New York, USA. Malmquist, G. and Danielsson, R. (1994) Alignment of chromatographic profiles for principal component analysis: a prerequisite for finger printing methods. Journal of Chromatography A 687: 71–88. Marquardt, B.J. and Wold, J.P. (2004) Raman analysis of fish: a potential method for rapid quality screening. Lebensmittel-Wissenschaft und -Technologie 37: 1–8. Martens, H. and Martens, M. (2000) Modified jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Quality and Preference 11: 5–16. Martens, H. and Martens, M. (2001) Multivariate Analysis of Quality. John Wiley & Sons Ltd., Chichester, UK. Martens, H. and Næs, T. (1989) Multivariate Calibration. John Wiley & Sons Ltd., Chichester, UK. Martinez, I., Bathen, T., Standal, I.B., Halvorsen, J., Aursand, M., Gribbestad, I.S. and Axelson, D.E. (2005) Bioactive compounds in cod (Gadus morhua) products and suitability of 1H NMR metabolite profiling for classification of the products using multivariate data analyses. Journal of Agricultural and Food Chemistry 53: 6889–6895. Lin, M., Mousavi, M., Al-Holy, M., Cavinato, A.G. and Rasco, B.A. (2006) Rapid near infrared spectroscopic method for the detection of spoilage in rainbow trout (Oncorhynchus mykiss) fillet. Journal of Food Science 71: S18-S23. Morita, K., Kubota, K. and Aishima, T. (2003) Comparison of aroma characteristics of 16 fish species by sensory evaluation and gas chromatographic analysis. Journal of the Science of Food and Agriculture 83: 289–297. Jensen, K.N., Jørgensen, B.M., Nielsen, H.H. and Nielsen, J. (2005) Water distribution and mobility in herring muscle in relation to lipid content, season, fishing ground and biological parameters. Journal of the Science of Food and Agriculture 85: 1259–1267. Nielsen, N.-P.V., Carstensen, J.M. and Smedsgaard, J. (1998) Aligning of single and multiple wavelength chromatographic profile for chemometric data analysis using correlation optimized warping. Journal of Chromatography A 805: 17–35.

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Nilsen, H., Esaiassen, M., Heia, K. and Sigernes, F. (2002) Visible/near-infrared spectroscopy: a new tool for the evaluation of fish freshness? Journal of Food Science 67: 1821–1826. Næs, T., Isaksson, T., Fearn, T. and Davies, T. (2002) Multivariate Calibration and Classification. NIR Publications, Chichester, UK. Ofstad, R., Egelandsdal, B., Kidman, S., Myklebust, R., Olsen, R.L. and Hermansson, A.M. (1996) Liquid loss as effected by post mortem ultrastructural changes in fish muscle: cod (Gadus morhua L.) and salmon (Salmo salar). Journal of the Science of Food and Agriculture 71: 301–312. Olafsdottir, G., Lauzon, H.L., Martinsdottir, E. and Kristbergsson, K. (2006) Influence of storage temperature on microbial spoilage characteristics of haddock fillets (Melanogrammus aeglefinus) evaluated by multivariate quality prediction. International Journal of Food Microbiology 111: 112–125. Pink, J., Naczk, M. and Pink, D. (1998) Evaluation of the quality of frozen minced red hake: use of Fourier transform infrared spectroscopy. Journal of Agricultural and Food Chemistry 46: 3667–3672. Rodrigues, M.J., Ho, P., Lopez-Caballero, M.E., Bandarra, N.M. and Nunes, M.L. (2005) Chemical, microbiological, and sensory quality of cod products salted in different brines. Journal of Food Science 70: M1-M6. Sferlazzo, G., Franco, M.A., del Caro, A., Madau, M.E., Cristini, A. and Menghini, V. (1995) Discrimination of tuna (Neothynnus albacora) fishing-sites using chemical parameters elaborated by multivariate statistical techniques. Italian Journal of Food Science 7: 395–402. Smilde, A., Bro, R. and Geladi, P. (2004) Multi-way Analysis. John Wiley & Sons Ltd., Chichester, UK. Stone, M. (1974) Cross-validatory choice and assessment of statistical prediction. Journal of the Royal Statistical Society B 39: 111–133. Sveinsdottir, K., Hyldig G., Martinsdottir, E., Jørgensen, B. and Kristbergsson, K. (2003) Quality index method (QIM) scheme developed for farmed Atlantic salmon (Salmo salar). Food Quality and Preference 14: 237–245. Svensson, V.T., Nielsen, H.H. and Bro, R. (2004) Determination of the protein content in brine from salted herring using near-infrared spectroscopy. Lebensmittel-Wissenschaft und -Technologie 37: 803–809. Tomasi, G., van den Berg, F. and Andersson, C. (2004) Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. Journal of Chemometrics 18: 231–241. Tritt, K.L., O’Bara, C.J. and Wells, M.J.M. (2005) Chemometric discrimination among wild and cultured age-0 largemouth bass, black crappies, and white crappies based on fatty acid composition. Journal of Agricultural and Food Chemistry 53: 5304–5312. Uddin, M., Okazaki, E., Turza, S., Yumiko, Y., Tanaka, M. and Fukuda, Y. (2005) Non-destructive visible/NIR spectroscopy for differentiation of fresh and frozen-thawed fish. Journal of Food Science 70: C506-C510. Vandeginste, B.G.M., Massart, D.L., Buydens, L.M.C., de Jong, S., Lewi, P.J. and Smeyers-Verbeke, J. (1998) Handbook of Chemometrics and Qualimetrics: Part B (Data Handling in Science and Technology 20B). Elsevier, Amsterdam, The Netherlands. Vazquez, M.J., Lorenzo, R.A. and Cela, R. (2003) The use of an ‘electronic nose’ device to monitor the ripening process of anchovies. International Journal of Food Science & Technology 38: 273–284. Warm, K., Nelsen, J., Hyldig, G. and Martens, M. (2000) Sensory quality criteria for five fish species. Journal of Food Quality 23: 583–601. Warm, K., Martens, H. and Nielsen, J. (2001) Sensory quality criteria for five fish species predicted from near-infrared (NIR) reflectance measurement. Journal of Food Quality 24: 389–403.


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Westad, F., Nilsen, B.N. and Rødbotten, R. (2001) Prediction and classification of food quality using VIS/NIR spectroscopy and parsimonious models. New Food 4: 52–57. Wold, S. (1978) Cross-validatory estimation of the number of components in factor analysis and principal component models. Technometrics 20: 397–406. Wold, J.P., Jakobsen, T. and Krane, L. (1996) Atlantic salmon average fat content estimated by nearinfrared transmittance spectroscopy. Journal of Food Science 61: 74–77. Yamashita, Y., Omura, Y. and Okazaki, E. (2006) Distinct regional profiles of trace element content in muscle of Japanese eel Anguilla japonica from Japan, Taiwan, and China. Fisheries Science 72: 1109–1113.

Chapter 22

Traceability as a tool Erling P. Larsen and Begoña Pérez Villarreal

22.1

Introduction

In recent years, it has been very amusing to look up the word ‘traceability’ in all the dictionaries that we have come across. In the Concise Oxford Dictionary of Current English, sixth edition 1976, twelfth impression 1981, the word ‘traceability’ does not appear, but the word ‘trace’ appears with 11 different distinct meanings. If the word traceability is ‘Googled’ on the Internet, it gives around 7 million hits. A few years ago it only gave a couple of hundred. There are several definitions of traceability, but the site www.Wikipedia.org is a good place to start. It states that ‘Traceability refers to the completeness of the information about every step in a process chain.’ In the USA, the National Institute of Standards and Technology (NIST) has the following definition: ‘Traceability requires the establishment of an unbroken chain of comparisons to stated references.’ The most useful statement from NIST is: ‘In logistics, traceability refers to the capability for tracing goods along the distribution chain on a batch number or series number basis. Traceability is an important aspect for example in the automotive industry, where it makes recalls possible, or in the food industry where it contributes to food safety.’ There are several other definitions that are useful when working with the food sector. ISO 9000 (ISO 2000) defines traceability as ‘the ability to trace the history, application or location of that which is under consideration . . . when considering a product, traceability can relate to the origin of materials and parts, and the processing history.’ In recent years the European Union (EU) has been working on drawing up legislation to cover all aspects of the food sector, commonly called the ‘the General Food Law’ (EU 2002). Here the EU defines traceability as ‘the ability to trace and follow a food, feed, food-producing animal or substance intended to be, or expected to be incorporated into a food or feed, through all stages of production and distribution.’ The Codex Alimentarius Commission, which was created in 1963 by FAO and WHO to develop food standards, guidelines and related texts, put forward at its meeting in July 2004, in Geneva, the following definition of traceability to be added to its standard: ‘Traceability/Product tracing: the ability to follow the movement of a food through specified stage(s) of production, processing and distribution.’ As can be seen from these different definitions, the area of traceability is still not mature and a finally agreed definition has not yet been found. There are still differences between 458

Traceability as a tool

459

A supply chain with four links P

B-ID

P

B-ID

P

B-ID

P

B-ID

Internal traceability in each link One link in the chain External traceability between the links or across the links P

Product with product data: details of raw material, etc.

B-ID

Batch identification

Figure 22.1 Internal and external traceability with the most important activities in the supply chain.

the USA and the EU in the way the concept is perceived (Larsen 2003). This is caused mainly by different attitudes in placing traceability in its proper context. This will be discussed later in the chapter. Traceability is normally divided into two categories: internal traceability and external traceability (Derrick and Dillon 2004). Internal traceability is related to the product and the information relating to it internally in a factory, company and even inside a conglomerate of companies. External traceability relates to the product information that a company either receives or provides to the next links in the chain from primary producer to the end-user. There is an continuing discussion about whether internal traceability is of importance in the total supply chain. As long as the batches that enter and leave the company can be clearly identified, then traceability is maintained. What has happened to the product within the company is of no interest as long as the final product can be identified with the proper information attached to it. There is no limit to how large or small a batch can be. A company can choose to have a batch size of one product unit, one day or one year’s worth of production, or even an unlimited batch size of the total production (Frederiksen et al. 2004). It reduces to an economic calculation by the company, where the cost of having a finemeshed traceability system can be compared with the possibility of having to recall all the products belonging to the same batch. See Figure 22.1 for an illustration of internal and external traceability. The introduction of traceability in the fish sector is spreading slowly. Internal traceability is expanding most, whereas external traceability comes in fits and starts depending on the pressure from the authorities for a system to be put in place. Food scares are a constant threat and it is here that traceability has its justification in the future. The fish sector has traditionally been considered as something special compared with other food sectors, not to mention other industrial processing. The fresh fish trade handles highly perishable foods and special operations have to be managed to maintain an acceptable eating quality. The only comparable sector is the dairy sector, where fresh milk is handled. The shelf life of milk – normally pasteurised – and fresh fish in the fish retailers shops is

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generally less than 10 days old, although many fish species have a longer shelf life, for example up to 20 days for farmed salmon and 18 days for redfish (Pérez Villarreal and Pozo 1990; Sveinsdottir et al. 2003). The dairy sector is characterised by the same ownership of many of the links in the chain from the primary producer to final sale to the consumer. There are individual differences between the European countries: from the Scandinavian model with full ownership from producer, through dairy processing to transportation to the wholesaler, to the British model where some of the dairy processing plants still deliver to the customer’s address. In these cases, the supply chain is fairly simple and there is full traceability due to a high degree of internal traceability. The customer is used to seeing all the important data on the milk bottle or the carton. This has only recently been the case in the fresh fish sector. Fresh fish was sold unpacked, wrapped in paper or other suitable material, with no information, not even the name of the fish species. This has changed in recent years (Hansen and Fischer 2003). Multiple retail stores have entered the market for selling fresh fish, starting with the introduction of modified atmosphere packaging (MAP) technology (technology). This technology has increased the total shelf life, while giving space on the packaging for introducing information such as: the name of the fish species, the date of packaging, the weight and often a recipe to help with preparing the fish. This has lead to the ‘shop in shop’ concept, which is a staffed counter selling fresh fish inside the multiple. This is presumably going to be one of the strongest driving forces towards having full traceability in the fish sector, because the multiple is generally used to having traceability for all other goods in the shop and will, of course, consider it necessary to have it for the fish as well.

22.2

Traceability from older times to the present

Historically, traceability was an important part of trade and social organisation. Taxation on actual money income is a relatively new invention, dating back three centuries. Before that, taxes on different goods, or more precisely customs fees, were an integral part of daily life. Of course there were individual taxes such as land tax, tax on the number of fireplaces and even on coins where a piece was cut off. If goods were being transported, and that includes foods such as fish and fish products, and they were crossing a border, a city limit, or maybe just a bridge or a specific road, a fee had to be paid. This fee could go to the local owner, the local community or to the ruler of the area or country. One of the more famous examples is from Northern Europe, where wars have been fought over the income from the trade with codfish and herring. In Figure 22.2 the herring are so numerous that, according to legend, an axe could stand upright if it was stuck in a shoal of them. The King of Denmark taxed the number of people allowed to fish, the salt coming primarily from Germany and even the barrels where the herring was salted. This system demanded a very well-functioning traceability system. Different kinds of salt meant different taxes, which required a specific quality control. The barrels with the salted fish each had an individual number – a batch number in a bigger lot – which could be traced to the fisherman, the community he belonged to, the salt supplier and the transporter. This system created an organisation of quality controllers, tax collectors, customs officials, civil servants in the central administration, and so on. There was, of course, an ongoing fight between the producers to try to pay as little tax as possible and to sell the goods at as high a price as possible and the authorities/tax collectors who wanted full control and high tax levels.


461

Figure 22.2 An old engraving from the 13th century showing the abundance of herring in the Ore Sound between Denmark and Sweden.

These systems operated for centuries, but were lost when mass production was introduced in the 18th century. The next big step was the extensive mass production of foods, which began during the two World Wars in the 20th century, especially after the Second World War. It was, of course, important to have information about the place of production, but the lot numbers covered huge quantities of batches produced. The development towards the present situation possibly started with the introduction of the hazard assessment critical control point (HACCP) concept in the 1960s. HACCP was a new way of handling food production, originally developed to ensure absolutely safe foods for astronauts orbiting the Earth and later going to the Moon (the Pillsbury Company 1973). During recent decades, it has always been in the interests of the authorities to have safe and healthy food products for the population. However, a conflict has been building up between the wish to have safe products on the one hand and cheap products that can be mass produced on the other. In the 1980s and 1990s, some major scares occurred in the food sector. During these years, bovine spongiform encephalopathy (BSE)/Creutzfeldt–Jakob disease (CJD) spread from the UK to the rest of the world; there was foot-and-mouth disease in the UK; and in Belgium the dioxin issue was affecting chicken feed. The fish sector has until now avoided big food scares. So there has been a good opportunity to develop traceability systems adapted to the fish sector. This sector is confronted by some rough conditions: salty water, high humidity, high temperature variation and the multiple ownerships of the products – just to mention just a few of the obstacles. The one big challenge for future traceability systems is the matter of who owns the information that is sent back and forth. The major traceability research projects in the past

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decade have dealt with an ‘open’ approach, whereby the necessary information is shared along the entire chain. Developments in the commercial area have focused more on the ‘closed’ approach, the development of a central information management system which gives the ‘caretaker’ of this central system an advantage. One of the open approach research projects was the Danish ‘Info-Fish’, which operated by an Internet solution based on barcodes as information carriers (Frederiksen et al. 2001). At that time the use of barcodes required that practical solutions be found for use in the harsh saltwater and climate with changes in temperature. This meant not only finding labels that could withstand these conditions, but also on finding equipment that could operate for longer periods. The ‘Info-Fish’ project developed and performed a full-scale test of a traceability system in a fresh fish chain, starting on board a fishing vessel in the North Sea, going through the operation of collecting (unloading, sorting and repacking the catch), auction, wholesale and ending at the retail counter. The result of this project is that today, in the 21st century, more than 50 Danish fishing vessels are using systems that allow them to sort, weigh and pack their catch onboard the vessel and then sell it at auction with all the relevant information included. The information includes: fish species, weight, size, catch date, vessel identity, and even more information if necessary. The buyers of the fresh, gutted fish at auctions are paying more per kilogram than for traditional fresh fish, where only the name of the vessel is given in the fish boxes (personal comment). Fish auctions around Europe are currently one of the most visible places where traceability is actively used. At a normal, traditional auction, a key item of information is the number or name of the fishing boat that has caught and landed the boxes of fish that are for sale. The auctioneer calls out the name of the fishing vessel when the sale begins. This, combined with the name of the fish species, the weight-class/grading of the fish, the total weight of the fish for sale and the quality grading according to EU regulations (EU 1996), gives the buyer the necessary information to give a bid at the auction. In the past 10 years, electronic auction systems have been introduced in fish salerooms. Starting with auctions specialising in the sale of flowers and vegetables, electronic systems are now spreading throughout Europe. One of the first electronic auctions of fish was the Pefa system (http://www.pefa. com/), which now operates nine different auctions (Krott 2003). This system is based on an electronic clock that counts down, and the buyers stop the clock when they make their bid. In the clock itself, which can be seen in a selling room or on a computer screen anywhere in the world, several types of information are given: the fishing vessel, fish species, weight, sorting according to weight and, last but not least, the quality grading. All the information is kept electronically and can be used, for example, if a lot number is recalled. The concept of electronic auctions is spreading across European countries. In Spain there are several companies supplying slightly different systems. The Autec (Automatismes Electrònics i Control, SL) system is chosen as an example (www.autex.com). In 2006, there were already 52 systems running, placed in fishing port auctions along the Mediterranean cost of Spain and on the Azores. The auction system is a combination of an electronic auction and a traditional auction with an auctioneer. To illustrate how it works, and the integration of traceability elements, the auction in La Garrucha (Almería province in Spain) is illustrated. The buyers have their individual remote key to the auction system and press a button to make their bid. When the auctioneer calls the bids, the buyers use the remote control. The same procedure is followed when the electronic auction clock system is used. When the


463

Figure 22.3 Auction of swordfish in La Garrucha (Spain), as a combination of a traditional auction and an electronic auction, with a screen over the gate to the quayside and remote keys for the buyers to give their bid. Photograph by Erling Larsen.

clock starts, the price goes down until a buyer has made a bid. At the same time, all the traceable data is shown on a screen. Figures 22.3–22.5 show the electronic auction in operation. TraceFish is the short title for the ‘Traceability of Fish Products’ concerted action project, which ran from 2000 to 2002, coordinated by the Norwegian Institute of Fisheries and Aquaculture (www.tracefish.org). The TraceFish premise is that with increasing demands for information from buyers and consumers of food products, it is no longer practical to convey all the relevant data physically with the product. A more sensible approach is to mark each package with a unique identifier, and then transmit or extract all the relevant information electronically. The main outcome of TraceFish comprised three consensus-based standards for recording and exchanging traceability information in the seafood chains: one for the electronic transmission of the data; and two other standards for the information data to be traced along the different links of the fish chains, one for farmed fish and one for wild-caught fish. The two last standards can be seen on the homepage of GS1 (GS1 Standard (2006) www.gs1.org/ docs/traceability/GS1_fish_traceability.pdf.).

464


Figure 22.4 The electronic auction, showing boxes on a conveyer belt and the screen showing the most important data such as quality grade, fish species, name of fishing boat and starting price. Photograph by Erling Larsen.

Figure 22.5 After the auction is finished, a label with all the traceable data is printed out and placed in the fish box. This label is used by the buyer in the next link in the chain. Photograph by Erling Larsen.


465

These standards now form the basis of numerous traceability implementations in the industry, both privately funded projects and pilot R&D projects with public funding. Plans are being developed to duplicate the TraceFish process in other food chains that are to develop standards for the recording and electronic interchange of traceability information in other sectors (TraceFood: www.tracefood.org). Today, one of the major marketing subjects in the commercial trade with seafood products is the use of traceability. This stretches from shrimps (Traceshrimp: www.thaitraceshrimp. com), to full traceability in the production of fish products guaranteed by one the major suppliers of equipment to the sector (Marel hf: www.marel.com).

22.3

Traceability research in the seafood sector and other EU-funded food traceability projects

The implementation of traceability in the seafood sector is in progress worldwide. Research is taking place in scientific communities that deal with seafood research, but it has opened up to several new research areas such as information technology and consumer science. In recent years the interest has been to involve sustainability and resource management in the concept of traceability (Thompson et al. 2005). The ethical aspects of traceability have increased owing to the growing interest in, for example, the ‘Fair Price’ concept or in animal welfare (Gouveia 2007) In the EU-financed integrated research project SEAFOODplus (http://www.seafoodplus. org/), the traceability part is concentrated in three major areas: development of methods, testing and implementation of specific elements, and development of validated traceability systems in the seafood sector. The strategic impact of having validated traceability systems is to supply the European consumer with seafood of the required quality and functionality. At the same time, fraud can be reduced. The validated traceability systems developed will also be working on imported seafood products from countries outside the EU, improving the delivery of seafood to the European market and making the choice of retail products much larger for European consumers. In addition to reinforcing consumer confidence in safe and healthy seafood, the introduction of validated traceability systems will also improve the competitiveness and trustworthiness of the seafood processing industry and the whole seafood sector as such. Major elements in the delivered new traceability systems will be the development of an agreed vocabulary and the description of guidelines for good traceability practice, testing and developing technology for automated data capture, developing methods for traceability surveys, and estimating costs and benefits from implementating traceability practice and systems. Moreover, an improved overview of the diverse methods for authentication will be given, and selected methods will be tested and validated for general use in the seafood production chain. Since 2006, the EU has been funding a Specific Support Action project ‘Promoting European Traceability Excellence & Research’ (PETER: http://www.eu-peter.org/) (Table 22.1). This project will provide an international forum for focusing and disseminating the results of the European Commission’s 1100 million investment into research into food traceability during the first decade of the 21st century. There is an urgent need for rapid consolidation and dissemination of European expertise to developing countries and SMEs so that they can

466


Table 22.1 Projects of which the coordinators are members of the PETER project, and other projects where traceability is part of the scope and results are exchanged with PETER.

Projects of which the coordinators are members of PETER

Projects with traceability as a main subject or part of their scope

TRACE (IP) Co-Extra (IP) SEAFOODPlus (IP) GTIS CAP (SSA) GeoTraceAgri (FPV-RTD) DNA-Track (FPV-RTD) Oliv-TRACK (FPV-RTD) ALCUEFOOD (SSA) FoodTrace (FPV-CA)

TRACEBACK P-2-P Chill-On Σ-Chain SAFEED-PAP Ethical Traceability ERMES EUROLATSEA TraceFood

gain access to the global markets that now exist. After consolidation of the European research in the field, the main outcome of this project will be to promote international activities through workshops and conferences. A web-based communication vehicle will serve as a dialogue forum, and specific platforms will be created for discussion between the industry, the consumer and standardisation stakeholders.

22.4

Validation of traceability data

Now that some time has passed since the enforcement of the EU’s General Food Law regulation No. 178/2002 on 1 January 2005, we can observed that the food sector is now accustomed to hearing and speaking about traceability, and even putting into practice some internal traceability issues. Nevertheless, there is still much misunderstanding about what traceability is, and even the full meaning of traceability for the sector is still not entirely understood (EC 178/2002). In particular, for chain traceability – which is the scope of the EU’s General Food Law – the level of doubt increases with information issues and the way in which this information is handled, managed and interchanged. These problems must be solved specifically in the different food chains. One aspect that is generally not taken fully into account for the practical implementation of traceability in a food chain is the validation of the information and the way it is transmitted through the chain. Information can be separated into two main areas: z z

collection and handling of data; verification of the data.

In internal traceability, it can be assumed that the verification of data issued during the production process is not a difficult task and can be put into practice by applying regular quality management tools. This has been proven by several tests in the fish industry in Norway, Spain and Denmark.


467

However, there are some drawbacks or missing points on the practical implementation of traceability issues in a food chain. These are related to the guarantee of traceability or the verification of data, which can be called a validated traceability. A validated traceability chain is a system that can ensure clients’ and consumers’ confidence in the information given.

22.4.1

Traceable data

Multiple traceable data are important for the fish sector, and for each type of data a diverse methodology has been described for measuring it. In most cases, there are no standards that allow easy or simple comparison of data between interested parties, and not every methodology is suitable for each link in the fish chain. One of the main parameters important for traceability is the authentication of seafood products. Authenticity can be defined as the quality or condition of being authentic, trustworthy or genuine. If this definition is applied to the seafood sector, a distinction can be made between: z z z

identification of fish species; identification of geographic origin; discrimination between some production methods (for example farmed from wild fish) and/or processing conditions.

The identification of fish species is an important concern in order to label seafood products following EU regulations (EC 104/2000). Morphological characterisation is used in fishing vessels, harbours and retailers. However, when the external characteristics are removed, DNA-based methodologies, instead of those that are protein based, are now preferred for their discriminatory power over very closely related fish species and highly processed seafood products (see Chapters 16 and 17). Genetic techniques are somehow still limited in routine control laboratories owing to the cost of the equipment and the need to have qualified personnel, although this will change in the near future thanks to the reliability of the methods and the development of more simplified and rapid kits and probes (Martínez et al. 2005). The best method to validate these techniques is by the direct sequencing of DNA fragments amplified by polymerase chain reaction with forensically informative nucleotide sequencing (PCR–FINS) and then comparing them with sequences compiled in a reliable database (Pardo 2005) (AZTI: www.azti.es/dna_database/). Identification of the geographic origin of fish can be important when dealing with sustainability issues in protecting exploited commercial species, giving appropriate information to consumers (EC No. 2065/2001) and avoiding possible fraud in the labelling. Here, there is an even greater need for research because there is a lack of suitable methodologies to deal with regulations and labelling. Among them, isotope signatures analysed by isotope ratio mass spectrometry have been used for certain foodstuffs such as wines (official EU method) and water (being developed in an R&D EU project, TRACE), but not fish. Molecular techniques using microsatellites for differentiating between fish stocks (Pardo and Estonba 2006) seem to be a very promising methodology.

468


Nevertheless, some of the most important traceable data are related to fish safety and quality parameters, including: z z z z

volatile amines as criteria for chemical quality assessment; histamine and biogenic amine analysis; microbiological and sensory quality assessment methods; and biotoxins, viruses and other biological hazards.

For some of them, official methods exist, although most are time consuming and quite slow, showing results that could be used to make decisions along the fish chain. In general, some specific sensors and probes have recently appeared, but there is a lack of validation, and reference methods are needed to see the possibility of having them as rapid quality control tools for the fish industry. As the development of new, faster and simpler methods continues, identity, safety and quality parameters will be more frequently controlled, contributing to a safer and more reliable fish distribution chain. The establishment of a validated system for traceability management, including establishing standards for analysing relevant traceability parameters and inspection procedures in each link of the fish chain, is of primary importance in these circumstances.

22.5

Traceability in a global perspective

Food products are an integral part of the global trading system. Food is an international commodity: in the past 25 years the cost of transporting it from one end of the world to the other has fallen because of the increased use of containerisation. It costs as much to send a 20-foot freezer container from Europe to China as it does to send the same container from Northern Europe to Italy. At the borders, the World Trade Organization has been responsible for reducing previous obstacles in the form of import–export customs declaration papers and health certificates, the main function of which was to prevent imports that could compete with nationally produced products. The world is not yet free of these artificial barriers, but the major trading organisations such as the EU and the North American Free Trade Agreement (NAFTA: http://www.nafta-sec-alena.org/) are creating free-trade areas. This development puts more pressure on the need for a reliable traceability system for two reasons: first, for reasons of food safety so an effective recall procedure can be operational; secondly, because of the growing threat from terrorist activities, this demands a 100% certainty of the origin of the products in question. One of the best definitions of globalisation is given by the OECD: ‘a dynamic and multidimensional process of economic integration whereby national resources become more and more internationally mobile while economies become increasingly interdependent’ (OECD 2005). The global transportation of foods can be a risky business for the transporter. Traditionally the transporter guarantees that nothing untoward will happen to the load or cargo. Today, live fish and shellfish are transported in containers, both by plane and by cargo vessel. This demands much more management than transporting at a specific temperature in the container. The humidity and/or water flow must be managed very accurately, and the facilities in the port of destination must be able to support this kind of transport.


5,000,000

Consumers

5000

Retailers Outlets Buying

6

salers

Processing Fishermen

600

desks

Whole-

industry and

469

aquaculture

500 2000 4000

Figure 22.6 The supply chain funnel in Denmark, illustrating the concentration of power over the supply chain with seafood. The number people are estimated in each segment.

Nowadays, the trend is for fish caught in, for example, Europe to be transported to Southeast Asia or China for processing and then brought back to the European market. This demands not only a strict quality control, but also a well-functioning traceability system, where the individual batches can be easily recognised. The reason for this long transportation is not only a matter of saving on labour costs. The indirect reason is that the rationalisation and mechanisation of the fish-processing industry cannot keep up with the capability of developing countries to produce products with a higher yield and individual adaptation. On the other hand, several transportation links in the entire chain from the fisherman to the consumer are more the rule than the exception. So the total transportation time now takes 6 months in a reefer container instead of a total of 6 days in a refrigerated lorry. However, the traceability requirements are the same. If the same international standard is used for example a 128 barcode labelling and the TraceFish standard including a lot number, the level of traceability can just be as high or even higher than for fish sold the very day after it is caught. It is a myth that fish caught by an inshore fisherman is of a higher quality and a better traceability standard than fish caught in bigger quantities and produced in large fish-processing factories. There is an continuing discussion about which of the links is the most powerful. This is setting the agenda for the whole sector. Figure 22.6 clearly illustrates that the retail link is very important – in 2006 only six people at the buying desk of the major multi-retailers were taking decisions on what to buy. In future, perhaps only five big multiple retail chains will dominate Europe. Their influence will be significant. At present, everybody is on the threshold of introducing traceability based on radio frequency identification (RFID) tag technology. The biggest American multiple chain, Wal-Mart, has just postponed the introduction of RFID tags for the second time. It is striking that a low-price multiple chain such as Wal-Mart wants to introduce this technology, but a short look at the history of the company shows that its success has been based on non-food goods coming from the Third World. It has thereby developed a very strong management system to deal with imports. That has created a very good logistical system that the company now wants to apply to the food sector of its shops. In Europe, one of the biggest

470


multiple chains, Carrefour in France, has adopted the strategy that every supplier must put its information on traceability into the format of the Trace-One system, and Carrefour must have access to all the information. It is difficult to say how traceability will find its place among all the other documentation systems. However, a good estimate is that it will be integrated within safety systems. Traceability is on the one hand a very old business, and on the other a very young system that needs to mature and find its own place in the overall management system.

22.6

References

Derrick, S. and Dillon, M. (2004) A Guide to Traceability within the Fish Industry EUROFISH and Swiss Import Promotion Programme (SIPPO), Copenhagen. EC (European Commission) 1996 Regulation No. 2406/96 of the European Parliament and of the Council of 26 November 1996. Laying down common marketing standards for certain fishery products, Official Journal, No. L334, pp. 1–14. EC (Council Regulation) 2000 Regulation No 104/2000 of 17 December 1999. On the common organisation of the markets in fishery and aquaculture products. Official Journal L 017, 21/01/2000, pp. 22–52. EC (European Commission) 2002 Regulation No. 178/2002 of the European Parliament and of the Council of 28 January 2002. Laying down the general principles and requirements of food law, establishing the European procedures in matters of food safety. Official Journal L 031, 01/02/2002, pp. 1–24. Frederiksen, M., Østerberg, C., Silberg, S., Larsen, E. and Bremner, H.A. (2002) Info-fisk. Development and validation of an Internet based traceability system in a Danish domestic fresh fish chain. Journal of Aquatic Food Product Technology 11: 13–34. Frederiksen, M., Larsen, E. and Børresen, T. (2004) European traceability legislation in the context of seafood. In: The International Review of Food Science and Technology. International Union of Food Science and Technology (IUFoST), Sovereign Publications Limited, London, UK, pp. 38–41. Gouveia, R. (2007) The Consumer co-operatives’ perspective. In Trace 3rd Annual Meeting, Crete, Greece. www.trace.eu.org/je/greece/meeting/trace_m3_gouveia.php. Hansen, H.E. and Fischer, K. (2003) Demand for documentation of freshness of loose fresh fish. In: J.B. Luten, J. Oehlenschhläger, and G. Ólafsdóttir (Eds) Quality of Fish from Catch to Consumer. Wageningen Academic Publishers, Wageningen, The Netherlands, pp. 361–365. ISO (International Organization for Standardization) (2000) Quality Management Systems – Fundamentals and Vocabulary. European Standard (EN ISO 9000:2000, Pint 3.5.4) Committee for Standardisation, Brussels, Belgium. Krott, W. (2003) PEFA: Selling fish on the Internet across Europe – bridge between suppliers and remote demand for fresh fish. In: J.B. Luten, J. Oehlenschhläger, and G. Ólafsdóttir (Eds) Quality of Fish from Catch to Consumer. Wageningen Academic Publishers, Wageningen, The Netherlands, pp. 165–173. Larsen, E. (2003) Traceability in fish processing. In: M. Lees (Ed.) Food Authenticity and Traceability. Woodhead Publishing Limited, Cambridge, UK, pp. 507–517. Larsen, E. and Olesen, E. (2002) Life cycle analysis on farmed fish. Network of LCA in the Nordic Fish Sector, Nordic Council of Ministers, final workshop, Roskilde, Denmark. Martínez, I., James, D. and Loréal, H. (2005) Application of modern analytical techniques to ensure seafood safety and authenticity. FAO Technical Paper No. 455. OECD (2005) Measuring Globalisation: OECD Economic Globalisation Indicators. OECD, Paris, France, pp. 198.


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Pardo, M.A. (2005) Report on the methods to authenticate seafood products. SEAFOODplus-VALID internal report. Pardo, M.A. and Estonba, E. (2006) Authentication of the European anchovy from the Biscay Bay: an approach based on the use of molecular techniques. In: Proceedings of the 2006 TAFT-WEFTA Meeting. Pérez Villarreal, B. and Pozo, R. (1990) Chemical composition and ice spoilage of albacore (Thunnus alalunga). Journal of Food Science 55: 678–682. The Pillsbury Company (1973) ‘Food Safety Through the Hazard Analysis and Critical Control Point System’, Contract No. FDA 72–59, Research and Development Dept., The Pillsbury Company, Minneapolis, USA. Sveinsdottir, K., Hyldig, G., Martinsdottir, E., Jørgensen, B.M. and Kristbergsson, K. (2003) Quality index method (QIM) scheme developed for farmed Atlantic salmon (Salmo salar). Food Quality and Preference 14: 237–245. Thompson, M., Sylvia, G. and Morrissey, M.T. (2005) Seafood traceability in the Unites States: current trends, system design, and potential applications. Comprehensive Reviews in Food Science and Food Safety 1: 1–5.

Index

Acinetobacter spp, 319 adenosine triphosphate, 16, 72, 263 Aeromonas spp, 332, 334 alcohols, 112 actin, 184 actomyosin, 69 alkanes, 114 allergens, 357 amines, 112 amine decarboxylase activity, 44 amination, 42 amino acids, 32 decarboxylation, 17, 42 ammonia, 22 anisopropic model, 242 antifreeze glycoproteins, 189 antioxidant, 13 appearance, 412 aquaculture, 1, 2, 132 production, 2 artificial neural network, 278 artificial quality index (AQI), 131, 230, 240 astaxanthin, 134, 397 Atlantic cod, 16, 137 Atlantic salmon, 132, 135–136 QIM scheme, 432 authentication, 98, 263 DNA based methods, 363–387 protein based methods, 349–362 autolysis, 69 Bacillus cereus, 332 bacteria, 17 heat labile, 323 hydrogen sulphide producing, 321, 323, 325 lactic acid, 330 mesophilic, 321, 323 472

pathogenic, 332–336 psychrotrophic, 321 basic local alignment search tools (BLAST), 371 bilinear modelling, 446–451 biogenic amines, 42–67, 468 chemical quality index, 50 metabolism, 43 regulatory issues, 54 bony fish, 4 Brochothrix thermaspacta, 319, 332 bruises, 100 cadaverine, 42 calibration substances, 178 calorimeter, 174–178 cantaxanthin, 134, 153, 398 capillary electrophoresis, 59, 352 zone, 59 carbonyls, 112 Carnobacterium, 330, 332 carotenoids, 132, 397 cartilaginous tissue, 4 caviar, 203, 355 chemical composition, 92 chemical sensors, 105 chemiluminescence, 80 chilled storage, 111, 195, 279 Clostridium botulinum, 332–333 Clostridium perfrigens, 45, 332 chewiness, 214 chondrichthyes, 4 chroma, 128 CIE system, 127 CIELAB space, 127 clupeidae, 54 coagulation test, 358

Index Codex guidelines for sensory evaluation, 415 coherence, 243 collagen, 186 colorimeter, 130 colorimetric techniques, 108 colour measurement, 127 composition, 258 feed, 259 compression test, 222 connective tissue, 4, 10, 186, 214 consumer studies, 421 consumer tests, 440 Coryphaenidae, 54 CO-treatment, 201–203 creatine, 10 phosphate, 16 Creep test, 228 crustacean species, 1 cryoprotectants, 149–150 dark musculature, 4 data analysis, 91–92 data fusion, 116 data processing, 257 degree of difference, 427 denaturation, 180 deoxyribonucleic acid (DNA), 363 content, 364–366 databases, 379 isolation, 368 mitochondrial, 365 nuclear, 365 Descriptive sensory analysis (QDA), 438 deterioration, 259 developing countries, 1 dielectric measurements, 275 difference from control test, 427 difference test, 425 differential scanning calorimetry (DSC), 173 curves, 177, 187, 196, 200, 202 dimethyl trisulphide, 114 dimethylamine, 22, 25–26, 114 docosahexenoic acid, 10 double-frozen fish block, 150, 282 drip loss, 71 eicosapentaenoic acid, 10 electrical properties, 286–300 conductivity, 287 resistance, 286

473

electronic auction, 462 electronic nose, 105 electronic tongue, 105 Engraulidae, 54 Enterobacteriaceae, 319, 329 Enterococcus spp, 319, 330 enthalpy, 173 enzyme-linked immunosorbent assay (ELISA), 56, 357 enzymes, 16 Escherichia coli, 44, 332 EU quality grading scheme, 428 exon, 366 eye fluids, 33 FAO, 1 fat content, 3 fat determination, 93 fat soluble vitamins, 11, 13 fatty acids, 264 authenticity, 264, 389 positional distribution, 389 profiles, 392 fatty fish species, 3 firmness, 214, 246–248 Fischtester, 286, 294–296 fish, 4 autolysis, 42 captured, 1–2 chilled, 114 decomposition, 51 farmed, 310, 396 fillet, 4 freshwater, 3 geographic origin, 398–403 marine, 3 meal, 3 microflora, 50 mince, 139 muscle, 4, 349 odour, 109–111 oil, 95, 264, 401 organically farmed, 401 proteins, 349 protein hydrolysate, 203 roe, 13, 203 species, 1 Fish Barcode of Life Initiative (FISH-BOL), 380 flavour, 412

474

Index

food-deficient countries, 1 forensically informative nucleotide sequencing (FINS), 370 formaldehyde, 17, 24 freezing/thawing, 254, 294, 359 freshness, 79, 96, 114, 116, 246, 418 frozen storage, 144, 180, 197, 281, 308 gas chromatography. 56 gelation, 188 gels, 143 geosmin, 430 glass transition, 191 glycogen, 68 glycolysis, 16 glycoproteins, 189 grading schemes, 427–430 Gram-negative, 17 groundfish, 4 HACCP, 51, 420, 461 Hafnia alvei, 319 hardness, 214 headspace, 107 heating temperature, 357–359 heat stability, 179 high-resolution NMR, 259–260 histamine determination, 55, 57–58 histamine, 42 fish poisoning, 48 methods of analysis, 57 rapid methods, 58 High Performance Liquid Chromatography (HPLC), 56, 356 high-pressure processing, 156–159, 193 histidine, 17 H NMR imaging of fish, 253 hue, 128 Hunter space, 127 hydrocolloids, 141 hydrolysis, 32 hypoxanthin, 16, 72 indole, 31 image analysis, 244–247 image processing, 240 immunoassay, 357 inosine monophosphate, 26, 73 intron, 366 internal transcribed spacer (ITS), 366

invertebrates, 183 iodine, 15 irradiation, 149 isinglass, 186 isoelectric focusing (IEF), 352 native, 352 urea, 353 isotope ratio mass spectrometry (IRMS), 400 Klebsiella pneumoniae, 44 Kramer-test, 216 k-value, 68, 75 lactic acid, 68 bacteria, 319, 330 Lactobacillus spp, 319, 330 Lactococcus spp, 319 lean fish species, 3 light musculature, 4 lightness, 128 lipids, 10, 259–262 authentication, 388 profiles, 263 oxidation, 203, 262 profiles, 263 lipolysis, 17 Listeria monocytogenes, 322 low-field NMR, 257, 259 magnetic field, 252 magnetic resonance imaging (MRI), 253, 255 SPRITE technique, 256 marine fish, 4 medium fatty species, 3 metabolism, 16 metabolites, 262 phosphocreatine, 263 phosphate, 263 methylamines, 23 2-methylisoborneol, 430 microarrays, 378 microfurnaces, 178 microwave dielectric spectrum, 274 microbiological methods, 318–348, 468 aerobic plate count, 323 detection media, 321 direct plating, 320 incubation conditions, 320 molecular techniques, 336–338 most probable number, 336

Index

475

surface count technique, 320 total viable count, 323 microorganisms, 42 microsatellites, 366 minerals, 15 mitochondrial genes, 367 modified atmosphere packaging, 78, 145–148 moisture determination, 92 molluscan species, 1 Moraxella spp, 319 Morganella morganii, 44 Morganella psychrotolerans,, 45 multivariate data analysis, 91, 278, 413, 444–457 myofibrillar proteins, 180, 214 myoglobin, 201 myosin, 184 light chains, 350

31 P NMR, 262 polyamines, 42 Polymerase Chain Reaction (PCR), 336 quantitative, 378 species-specific primers, 369 universal primers, 369 Pomatomidae, 54 post mortem changes, 15–17, 69–76 Principal Component Analysis (PCA), 392–395, 445 cross validation, 451 loading plot, 450 score plot, 449 proteolysis, 16, 32, 70 proteome, 301 Pseudomonas spp, 44, 319, 325 puncture test, 219 putrescine, 42

23 Na imaging, 255 Natural Colour System (NCS), 132 nematode detection, 99 near infrared (NIR) reflectance spectroscopy. 92, 95 NIR spectroscopy, 87, 95 non-protein-nitrogen, 10 nuclear magnetic resonance (NMR), 252–253 nucleotide, 68–69 degradation, 72 nutritional composition, 4–9, 308

quality, 34 assessment, 49 index method (QIM), 116, 145, 242, 419, 430–438

odour, 412 off-flavours, 430 olfaction, 105–106 oscillatory test, 229 osteichthyes, 4 paired comparison test, 426 paramyosin, 184 partial least square model, 117–119 parasites, 99 parvalbumins, 350 pattern analysis, 242 pelagic fish, 4 β-phenylethylamine, 42 Photobacterium phosphoreum, 45, 319, 331 pH value, 33 phospholipids, 391 pI value, 352 pigmentation, 133

rainbow trout, 132 random amplified polymorphic DNA analysis (RAPD), 377 ranking, 426 receptor, 105 reducing substances, 30 refractive index, 33, 100 refrigerated storage, 144 relaxation curve, 258 restriction-fragment length polymorphism analysis (RFLP), 371 -SSCP, 377 ribonucleic acid (RNA), 366 ribosomal, 366 ribotyping, 331 rigor mortis, 16, 70, 215 safety, 47, 98 Salmonella spp, 335 salting, 199 salt distribution, 255 sarcoplasmic proteins, 184 scombridae, 54 sea bream, 137 seafood, 425 selenium, 15

476

Index

sensors, 105, 240 metal-oxide chemoresistor, 114 MOSFET, 114 amperometric, 115 conducting polymer, 115 quartz microbalance, 115 sensory analysis, 411–424 sensory attributes, 19 sensory characteristics, 413 sensory descriptors, 439 sensory evaluation, 34, 79, 411 consumer studies, 418, 421, 440 facilities, 415 methods, 425–443 product development, 418 research, 418 quality control, 308, 418 sample preparation, 416–417 sensory panel, 412 senses, 127, 412 Serratia spp, 319 shark, 185–186 shelf life, 149, 419, 431, 435 Shewanella putrefaciens, 53, 319, 324, 329 Shigella spp, 332 shortening of fillets, 16, 71 shrimps, 367, 396 single-strand conformation polymorphism and analysis (SSCP), 371–373 site-specific natural isotope fractionation analysis by NMR (SNIF-NMR), 400 smoking, 201 sodium dodecylsulphate polyacrylamide gel electrophoresis (SDS PAGE), 354–356 species differentiation, 373–377 flatfishes, 374 gadoids, 374 groupers, 375 hakes, 374 molluscs, 376 salmonids, 375 sharks, 376 shrimps, 367 snappers, 375 sturgeons, 375 tunas and bonitos, 373 Specific Spoilage Organisms (SSO), 325–328 spermidine, 42 spermine, 42 spin, 252

spoilage, 19, 24–25, 69 bacteria, 324 cod fillets, 111 microbial, 110 odours, 112 squid, 184 stable isotopes, 388, 399–402 stale, 112 Staphylococcus spp, 319, 332 stiffness, 220 storage time, 229–231 stress, 310 stress relaxation test, 226 structure tensor, 243 sulphur compounds, 112, 114 supply chain, 459 surimi, 139–144, 159, 180, 189 taint, 429 tarama, 372 taste, 412 tension analysis, 221 texture, 214, 412 measurement, 214 profile analysis (TPA), 224 thaw rigor, 71 thermal processing, 98, 152 thin-layer chromatography, 56 thiobarbituric acid, 49 Time Domain Spectroscopy (TDI), 273–285 time domain reflectrometry, 275–277 TMA-index, 27 Torrymeter, 287–294 Torry scheme, 428 total volatile basic nitrogen (TVB-N), 17, 19, 20–21, 114 ammonia, 22 distillation, 21 traceability, 458–471 external, 459 internal, 459 validation, 466 verification, 466 trace elements, 388 signatures, 402 traditional methods, 19–39 training, 413 transamination, 42 transflection measurement, 90, 96 transmission measurement, 90, 94

Index transmittance measurement, 90 triangle test, 426 trimethylamine, 22, 24–25, 114 index, 27 oxide, 17, 114 tristimulus, 128 Two-Dimensional Gel Electrophoresis (2DE), 301–317 authenticity, 305 gel staining, 303 image and data analysis, 303 methodology, 302 post mortem metabolism, 306 umami, 72 urea, 10, 353 uric acid, 72 urocanic acid, 50

Vibrio spp, 332, 334 Vibrionaceae, 319 VIS spectroscopy, 87 viscoelastic methods, 226 vitamins, 10–13 volatile acids, 29–30 volatile amines, 468 volatile compounds, 105, 109–110 wavelength, 90, 244 Warner-Bratzler test, 218 water holding capacity, 258 water soluble vitamins, 11, 14 whiteness, 139 world fishery production, 1–2 xanthine, 72

477

Fishery Products: Quality, Safety and Authenticity

Beverage quality and safety

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Assisted Reproductive Technologies Quality and Safety: Quality and Safety

Handbook for agricultural and fishery products import regulations 2009

Biodiesel- Quality, Emissions and By-Products

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Beer: quality, safety and nutritional aspects

Handbook of Organic Food Safety and Quality

Handbook of Organic Food Safety and Quality

Reliability, quality, and safety for engineers

Safety and Quality Issues in Fish Processing

Reliability, Quality, and Safety for Engineers

Food Chain Integrity: A Holistic Approach to Food Traceability, Safety, Quality, and Authenticity

Microbiological Safety and Quality of Food

Reliability, Quality, and Safety for Engineers

Improving the Safety and Quality of Eggs and Egg Products: Volume 2: Egg safety and nutritional quality (Food Science, Technology and Nutrition)

Food authenticity and traceability

Evaluation of Herbal Medicinal Products: Perspectives on Quality, Safety and Efficacy

Handbook of Indices of Food Quality and Authenticity

Romanticism, Sincerity and Authenticity

Safety and Reliability of Industrial Products, Systems and Structures

Guidelines for Integrating Process Safety Management, Environment, Safety, Health, and Quality (Center for Chemical Process Safety)

The SAGES Manual of Quality, Outcomes and Patient Safety

Case Studies in Food Microbiology for Food Safety and Quality

Irradiation to Ensure the Safety and Quality of Prepared Meals

Handbook of Seafood Quality, Safety and Health Applications

Improving the Safety and Quality of Milk: Volume Two, Improving Quality in Milk Products (Woodhead Publishing Series in Food Science, Technology and Nutrition)

Guidelines for Integrating Process Safety Management, Environment, Safety, Health, and Quality (Center for Chemical Process Safety (Ccps).)

Safety

The Ethics of Authenticity

Fishery Products: Quality, Safety and Authenticity

Beverage quality and safety

Beverage Quality and Safety

Assisted Reproductive Technologies Quality and Safety: Quality and Safety

Handbook for agricultural and fishery products import regulations 2009

Biodiesel- Quality, Emissions and By-Products

Improving Farmed Fish Quality and Safety

Beer: quality, safety and nutritional aspects

Handbook of Organic Food Safety and Quality

Handbook of Organic Food Safety and Quality

Reliability, quality, and safety for engineers

Safety and Quality Issues in Fish Processing

Reliability, Quality, and Safety for Engineers

Food Chain Integrity: A Holistic Approach to Food Traceability, Safety, Quality, and Authenticity

Microbiological Safety and Quality of Food

Reliability, Quality, and Safety for Engineers

Improving the Safety and Quality of Eggs and Egg Products: Volume 2: Egg safety and nutritional quality (Food Science, Technology and Nutrition)

Food authenticity and traceability

Evaluation of Herbal Medicinal Products: Perspectives on Quality, Safety and Efficacy

Handbook of Indices of Food Quality and Authenticity

Romanticism, Sincerity and Authenticity

Safety and Reliability of Industrial Products, Systems and Structures

Guidelines for Integrating Process Safety Management, Environment, Safety, Health, and Quality (Center for Chemical Process Safety)

The SAGES Manual of Quality, Outcomes and Patient Safety

Case Studies in Food Microbiology for Food Safety and Quality

Irradiation to Ensure the Safety and Quality of Prepared Meals

Handbook of Seafood Quality, Safety and Health Applications

Improving the Safety and Quality of Milk: Volume Two, Improving Quality in Milk Products (Woodhead Publishing Series in Food Science, Technology and Nutrition)

Guidelines for Integrating Process Safety Management, Environment, Safety, Health, and Quality (Center for Chemical Process Safety (Ccps).)

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