Advances in Food and Nutrition Research, Volume 61 (Advances in Food & Nutrition Research)

Advances in FOOD AND NUTRITION RESEARCH VOLUME 61 ADVISORY BOARDS KEN BUCKLE University of New South Wales, Austral...

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Advances in

FOOD AND NUTRITION RESEARCH VOLUME

61

ADVISORY BOARDS KEN BUCKLE University of New South Wales, Australia

MARY ELLEN CAMIRE University of Maine, USA

ROGER CLEMENS University of Southern California, USA

HILDEGARDE HEYMANN University of California, Davis, USA

ROBERT HUTKINS University of Nebraska, USA

RONALD JACKSON Quebec, Canada

HUUB LELIEVELD Global Harmonization Initiative, The Netherlands

DARYL B. LUND University of Wisconsin, USA

CONNIE WEAVER Purdue University, USA

RONALD WROLSTAD Oregon State University, USA

SERIES EDITORS GEORGE F. STEWART

(1948–1982)

EMIL M. MRAK

(1948–1987)

C. O. CHICHESTER

(1959–1988)

BERNARD S. SCHWEIGERT (1984–1988) JOHN E. KINSELLA

(1989–1993)

STEVE L. TAYLOR

(1995–

)

Advances in

FOOD AND NUTRITION RESEARCH VOLUME

61 Edited by

STEVE L. TAYLOR University of Nebraska, Lincoln

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Academic Press is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 32 Jamestown Road, London NW1 7BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Linacre House, Jordan Hill, Oxford OX2 8DP, UK First edition 2010 Copyright # 2010 Elsevier Inc. 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 without the prior written permission of the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material. Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made. ISBN: 978-0-12-374468-5 ISSN: 1043-4526 For information on all Academic Press publications visit our website at elsevierdirect.com Printed and bound in USA 10 11 12 10 9 8 7 6 5 4 3 2 1

CONTENTS

Contributors

1. Visual Perception of Effervescence in Champagne and Other Sparkling Beverages

vii

1

Ge´rard Liger-Belair I. II. III. IV. V.

Introduction Within a Champagne Bottle The Bubble Nucleation Process During the Bubble Rise CO2 Volume Fluxes Outgassing from Champagne Glasses in Tasting Conditions VI. Close-Up on Bubbles Bursting at the Liquid Surface Acknowledgments References

2. Chemometric Brains for Artificial Tongues

2 3 12 27 34 43 53 53

57

Paolo Oliveri, M. Chiara Casolino, and Michele Forina I. Introduction II. Terminology III. History IV. Main Application Sectors V. Analytical Techniques VI. Chemometrics VII. Artificial Tongue Applications in the Food Science VIII. Conclusions References

58 61 62 63 66 69 98 108 109

3. Photodynamic Treatment: A New Efficient Alternative for Surface Sanitation

119

Lubov Brovko I. II. III. IV.

Introduction Interaction of Light with Matter and History of PDT Mechanisms of Photodynamic Production of Cytotoxic Species Mechanisms of Photodynamic Killing of Bacteria and Viruses

120 121 124 126

v

vi

Contents

V. Examples of Photoactive Dyes used for Photodynamic Killing of Microorganisms VI. PDT for Environmental Cleaning and Disinfection VII. Conclusions References

4. Microoxidation in Wine Production

133 138 143 144

149

Paul A. Kilmartin I. Introduction II. Microoxygenation in Industry III. Oxidation Processes in Wine IV. Microoxygenation Research Findings V. Final Comments References

5. The Morama Bean (Tylosema esculentum): A Potential Crop for Southern Africa

150 151 154 159 181 182

187

Jose C. Jackson, Kwaku G. Duodu, Mette Holse, Margarida D. Lima de Faria, Danie Jordaan, Walter Chingwaru, Aase Hansen, Avrelija Cencic, Martha Kandawa-Schultz, Selalelo M. Mpotokwane, Percy Chimwamurombe, Henrietta L. de Kock, and Amanda Minnaar I. Introduction II. Agronomic Characteristics III. Chemistry, Nutritional, and Health Potential IV. Food-Processing Applications and Utilization V. Potential Marketing Strategies for Morama-Processed Products VI. Socio-Economic Analysis of Communities Where Morama is Found VII. Challenges and Future Research References Index

189 190 195 213 223 230 238 239 247

CONTRIBUTORS Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Lubov Brovko

Canadian Research Institute for Food Safety, University of Guelph, Guelph, Ontario, Canada (119) M. Chiara Casolino

Department of Drug and Food Chemistry and Technology, University of Genoa, Genoa, Italy (57) Avrelija Cencic

Department of Microbiology, Biochemistry, Molecular Biology and Biotechnology, University of Maribor, Hocˇe, Slovenia (187) Walter Chingwaru

Department of Microbiology, Biochemistry, Molecular Biology and Biotechnology, University of Maribor, Hocˇe, Slovenia (187) Percy Chimwamurombe

Department of Biochemistry and Chemistry, and Department of Biological Sciences, University of Namibia, Windhoek, Namibia (187) Margarida D. Lima de Faria

Department of Human Sciences, Programme for Global Development, Tropical Research Institute–IICT, Rua da Junqueira, Lisbon, Portugal (187) Henrietta L. de Kock

Department of Food Science, University of Pretoria, Pretoria, South Africa (187) Kwaku G. Duodu

Department of Food Science, University of Pretoria, Pretoria, South Africa (187) Michele Forina

Department of Drug and Food Chemistry and Technology, University of Genoa, Genoa, Italy (57) Aase Hansen

Department of Food Science, Quality and Technology Section, University of Copenhagen, Frederiksberg C, Denmark (187)

vii

viii

Contributors

Mette Holse

Department of Food Science, Quality and Technology Section, University of Copenhagen, Frederiksberg C, Denmark (187) Jose C. Jackson

Centre for Scientific Research, Indigenous Knowledge and Innovation (CESRIKI), University of Botswana, Botswana (187) Danie Jordaan

Market Matters Inc., South Africa, Ithaca, New York, USA (187) Martha Kandawa-Schultz

Department of Biochemistry and Chemistry, and Department of Biological Sciences, University of Namibia, Windhoek, Namibia (187) Paul A. Kilmartin

Wine Science Programme, The University of Auckland, Auckland, New Zealand (149) Ge´rard Liger-Belair

Groupe de Spectrome´trie Molećulaire et Atmosphe´rique, UMR CNRS 6089, UFR Sciences Exactes et Naturelles, BP 1039, 51687 Reims Cedex 2; Laboratoire d’Œnologie et Chimie Appliqueé, Unite´ de Recherche sur la Vigne et le Vin de Champagne (URVVC), Universite´ de Reims Champagne-Ardenne, Reims Cedex 2, France (1) Amanda Minnaar

Department of Food Science, University of Pretoria, Pretoria, South Africa (187) Selalelo M. Mpotokwane

National Food Technology Research Centre, Kanye, Botswana (187) Paolo Oliveri

Department of Drug and Food Chemistry and Technology, University of Genoa, Genoa, Italy (57)

CHAPTER

1 Visual Perception of Effervescence in Champagne and Other Sparkling Beverages Ge´rard Liger-Belair*,†,1

Contents

I. Introduction II. Within a Champagne Bottle A. Where do CO2 molecules dissolved in champagne come from? B. The pressure under the cork C. The chemical composition of champagne D. Uncorking the bottle III. The Bubble Nucleation Process A. A critical radius required for bubble nucleation B. ‘‘Natural’’ bubble nucleation C. Entrapping an air pocket within a fiber D. Modeling the repetitive bubble nucleation from a cellulose fiber E. Evidence for bubbling instabilities F. ‘‘Artificial’’ bubble nucleation IV. During the Bubble Rise A. Bubble growth B. Average bubble size V. CO2 Volume Fluxes Outgassing from Champagne Glasses in Tasting Conditions A. Flute versus coupe B. The role of temperature

2 3 3 6 8 9 12 12 13 14 18 23 27 27 27 31 34 34 42

* Groupe de Spectrome´trie Molećulaire et Atmosphe´rique, UMR CNRS 6089, UFR Sciences Exactes et {

1

Naturelles, BP 1039, 51687 Reims Cedex 2, France Laboratoire d’Œnologie et Chimie Appliqueé, Unite´ de Recherche sur la Vigne et le Vin de Champagne (URVVC), Universite´ de Reims Champagne-Ardenne, Reims Cedex 2, France Corresponding author: Ge´rard Liger-Belair, E-mail address: [email protected]

Advances in Food and Nutrition Research, Volume 61 ISSN 1043-4526, DOI: 10.1016/S1043-4526(10)61001-7

#

2010 Elsevier Inc. All rights reserved.

1

2

Ge´rard Liger-Belair

VI. Close-Up on Bubbles Bursting at the Liquid Surface A. The bursting process as frozen by high-speed photography B. A paternoster for surface active molecules C. When champagne bubbles dress up like flowers D. Avalanches of bursting events in the bubble raft? Acknowledgments References

Abstract

43 43 45 47 51 53 53

The so-called effervescence process, which enlivens champagne, sparkling wines, beers, and carbonated beverages in general, is the result of the fine interplay between CO2-dissolved gas molecules, tiny air pockets trapped within microscopic particles during the pouring process, and some liquid properties. This chapter summarizes recent advances obtained during the last decade concerning the physicochemical processes behind the nucleation, rise, and burst of bubbles found in glasses poured with sparkling beverages. Those phenomena observed in close-up through high-speed photography are often visually appealing. Moreover, the kinetics of gas discharging from freshly poured glasses was monitored with time, whether champagne is served into a flute or into a coupe. The role of temperature was also examined. We hope that your enjoyment of champagne will be enhanced after reading this fully illustrated review dedicated to the deep beauties of nature often hidden behind many everyday phenomena.

I. INTRODUCTION From a strictly chemical point of view, Champagne and other sparkling wines are multicomponent hydroalcoholic systems supersaturated with CO2-dissolved gas molecules, formed during the second, alcoholic, fermentation process (Liger-Belair, 2002, 2003, 2004). As soon as a bottle of champagne or sparkling wine is uncorked, the progressive release of CO2-dissolved gas molecules is responsible for bubble formation, the so-called effervescence process. It is worth noting that approximately 5 L of CO2 must escape from a typical 0.75 L champagne bottle. To get an idea of how many bubbles are potentially involved all along the degassing process from this single bottle, we can divide this volume of CO2 to be released by the average volume of a typical bubble of 0.5 mm in diameter. A huge number close to 108 is found. Actually, champagne and other sparkling wine tasting mainly differs from still noneffervescent wine tasting due to the presence of those myriad of bubbles continuously

Visual Perception of Effervescence in Champagne and Other Sparkling Beverages

3

B

A

FIGURE 1.1 Photograph of a typical flute poured with champagne (A), and close-up on particles acting as bubble nucleation sites freely floating in the bulk of the flute, thus creating lovely bubble trains in motion in the champagne bulk (B) (#Alain Cornu/ Collection CIVC).

rising through the liquid medium. This is the reason why considerable efforts have been conducted in the last few years in order to better illustrate, detect, understand, and finally control each and every parameter involved in the bubbling process. Without bubbles, champagne and other sparkling wines would be unrecognizable as such (see Fig. 1.1), but the role of effervescence goes far beyond the sole esthetical point of view. This chapter covers recent progress in the field of champagne science.

II. WITHIN A CHAMPAGNE BOTTLE A. Where do CO2 molecules dissolved in champagne come from? The modern production of champagne is not so far removed from that empirically developed by the Benedictine monk Dom Pierre Pe´rignon in the late seventeenth century. This method is also used outside

4


the Champagne region. Sparkling wines produced as such are labeled me´thode traditionelle. Indeed, most American and Australian sparkling winemakers use this method to elaborate their own sparkling wines. This method involves several distinct steps.

1. A first alcoholic fermentation Three grape cultivars are grown in the 75,000 acres of the Champagne vineyards: Chardonnay (a white grape), Pinot Meunier, and Pinot Noir (both dark grapes). Usually around mid-september, the grapes harvested from these vineyards are pressed to make a juice, called ‘‘the grape must.’’ After pressing, the must is transferred into an open vat where yeast (Saccharomyces cerevisiae) is added. Generally speaking, the key metabolic process during winemaking is alcoholic fermentation: the conversion of sugars into ethanol and carbon dioxide by yeast. The process of fermentation was first scientifically described by the French chemist JosephLouis Gay Lussac, in 1810, when he demonstrated that glucose is the basic starting block for producing ethanol: C6 H12 O6 ! 2CH3 CH2 OH þ 2CO2

(1)

The manner in which yeast contributes to the fermentation process was not clearly understood until 1857, when the French microbiologist Louis Pasteur discovered that not only does the fermentation process require any oxygen, but also alcohol yield is actually reduced by its presence. The amount of ethanol generated by this first alcoholic fermentation is about 11%. At this step, ‘‘champagne’’ is still actually a noneffervescent white wine, because the carbon dioxide produced during the first alcoholic fermentation is allowed to escape into the atmosphere.

2. The art of blending Because it is rare that a single wine of a single vintage from a single vineyard and grape variety will provide the perfect balance of flavor, sugar level, and acidity necessary for making a fine champagne, wine makers will often mix several different still wines. This is called the assemblage (or blending) step, and it is carried out directly after the first alcoholic fermentation is complete. Blending is considered a key step in the art of champagne-making. A cellar master will sometimes blend up to 80 different wines from various grape varieties, vineyards, and vintages to produce one champagne. The blending of still wines originally made from the three grape cultivars forms a base wine, which will then undergo a second fermentation—the key step in producing the ‘‘sparkle’’ in champagne and other sparkling wines.


5

3. The prise de mousse: A second alcoholic fermentation Once the base wine is created, sugar (about 24 g/L) and yeast are added. The entire concoction is put into thick-walled glass bottles and sealed with caps. The bottles are then placed in a cool cellar (12–14 C), and the wine is allowed to slowly ferment for a second time, producing alcohol and carbon dioxide again. Actually, during this second fermentation process which occurs in cool cellars, the bottles are sealed, so that the CO2 molecules cannot escape and progressively dissolve into the wine. Therefore, CO2-dissolved molecules into the wine and gaseous CO2 molecules under the cork progressively establish equilibrium—an application of Henry’s law which states that the partial pressure of a given gas above a solution is proportional to the concentration of the gas dissolved into the solution, as expressed by the following relation: c ¼ kH PCO2

(2)

where c is the concentration of dissolved CO2 molecules, PCO2 is the partial pressure of CO2 molecules in the vapor phase, and kH is the Henry’s law constant. For a given gas, kH is strongly temperature dependent. The lower the temperature, the higher the Henry’s law constant, and therefore the higher the solubility. In champagne and other sparkling wines, Agabaliantz thoroughly examined the solubility of dissolved CO2 molecules as a function of both temperature and wine parameters (Agabaliantz, 1963). His empirical relationships are still in use nowadays by champagne and other sparkling winemakers. For a typical sparkling wine elaborated according to the me´thode traditionnelle, Agabaliantz established the temperature dependence of the Henry’s law constant, which is displayed in Table 1.1. Thermodynamically speaking, the behavior of Henry’s law constant as a function of temperature can be conveniently expressed with a Van’t Hoff like equation as follows: DHdiss 1 1 kH ðyÞ ¼ k298 K exp (3) ℜ y 298 where DHdiss is the dissolution enthalpy of CO2 molecules in the liquid medium (in J/mol), ℜ is the ideal gas constant (8.31 J/K/mol), and y is the absolute temperature (in K). By fitting Agabaliantz data with the latter equation, it is worth noting that the dissolution enthalpy of CO2 molecules in champagne may be evaluated (Liger-Belair, 2005). The best fit to Agabaliantz data was found with DHdiss 24,800 J/mol (see Fig. 1.2). In comparison, the dissolution enthalpy of CO2 molecules in pure water is about 19,900 J/mol (Lide and Frederikse, 1995).

6


TABLE 1.1 Henry’s law constant of CO2 in champagne as a function of temperature, for a typical champagne with 12.5% (v/v) of ethanol and 10 g/L of sugars Temperature ( C)

Henry’s law constant kH (kg/m3/atm)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

2.98 2.88 2.78 2.68 2.59 2.49 2.41 2.32 2.23 2.16 2.07 2.00 1.93 1.86 1.79 1.73 1.67 1.60 1.54 1.48 1.44 1.40 1.34 1.29 1.25 1.21

Compiled from the data by Agabaliantz (1963).

B. The pressure under the cork Following Eq. (1), 24 g/L of sugar added in closed bottles to promote the second alcoholic fermentation produces approximately 11.8 g/L of CO2 within each bottle. Therefore, a typical 75-cl champagne bottle contains close to 9 g of CO2 molecules. By use of the molar mass of CO2 (44 g/mol), and the molar volume of an ideal gas (close to 24 L/mol at 12 C), it can be deduced that about 5 L of gaseous CO2 is trapped into a single bottle of champagne (i.e., six times its own volume!). Because the solubility of CO2 strongly depends on the champagne temperature, the pressure of gaseous CO2 under the cork also strongly

7


kH (kg/m3/atm)

4

3

2

1 0

5

10

15

20

25

Temperature (°C)

FIGURE 1.2 Henry’s law constant as a function of temperature (O) (redrawn from Agabaliantz data); the dashed line is the best fit to Agabaliantz data, drawn with the Van’t Hoff like Eq. (3) and with DHdiss 24,800 J/mol.

depends, in turn, on the champagne temperature. The physicochemical equilibrium of CO2 molecules within a champagne bottle is ruled by both Henry’s law (for CO2-dissolved gas molecules) and the ideal gas law (for the gaseous CO2 in the headspace under the cork). Moreover, the conservation of the total mass of CO2 molecules (dissolved into the wine and in the vapor phase under the cork) applies, since bottles are hermetically closed. Therefore, by combining the two aforementioned laws with mass conservation, the following relationship can easily be determined which links the pressure P of gaseous CO2 under the cork (in bars) with both temperature and the bottle’s parameters as P

mℜy 4:4 103 v þ ðkH ℜyÞV

(4)

where m is the total mass of CO2 within the bottle (in g), y the champagne temperature (in K), ℜ the ideal gas constant (8.31 J/K/mol), kH the Henry’s law constant given in Table 1.1 (in g/L/bar), V volume of champagne within the bottle (in L), and v is the volume of the gaseous headspace under the cork (in L). For a typical champagne bottle with V ¼ 75 cl, a volume in the headspace of v ¼ 25 mL, and a total mass of CO2 trapped within the bottle of

8


12

P (bars)

10

8

6

4

2 0

5

10

15 θ (⬚C)

20

25

30

FIGURE 1.3 Pressure of gaseous CO2 under the cork of a typical 75 cl champagne bottle as a function of champagne temperature.

m ¼ 9 g, the variation of the pressure P under the cork with the champagne temperature y is displayed in Fig. 1.3. At the temperature of champagne tasting (usually between 8 and 10 C), the pressure within a typical 75 cl champagne bottle is close to 5 bars (i.e., 5 105 N/m2).

C. The chemical composition of champagne From the point of view of the chemist, champagne can indeed be viewed as a multicomponent aqueous solution. The chemical composition of a typical Champagne wine is reported in Table 1.2 (Dussaud, 1993). Typically, gases like CO2 undergo specific reactions with water. Equilibrium is established between the dissolved (CO2)aq and H2CO3, the carbonic acid: ðCO2 Þaq þ H2 O $ H2 CO3

(5)

Moreover, carbonic acid is a weak acid that dissociates in two steps: H2 CO3 þ H2 O $ H3 Oþ þ HCO3 pKa1 ðat 25 CÞ ¼ 6:37

(6)

HCO3 þ H2 O $ H3 Oþ þ CO3 2 pKa2 ðat 25 CÞ ¼ 10:25

(7)

However, as the pH of champagne and other sparkling wines is relatively low (of order of 3.2), no carbonated species (CO32, HCO3)


TABLE 1.2

9

Average composition of a typical Champagne wine (Dussaud, 1993)

Compound

Quantity

Ethanol CO2 Glycerol Tartaric acid Lactic acid Sugars Proteins Polysaccharides Polyphenols Amino acids Volatile Organic Compounds (VOC) Lipids Kþ Ca2þ Mg2þ SO42 Cl

12.5 v/v% 10–12 g/L 5 g/L 2.5–4 g/L 4 g/L 10–50 g/L 5–10 mg/L 200 mg/L 100 mg/L 0.8–2 mg/L 700 mg/L 10 mg/L 200–450 mg/L 60–120 mg/L 50–90 mg/L 200 mg/L 10 mg/L

Typically, pH 3.2 and the ionic strength is 0.02 M.

should coexist with dissolved CO2. Recently, the 13C Magnetic Resonance Spectroscopy (MRS) technique was used as an unintrusive and nondestructive method to determine the amount of CO2 dissolved in closed bottles of Champagne and other sparkling wines (Autret et al., 2005). Different well-separated peaks were recorded in a 13C spectrum, as can be seen in Fig. 1.4: (i) the quadruplet of CH3 group of ethanol appears at 17.9 ppm, (ii) the triplet of CH2(–OH) group of ethanol at 57.3 ppm, and (iii) the singlet of CO2 appears at 124.4 ppm, thus confirming the absence of other carbonated species (CO32, HCO3) in the liquid matrix (contrary to fizzy waters, e.g., where pH values near neutrality enable the aforementioned carbonated species to cohabit with CO2-dissolved gas molecules).

D. Uncorking the bottle Have you ever thought about the velocity reached by an uncontrolled champagne cork popping out of a bottle? Measurements conducted in our laboratory in Reims led to typical velocities ranging from 50 to 60 km/h (Liger-Belair, 2009). When opening a bottle of champagne (or carbonated beverage in general), anyone has also already noticed the cloud of fog forming right above the bottle neck (as wonderfully illustrated by the photograph displayed in Fig. 1.5 taken by Jacques Honvault).

10


CH2(−OH) CH3

Ethanol CO2

130 120 110 100 90

80

70

60

50

40

30

20

10

(ppm) 13

FIGURE 1.4 C spectrum recorded to measure the CO2 concentration in a typical Champagne wine (Autret et al., 2005); it is clear that no carbonated species (CO32, HCO3) coexist with dissolved CO2.

FIGURE 1.5 Uncontrolled champagne cork popping out of a bottle; the cloud of fog forming right above the bottle neck clearly appears (#Jacques Honvault).


11

This cloud of fog is due to a significant drop in temperature in the headspace below the champagne surface, caused by the sudden gas expansion when the bottle is uncorked. Actually, this sudden temperature drop is responsible for the instantaneous condensation of water vapor into the form of this characteristic cloud of fog. Assuming an adiabatic expansion experienced by the gas volume of the headspace (from about 5 to 1 atm), the corresponding theoretical drop in temperature experienced by the gas volume may easily be accessed by the following and well-known relationship: Pð1GÞ yG ¼ constant

(8)

where P, y, and G are the pressure, temperature, and ratio of specific heats of the gas volume experiencing adiabatic expansion, respectively. The ratio of specific heats for CO2 molecules being 1.3, an adiabatic expansion from about 5–1 atm when uncorking the bottle corresponds to a theoretical drop in temperature close to 90 C. No wonder, traces of water vapor immediately condensate into the form of a small cloud. Moreover, in addition to this sudden temperature drop experienced by gases from the headspace, the fall of CO2 partial pressure above the champagne surface linked with bottle uncorking leads to a huge consequence concerning the thermodynamic equilibrium of CO2-dissolved molecules. Since the partial pressure of CO2 falls above the champagne surface, the CO2 dissolved in champagne is not in equilibrium any longer with its partial pressure in the vapor phase. Champagne enters a metastable state, that is, it contains CO2 molecules in excess in comparison with what Henry’s law states. To recover a new stable thermodynamic state corresponding to the partial pressure of CO2 molecules in the atmosphere (about only 3.5 10 4 atm), almost all the carbon dioxide molecules dissolved into the champagne must escape. The champagne becomes supersaturated with CO2. Before proceeding further, it is important to define the supersaturating ratio, used for quantifying CO2 molecules in excess in a carbonated liquid. The supersaturating ratio S is defined as follows (Lubetkin and Blackwell, 1988): S¼

cL 1 c0

(9)

where cL is the concentration of CO2 in the liquid bulk and c0 is the equilibrium concentration of CO2 corresponding to a partial pressure of gaseous CO2 of 1 atm. As soon as S > 0, a supersaturated liquid enters a metastable state and must degas to recover a supersaturating ratio equal to zero. In the case of Champagne wines, just after uncorking the bottle, cL is the equilibrium concentration of CO2 in the liquid bulk corresponding to a partial

12


pressure of CO2 of about 5 atm. Because there is a strict proportionality between the concentration of dissolved CO2 and its partial pressure in the vapor phase (as expressed by Henry’s law), cL/c0 5. Therefore, just after uncorking the bottle, the supersaturating ratio of champagne is approximately S 4, and champagne must degas. Actually, there are two mechanisms for gas loss: (i) losses due to diffusion through the surface of the liquid (invisible by the naked eye) and (ii) losses due to bubbling (the so-called effervescence process). But, how and where do all these bubbles form or nucleate?

III. THE BUBBLE NUCLEATION PROCESS A. A critical radius required for bubble nucleation Generally speaking, carbonated beverages are weakly supersaturated with CO2-dissolved gas molecules. In weakly supersaturated liquids such as champagne and other sparkling wines, bubbles do not just pop into existence ex nihilo. Actually, to cluster into the form of bubbles, CO2dissolved gas molecules must cluster together and push their way through the liquid molecules that are held together by Van der Waals attractive forces. Bubble formation is therefore limited by an energy barrier (for a complete review, see the paper by Lugli and Zerbetto, 2007). This is the reason why in weakly supersaturated liquids, bubble formation and growing require preexisting gas cavities with radii of curvature large enough to overcome the nucleation energy barrier and grow freely (Lubetkin, 2003). This critical radius, denoted r*, can easily be accessed by using standard thermodynamic arguments, or by using simple arguments based on classical diffusion principles (Liger-Belair and Rochard, 2008). The critical radius r* of gas pockets required to enable bubble production in a carbonated beverage expresses as follows: r

2g P0 S

(10)

where g is the surface tension of the liquid medium, of order of 50 mN/m in champagne and other sparkling wines (Dussaud, 1993), and P0 is the atmospheric pressure (P0 105 N/m2). At the opening of a champagne bottle, because S 4, the critical radius required to enable bubble nucleation is of the order of 0.25 mm. Jones et al. (1999) made a classification of the broad range of nucleation likely to be encountered in liquids supersaturated with dissolved gas molecules ( Jones et al., 1999). Bubble formation from preexisting gas cavities larger than the critical size is referred to as nonclassical heterogeneous bubble nucleation (type IV bubble nucleation, following their


13

nomenclature). Generally speaking, effervescence in a glass of champagne or sparkling wine may have two distinct origins. It can be ‘‘natural’’ or ‘‘artificial.’’

B. ‘‘Natural’’ bubble nucleation Natural effervescence is related to the bubbling process from a glass which has not experienced any specific surface treatment. Closer inspection of such glasses poured with champagne and other sparkling wines was conducted through a high-speed video camera fitted with a microscope objective (see the photograph displayed in Fig. 1.6). It revealed that most of the bubble nucleation sites were found to be located on preexisting gas cavities trapped inside hollow and roughly cylindrical cellulose– fiber-made structures on the order of 100-mm long with a cavity mouth of several micrometers (see Fig. 1.7) (Liger-Belair, 2002, 2003; Liger-Belair et al., 2004). The hollow cavity (a kind of tiny channel within the fibers) where a gas pocket is trapped during the pouring process is called the lumen. It can be clearly noticed from Fig. 1.7 that the radii of curvature of gas pockets trapped inside the fiber’s lumen are much higher than the aforementioned critical radius r*. Fibers probably adhere on the flute wall due to electrostatic forces (especially if the glass or the flute is vigorously wiped by a towel). Natural effervescence may also arise from tartrate crystals precipitated on the glass wall and resulting from the evaporation process after rinsing the glass with tap water. Therefore, there is a substantial variation concerning the ‘‘natural’’ effervescence between flutes depending on how the flute was cleaned and how and where it was left before serving.

FIGURE 1.6 High-speed video camera used to visualize bubble nucleation sites in a glass poured with champagne (photograph by Hubert Raguet).

14


Gas pockets trapped inside the fibers’ lumens

FIGURE 1.7 Three typical cellulose fibers adsorbed on the wall of a glass poured with champagne; the gas pockets trapped inside the fibers’ lumen and responsible for bubble formation clearly appear (bar ¼ 100 mm) (Photographs by Ge´rard Liger-Belair).

The mechanism of bubble release from a fiber’s lumen has already been described in recent papers (Liger-Belair et al., 2005b, 2006a). In very short, after opening a bottle of champagne or sparkling wine, the thermodynamic equilibrium of CO2 molecules dissolved in the liquid medium is broken. CO2-dissolved molecules become in excess in comparison with what the liquid medium can withstand. Therefore, CO2 molecules will escape from the liquid medium through every available gas–liquid interface to reach a vapor phase. Actually, once the sparkling beverage is poured into a glass, the tiny air pockets trapped inside the collection of fibers adsorbed on the glass wall offer gas–liquid interfaces to CO2-dissolved molecules, which cross the interface toward the gas pockets. In turn, gas pockets grow inside the fibers’ lumen. When a gas pocket reaches the tip of a fiber, a bubble is ejected, but a portion of the gas pocket remains trapped inside the fiber’s lumen, shrinks back to its initial position, and the cycle starts again until bubble production stops through lack of dissolved gas molecules (see the very typical time sequence displayed in Fig. 1.8).

C. Entrapping an air pocket within a fiber Cellulose fibers are in the form of hollow tubes of several hundreds of micrometers long and with a cavity mouth of several micrometers wide. The fiber wall section consists of densely packed cellulose microfibrils, with a preferential orientation along the fiber axis. Cellulose microfibrils consist of glucose units bounded in a b-conformation favoring straight


15

(1)

(2)

(3)

(4)

(5)

(6)

FIGURE 1.8 Time sequence illustrating one period of the cycle of bubble production from the lumen of a typical hollow cellulose fiber adsorbed on the wall of a glass poured with champagne; from Frame 1 to Frame 5, the time interval between successive frames is about 200 ms, but from Frame 5 to Frame 6, the time interval is only 1 ms (bar ¼ 50 mm) (Photographs by Ce´dric Voisin and Ge´rard Liger-Belair).

polymer chains. The different structural levels of a cellulose fiber are presented in Fig. 1.9. For a current review on the molecular and supramolecular structures of cellulose, see the article by O’Sullivan and references therein (O’Sullivan, 1997). From the physics point of view, cellulose fibers can indeed be considered as tiny roughly cylindrical capillary tubes of radius r and length h. Consequently, a wetting liquid placed into contact with this highly hydrophilic material penetrates it by capillary action. Actually, in capillaries with radii much smaller than the capillary length, gravity may be

16


h ≈ 100 µm–1 mm

d ≈ 10–20 µm

Fibre wall

e Lumen

Cellulose micro fibrils network

FIGURE 1.9 The different structural levels of a typical cellulose fiber; the fiber wall consists of closely packed cellulose microfibrils oriented mainly in the direction of the fiber.

neglected. Therefore, being the viscosity of the liquid phase, g being the surface tension of liquid, z being the distance of penetration at time t, and y the effective contact angle between the liquid and the capillary wall, the overall balance of forces on the liquid in the capillary may be expressed as " 2 # d2 z dz 2g cosy 8z dz 2 (11) ¼ r z 2þ dt dt r r dt The left-hand side of the latter equation is related to the liquid inertia, whereas both terms in the right-hand side are related to capillarity (the driving force), and viscous resistance, respectively. Under steady conditions, capillarity is balanced by the viscous drag of the liquid, and the famous Lucas–Washburn’s equation can be derived (De Gennes et al., 2002): z2 ¼

rg cosy t 2

(12)

Let us imagine a liquid edge spreading with a velocity v along a solid surface where cellulose fibers are adsorbed. This is basically what happens


17

when you fill a glass with a liquid. Actually, a liquid edge progressively advances along the vertical glass wall at a velocity v of order of several centimeter per second. As soon as the wetting liquid gets in touch with the fiber, some liquid progressively penetrates and fills the fiber’s lumen by capillary rise. Finally, a gas pocket may be trapped within the fiber if the time t taken by the liquid to completely fill the lumen by capillary action is greater than the characteristic time T taken by the liquid edge to completely submerge the fiber inside the liquid (see the scheme displayed in Fig. 1.10). By retrieving Eq. (12) with the characteristic fiber’s parameters defined in Fig. 1.10, the characteristic time required to completely fill the fiber’s lumen by capillary action may be expressed as: t¼

2h2 rg cosy

(13)

Considering a fiber with a length h, inclined by an angle a with regard to the liquid edge advancing over it at a velocity v, leads to the following time required for the fiber to be completely submerged: T¼

h sina v

(14)

The condition of gas entrapment inside the fiber therefore expresses as t > T, that is, 2h2 h sina > rg cosy v

(15)

Air

z

h

α

v v

u d = 2r Champagne

FIGURE 1.10 From the physics point of view, a fiber may be seen as a tiny capillary tube which gets invaded by a wetting liquid placed into contact with one of the fiber’s tip; v is the velocity of the liquid edge advancing over the fiber, and u is the velocity at which the meniscus advances inside the fiber’s lumen by capillary action (Liger-Belair et al., 2005b).

18


Because cellulose is a highly hydrophilic material, the contact angle of an aqueous liquid on it is relatively small (about 30 with pure water). Consequently, cosy 1. Finally, the condition of entrapment may be rewritten as follows: h g > r sina 2v

(16)

with the geometric parameters of the cellulose fiber lying on the left-hand side of Eq. (16) and the liquid parameters lying on the right-hand side of Eq. (16). The entrapment of an air pocket inside the lumen of a fiber during the filling of a glass is therefore favored by the following conditions, depending on both fiber and liquid parameters: (i) as elongated fibers as possible (h long) (ii) of small lumen’s radii r, (iii) fibers as horizontal as possible with regard to the liquid edge (i.e., sin a small), (iv) liquids with a small surface tension g, (v) and a high viscosity , and finally, (vi) a high velocity for the liquid edge advancing along the glass wall. It is worth noting that both conditions (iv) and (v) imply that hydroalcoholic carbonated beverages are more favorable than fizzy waters to entrap air pockets inside cellulose fibers during the pouring process. Actually, the surface tension of champagne and beer is of the order of 50 mN/m (i.e., about 20 mN/m less than the surface tension of pure water), and their dynamic viscosity is about 50% higher than that of pure water.

D. Modeling the repetitive bubble nucleation from a cellulose fiber As seen in Fig. 1.8, the whole process leading to the production of a bubble from a cellulose fiber’s tip can be coarsely divided in two main steps: (i) the growth of the gas pocket trapped inside the fiber’s lumen (from frame 1 to frame 5) and (ii) the bubble detached as the gas pocket reaches the fiber’s tip (from frame 5 to frame 6). Actually, it is clear from the numerous close-up time sequences taken with the high-speed video camera, that the time scale of the bubble detachment is always very small ( 1 ms) compared with the relatively slow growth of the gas pocket (several tens to several hundreds of meterseconds). Therefore, the whole cycle of bubble production seems to be largely governed by the growth of the gas pocket trapped inside the fiber’s lumen. This tiny gas pocket was modeled as a slug-bubble growing trapped inside an ideal cylindrical microchannel and being fed with CO2-dissolved molecules diffusing (i) directly from both ends of the gas pocket and (ii) through the fiber wall, which consists of closely packed cellulose microfibrils oriented mainly in the direction of the fiber (Liger-Belair et al., 2004; Topgaard,


19

A

Gas pocket

B z

Gas pocket

r

cB λ

Boundary layer cL Liquid bulk

FIGURE 1.11 Real gas pocket trapped within the lumen of a cellulose fiber acting as a bubble nucleation site in a glass poured with champagne (A), modeled as a slug-bubble trapped inside an ideal cylindrical microchannel and being fed with CO2-dissolved molecules diffusing, (i) directly from the liquid bulk through both ends of the gas pocket and (ii) through the wall of the microchannel (B) (bar ¼ 50 mm).

2003). A scheme is displayed in Fig. 1.11, where the geometrical parameters of the tiny gas pocket growing by diffusion are defined. Taking into account the diffusion of CO2-dissolved molecules from the liquid bulk to the gas pocket, the growth of this gas pocket with time t was linked with both liquid and fiber parameters as follows (Liger-Belair et al., 2005b): 8 zðtÞ ðz0 þ AtÞ expðt=tÞ At > < ðP þ 2g=rÞrl 4ℜyD0 Dc (17) with t ¼ and A ¼ > : 2ℜyD? Dc ðP þ 2g=rÞl where z is the length of the gas pocket, z0 the initial length of the gas pocket before it starts its growth through the lumen, at each cycle of bubble production (see e.g., frame 1 and frame 6 in Fig. 1.8), P the ambient pressure, D0 the diffusion coefficient of CO2-dissolved molecules in the liquid bulk, D? the diffusion coefficient of CO2-dissolved molecules through the fiber wall (and therefore perpendicular to the cellulose

20


microfibrils), Dc ¼ cL cB ¼ cL kHPB ¼ cL kH(P0 þ 2g / r) the difference in CO2-dissolved concentrations between the liquid bulk and the close vicinity of the gas pocket surface in equilibrium with the gaseous CO2 in the gas pocket, and l is the boundary layer thickness where a linear gradient of CO2-dissolved concentration is assumed. In the previous work, the transversal diffusion coefficient D? of CO2 molecules through the fiber wall was approached and properly bounded by D? / D0 0.1 and D? / D0 0.3 (Liger-Belair et al., 2004). For modeling purposes, an intermediate value of about D? 0.2D0 was proposed and will be used hereafter. The whole cycle of bubble production being largely governed by the growth of the gas pocket trapped inside the fiber’s lumen, the period of bubble formation from a single cellulose fiber is therefore equal to the total time T required by the tiny gas pocket to grow from its initial length, denoted z0, to its final length, denoted zf, as it reaches the fiber’s tip (see frame 5 in Fig. 1.8). By retrieving Eq. (17), it is therefore possible to access the frequency of bubble formation f (expressed in s 1) from a single fiber as follows: f

1 2ℜyD? Dc T rlðP0 þ 2g=rÞ ln½ðzf þ 10rÞ=ðz0 þ 10rÞ

(18)

To go further on with the dependence of the bubbling frequency with both liquid and fiber parameters, we can replace in Eq. (18) the diffusion coefficient D0 by its theoretical expression approached through the wellknown Stokes–Einstein equation (D0 kBy /6pd), kB being the Boltzman constant (1.38 10 23 J/K), and d being the characteristic size of the CO2 molecule’s hydrodynamic radius (d 10 10 m). By replacing in Eq. (18) each parameter by its theoretical expression and each constant by its numerical value, the variation of the bubbling frequency as a function of the various pertinent parameters involved may be rewritten as follows (in the MKSA system): f 2:4 1014

y2 ½cL kH ðP þ 2g=rÞ rðP þ 2g=rÞl ln½ðzf þ 10rÞ=ðz0 þ 10rÞ

(19)

The boundary layer thickness l was indirectly approached and found to be of the order of 20 mm (Liger-Belair et al., 2005b). Finally, let us apply the latter equation to the standard textbook case fiber displayed in Fig. 1.8 and modeled in Fig. 1.11 (i.e., r 5 mm, z0 20 mm, and zf 100 mm). Eq. (19) may therefore be rewritten as follows, by replacing the fiber’s parameters r, z0, zf, and l by their numerical value: f 5:2 108

y2 ½cL kH ðP þ 0:2Þ ðP þ 0:2Þ

(20)


21

In the latter expression, f is expressed in bubbles/s, cL is expressed in g/L, kH in g/L/atm, P in atm, and in kg/m/s, to fit the standards used in enology. We will discuss the relative influence of the following parameters on the average bubbling frequency: (i) the concentration cL of CO2-dissolved molecules, (ii) the liquid temperature y, and (iii) the ambient pressure P. (i) Following Eq. (20), every other parameter being constant, the dependence of the theoretical average bubbling frequency f with the CO2-dissolved concentration cL is in the form f ¼ acL b. By use of a high-speed video camera fitted with a microscope objective, a few cellulose fibers acting as bubble nucleation sites on the wall of a glass poured with champagne were followed with time during the whole gas-discharging process (which may last up to several hours). This method is developed in minute details in Liger-Belair et al. (2001b). The dependence of the experimental bubbling frequency fexp with cL was found to follow a linear-like cL dependence, as expected from the model developed above. Therefore, the frequency of bubble formation from a given nucleation site is found to progressively decrease with time, because the concentration cL of CO2-dissolved molecules progressively decreases as CO2 continuously desorbs from the champagne matrix. Furthermore, it is worth noting that the bubbling frequency of a given nucleation site vanishes (i.e., the bubble release ceases, f ! 0 bubble/s), although the CO2-dissolved concentration cL remains higher than a critical value, as shown in Liger-Belair et al. (2001b). Actually, following both Laplace’s and Henry’s law, the curvature r of the CO2 pocket trapped inside the fiber’s lumen induces in the close vicinity of the trapped CO2 pocket a concentration cB of CO2-dissolved molecules of order of kH(P0 þ 2g/r). Consequently, as soon as the concentration of CO2-dissolved in the liquid bulk reaches a critical value cL∗ ¼ cB kH(P0 þ 2g / r), the diffusion toward the gas pocket ceases and the given nucleation site stops releasing bubbles (simply because Dc, the driving force of diffusion, vanishes as cL cL*). Let us apply the latter condition to the characteristic radius of a cellulose fiber (r 5 mm). At 10 C, the critical concentration cL* below which bubble release becomes impossible is therefore: cL∗ kH ðP0 þ 2g=rÞ 2:07 105 105 þ 2 5 102 =5 106 2:5 g=L (21) (ii) The dependence of the bubbling frequency with the liquid temperature is much more difficult to test experimentally in real consuming conditions. Actually, we needed time to decrease or increase the liquid temperature, and we found no satisfying possibility of modifying the liquid temperature without significantly losing CO2-dissolved molecules which continuously desorb from the supersaturated liquid matrix due to

22


diffusion through the liquid surface and due to bubbling from the numerous nucleation sites found in the flute. We will nevertheless discuss the theoretical influence of the liquid temperature by retrieving Eq. (20). In Eq. (20), the temperature directly appears as y2, but it is worth noting that the Henry’s law constant kH, as well as the champagne dynamic viscosity , is strongly temperature dependent (Liger-Belair and Rochard, 2008). Therefore, increasing the liquid temperature by 10 K (let us say from 278 to 288 K, which is approximately the range of champagne-tasting temperatures) increases the theoretical bubbling frequency by about 50%. For the fiber displayed in Fig. 1.8 (r 5 mm, z0 20 mm, and zf 100 mm) and with cL 12 g/L, the theoretical temperature dependence of the bubbling frequency is displayed in Fig. 1.12. (iii) Increasing or decreasing the ambient pressure P also significantly modifies the corresponding average bubbling frequency f. For the fiber displayed in Fig. 1.8 (r 5 mm, z0 20 mm, and zf 100 mm) and with cL 12 g/L, the theoretical pressure dependence of the bubbling frequency is displayed in Fig. 1.13. Reducing the ambient pressure to only 0.3 atm (e.g., on the top of Mount Everest) would increase the average bubbling frequency by a factor of almost 3. This is basically the same phenomenon which is responsible for gas embolism in divers who have

20

f (bubbles/s)

18

16

14

12

10 278

280

282

284 θ (⬚ K)

286

288

FIGURE 1.12 Theoretical dependence of the bubbling frequency f with the temperature y, as expected from the model displayed in Eq. (20), in the range of usual champagnetasting temperatures (from 5 to 15 C), and for the textbook case fiber displayed in Fig. 1.7.


23

1000

f (bubbles/s)

100

10

1 0

1

2 P (atm)

3

4

FIGURE 1.13 Theoretical dependence of the bubbling frequency f with the ambient pressure P (at 20 C), as expected from the model displayed in Eq. (20) for the textbook case fiber displayed in Fig. 1.7.

breathed high-pressure air under water if they resurface too quickly. Inversely, increasing the ambient pressure to 2 atm decreases the average bubbling frequency by a factor of about 2 compared to that at sea level.

E. Evidence for bubbling instabilities The regular and clockwork release of bubbles from a cellulose fiber is indeed the most common and usual way of forming bubbles, but cellulose fibers were recently found to experience other various and sometimes very complex rhythmical bubbling regimes (Liger-Belair et al., 2005a, 2006b). After pouring champagne into a flute, a thorough examination (even by the naked eye) of the bubble trains rising toward the liquid surface recently revealed a curious and quite unexpected phenomenon. As time proceeds, during the gas-discharging process from the liquid matrix, some of the bubble trains showed abrupt transitions during the repetitive and rhythmical production of bubbles. Visually speaking, the macroscopic pertinent parameter which is characteristic from the successive bubbling regimes is the interbubble distance between the successive bubbles of a given bubble train. In Fig. 1.14, micrographs of a bubble train in its successive rhythmical bubbling regimes while degassing are displayed. The duration of a given bubbling regime may vary from a few

24


Arrow of time

A Period-2

B

C

D

E

Period-1

Period-2

Period-1

F

G Period-1

FIGURE 1.14 Time sequence (from left to right) showing a bubble nucleation site at the bottom of a flute poured with champagne blowing bubbles through different and well-established bubbling regimes (bar ¼ 1 mm; photographs by Ge´rard Liger-Belair).

seconds to several minutes. In frame (a), bubbles are seen to be generated from a period-2 bubbling regime which is characterized by the fact that two successive bubbles rise by pairs. Then, the bubbling regime suddenly changes, and a multiperiodic bubbling regime arises which is displayed in frame (b). Later, in frame (c), clockwork bubbling in period-1 occurs where the distance between two successive bubbles increases monotonically as they rise, and so on. This nucleation site experienced other various bubbling regimes during its life, until it finally ended in a clockwork period-1 bubbling regime presented in frame (g).


25

Such a curious and unexpected observation raises the following question: what is/are the mechanism(s) responsible for the transitions between the different bubbling regimes? To better identify the fine mechanisms behind this rhythmical production of bubbles from a few nucleation sites, some of them experiencing bubbling transitions were filmed in situ by use of a high-speed digital video camera. Two time sequences are displayed in Figs. 1.15 and 1.16, where bubbles are blown in a period-2 and in a very erratic way, respectively. The lumen of the cellulose fiber displayed in Fig. 1.15 presents only one gas pocket, whereas the fiber’s lumen displayed in Fig. 1.16 clearly shows two gas pockets periodically touching and connecting themselves through a tiny gas bridge (see frames 3 and 4 of Fig. 1.16). The micrometric gas bridge connecting the two gas pockets and disturbing the overall production of bubbles is enlarged in Fig. 1.17. This tiny gas bridge is a likely source of bubbling instabilities. Recently, a model was built which takes into account the coupling between the bubbling frequency and the frequency of the single gas pocket which oscillates while trapped inside the fiber’s lumen (e.g., as in Fig. 1.15). The previously published data showed a general rule concerning bubbling instabilities arising from some fibers presenting just one trapped gas pocket. In this previous paper, the successive rhythmical bubbling regimes followed the so-called ‘‘period-adding scenario’’ (Liger-Belair et al., 2005a).

(1)

(2)

(3)

Gas pocket trapped inside the fibre’s lumen

FIGURE 1.15 Close-up time sequence illustrating a tiny cellulose fiber acting as a bubble nucleation site in its period-2 bubbling regime (i.e., bubbles are blown by pairs); the time interval between two successive frames is 40 ms (bar ¼ 50 mm; photographs by Ge´rard Liger-Belair).

26


FIGURE 1.16 Two gas pockets are interacting in the lumen of this cellulose fiber, thus disturbing the periodicity of the bubbling regime; the black arrows point the various gas pockets interacting; the time interval between two successive frames is 10 ms (bar ¼ 100 mm; photographs by Ge´rard Liger-Belair).

FIGURE 1.17 Detail of the cellulose fiber displayed in Fig. 1.16, which clearly shows the establishment of a micrometric gas bridge between the two gas pockets trapped inside the fiber’s lumen (bar ¼ 10 mm; photograph by Ge´rard Liger-Belair).

Nevertheless, this previously published scenario does not fit the various ways of forming bubbles from more complex cellulose fibers able to entrap numerous gas pockets, as shown in Fig. 1.16. Numerous fibers, such as those shown in the present paper, presented a sequence of various bubbling instabilities which is not reproduced by our previous model. A huge collection of successive rhythmical bubbling regimes has already been observed, and the highest recorded periodicity was observed for a fiber presenting a period-12 bubbling regime (LigerBelair et al., 2006b). At the moment, we could not find any general rule


27

with fibers presenting numerous gas pockets interacting together, but the close-up observation and the discovery of the multiple gas pockets interacting together are considered as a step toward a deeper understanding of the successive rhythmical bubbling regimes arising from complex fibers. The huge diversity of our observations, in terms of the various successive bubbling regimes seems to be directly linked with the ‘‘natural’’ variability of cellulose fibers (in terms of size, lumen diameter, inner wall properties, etc.).

F. ‘‘Artificial’’ bubble nucleation Artificial effervescence is related to bubbles nucleated from glasses imperfections done intentionally by the glassmaker to promote or to eventually replace a deficit of ‘‘natural’’ nucleation sites. Actually, it has been known for decades that bubbles may arise from microscratches on the glass wall (Lynch and Bamforth, 2002; Ronteltap et al., 1991) Those microscratches are geometrically able to trap tiny air pockets when champagne is poured into the glass (as cellulose fibers do). Those microscratches on a glass can be done by essentially two techniques: sandblast or laser engraving. Nevertheless, effervescence produced from scratches intentionally done by the glassmaker does not resemble that arising from tiny individual cellulose fibers. A rendering of such microscratches releasing bubbles at the bottom of a champagne flute is displayed in Fig. 1.18. It is worth noting that the repetitive bubbling process arising from artificial bubble nucleation is much more vigorous and chaotic than the bubbling process from tiny cellulose fibers. Glasses engraved at their bottom are thus indeed easily recognizable, with a characteristic bubble column rising on their axis of symmetry (Liger-Belair et al., 2007; Polidori et al., 2008). Effervescence promoted by engraved glasses is indeed visually quite different than that naturally promoted by cellulose fibers, but the difference is also suspected to go far beyond the solely esthetical (and rather subjective) point of view. Differences are strongly suspected concerning the kinetics of CO2 and flavor release all along champagne tasting.

IV. DURING THE BUBBLE RISE A. Bubble growth After being born on micrometric gas pockets trapped inside impurities of the glass wall, bubbles rise toward the liquid surface due to their own buoyancy. While rising, they continue to grow in size by continuously absorbing carbon dioxide molecules dissolved into the liquid ‘‘matrix’’, as

28


B

C

A

2.5 mm

3.5 mm

FIGURE 1.18 At the bottom of this flute, on its axis of symmetry, the glassmaker has engraved a small ring (done with adjoining laser beam impacts) (A); single laser beam impact as viewed through a scanning electron microscope (bar ¼ 100 mm) (B); effervescence in this flute is promoted from these ‘‘artificial’’ microscratches into the form of a characteristic and easy recognizable vertical bubbles column rising on its axis of symmetry (bar ¼ 1 mm; photographs by G. Polidori and F. Beaumont) (C).

it is clearly illustrated on the photograph displayed in Fig. 1.19. Growing bubbles thus continuously accelerate along their way through the champagne. This continuous acceleration is also betrayed, on high-speed photographs, by the continuously increasing spacing e between the successive bubbles of a given bubble train, as seen in Fig. 1.19, for example. High-speed photography and strobe lighting were used to monitor the motion of bubbles (Liger-Belair, 2002). It was found that the bubble radius R of bubbles increases at a constant rate k ¼ d R /d t, as they rise toward the liquid surface. Thus, RðtÞ ¼ R0 þ kt

(22)

where R0 is the bubble radius as it detaches from the nucleation site. R0 is of the same order of magnitude than the radius of the mouth of the cellulose fiber which acts as a nucleation site, that is, around 5–10 mm (Liger-Belair, 2002; Liger-Belair et al., 2005b). The first experimental observations about the growth and rise of bubbles in carbonated beverages were conducted in the early 1990s, with bubbles rising and growing in a glass of beer (Shafer and Zare,


29

e

FIGURE 1.19 A characteristic bubble train promoted by the repetitive bubble formation processes from a single cellulose fiber; bubbles are clearly seen growing during their way up (bar ¼ 1 mm; photograph by Ge´rard Liger-Belair).

1991). Shafer and Zare also proved that bubbles’ diameter linearly increases with time as bubbles rise toward the liquid surface. Three minutes after pouring, experiments conducted with Champagne and other sparkling wines led to growth rates k ranging between 350 and 400 mm/s, at 20 C. (Liger-Belair, 2002). Experiments were also performed with bubbles rising in beer glasses. In beer, 3 min after pouring, bubble growth rates were found to lay around 100–150 mm/s, that is, about three times less than those in Champagne and other sparkling wines (Liger-Belair, 2002). By use of mass transfer equations, the growth rate k of bubbles rising in champagne and beer (at low to moderate Reynolds numbers) was also modeled and linked with some physicochemical properties of liquids as follows (in the MKSA system): k¼

1=3 dR ℜy 2=3 2arg D0 Dc 0:63 dt PB 9

(23)

where y is the liquid temperature, ℜ the ideal gas constant, PB the pressure inside the rising bubble, D0 the diffusion coefficient of CO2 molecules through the liquid bulk, r and are, respectively, the liquid density and viscosity, g the acceleration due to gravity, a a numerical

30


prefactor close to 0.7 for Champagne and other sparkling wines bubbles, h the distance traveled by the bubble from its nucleation site, and Dc (the driving force responsible for the diffusion of CO2 into the rising bubble) is the difference in dissolved-CO2 concentrations between the liquid bulk and the close vicinity of the bubble surface in equilibrium with the gaseous CO2 into the rising bubble (see Fig. 1.20). Strictly speaking, the pressure PB inside the rising bubble is the sum of three terms: (i) the atmospheric pressure P0, (ii) the hydrostatic pressure rgH, and (iii) the Laplace pressure 2g/R, originated in the bubble’s curvature. H is the depth at which the bubble rises and g is the surface tension of the liquid medium. However, H varying from several millimeters to several centimeters, the surface tension of champagne being of the order of 50 mN m 1, and bubbles’ radii varying from several tens to several

Champagne Bubble

Gas/liquid interface

Liquid bulk cL CO2 bubble Δc = cL−cB λ

cB = kHPB ≈ kHP0

Boundary layer where a gradient Adsorption layer in equilibrium with the CO2 gas into the bubble and of dissolved CO2 exists. where Henry’s law locally applies.

FIGURE 1.20 surface.

Carbon dioxide concentrations in the close vicinity of the CO2 bubble


31

hundreds of micrometers, the contribution of both hydrostatic and Laplace pressures is clearly negligible in front of the atmospheric pressure P0. Let us test the applicability of Eq. (23) in case of rising and expanding champagne bubbles, at 20 C. By using known values of r and in champagne (Liger-Belair, 2002) a ¼ 0.7, D0 1.410 9 m2/s, as measured by Nuclear Magnetic Resonance (Liger-Belair et al., 2003), and the difference in CO2 concentrations between the liquid bulk and the close vicinity of the bubble surface Dc 10 g/L 227 mol/m3, one finds 1=3 8:31 293 2 0:7 103 9:8 9 2=3 k 0:63 1:4 10 227 105 9 1:5 103 500mm=s (24) which is in quite good accordance with the order of magnitude of experimentally observed growth rates (Liger-Belair, 2002). This value nevertheless slightly overestimates experimentally observed growth rates probably because, 3 min after pouring, champagne already lost a significant part of its dissolved-CO2 content, thus decreasing Dc below its initial value of 227 mol/m3. Very recently also, Zhang and Xu proposed a model for the growth rate of rising bubbles in both champagne and beer (Zhang and Xu, 2008).

B. Average bubble size Because champagne and other sparkling wine tasters are often concerned with the size of bubbles formed in the wine (a proverb says that the smaller the bubbles, the better the wine), much attention was paid recently to model the average size of ascending bubbles. Actually, the final average size of ascending bubbles is the result of a combination between their growth rate and their ascending velocity. Recent calculations, based on mass transfer equations, linked the final average bubbles’ size with various physicochemical and geometrical parameters (Liger-Belair, 2005, 2006). The following dependence of the ascending bubble radius R with some of the liquid parameters was derived (in the MKSA system): R 1:24

9 2arg

2=9

ℜy PB

1=3

D0 2=9 ðDcÞ1=3 h1=3

(25)

To go further on with the dependence of bubbles’ radii with some few parameters, we can also replace in the latter equation the diffusion coefficient D0 by its theoretical expression approached through the well-known Stokes–Einstein equation (D0 kBy /6pd). The following relationship expressed in the MKSA system was thus obtained:

32


3kB Rðh;y; . . .Þ 2:5 4par

2=9

1 rg

2=9

1 P0

1=3

y5=9 ðcL cB Þ1=3 h1=3

(26)

It is worth noting that the dependence of the bubble size with the liquid viscosity vanishes. Finally, by replacing in Eq. (26), kB, a, and d by their known numerical values, and by developing cB as kHP0, one obtains 3 5=9

R 2:7 10 y

1 rg

2=9

cL kH P0 P0

1=3 h1=3

(27)

Otherwise, because the liquid density r does not significantly vary from one champagne to another (and even from one carbonated beverage to another), we will discuss and put the accent on the influence of the following parameters on the bubble size: (i) the traveled distance h, (ii) the liquid temperature y, (iii) the gravity acceleration g, (iv) the ambient pressure P0, and (v) the carbon dioxide content cL. (i) The longer the traveled distance h, the larger the bubble size. This dependence of the bubble size with its traveled distance through the liquid means that, during champagne tasting, the average bubble size at the champagne surface varies from one glass to another. In a narrow flute, for example, the level of champagne poured is about three times higher than that in a typical coupe (with a shallower bowl and a much wider aperture). Therefore, the average bubbles’ diameters in the flute will be larger than those in the coupe by a factor of about Rflute / Rcoupe 31/3 1.45 (i.e., bubbles about three times larger in volume), as seen in the photograph displayed in Fig. 1.21, which compares the average size of bubbles after pouring, whether champagne is served into a flute or into a coupe. (ii) In Eq. (27), the temperature appears directly as y5/9, but we should not forget that the Henry’s law constant kH is also strongly temperature dependent (see Fig. 1.2) and conveniently expressed by the Van’t Hoff Eq. (3). The temperature being expressed in K, the temperature dependence of the bubble size is nevertheless quite weak. Increasing the liquid temperature by 10 K (let us say from 278 to 288 K, which is approximately the range of champagne-tasting temperature) makes bubbles grow from about only 5–6% in diameter. (iii) The gravity acceleration which is the driving force behind the bubble rise (through buoyancy) also plays quite an important role in the final bubble’s size. This could indeed be easily evidenced during a parabolic flight where the acceleration changes from microgravity (close to zero-g) to macrogravity (up to 1.8 g). On the Moon, for example, where the gravity is about one-sixth the gravity on Earth, the average


A

33

B

FIGURE 1.21 Bubbles’ size distribution at the free surface of champagne, 30 s after pouring, whether champagne is served in the coupe (A) or in the flute (B) engraved on its bottom. Because of the drastic glass washing protocol, bubbles were generated only from nucleation sites of the ring-shaped engravements (lying respectively, 2.9 and 7.4 cm below the free surface area, whether champagne is served in the coupe or in the flute; photographs by Ge´rard Liger-Belair).

bubbles’ size would increase by a factor of about gMoon / gEarth 62/9 1.49 (i.e., bubbles almost 50% larger in diameter and therefore more than three times larger in volume). (iv) The pressure inside the rising bubble is equivalent to the ambient pressure P0 (for the reasons detailed in the latter paragraph). Usually, at the sea level, this pressure is equivalent to 1 atm (or 105 N/m). Reducing the atmospheric pressure to only 0.3 atm (on the top of Mount Everest, e.g.) would increase the average bubble diameter by about 55% (and therefore by a factor of almost 4 in volume). (v) The carbon dioxide content of the liquid medium cL also influences the final average bubbles’ size. This is the main reason why bubbles in beer are significantly smaller than bubbles in champagne and other sparkling wines. Actually, the carbon dioxide content in beers may classically vary from about 4 to 7 g/L, whereas the carbon dioxide content in champagne and other sparkling wines may rather vary from 10 up to 12 g/L (i.e., cL is approximately two times higher in champagne than in beer). Reducing cL by a factor 2 in Eq. (27) would decrease the theoretical average bubble size by about 40% (thus leading to bubbles almost five times smaller in volume). The two

34


A

B

FIGURE 1.22 Three minutes after pouring, bubbles rising in a glass of beer (A) show diameters much lower than those of bubbles rising in a flute poured with champagne (B) (bar ¼ 1 mm); the very significant difference between the bubble size in champagne and beer is mainly due to amounts of dissolved-CO2 about two times higher in champagne than in beer (photographs by Ge´rard Liger-Belair).

photographs displayed in Fig. 1.22 illustrate the significant difference in bubble size between a standard commercial champagne and a standard commercial beer, both showing very typical bubbling behavior. Moreover, after pouring champagne into a flute, due to bubbling and diffusion through the surface of champagne, CO2 molecules progressively escape from the liquid medium. Subsequently, the dissolved carbon dioxide content cL in the liquid medium progressively decreases. Therefore, as time proceeds during champagne tasting, the average bubbles’ size at the liquid surface progressively decreases, as can be clearly seen in the sequence displayed in Fig. 1.23.

V. CO2 VOLUME FLUXES OUTGASSING FROM CHAMPAGNE GLASSES IN TASTING CONDITIONS A. Flute versus coupe In case of champagne and other sparkling wine tasting, two quite emblematic types of drinking vessels have coexisted for decades: (i) the classical flute, namely, a long-stemmed glass with a deep tapered bowl

35


A

B

C

D

FIGURE 1.23 Time sequence showing successive top views of a flute poured with champagne and followed as time proceeds; (A) immediately after pouring, (B) 3 min after pouring, (C) 10 min after pouring, and (D) 25 min after pouring; it clearly appears that the average bubbles’ size decreases as time proceeds, as well as the average number of floating bubbles (photographs by Ge´rard Liger-Belair).

and a narrow aperture, and (ii) the classical coupe, namely, a shallower glass with a much wider aperture. Advantages and disadvantages of both glass shapes have long been debated, nevertheless without bringing any sensory and/or analytical data. Due to their very different geometrical properties, the kinetics of CO2 release has therefore been supposed to significantly differ between these two different kinds of champagnedrinking vessels. Very recently, measurements of CO2 volume fluxes outgassing from champagne were done, during the first 10 min following pouring (Liger-Belair et al., 2009c). In order to avoid the randomly located ‘‘bubbling environment’’ inevitably provided in glasses showing natural effervescence, single standard flutes and coupes engraved on their bottoms were used for this set of experiments (thus providing a ‘‘standardized’’ and artificial effervescence, as that displayed in Fig. 1.18). Between the successive pouring and time series data recordings, the flute and the

36


coupe were systematically and thoroughly washed in a dilute aqueous formic acid solution, rinsed using distilled water, and then compressed air dried. This drastic treatment limits the potential formation of calcium carbonate crystals on the flute wall as well as the adsorption of any dust particle acting as ‘‘natural’’ bubble nucleation sites. With such a surface treatment, CO2 bubble nucleation is strictly restricted to the bubble nucleation sites of the ring-shaped engravement, so that differences in the kinetics of CO2 release from one type of drinking vessel to another are attributed only to geometrical differences between them (assuming identical etching).

1. Evidence for losses of dissolved CO2 during champagne serving The pouring process is far from being consequence-less with regard to the dissolved CO2 concentration cL (Liger-Belair et al., 2009b,c). During the several seconds of the pouring process inevitably preceding tasting, champagne undergoes highly turbulent and swirling flows. During this phase, champagne loses quite a significant part of its initial content in dissolved CO2. Consequently, as soon as champagne is served and ready to drink, CO2 dissolved in champagne is suspected to be well below its initial content of about 11.5 g/L (as chemically measured inside a bottle, after uncorking, but before pouring). The initial bulk concentration of dissolved CO2 after pouring, denoted ci, was chemically accessed by using carbonic anhydrase. The official method used to measure the dissolved CO2 content is thoroughly detailed in two recent papers (Autret et al., 2005; Liger-Belair et al., 2009b). To enable a statistical treatment, six successive CO2-dissolved measurements were systematically done for each type of drinking vessel, after six successive pouring (from six distinct bottles). Champagne was found to initially hold (after pouring) a concentration of CO2-dissolved molecules of ciflute ¼ 7.4 0.4 g/L in the flute, and cicoupe ¼ 7.4 0.5 g/L in the coupe (i.e., approximately 4 g/L less in both types of drinking vessels after pouring than inside the bottle, before pouring). Table 1.3 summarizes the geometrical and analytical pertinent parameters linked with each type of drinking vessel poured with 100 mL of champagne. Turbulences of the pouring process therefore cause TABLE 1.3 Pertinent parameters linked with each type of drinking vessel filled with 100 mL of champagne Type of Liquid level after vessel pouring, h (cm)

Surface area offered to gas discharge, A (cm)

Initial concentration of dissolved CO2, ci (g/L)

Flute 7.4 Coupe 2.9

21.2 60.8

7.4 0.4 7.4 0.5


37

significant and quite unexpected loss of dissolved CO2 during champagne serving. It is worth noting that losses of CO2-dissolved molecules during the pouring process are of the same order of magnitude whether champagne is served in the flute or in the coupe, despite a significantly longer pouring process in the case of the flute (mainly due to an excess of foam restricted in narrower aperture, which forces the taster to pour champagne into the flute in two or three steps in order to avoid champagne overflow). This observation first appeared counter intuitive to us. Actually, losses of dissolved CO2 induced by a longer pouring process in the flute could be counterbalanced by the much larger area offered to CO2-dissolved molecules escape in case of the coupe aperture.

2. Influence of each type of drinking vessel on its loss of dissolved CO2 concentration with time For each type of drinking vessel, the corresponding loss of dissolved CO2 concentration with time along the first 10 min following pouring is displayed in Fig. 1.24. 8 Flute Coupe

7

[CO2] (g/L)

6

5

4

3

Immediately after pouring

2 0

100

200

300

400

500

600

t (s)

FIGURE 1.24 Progressive loss of CO2-dissolved concentrations (in g L 1) all along the first 10 min following the pouring process in a flute and in a coupe, respectively, filled with 100 mL of champagne; each datum of each time series is the arithmetic average of six successive values recorded from six successive pouring; standard deviations correspond to the root-mean-square deviations of the values provided by the six successive data recordings.

38


Despite significant standard deviations (mainly attributed to the difficult repeatability of the manual pouring process between the six successive pouring and time series data records conducted for each type of drinking vessel), it is clear from Fig. 1.24 that the progressive loss of dissolved CO2 concentration with time is significantly higher when champagne is served in the coupe than in the flute. From the taster point of view, this observation is of importance for both the visual aspect of champagne and its ‘‘mouth feel’’ sensation. Actually, it was recently shown that the higher the concentration of dissolved CO2 in champagne, the higher the kinetics of bubble nucleation, the larger the average bubbles’ size, and finally the more effervescence in the glass (Liger-Belair, 2005). Moreover, it is also well known in champagne and other sparkling wine tasting that the higher the concentration of dissolved CO2, the higher the ‘‘fizzy’’ sensation when bubbles burst over the tongue (Liger-Belair, 2009; Liger-Belair and Rochard, 2008). To the best of our knowledge, this is the first set of analytical results concerning the influence of glass shape on its progressive loss of dissolved CO2 concentrations with time in tasting conditions, and therefore the first analytical proof that a longstemmed glass with a deep tapered bowl and a narrow aperture prolongs the drink’s chill and helps to retain its effervescence by contrast with a shallower coupe of much wider aperture.

3. Influence of each type of drinking vessel on CO2 volume fluxes outgassing from it In champagne and other sparkling wine tasting, in addition to the visual aspect of effervescence and mouth feel—both depending (among many other parameters) on the dissolved-CO2 concentration c(t)—an other important aspect is the smell or ‘‘nose’’ of the wine, the so-called ‘‘bouquet’’ (Priser et al., 1997; Tominaga et al., 2003). Effervescence of champagne and other sparkling wines is suspected to promote the development of aromas in the headspace above the glass. The myriad of bubbles nucleating on the flute’s wall and traveling through the wine’s bulk is expected to enhance the release of volatile organic compounds by considerably enhancing exchange surfaces between the wine and the atmosphere. It is worth noting that, in a typical flute poured with champagne or sparkling wine, approximately 50,000 bubbles would have already nucleated, rose, and finally exploded during the first 3 min following pouring (Liger-Belair, 2002). Each bubble having a diameter close to 1 mm, this cloud of 50,000 bubbles totalizes a global exchange surface of approximately 1500 cm2 during the first 3 min following pouring. This is huge compared with the area of the air–wine interface which rarely exceeds 20 cm2 in a classical flute. However, each bubble collapsing at the wine’s surface inevitably frees its tiny CO2 volume. Consequently, the inevitable counterparty of the ‘‘exhausting’’ aromas effect attributed to


39

bubbles’ exchange surfaces is to progressively bring gaseous CO2 in the headspace above the wine’s surface. It is indeed well known that a sudden and abundant quantity of CO2 may irritate the nose during the evaluation of aromas (Liger-Belair and Rochard, 2008). The analytical parameter which characterizes the progressive release of gaseous CO2 desorbed from a glass poured with champagne is the total volume flux of CO2 escaping from the wine–air interface. The total CO2 volume fluxes outgassing from each type of drinking vessel poured with 100 mL of champagne are presented in the graph displayed in Fig. 1.25, during the first 10 min following the pouring process. Experimentally, during approximately the first 3 min following the pouring process, it is clear that total CO2 volume fluxes are significantly higher when champagne is served in the coupe than in the flute. Nevertheless, this tendency reverses from about 3 min until 10 min after pouring, and total CO2 volume fluxes outgassing from the flute become higher than those outgassing from the coupe (see the inset in Fig. 1.25). In a recent paper, it was demonstrated that the driving force behind the progressive desorption of CO2 from a glass poured with champagne

2.0 Coupe Flute

1.8

Total CO2 volume flux (cm3 s–1)

1.6 1.4

0.4

1.2

0.3

1.0

0.2

0.8

0.1

0.6

0.0 200

300

400

500

600

0.4 Immediately after pouring

0.2 0.0 0

100

200

300 t (s)

400

500

600

FIGURE 1.25 Total CO2 volume fluxes recordings (in cm3 s 1) desorbing from a flute and coupe, respectively, filled with 100 mL of champagne, all along the first 10 min following the pouring process; each datum of each time series is the arithmetic average of six successive values recorded from six successive pourings.

40


was its bulk concentration cL of dissolved CO2 (Liger-Belair et al., 2009b). Therefore, it seemed pertinent to propose a correlation between the CO2 volume flux outgassing from the flute and the continuously decreasing bulk concentration cL of dissolved CO2. To do so, time series data recordings displayed in Figs. 1.24 and 1.25 were combined. Time was eliminated so that the total CO2 volume flux outgassing from each type of drinking vessel was plotted as a function of champagne dissolved CO2 concentration cL. Correlations between total CO2 volume fluxes and dissolved CO2 concentrations in champagne are displayed in Fig. 1.26, for each type of drinking vessel. It is clear from Fig. 1.26 that, for a given dissolved CO2 concentration of champagne, total CO2 volume fluxes are significantly higher when champagne is served into a coupe than when it is served into a flute. Nevertheless, it is worth noting that from the point of view of champagne and other sparkling wine tasting, the pertinent parameter which characterizes a given type of drinking vessel with regard to its progressive CO2 release with time is not really the total CO2 volume flux outgassing from it. Actually, the open aperture of tasters’ nostrils being obviously much

2.0 Flute Coupe

Total CO2 volume flux (cm3 s–1)

1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 2

3

4

5 cL

6

7

8

(g L–1)

FIGURE 1.26 Total CO2 volume fluxes recordings (in cm3 s 1) desorbing from a flute and coupe, respectively, filled with 100 mL of champagne as a function of their dissolved-CO2 concentration; each data of each time series is the arithmetic average of six successive values recorded from six successive pourings; experimental data were fitted with polynomial functions which appear as dashed lines.


41

smaller than the surface area of the drinking vessel offered to total gas discharge, we propose a more adapted analytical parameter in order to compare the progressive CO2 release from various glasses, namely, the CO2 volume flux per unit surface area, denoted FUS, and deduced as follows for each type of drinking vessel: FUS ¼

FT A

(28)

with A being the free surface area of the given type of drinking vessel (given in Table 1.3). CO2 volume fluxes per unit surface area outgassing from each type of drinking vessel poured with 100 mL of champagne are presented in Fig. 1.27, during the first 10 min following the pouring process. It is clear from Fig. 1.27 that CO2 volume fluxes outgassing per unit surface area are much higher in the flute than in the coupe, during the 10 min following the pouring process. From the point of view of champagne tasting, it means that the flute should finally enable higher concentrations of gaseous CO2 above the champagne free surface during tasting (due to its narrower open aperture compared to that of the coupe), despite lower total CO2 volume fluxes in the first 3 min following pouring. Further determination of the gaseous CO2 concentration above

70 Flûte Coupe

60

FUS (mm3 s–1 cm–2)

50 40 30 20 10 0 0

100

200

300 t (s)

400

500

600

FIGURE 1.27 CO2 volume fluxes per unit surface (in mm3 s 1 cm 2) as determined with Eq. (28), desorbing from a flute and a coupe, respectively, filled with 100 mL of champagne, all along the first 10 min following the pouring process.

42


the champagne free surface, a parameter useful for champagne tasting, will be achieved by the use of gas microchromatography in a near future.

B. The role of temperature In order to test the influence of champagne temperature on the kinetics of CO2 release from a flute, experiments were performed at three sets of champagne temperature: 4, 12, and 20 C, respectively, in a temperaturecontrolled room (Liger-Belair et al., 2009b). The total CO2 volume fluxes outgassing from the flute poured with champagne, during the first 10 min following pouring and for each champagne temperature, are presented in the graph displayed in Fig. 1.28. Experimentally, it is clear that the lower the champagne temperature, the lower CO2 volume fluxes outgassing from the flute and especially in the early moments following the pouring process. As in the preceding paragraph, correlations between CO2 volume fluxes outgassing from the flute and dissolved CO2 concentrations in champagne were done. They are displayed in Fig. 1.29, for each 1.2 T = 4 ⬚C T = 12 ⬚C T = 20 ⬚C

CO2 volume flux (cm3 s–1)

1.0

0.8

0.6

0.4

0.2

0.0 0

100

200

300 t (s)

400

500

600

FIGURE 1.28 CO2 volume fluxes recordings (in cm3 s 1) desorbing from a 100-mL flute poured with champagne, all along the first 10 min following the pouring process, and at three different champagne temperatures; each datum of each time series is the arithmetic average of six successive values recorded from six successive pourings; standard deviations correspond to the root-mean-square deviations of the values provided by the six successive data recordings.


43

1.2 T = 4 ⬚C T = 12 ⬚C T = 20 ⬚C

CO2 volume flux (cm3 s–1)

1.0

0.8

0.6

0.4

0.2

0.0 2

3

4

5

6

7

cL (g L–1)

FIGURE 1.29 CO2 volume fluxes recordings (in cm3 s 1) desorbing from a 100-mL flute poured with champagne as a function of its dissolved-CO2 concentration, and at three different champagne temperatures.

champagne temperature. It is clear from Fig. 1.29 that, for a given dissolved CO2 concentration of champagne, the lower the champagne temperature, the lower CO2 volume fluxes outgassing from the flute.

VI. CLOSE-UP ON BUBBLES BURSTING AT THE LIQUID SURFACE A. The bursting process as frozen by high-speed photography A champagne bubble reaches the liquid surface with a size that depends on the distance h traveled from its nucleation site, as expressed in Eq. (27). Experimentally, it was observed that bubble diameters rarely exceed about 1 mm. At the free surface, the shape of a bubble results from a balance between two opposing effects: the buoyancy FB, of order of rgpR3, which tends to make it emerge from the liquid surface and a capillary force FC inside the hemispherical thin liquid film, of the order of spR, which tends to maintain the bubble below the liquid surface. In the case of champagne millimetric bubbles, buoyancy can be neglected in comparison to capillary effects. Consequently, like a tiny iceberg, a bubble only slightly emerges from the liquid surface, with most of its volume

44


remaining below the free surface. The emerged part of the bubble, the bubble-cap, is essentially a spherically shaped film of liquid, which gets thinner and thinner as the liquid drains back into the liquid bulk. A bubble-cap which has reached a critical thickness of about 100 nm becomes so thin and sensitive to such disturbances as vibrations and temperature change that it finally ruptures (Liger-Belair et al., 2001a). For bubbles of millimeter size, the disintegration of the bubble-cap takes from 10 to 100 ms. During this extremely brief initial phase, the bulk shape of the bubble is literally ‘‘frozen’’, and a nearly millimetric open cavity remains as a tiny indentation in the liquid surface (see the high-speed photograph displayed in Fig. 1.30). Then, a complex hydrodynamic process ensues, causing the collapse of the submerged part of the bubble and projecting into the air a liquid jet which quickly breaks up into tiny droplets of liquid (called jet drops). This process is indeed characteristic of every carbonated beverage. Generally speaking, the number, size, and velocity of jet drops produced during bubble collapse depend on the size of the initial bursting bubble (Spiel et al., 1994, 1995, 1997, 1998). In Fig. 1.31, the close-up high-speed photograph of a tiny liquid jet caused by the collapse of a champagne bubble is displayed (Liger-Belair et al., 2001a).

FIGURE 1.30 The bubble-cap of a bubble at the champagne surface has just ruptured (on a time scale of 10–100 ms); during this extremely brief initial phase, the bulk shape of the bubble has been ‘‘frozen,’’ and a nearly millimetric open cavity remains as a tiny indentation in the liquid surface (bar ¼ 1 mm; photograph by Ge´rard Liger-Belair).


45

FIGURE 1.31 The collapsing bubble cavity gives rise to a high-speed liquid jet above the champagne surface (bar ¼ 1 mm; photograph by Ge´rard Liger-Belair).

B. A paternoster for surface active molecules As champagne or sparkling wine is poured into a glass, the myriad of ascending bubbles collapses and therefore radiates a multitude of tiny droplets above the free surface, into the form of very characteristic and refreshing aerosols, as shown in the photograph displayed in Fig. 1.32. Those tiny droplets ejected up to several centimeters above the liquid surface, partly evaporate themselves, thus accelerating the transfer of the numerous aromatic volatile organic compounds above the liquid surface. This very characteristic fizz considerably enhances the flavor release in comparison with that from a flat wine, for example. Laser tomography techniques were applied to freeze the huge number of bursting events and the myriad of droplets ejected above champagne glasses in real consuming conditions (see the tomography of the droplets’ cloud above the surface of a coupe displayed in Fig. 1.33; Liger-Belair et al., 2008). Moreover, based on a phenomenological analogy between the fizz of the ocean and the fizz in Champagne wines, it was hypothesized a few years ago, that aerosols found in the headspace above a glass poured with champagne could considerably enhance the fragrance release of champagne by bringing chemical compounds to the taster’s nostrils, showing both surface activity and organoleptic interest (Liger-Belair et al., 2001a). Very recently, ultra high resolution mass spectrometry was used in order to analyze the aerosols released by champagne bubbles (Liger-Belair et al., 2009a). Compared with the liquid bulk in the glass itself, the aerosols

46


FIGURE 1.32 The collapse of hundreds of bubbles at the free surface radiates a cloud of tiny droplets which is characteristic of champagne and other sparkling wines and which complements the sensual experience of the taster (#Alain Cornu/Collection CIVC).

FIGURE 1.33 The cloud constituted by myriads of tiny droplets ejected from bubbles’ bursting above the surface of a coupe, as seen through laser tomography technique; the droplets’ trajectories are materialized by blue streaks of light during the 1 s-exposure time of a digital photo camera (photograph by G. Liger-Belair, F. Beaumont, and G. Polidori).


47

35 30 25

Cf

20 15 10 5 0 200

300

400

500

600 m/z

700

800

900 1000

FIGURE 1.34 Concentration factors analysis of all masses present in the mass spectra of champagne aerosols and bulk, respectively (in the whole mass range m/z 150–1000).

contained an overconcentration of compounds known to be aromatic or the precursors of aromas (see Fig. 1.34). Each dot displayed in Fig. 1.34 represents the concentration factor of a given compound found in the aerosols (i.e., the ratio of its concentration in the aerosols to its concentration in the bulk, below the champagne surface). These compounds, mostly including saturated and unsaturated fatty acids, act as surfactants (i.e., as double-ended compounds with one end attracted to the liquid phase and another that shuns it). It was suggested that champagne bubbles drain these compounds out of the liquid bulk toward the liquid surface, with the hydrophobic end attracted by the bubble’s airy inside and the hydrophilic end attracted by the liquid outside. The bubbles then rise to the surface of the glass where they pop, releasing the compounds as aerosols. A detailed scheme of this mechanism is displayed in Fig. 1.35, together with high-speed photographs of the bursting process. This recent discovery supports the idea that rising and collapsing bubbles act as a continuous paternoster lift for aromas in every glass of champagne. Aerosols were thus found to hold the organoleptic ‘‘essence’’ of champagne.

C. When champagne bubbles dress up like flowers The close observation of bubbles collapsing at the free surface of a glass poured with champagne also revealed another unexpected and lovely phenomenon. A few seconds after pouring, and after the collapse of the

48


Collecting glass slide Mixed amphiphilic monolayer

Bubble burst

Bursting jet

Bubble

A

B

C

FIGURE 1.35 Scheme of the ‘‘bubble bursting’’ mechanism responsible for the ejection of champagne aerosols overconcentrated with compounds showing both surface activity and aromatic properties (these compounds appear as red dots); below are displayed high-speed photographs of a bubble collapse, leading to the projection of a liquid jet which quickly breaks up into tiny droplets (bar ¼ 1 mm).

foamy head, the surface of a champagne flute is covered with a layer of bubbles—a kind of bubble raft, also called bidimensional foam, where each bubble is generally surrounded by six neighboring bubbles (see Fig. 1.36). Scientifically speaking, bubbles arrange themselves in an approximate hexagonal pattern, strikingly resembling those in beeswax. While snapping pictures of the bubble raft after pouring, Liger-Belair also accidentally took some pictures of bubbles collapsing close to one another in the raft. When the bubble-cap of a bubble ruptures and leaves an open cavity at the free surface, adjacent bubble-caps are sucked toward this empty cavity and create unexpected and short-lived flower-shaped structures, unfortunately invisible to the naked eye (see the high-speed photograph displayed in Fig. 1.37; Liger-Belair et al., 2001b; Liger-Belair and Jeandet, 2003). Shear stresses induced by bubbles trapped in the close vicinity of a

FIGURE 1.36 A few seconds after pouring, and after the collapse of the foamy head, the surface of a champagne flute is covered with a layer of quite monodisperse millimetric bubbles, where bubbles arrange themselves in an approximate hexagonal pattern, strikingly resembling those in beeswax (photograph by Ge´rard Liger-Belair).

FIGURE 1.37 Flower-shaped structure found during the collapse of bubbles in the bubble raft at the free surface of a flute poured with champagne (bar ¼ 1 cm; photograph by Ge´rard Liger-Belair).

50


FIGURE 1.38 Shear stresses experienced by bubbles adjacent to a collapsing one at the free surface of a flute poured with champagne (bar ¼ 1 cm; photograph by Ge´rard LigerBelair).

collapsing one are even better visualized on the high-speed photograph displayed in Fig. 1.38, where the bubble raft is not complete. Such behavior first appeared counter intuitive to me. Paradoxically, adjacent bubblecaps are sucked and not blown-up by bursting bubbles, contrary to what could have been expected at first glance. Actually, after the disintegration of a bubble-cap, the hexagonal symmetry around adjoining bubbles is suddenly locally broken. Therefore, the symmetry in the field of capillary pressure around adjoining bubbles is also locally broken. Capillary pressure gradients all around the now empty cavity are detailed in Fig. 1.39. Signs indicate a pressure above/ below the atmospheric pressure P0. Finally, inertia and gravity being neglected, the full Navier–Stokes equation applied to the fluid within the thin liquid film of adjoining bubble-caps drawn by capillary pressure gradients, reduces itself to a simple balance between the capillary pressure gradients and the viscous dissipation as follows: ! D! u S ¼ rP (29) S

where u is the velocity in the thin liquid film of adjacent bubble-caps, is ! the champagne viscosity, r P are the capillary pressure gradients, and S being the axial coordinate which follows the bubble-cap’s curvature and along which the fluid within the thin film is displaced. The asymmetry in the capillary pressure gradients distribution around a bubble-cap adjacent to an empty cavity is supposed to be the main


51

P0 P0 ΔP1≈ γ/R

(∇P)S

ΔP

ΔP ≈ 2γ/R s

u

Bubble-cap Air

Free surface

2R

Champagne R

R

Bubble (∇P)S ∝(ΔPC −ΔP1)/R ≈ −3γ/R 2 P0 ΔPC ≈ −2γ/R

FIGURE 1.39 Schematic transversal representation of the situation, as frozen after the disintegration of the central bubble-cap.

driving force of the violent sucking process experienced by a bubble-cap in touch with a bursting bubble. Actually, due to higher capillary pressure gradients, the liquid flows that develop in the half part of the bubble-cap close to the open cavity are thus expected to be higher than those which develop in the rest of the bubble-cap. It ensues a violent stretching of adjoining bubble-caps toward the now empty cavity, which is clearly visible on the photographs displayed in Figs. 1.37 and 1.38. More recently, those flower-shaped structures have been observed during the coarsening of bidimensional aqueous foams, obtained by mixing a surfactant, sodium dodecyl sulphate (SDS), with pure water (Ritacco et al., 2007). But it is worth noting that this lovely and short-lived process was first observed at the top of a champagne flute.

D. Avalanches of bursting events in the bubble raft? Actually, avalanches of popping bubbles were put in evidence during the coarsening of bidimensional and three-dimensional aqueous foams (Ritacco et al., 2007; Vandewalle et al., 2001). How does the bubble raft behave at the surface of a flute poured with champagne? Does a bursting

52


bubble produce a perturbation which extends to the neighboring bubbles and induce avalanches of bursting events which finally destroy the whole bubble raft? In the case of champagne wines, a few time sequences of bubbles bursting in the bubble raft have been captured with a high-speed video camera. One of them is displayed in Fig. 1.40. Between Frame 1 and Frame 2, the bubble pointed with the black arrow has disappeared. In Frame 2, neighboring bubbles are literally sucked toward this now bubble-free area. Then, neighboring bubbles oscillate during a few milliseconds and progressively recover their initial hemispherical shape. In conclusion, in the case of bubbles adjacent to the collapsing ones, despite high shear stresses produced by a violent sucking process, bubbles adjacent to the collapsing ones were never found to This bubble-cap is about to rupture

1

2

3

4

5

6

FIGURE 1.40 Time sequence illustrating the dynamics of adjoining bubbles in touch with a collapsing one at the free surface of flute poured with champagne; the whole process was filmed at 1500 frames/s; from frame 4, in the center of the empty cavity left by the collapsing bubble, a tiny air-bubble entrapment is observed (bar ¼ 1 mm; photographs by Ge´rard Liger-Belair).


53

rupture and collapse in turn, thus causing a chain reaction. At the free surface of a flute poured with champagne, bursting events appear to be spatially and temporally noncorrelated. The absence of avalanches of bursting events seems to be linked to the champagne viscosity (which is about 50% higher than that of pure water; Ritacco et al., 2007). It can also be noted that a tiny daughter bubble, approximately 10 times smaller than the initial central bubble, has been entrapped during the collapsing process of the central cavity (as clearly seen in Frames 3 and 4 of Fig. 1.40). Bubble entrapment during the collapsing process was already experimentally and numerically observed with single millimetric collapsing bubbles (Duchemin et al., 2002; Herman and Mesler, 1987), including champagne bubbles (Liger-Belair et al., 2001a).

ACKNOWLEDGMENTS This research was partially supported by the Europol’Agro institute and by the Conseil Geńe´ral de la Marne. Thanks are due to Champagne Moe¨t & Chandon and Pommery for regularly supplying us with wine samples, and to Jean-Claude Colson and AROCU for encouraging and supporting our research. The author is also grateful to the CIVC for providing them with the photograph displayed in Fig. 1.1, and to Jacques Honvault for providing them with the photograph displayed in Fig. 1.5. Ge´rard Liger-Belair finally warmly thanks Clara Cilindre, Re´gis Gougeon and Philippe Schmitt-Kopplin for helpful discussions about the role played by bursting bubbles on the release of aromas.

REFERENCES Agabaliantz, G. G. (1963). Bases scientifiques de la technologie des vins mousseux. Bull. O.I.V. 36, 703–714. Autret, G., Liger-Belair, G., Nuzillard, J.-M., Parmentier, M., Dubois de Montreynaud, A., Jeandet, P., Doan, B. T., and Beloeil, J.-C. (2005). Use of magnetic resonance spectroscopy for the investigation of the CO2 dissolved in champagne and sparkling wines: A nondestructive and unintrusive method. Anal. Chim. Acta. 535, 73–78. De Gennes, P.-G., Brochard-Wyart, F., and Que´re´, D. (2002). Gouttes, bulles, perles et ondes. Belin, Paris. Duchemin, L., Popinet, S., Josserand, C., and Zaleski, S. (2002). Jet formation in bubbles bursting at a free surface. Phys. Fluids 14, 3000–3008. Dussaud, A. (1993). Etude des proprie´te´s de surface statiques et dynamiques de solutions alcooliques de proteínes: Application a` la stabilite´ des mousses de boissons alcooliseés. Ph.D. Thesis, ENSIAA, Massy, France. Herman, J. and Mesler, R. (1987). Bubble entrainment from bursting bubbles. J. Colloid Interface Sci. 117, 565–569. Jones, S. F., Evans, G. M., and Galvin, K. P. (1999). Bubble nucleation from gas cavities: A review. Adv. Colloid Interface Sci. 80, 27–50. Lide, D. R. and Frederikse, H. P. (1995). CRC Handbook of Chemistry and Physics. 76th edn. CRC Press, Boston. Liger-Belair, G. (2002). Physicochemical approach to the effervescence in Champagne wines. Ann. Phys. (Paris) 27, 1–106. Liger-Belair, G. (2003). The science of bubbly. Sci. Am. 288, 80–85.

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Liger-Belair, G. (2004). Uncorked: The Science of Champagne. Princeton University Press, Princeton. Liger-Belair, G. (2005). The physics and chemistry behind the bubbling properties of champagne and sparkling wines: A state-of-the-art review. J. Agric. Food Chem. 53, 2788–2802. Liger-Belair, G. (2006). Nucleátion, ascension et ećlatement d’une bulle de champagne. Ann. Phys. (Paris) 31, 1–133. Liger-Belair, G. (2009). Le Champagne: Effervescence! La science du champagne. Odile Jacob, Paris. Liger-Belair, G. and Jeandet, P. (2003). Capillary-driven flower-shaped structures around bubbles collapsing in a bubble raft at the surface of a liquid of low viscosity. Langmuir 19, 5771–5779. Liger-Belair, G. and Rochard, J. (2008). Les vins effervescents, du terroir a` la bulle. Dunod, Paris. Liger-Belair, G., Lemaresquier, H., Robillard, B., Duteurtre, B., and Jeandet, P. (2001a). The secrets of fizz in champagne wines: A phenomenological study. Am. J. Enol. Vitic. 52, 88–92. Liger-Belair, G., Robillard, B., Vignes-Adler, M., and Jeandet, P. (2001b). Flower-shaped structures around bubbles collapsing in a bubble monolayer. C. R. Phys. 2, 775–780. Liger-Belair, G., Prost, E., Parmentier, M., Jeandet, P., and Nuzillard, J.-M. (2003). Diffusion coefficient of CO2 molecules as determined by 13C NMR in various carbonated beverages. J. Agric. Food Chem. 51, 7560–7563. Liger-Belair, G., Topgaard, D., Voisin, C., and Jeandet, P. (2004). Is the wall of a cellulose fiber saturated with liquid whether or not permeable with CO2 dissolved molecules: Application to bubble nucleation in champagne wines. Langmuir 20, 4132–4138. Liger-Belair, G., Tufaile, A., Robillard, B., Jeandet, P., and Sartorelli, J.-C. (2005a). Periodadding route in sparkling bubbles. Phys. Rev. E 72, 037204. Liger-Belair, G., Voisin, C., and Jeandet, P. (2005b). Modeling non-classical heterogeneous bubble nucleation from cellulose fibers: Applications to bubbling in carbonated beverages. J. Phys. Chem. B 109, 14573–14580. Liger-Belair, G., Parmentier, M., and Jeandet, P. (2006a). Modeling the kinetics of bubble nucleation in champagne and carbonated beverages. J. Phys. Chem. B 110, 21145–21151. Liger-Belair, G., Tufaile, A., Jeandet, P., and Sartorelli, J.-C. (2006b). Champagne experiences various rhythmical bubbling regimes in a flute. J. Agric. Food. Chem. 54, 6989–6995. Liger-Belair, G., Religieux, J.-B., Fohanno, S., Vialatte, M.-A., Jeandet, P., and Polidori, G. (2007). Visualization of mixing flow phenomena in champagne glasses under various glass-shape and engravement conditions. J. Agric. Food Chem. 55, 882–888. Liger-Belair, G., Polidori, G., and Jeandet, P. (2008). Recent advances in the science of champagne bubbles. Chem. Soc. Rev. 37, 2490–2511. Liger-Belair, G., Cilindre, C., Gougeon, R., Lucio, M., Gebefu¨gi, I., Jeandet, P., and SchmittKopplin, P. (2009a). Unraveling different chemical fingerprints between a champagne wine and its aerosols. Proc. Natl. Acad. Sci. USA 106, 16545–16549. Liger-Belair, G., Villaume, S., Cilindre, C., and Jeandet, P. (2009b). Kinetics of CO2 fluxes outgassing from champagne glasses in tasting conditions: The role of temperature. J. Agric. Food Chem. 57, 1997–2003. Liger-Belair, G., Villaume, S., Cilindre, C., Polidori, G., and Jeandet, P. (2009c). CO2 volume fluxes outgassing from champagne glasses in tasting conditions: Flute versus coupe. J. Agric. Food Chem. 57, 4939–4947. Lubetkin, S. D. (2003). Why is it much easier to nucleate gas bubbles than theory predicts? Langmuir 19, 2575–2587. Lubetkin, S. D. and Blackwell, M. (1988). The nucleation of bubbles in supersaturated solutions. J. Colloid Interface Sci. 126, 610–615.


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Lugli, F. and Zerbetto, F. (2007). An introduction to bubble dynamics. Phys. Chem. Chem. Phys. 9, 2447–2456. Lynch, D. M. and Bamforth, C. W. (2002). Measurement and characterisation of bubble nucleation in beer. J. Food. Sci. 67, 2696–2701. O’Sullivan, A. (1997). Cellulose: The structure slowly unravels. Cellulose 4, 173–207. Polidori, G., Beaumont, F., Jeandet, P., and Liger-Belair, G. (2008). Visualization of swirling flows in champagne glasses. J. Visual. 11, 184. Priser, C., Etievant, P. X., Nicklaus, S., and Brun, O. (1997). Representative champagne wine extracts for gas chromatography olfactometry analysis. J. Agric. Food Chem. 45, 3511–3514. Ritacco, H., Kiefer, F., and Langevin, D. (2007). Lifetime of bubble rafts: Cooperativity and avalanches. Phys. Rev. Lett. 98, 244501. Ronteltap, A. D., Hollemans, M., Bisperink, C. G., and Prins, A. (1991). Beer foam physics. Master Brew. Ass. Am. Tech. Quart. 28, 25–32. Shafer, N. E. and Zare, R. N. (1991). Through a beer glass darkly. Phys. Today 44, 48–52. Spiel, D. E. (1994). The number and size of jet drops produced by air bubbles bursting on a fresh water surface. J. Geophys. Res. 99, 10289–10296. Spiel, D. E. (1995). On the birth of jet drops from bubbles bursting on water surfaces. J. Geophys. Res. 100, 4995–5006. Spiel, D. E. (1997). More on the births of jet drops from bubbles bursting on seawater surfaces. J. Geophys. Res. 102, 5815–5821. Spiel, D. E. (1998). On the birth of film drops from bubbles bursting on seawater surfaces. J. Geophys. Res. 103, 24907–24918. Tominaga, T., Guimbertau, G., and Dubourdieu, D. (2003). Role of certain volatile thiols in the bouquet of aged Champagne wines. J. Agric. Food Chem. 51, 1016–1020. Topgaard, D. (2003). Nuclear Magnetic Resonance Studies of Water Self-diffusion in Porous System. Ph.D. thesis, Lund University, Sweden. Vandewalle, N., Lentz, J.-F., Dorbolo, S., and Brisbois, F. (2001). Avalanches of popping bubbles in collapsing foams. Phys. Rev. Lett. 86, 179–182. Zhang, Y. and Xu, Z. (2008). Fizzics of bubble growth in beer and champagne. Elements 4, 47–49.

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CHAPTER

2 Chemometric Brains for Artificial Tongues Paolo Oliveri,1 M. Chiara Casolino, and Michele Forina

Contents

Introduction Terminology History Main Application Sectors Analytical Techniques A. Potentiometry B. Voltammetry C. Impedance spectroscopy VI. Chemometrics A. Multivariate design of experiments B. Preprocessing C. Exploratory analysis D. Classification and class-modeling E. Regression F. Validation VII. Artificial Tongue Applications in the Food Science VIII. Conclusions References

Abstract

The last years showed a significant trend toward the exploitation of rapid and economic analytical devices able to provide multiple information about samples. Among these, the so-called artificial tongues represent effective tools which allow a global sample

I. II. III. IV. V.

58 61 62 63 66 67 68 68 69 71 73 79 83 93 96 98 108 109

Department of Drug and Food Chemistry and Technology, University of Genoa, Genoa, Italy Corresponding author: Paolo Oliveri, E-mail address: [email protected]

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characterization comparable to a fingerprint. Born as taste sensors for food evaluation, such devices proved to be useful for a wider number of purposes. In this review, a critical overview of artificial tongue applications over the last decade is outlined. In particular, the focus is centered on the chemometric techniques, which allow the extraction of valuable information from nonspecific data. The basic steps of signal processing and pattern recognition are discussed and the principal chemometric techniques are described in detail, highlighting benefits and drawbacks of each one. Furthermore, some novel methods recently introduced and particularly suitable for artificial tongue data are presented.

I. INTRODUCTION The evolution of food processing from small-scale craft production to an industrial scale of production of food created several new needs. Among these, the possibility of performing an analytical supervision, monitoring every production stage, is one of the most important. Such control may address two main issues. The first one is a research-and-development aim: to study all the factors involved in the process, in order to ascertain the optimal production conditions, which are able to provide a product with the highest consumer-satisfaction rate while keeping costs and time as low as possible. The second purpose concerns the normal production phase and involves continuous monitoring of the manufacturing processes during regular daily manufacturing for the ability to determine, in real time, any eventual faults. Also in this case, the final aim is to achieve significant cost and time savings. Another important analytical requirement concerns the quality control of the incoming ingredients and of the outgoing finished product. Quality, according to Taguchi (1986), is intended as the minimization of the variability about a target value, as indicated in Fig. 2.1.

No

Quality

High variability

Yes

Information

FIGURE 2.1 Implications of variability amount.

Chemometric Brains for Artificial Tongues

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All these issues require the execution of analyses with a very high frequency. As a result, the demand for instrumentation able to provide chemical and physical information rapidly and cheaply is constantly increasing. Furthermore, analytical techniques should be performed without destruction of samples or, at least, with a minimal sample consumption, thus facilitating at-, on-, and in-line implementation. They should be easy to perform, automatable, and robust. From the consumer’s point of view as well, controls on food products are needed, from a food safety and traceability perspective. The problem of adulteration and falsification of food has ancient roots which date back to the Roman Age and continue, through the Middle and Modern Ages, till nowadays: the deployment of new analytical tools for fraud detection has caused a parallel progress in the adulteration procedures, which has gradually evolved from coarse and rudimentary systems to highly sophisticated and scarcely detectable strategies. For these reasons, in the last decades, there has been an increasing attention, among national and international authorities, toward food safety and authenticity problems: the Food and Agricultural Organization (FAO) and the World Health Organization (WHO) played a chief role in regulation and control. During the years 1961–1963, the Codex Alimentarius Commission (CODEX) has been instituted as an intergovernmental organization devoted to establish international food standards, such as hygienic practice policies, food labeling codes, limits for additives, contaminants, pesticides, veterinary drug residues, and so on (Lupien, 2002). The CODEX efforts over the past fifty years have led to far more than 200 standards for different food products and guidelines for labeling, sampling, and analysis (http://www.codexalimentarius.net/). Also the consumer’s needs—exactly like the manufacturer’s ones— require rapid, effective, low-cost, and nondestructive analytical techniques, in order to accomplish a widespread control and achieve broader quality assurances saving the costs. Instrumental devices able to address all these requirements have been defined by Valca´rcel and Ca´rdenas (2005) as ‘‘vanguard analytical strategies’’. Frequently, such methods provide nonspecific information, which provides a global sample characterization analogous to a fingerprint and sometimes can also be used to identify or quantify specific analytes in a sample. Such nonspecific techniques, in comparison with the classical specific analytical approaches, usually offer several advantages, the most significant of which are high speed, low costs, no or minimal sample pretreatments, no destruction of the sample, no requirement for highly skilled personnel, the possibility of at-, on-, and in-line deployments, automation, and transportability. Typical examples are constituted by spectrophotometric techniques and by the so-called artificial noses and

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artificial tongues. These latter devices have been developed with the initial aim of reproducing the mammalian sensorial evaluation of samples in many potential application fields: analysis of bioactive and/or toxic substances, like pharmaceutical formulations and environmental samples; replacement of human sensory panels, overcoming some related hurdles like limitations in the number of samples assessable in each test session, high costs, and low repeatability and reproducibility of the responses. The sensations in mammals result from neural processing of thousands of stimuli coming from many different peripheral sensors. These two main elements—inputs from terminal sensors and central processing—are exactly what the sensory-like analytical techniques aim at reproducing: the sensorial stimulus is replaced by the response of an instrumental sensor or a sensor array suitably arranged, while the elaboration stage consists of proper processing of instrumental data (see Fig. 2.2). Since both sensorial stimuli and brain elaboration in nature are multiple and simultaneous, in order to reproduce such mechanisms also the instrumental data processing should be conducted in a multivariate way. In parallel, during recent years, several applications of artificial noses and tongues were demonstrated to be suitable not only for a sensory-like evaluation but for a wider-ranging characterization of the samples. Nonspecific analytical responses, in fact, may provide information about the

Multivariate processing

Transmission

Transduction

Sensorial input

FIGURE 2.2 Parallelism between sensorial mechanisms in mammals and sensory-like analytical techniques.


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geographical origin of a product, the quality of the ingredients employed, the type of manufacturing process, and so on. As a consequence of this big potential, the application fields of artificial noses and tongues have been considerably extended, embracing a number of sectors other than food science. Anyway, whatever the instrument and the application, an aspect is unvarying: the only way to obtain valuable information from these analytical systems is to process data by means of multivariate tools, that is to say, to make use of chemometrics. Obviously, the quality of the final information depends both on the quality of the sensors and on that of the brain. In this review, an in-depth overview is presented, tracing an outline of the chemometric techniques most widely applied in the relatively brief history of artificial tongues, highlighting benefits and drawbacks of each one. Furthermore, some chemometric methods recently introduced and particularly suitable for artificial tongue data processing are discussed.

II. TERMINOLOGY The term artificial tongue is used in two main branches of science. The first one concerns the neurophysiological studies aimed at developing perceptual supplementation devices, with biomedical engineering applications to human disabilities. The second utilization of the term artificial tongue concerns, instead, the laboratory analytical instruments used in combination with chemometric techniques to obtain complex information (often sensory-like, but not only) on samples. As for this latter meaning, also the synonymous electronic tongue is frequently used, particularly for electroanalytical devices. In order to better describe the utilization purposes, some authors proposed a distinction between electronic tongues and taste sensors: the former term should have a wider meaning, embracing all the possible applications, while the latter should exclusively refer to sensory-like evaluations. A sensor is a device able to respond to the presence of one or many given substances in a more-or-less selective way, by means of a reversible chemical interaction: it may be employed for qualitative or quantitative determinations (Cattrall, 1997). All sensors are composed of two parts: the responsive region and the transducer. The responsive region is responsible for sensitivity and selectivity of the sensor, while the transducer converts energy from one to another form, providing a signal which is informative about the system analyzed. Usually, basic signalprocessing electronics, and control and display units complete the device.

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Nowadays, the signals are almost always digitally recorded and stored for subsequent processing, which involves the application of chemometric pretreatment and pattern recognition tools. A wide number of sensor types have been described in the literature, from optical to mass spectrometry-based devices, but the sensors most commonly used in artificial tongues are electrochemical.

III. HISTORY The history of electrochemical sensors began in the thirties of the twentieth century, when the pH-sensitive glass electrode was deployed, but no noteworthy development was carried out till the middle of that century. In 1956, Clark invented his oxygen-sensor based on a Pt electrode; in 1959, the first piezoelectric mass-deposition sensor (a quartz crystal microbalance) was produced. In the sixties, the first biosensors (Clark and Lyons, 1962) and the first metal oxide semiconductor-based gas sensors (Taguchi, 1962) started to appear. Nevertheless, it was only starting from the seventies and the eighties that the technology began to deploy highly sophisticated devices, which are continuously evolving toward an increasing miniaturization and the development of portable instrumentation. The first utilization of the term artificial tongue dates to 1978, when H.W. Harper and M. Rossetto presented an apparatus based on conductance measurements able to mimic the taste stimulus delivery systems (Harper and Rossetto, 1978). This pioneering work has represented an isolated study for several years. The first example of a factual taste sensor was developed, in fact, by Toko and coworkers in 1990 (Hayashi et al., 1990; Toko et al., 1990). It was based on ionsensitive lipid membranes and it was claimed to be able to respond to the basic tastes of the human tongue: sour, sweet, bitter, salt, and umami. The term electronic tongue was created in 1996, at the Eurosensors X Conference by A. Legin, C. Di Natale and coworkers (Di Natale et al., 1996; Legin et al., 1996). Starting from the nineties, the number of original research papers on ISI journals has been gradually increasing for almost one decade, showing the maximum expansion between 2003 and 2008, as it is noticeable in the bar-plot of Fig. 2.3. A few commercial instruments have become available, for instance, from Insent Inc. (Atsugi-chi, Japan) and Alpha MOS (Toulouse, France). They are potentiometric devices for taste sensing, mainly used in the pharmaceutical sector.


63

35

Number of publications

30 25 20 15 10 5 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Publication year

FIGURE 2.3 Trend of electronic tongue original research papers over the period 1996–2009. Data obtained from a literature search using SciFinder Scholar.

IV. MAIN APPLICATION SECTORS Taste sensors, and, more generally, artificial tongues may work in two main modalities: qualitative and quantitative. The qualitative approach usually consists in the identification and classification of samples on the basis of some particular property, like, for instance, the taste or the geographical origin. Quantitative applications, instead, may be either the simultaneous calibration of multiple analytes or the prediction of sensorial attributes and/or chemical-physical parameters. Not rarely, in the same study, both of the approaches are followed: data collected with the same instrument from the same samples are treated by different chemometric strategies to obtain qualitative and quantitative information, respectively. For example, the nonspecific voltammetric profiles recorded in wine samples may be used both for characterizing wines on the basis of their provenance and for quantifying parameters of oenological interest. From examination of almost 230 papers published in ISI journals over the period 1996–2009, it emerges that the many sectors in which artificial tongues have found applications go from the industrial plant processmonitoring to biomedical and clinical studies (see Fig. 2.4). As for this latter field, a number of sensors for the determination of several clinical

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Environmental Pharmaceutical Clinical Food

Industrial monitoring

FIGURE 2.4 Main application sectors of electronic tongues. Data obtained from a literature search over the period 1996–2009, using SciFinder Scholar.

parameters such as urea, glucose, and triglycerides and for the diagnosis of several diseases by nonspecific analysis of biological liquids have been deployed (Ciosek et al., 2008; Mottram et al., 2007; Sangodkar et al., 1996; Sohn et al., 2005; Sukeerthi and Contractor, 1999; Wang et al., 2007). Another considerable employment sector is that of environmental analyses, particularly in relation to the detection of heavy metal traces and various organic contaminants in water, such as pesticides and residuals from industrial plants (Aoki et al., 2009; Calvo et al., 2008; Carvalho et al., 2007; Constantino et al., 2004; Cortina et al., 2006; Di Natale et al., 1997; Gutes et al., 2005; Gutierrez et al., 2008; Hu et al., 2008; Ipatov et al., 2008; Kulapina and Mikhaleva, 2005; Makarova and Kulapina, 2009; Martinez-Manez et al., 2005; Men et al., 2004, 2005; Mikhaleva and Kulapina, 2006; Mourzina et al., 2001; Olsson et al., 2008; Turek et al., 2009; Valdes-Ramirez et al., 2009). A further widespread area of utilization, particularly of the taste sensors, is represented by pharmaceutical technology studies: in fact, in order to enhance patient compliance to oral drugs, there is an incessant search for pharmaceutical formulations able to mask the bitter and/or disagreeable taste which characterizes many active principles; however, since such formulations are biologically active, traditional taste assessments by means of panel tests are a problematical task. For this reason, instrumental devices, such as artificial tongues, able to provide a response correlated with human taste perception are enormously advantageous (Agresti et al., 2008; Kayumba et al., 2007; Krishna Kumar, 2006; Legin et al., 2004; Li et al., 2007a; Lorenz et al., 2009; Sadrieh et al., 2005; Takagi et al., 2001; Tokuyama et al., 2009; Zheng and Keeney, 2006). Anyway, the principal use of artificial tongues is within the food sciences. The applications concern almost exclusively liquid food: mainly wine (about 18% of the studies examined), fruit juices (almost 15%), mineral water (about 13%), followed by infusions like tea and coffee, soft drinks, milk, beer, and other alcoholic beverages. All these liquid foods are characterized by both low-viscosity and high-polarity values,

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which provide them a suitable electrical conductivity. Instead, lowpolarity and high-viscosity liquids, such as vegetable oils and honey, are rather less studied, due to the intrinsic difficulties in performing electroanalytical measurements in such media. Only a few examples of application to solid foods, like vegetables and fish, can be found in the literature: in all these cases, the analytical protocol includes more-or-less complex sample pretreatment stages (Bengtsson et al., 2007; Beullens et al., 2006; Ciosek et al., 2006c; Rudnitskaya et al., 2006a,b). Some, especially early, studies based on electronic tongues explored the possibility of differentiating liquid foods with very different natures-like, for example, mineral water, wine, milk, coffee, and so on, as in the example of Fig. 2.5 (Ciosek and Wroblewski, 2007; Gallardo et al., 2005; Vlasov et al., 2000). Such studies are of limited practical interest since, in everyday life, it is possible—for example—to easily distinguish coffee from fruit juice without making use of electroanalysis and chemometrics. Nevertheless, in many cases, these studies aimed at assessing the potential of novel sensors and served as the bases for subsequent research work and technical improvements. Examples of more advanced applications are, for instance, the discrimination among different brands of the same food (Ciosek and Wroblewski, 2006; Ciosek et al., 2004a,b, 2005, 2006a–d, 2007, 2009; Legin et al., 1999; Martina et al., 2007; Riul et al., 2003a,b; Winquist et al., 2000).

0.6 Beer Fruit juice

0.5

0.4 PC2

Mineral water 0.3

Coffee

0.2

Tea Soft drinks

0.1 –1.0

–0.8

–0.6

–0.4

–0.2

0.0

0.2

0.4

PC1

FIGURE 2.5 Differentiation of beverages of very different nature by means of an electronic tongue based on potentiometric sensor arrays (reproduced from Vlasov et al., 2000, with permission).

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Other interesting applications arise from studies concerning vegetable-derived foods, where the differentiation is carried out according to the botanical variety or cultivar of the plant involved (Beullens et al., 2008; Dias et al., 2008; Kantor et al., 2008; Rudnitskaya et al., 2006b). In other cases, the attention is focused onto the geographical origin (Buratti et al., 2004; Cosio et al., 2006; Oliveri et al., 2009). A considerable part of the studies based on artificial tongues within food sciences aims at evaluating food taste properties, like bitterness and astringency and/or typical descriptors of sensorial analysis (Buratti et al., 2006; Buratti et al., 2007; Fung et al., 2004; Legin et al., 2003; Li et al., 2007b; Puech et al., 2007; Rudnitskaya et al., 2009a; Scampicchio et al., 2006). Such applications usually present worse results than those obtained for the prediction of chemical quantities and objective chemical–physical descriptors. The reasons for this inferiority do not come—or, at least, not only—from instrumental inadequacy, but from the poor repeatability and reproducibility which inexorably characterize sensorial analyses, in spite of the accurate training of the panels and their considerable remuneration. In some interesting studies, artificial tongues are employed to evaluate the effect of a number of process factors on the quality of the finished products (Esbensen et al., 2004; Rollm de Moura et al., 2007; Rudnitskaya et al., 2009b) or the effects of storage time and conditions (Apetrei et al., 2007; Cosio et al., 2007; Kantor et al., 2008; Parra et al., 2006a; RodriguezMendez et al., 2007). Other important research activities aim at carrying out the setting up of analytical methods specifically committed to fraud detection (Chen et al., 2008; Dias et al., 2009; Legin et al., 2005; Parra et al., 2006b).

V. ANALYTICAL TECHNIQUES Several analytical devices have found application as test sensors or, more generally, as electronic tongues for characterizing foods or food ingredients, being able to provide information related to the human sensorial perception or to other important features. There are some examples of electronic tongues based on optical techniques as well as on mass measurements, but the analytical methods that have been most widely exploited in this field are, without any doubt, the electrochemical ones, as shown in Fig. 2.6. Within the electroanalytical sector, potentiometry and voltammetry are the principal methods applied in electronic tongue studies, followed by impedance spectroscopy.


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Others Optical methods Conductometry Impedance spectroscopy Voltammetry Potentiometry 0

20

40 60 80 Number of publications

100

120

FIGURE 2.6 Main analytical techniques applied as electronic tongues. Data obtained from a literature search over the period 1996–2009, using SciFinder Scholar.

A. Potentiometry Figure 2.6 shows that almost one-half of the artificial tongues described in the scientific literature of the last decade are based on potentiometric devices. Potentiometry is a technique traditionally employed for the quantification of ions in a liquid solution. It is a static electroanalytical method, that is, there is no current flow inside the measurement cell (i ¼ 0). The measurement cell is constituted by two electrodes which are immersed in the solution containing the analytes. A voltmeter measures the potential difference between the two electrodes, which is a function of the concentration (actually, the activity) of the analytes, as described by the wellknown Nerst’s equation (Kissinger and Heineman, 1996). The most common analytical applications require one of the two electrodes to be characterized by an unchanging potential, known and independent of the characteristics of the solution being analyzed. Such a device is called the reference electrode. One of the most commonly used is the Ag/AgCl electrode, which consists of a silver wire coated with silver chloride and immersed into a solution saturated by chloride ions; a porous plug serves as a connection bridge with the outer solution. The other electrode, called the indicator electrode, is directly involved in the interaction with the analyte and it is usually selective toward particular species. A huge number of different devices have been deployed with various characteristics, for a lot of applications. Typically, in the potentiometric electronic tongues, several selective electrodes are employed together, constituting a sensor array, with the aim of providing a global characterization of the sample, similar to the evaluation made by human senses. This concept has been synthesized by Toko with the expression global selectivity (Toko, 1998): in the electronic tongue philosophy, the explicit quantification of single analytes is not the important aspect. On the contrary, it is often an implicit step in determining sensorial and other particular properties, which may be a function of the proportions between hundreds or even thousands of species in a sample.

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B. Voltammetry Voltammetry is the second most utilized technique for electronic tongue devices (see Fig. 2.6). It is a dynamic electroanalytical method, that is, a current flow passes through the measurement cell (i 6¼ 0). Voltammetry consists of the measurement of current at a controlled potential: constant or, more frequently, varying. In the classic three-electrode cell configuration, the current flows between two electrodes, called working and counter (or auxiliary) respectively, while the potential is controlled between the working and a third electrode, the reference (Kissinger and Heineman, 1996). The signal recorded is generally the current versus potential profile, which is called voltammogram. In voltammetry, different measurement modalities can be deployed on the basis of the nature of the potential versus time variation. In chronoamperometry, which is employed in a few electronic tongue systems (Cortina et al., 2008; Han et al., 2004), the potential is kept constant, while the current variations, resulting from faradic processes occurring at the electrode, are monitored as a function of time. In linear sweep voltammetry, the potential is varied linearly versus time, and current peaks are registered in correspondence to oxidation or reduction (depending on the potential variation verse) of the analytes. The potential value associated to a peak is characteristic of the specie being oxidizing or reducing, while the peak height can be employed for quantitative purposes. Two closely related techniques are staircase and square-wave voltammetry: in the first modality, the potential is varied stepwise versus time; in the second modality, a pulse square-wave is superimposed onto the staircase potential variation. In the differential acquisition mode, the current is measured immediately before each potential change, and the current difference is plotted as a function of potential. This way, the effect of the charging current can be decreased. Another evolution from the linear sweep mode is cyclic voltammetry, namely, a sequential combination of two (or more) linear sweep potential scans in the opposite direction: for this reason, the current versus potential response supplies information about the reversibility of redox systems.

C. Impedance spectroscopy Impedance spectroscopy is a versatile electrochemical tool, helpful to characterize the intrinsic dielectric properties of various materials. The basis of this technique is the measurement of the impedance (opposition to alternating current) of a system, in response to an exciting signal over a range of frequencies (Bard and Faulkner, 2001).


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Impedance spectroscopy may provide quantitative information about the conductance, the dielectric coefficient, the static properties of a system at the interfaces, and its dynamic changes due to adsorption or charge-transfer phenomena. Since in this technique an alternating current with low amplitude is employed, a noninvasive observation of samples with no or low influence on the electrochemical state is possible.

VI. CHEMOMETRICS Chemometrics is a chemical discipline born for interpreting and solving multivariate problems in the field of analytical chemistry. Svante Wold used for the first time, in 1972, the name chemometrics for identifying the discipline that performs the extraction of useful chemical information from complex experimental systems (Wold, 1972). In 1997, D.L. Massart suggested the following definition: ‘‘Chemometrics is a chemical discipline that uses mathematics, statistics and formal logic (a) to design or select optimal experimental procedures; (b) to provide maximum relevant chemical information by analyzing chemical data; and (c) to obtain knowledge about chemical systems’’ (Massart et al., 1997). The first of these aims is addressed by multivariate design of experiments (MDOE), while pattern recognition techniques deal with the remaining objectives. Since the quality of a result depends on the distribution of the experiments in the experimental domain, the main purpose of MDOE is to select the set of experiments resulting in the highest possible information amount, given the experimental limitations and the budget available (i.e., the maximum number of performable experiments). With the tools of MDOE, it is possible to find optimal conditions for multiple factors, considering their interactions, within almost every type of multivariate procedure, from analytical methods to organic synthesis and industrial processes. Despite its considerable effectiveness and the easy-to-understand concepts on which it is based, MDOE is applied in a minimal number of scientific studies. MDOE techniques are employed in just 5 over the nearly 230 original research papers on electronic tongues published during the last decade and examined for this review. (Holmin et al., 2004; Labrador et al., 2009; Li et al., 2007a; Rollm de Moura et al., 2007; Rudnitskaya et al., 2009b). On the other hand, pattern recognition tools are widely employed for processing data in the field of electronic tongues and, more generally, of artificial senses. Nowadays, a large number of chemometric techniques, which are schematized in Fig. 2.7, are available, giving the

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Supervised Unsupervised Validation

Regression Class-modeling Classification Clustering PCA

Quantitative answers

Qualitative answers

Exploratory analysis

FIGURE 2.7

Main steps of pattern recognition.

possibility of achieving several different results from the analysis of a data set, namely: recognition of the presence of structures (clusters, correlation) among

the objects and/or the variables studied (exploratory analysis, unsupervised) deployment of mathematical models for prediction of qualitative responses (classification and class-modeling analysis, supervised) deployment of mathematical models for prediction of quantitative responses (regression analysis, supervised). Several very important accessory tools, for example for data preprocessing and variable selection, complete the chemometric pattern recognition arsenal. As the definition says, a model is a description of a real phenomenon performed by means of mathematical relationships (Box and Draper, 1987). It follows that a model is not the reality itself: it is just a simplified representation of reality. Chemometric models, different from the models developed within other chemical disciplines (such as theoretical chemistry and, more generally, physical chemistry), are characterized by an elevated simplicity grade and, for this reason, their validity is often limited to restricted ranges of the whole experimental domain. Nevertheless, chemometric models are not developed with the aim of supporting a theory or describing a phenomenon from a general point of view, but with the aim of obtaining answers for particular real problems. Therefore, if the validity range of a model corresponds to the region of


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practical utility and interest, it means that such a model is functional to the aim for which it has been developed. George E.P. Box synthesized all these concepts in a sentence: ‘‘all models are wrong, but some are useful’’ (Box and Draper, 1987). From this perspective, the ultimate aim of chemometrics should be the development of useful models. Such a target can be accomplished only by means of a deep chemical knowledge of the problem to be solved, of the type of data to be handled, and finally, of the tools for multivariate analysis. The development of models always requires these models to be carefully validated (see Fig. 2.7), by means of a proper validation strategy, in order to provide information about the actual validity and usefulness in relation to the problem studied. If not validated, a model is not exploitable in the practice, since the reliability of its outcomes is completely unknown.

A. Multivariate design of experiments MDOE embraces a number of tools which permit experiments to be conducted in the most efficient possible way, achieving several interesting results such as screening of the important factors, optimization of manufacturing and analytical procedures, minimization of costs and pollution, and robustness testing of products and processes. When a new analytical method is being developed, MDOE is extremely useful, since it is able to identify the key experimental factors and the optimal experimental conditions, by performing a minimal number of experiments, that is, with the lowest possible effort and costs. The first stage of any experimental design is the problem formulation, a basic step in which the objectives and thus the response variable to be optimized should be defined. After that, it is essential to identify all the factors that might have an influence on the selected responses, and for each factor, variability levels that take into account eventual constraints. Then, a screening method may identify which factors have a significant effect on the response. At this point, a model has to be postulated based on the significant factors: the main types are linear, linear with interactions, and quadratic polynomial models. The next step is the definition of the experimental plan, which is closely dependent on the model chosen and on the number of factors. When few factors ( f from two to four) are studied, the full factorial design is the most common approach. The full factorial scheme is the basis for all classical experimental designs, which may be used in more complex situations. For a general two-level full factorial design, each factor has to be considered at a low level (coded as 1) and a high level

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(coded as þ 1), whose values are defined by the operator on practical bases. The experimental plan derives from all the possible level combinations (2f) for all the factors studied: in the case of two factors, the number of experimental conditions is 22 and the possible combinations are low– low, low–high, high–low, and high–high; they are located at the vertices of a square and, consequently, they explore the whole experimental domain. In the case of three factors, the number of experimental conditions is 23, located at the vertices of a cube, as shown in Fig. 2.8. From the experiments performed under each condition tested, the response variable is measured and the values obtained are used to build the model, which usually also estimates the interactions between variables (i.e., the effect that the value of one factor has on changing other factors). The complete mathematical model is therefore the following: Y ¼ b0 þ b1 X1 þ b2 X2 þ b3 X3 þ b12 X1 X2 þ b13 X1 X3 þ b23 X2 X3

þ b123 X1 X2 X3

(1)

where Y is the response variable and X1, X2, and X3 are the three factors. With just eight experiments, it is possible to estimate a constant term (b0), the three linear terms (b1, b2, and b3), the three two-term interactions (b12, b13, and b23), and the three-term interaction (b123). Usually, the model is validated by predicting the response value in the point corresponding to the center of the experimental domain (the center point in Fig. 2.8). When the number of factors is higher and/or when the model postulated is more complex, more advanced strategies can be employed, such

Factor 3

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as the fractional factorial design, central composite design, and D-optimal design, which allows the choice of a set of informative experiments by way of optimization criteria (Box et al., 1978; Leardi, 2009).

B. Preprocessing In the case of the complex analytical signals arising from artificial tongue instruments, a number of preprocessing tools may be employed for three main purposes, namely: elimination or reduction of random noise elimination or reduction of unwanted systematic variations data reduction or compression.

Unwanted systematic variations may be due to instrumental hurdles, to the experimental conditions and/or to physical characteristics of the samples. In general, it might be advantageous to avoid unwanted signal variations by improving the experimental settings, but this approach is not always feasible. Furthermore, in many cases, the effort that is needed (complex sample pretreatments, accurate temperature control, and so on) may be so high that it becomes incompatible with the ideal characteristics of a vanguard analytical system, which has to be cost and time saving and as simple as possible (Valca´rcel and Ca´rdenas, 2005; Vlasov et al., 2005). For these reasons, when it is possible, it may be preferred to remove the signal variations afterward, by data preprocessing, following the saying ‘‘math is cheaper than physics’’ (Kohler et al., 2009).

1. Row preprocessing Row preprocessing acts on each single signal, independent of the other ones, and the result is the correction of systematic differences among signals. Figure 2.9 and Table 2.1 summarize the effects of a number of common row pretreatments, discussed below, in correcting for unwanted signal variations such as addictive (baseline shifts), multiplicative (baseline drifts), and global intensity variation effects. Each signal (yi), defined by V variables (e.g., the current values in a voltammogram), is corrected individually by subtracting its mean value (yi ) from each single value (yi,v). The values of the row-centered signal (yi, v*) are obtained by yi yi;v ¼ yi;v

(2)

After transformation, the mean value of each signal is equal to 0: for this reason, row centering removes systematic location differences, namely baseline shifts, from a set of signals, as it is shown in Fig. 2.9A.

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FIGURE 2.9 Effectiveness of a number of row pretreatments in eliminating addictive, multiplicative, and global intensity effects.


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TABLE 2.1 Summary of the corrections for unwanted signal variations, practicable by application of appropriate row pretreatments Addictive

Row centering SNV First derivative Second derivative Differences

Multiplicative

✓ ✓ ✓ ✓ ✓

Intensity

✓ ✓ ✓

a. Standard normal variate transform Standard normal variate transform (SNV), or row autoscaling, is a mathematical method for signal transformation, particularly applied in spectroscopy, which is useful to remove slope variations and to correct for both baseline shifts and global intensity variations (Barnes et al., 1989) (see Fig. 2.9A). Each signal (yi) is row-centered, as described above, and then scaled by dividing the single values by the signal standard deviation (si). The values of the SNV transformed signal are obtained by yi;v ¼

yi;v yi si

(3)

After transformation, each signal presents a mean value equal to 0 and a standard deviation equal to 1: both location and dispersion systematic differences are corrected. SNV has the peculiarity of potentially shifting informative regions all along the signal range, so that the interpretation of the results referring to the original signals may be deceiving (Fearn, 2009).

b. First and second order derivation after smoothing The numerical differentiation of digitized signals has many uses in analytical signal processing. It allows a signal resolution enhancement, increasing the apparent resolution of overlapping peaks, accentuates small structural differences between nearly identical signals, and corrects for baseline shifts and drifts, depending on the derivation order, as shown in Fig. 2.9A (Taavitsainen, 2009). In particular, the first derivative of a signal y ¼ f(x) is the rate of change of y with x (i.e., y0 ¼ dy/dx), which can be interpreted as the slope of the tangent to the signal at each point. It returns null segments in correspondence to constant bands of the original signal and provides a correction for addictive effects.

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The second derivative can be considered as a further derivation of the first derivative (y00 ¼ d2y/dx2); it represents a measure of the curvature of the original signal, that is, the rate of change of its slope. The second derivative returns null segments in correspondence to bands characterized by a constant slope in the original signal and provides a correction for both addictive and multiplicative effects. A disadvantageous consequence of derivation may be an enhancement of the noise, which is usually characterized by high-frequency slope variations. To overcome this hurdle, signals are firstly smoothed, often by using the Savitzky–Golay algorithm, which is a moving window averaging method (Savitzky and Golay, 1964). A third-degree smoothing polynomial and a window size of 5–15 datapoints are suggested.

c. Difference between forward and backward currents Computing the difference, point by point, between the forward and the backward current values evaluated at the same potential, is a peculiar pretreatment for cyclic voltammograms (Oliveri et al., 2009), that is able to eliminate both baseline shifts and drifts (see Fig. 2.9B), and therefore improve the availability of useful information in data analysis. The number of variables in the resulting data matrix is half of the original one.

2. Column preprocessing Column preprocessing corrects each variable individually and the result is the correction of systematic differences among variables.

a. Column centering Each variable defining a set of signals (e.g., the current values at different potentials in a set of voltammograms) is corrected individually by subtracting its mean value (yv ) from each single value (yi,v). The column-centered values (yi;v ) are obtained by yi;v ¼ yi;v yv

(4)

After transformation, the mean value of each column is equal to 0, so that systematic location differences among variables are eliminated.

b. Column autoscaling Column autoscaling is often a default data pretreatment in chemometrics, particularly in the case of variables of different nature. Each variable is corrected individually by subtracting its mean (yv ) from each of its values and then dividing by its standard deviation (sv). The autoscaled values are dimensionless and are computed by yi;v ¼

yi;v yv sv

(5)


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This column preprocessing eliminates systematic location and dispersion differences among heterogeneous variables, giving all of them the same a priori importance (mean values equal to 0 and standard deviations equal to 1), and enhances differences among the samples. Also, in the case of signals like current/potential profiles, in which all the variables have the same nature and measurement unit, column autoscaling may be important if there are variables characterized by a relatively low mean value and/or standard deviation, which enclose useful information. Otherwise, this pretreatment may decrease the signal-to-noise ratio, since the same weight is given both to the noisy parts of the signal and to the informative features.

3. Signal compression and variable reduction Not rarely, analytical signals are composed of hundreds or thousands of variables. Many of them are uniquely or predominantly associated with noise, while contiguous informative variables are often very intercorrelated, so that they carry redundant information. For these reasons, chemometric tools to perform the selection of a limited number of informative predictors and to realize data compression are enormously profitable. A reliable way to compress a set of signals is to perform principal component analysis (PCA) (see Section VI.C) and then use a limited number of significant principal components (PCs) as new variables to describe the samples.

a. Wavelet transform The wavelet transform (WT) is a technique developed to study the frequency components of a signal, similar to the Fourier transform, with the main aim of compressing data. The WT is based on the repeated application of low-pass filters (whose outputs are called approximations) and high-pass filters (whose outputs are called details) to a signal. There are many types of filters, which constitute the families of wavelets. The simplest is the Haar filter, based on semisums and semidifferences of consecutive signal elements (Haar, 1910). The discrete wavelet transform (DWT) scheme computes, in each level, approximations and details of the approximations of the previous level, originating the so-called Mallat pyramid. Instead, the wavelet packet transform (WPT) applies the filters also to the details at each level, producing a more extended tree. A set of wavelet coefficients (approximations and details) from which it is possible to reconstruct perfectly the original signal is called basis. Both for DWT and WPT, it is possible to define a number of different coefficient combinations, that is, different possible bases; in general, WPT allows more alternatives than DWT. The so-called best basis is the one, among all possible wavelet bases, which optimizes a given criterion function, a cost function, like threshold or entropy (Mallet et al., 2000; Soman and Ramachandran,

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2005). Usually, several coefficients are very small, so that they can be omitted without a significant negative effect on the reconstructed signal and, hopefully, with the elimination of noise. The result is the compression of the signal. Generally, chemometrics handles data sets constituted by many objects described by the same variables. In this perspective, the application of wavelet transform should be performed, obtaining, for all the objects, a single basis formed by the same coefficients, the so-called common best basis. Usual procedures for the selection of the common best basis are based on maximum variance criteria (Walczak and Massart, 2000). For instance, the variance spectrum procedure computes at first the variance of all the variables and arranges them into a vector, which has the significance of a spectrum of the variance. The wavelet decomposition is applied onto this vector and the best basis obtained is used to transform and to compress all the objects. Instead, the variance tree procedure applies the wavelet decomposition to all of the objects, obtaining a wavelet tree for each of them. Then, the variance of each coefficient, approximation or detail, is computed, and the variance values are structured into a tree of variances. The best basis derived from this tree is used to transform and to compress all the objects.

b. SELECT: A decorrelation–selection algorithm As already discussed in the case for almost continuous signals such as voltammograms, not all variables contribute unique or useful information. The elimination of predictors of limited or negligible utility—noisy or redundant—may increase the efficiency of models and/or make their interpretation simpler. Stepwise decorrelation of variables was introduced by B. Kowalski and C.F. Bender as an algorithm with the name SELECT (Kowalski and Bender, 1976), a supervised Gram–Schmidt orthogonalization. This algorithm can be used both for classification/class-modeling problems and multivariate quantitative calibration. It is based on a sequential identification and decorrelation of individual variables. For example, SELECT first identifies the variable with the largest discriminant power (e.g., the Fisher weight) in the case of classification and class-modeling problems. This first selected variable is decorrelated from all the others so that these latter ones become orthogonal to that selected. After decorrelation, the selected variable is set aside from the variable set and the process repeated with another identification. This identification and decorrelation procedure continues until the decorrelated predictors reach a discriminant power value less than a prefixed cut-off level. In the case of regression, the selection criterion is based on the correlation coefficient of predictors with a given response variable.


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C. Exploratory analysis 1. Principal component analysis PCA is one of the most widely employed and most useful tools in the branch of exploratory analysis. It has been used in more than half of the 230 electronic tongue studies examined for this review, as shown in Fig. 2.10. It offers a general overview of the problem studied, showing the interrelations existing among objects and between objects and variables. Also intercorrelations among variables can be estimated, and this knowledge may be used subsequently, for instance, for eliminating redundant information. Since a high variability (i.e., a high variance value) is synonymous with a high amount of information (see Fig. 2.1), the PCA algorithms search for the maximum variance direction, in the multidimensional space of the original data, preferably passing through the data centroid, which means that data have to be at least column-centered. The maximum variance direction, which can be expressed as a linear combination of the original variables, represents the first PC . The second PC is the direction which keeps the maximum variance among all directions orthogonal to the first PC. Therefore, the second PC explains the maximum information not explained by the first one or, in other words, these two new variables MDOE Preprocessing Clustering PCA k-NN SIMCA LDA PLS-DA ANN PCR OLS PLS 0

20

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FIGURE 2.10 Main chemometric techniques applied in electronic tongue studies. Data obtained from a literature search over the period 1996–2009, using SciFinder Scholar. MDOE, multivariate design of experiments; PCA, principal component analysis; k-NN, k-nearest neighbors; SIMCA, soft independent modeling of class analogy; LDA, linear discriminant analysis; PLS-DA, partial least squares discriminant analysis; ANN, artificial neural networks; PCR, principal component regression; OLS, ordinary least squares; PLS, partial least squares.

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are not intercorrelated among them. The process goes on identifying the subsequent PCs: it may stop at reaching a variance cut-off value or continue until all the variability enclosed in the original data has been explained. Since the variance values depend on the measurement unit of variables, it becomes difficult to compare and impossible to combine information from variables of different nature, unless properly normalized: column autoscaling is the transform most commonly applied. Each object can be projected in the space defined by the new variables: the coordinate values obtained are called scores. As already stated, the PCs are expressible as linear combinations of the original variables: the coefficients which multiply the variables are called loadings. They represent the cosine values (director cosines) of the angles between the PCs and the original variables. These values may vary between 1 and þ 1, indicating the importance of defining a given PC: the larger the cosine absolute value, the closer the two directions, thus the larger the contribution of the original variable to the PC. In terms of matrix algebra, the rotation from the space of the original variables to the PC space is performed by means of the loading orthogonal matrix, L: SNV ¼ XNV LVV

(6)

where S is the score matrix and X is the original matrix, constituted by N objects (rows) described by V variables (columns). The key feature of PCA is in its high capability for representing large amounts of complex information by way of simple bidimensional or tridimensional plots. In fact, the space described by two or three PCs can be used to represent the objects (score plot), the original variables (loading plot), or both objects and variables (biplot). For instance, if the first two PCs (low-order) are drawn as axes of a Cartesian plane, we may observe in this plane a fraction of the information enclosed in the original multidimensional space which corresponds to the sum of the variance values explained by the two PCs. Since PCs are not intercorrelated variables, no duplicate information is shown in PC plots. In Fig. 2.11, an example of a biplot is shown (Rudnitskaya et al. 2009b). From the inspection of the graph, it is possible to get information about the repeatability of the measurements (replicate analyses on wine samples), the discrimination among samples (wines of three vintages), the intercorrelation between variables (responses of potentiometric sensors), and their discriminatory importance. In the case of almost continuous signals such as voltammograms, it may be useful to represent the loading profiles, eventually superimposed

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2004 2005 2006

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FIGURE 2.11 Example of biplot. The scores (filled symbols) are replicates of analyses of wine samples of three vintages (2004, 2005, and 2006, respectively), while the loadings (stars) represent the potentiometric sensors used for the measurements (A ¼ anionsensitive sensors, C ¼ cation-sensitive sensors, G ¼ redox-sensitive sensors, pH ¼ pH sensor) (reproduced from Rudnitskaya et al. 2009b, with permission).

to a selected signal or an average signal profile, in order to directly visualize which parts of the original signals give the highest contribution in defining a particular PC. As already remarked, particular caution in interpretations has to be observed when a row normalization such as the SNV transform has been applied to the signals. In fact, such a transform might shift or smear the information contained in one region of the original signals (Fearn, 2009). As already stated, maximum variance means maximum information. Nevertheless, it has to be noted that the maximum information does not always correspond to the most useful information for the solution to a particular problem. In fact, there are cases in which the maximum variability characterizing a data set refers to factors that are not related to the features of interest. In such cases, the useful information might be extracted by higher-order PCs, explaining a minor (but, in this case, effective) variance fraction ( Jolliffe, 2002). For example, if the aim of a study is the discrimination of wine samples on the basis of the oenological production region and the samples have been collected spanning three vintages, the highest variability in the data, thus captured by the loworder PCs, will very probably be year-related. On the other hand, an

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examination of the scores on higher-order PCs may show groupings due to sample provenance. PCA is a useful tool not only for visualizing information; it can also be employed as a strategy for feature reduction and noise reduction, as indicated in Section VI.C.

2. Clustering Clustering is a branch of exploratory analysis able to provide answers about the presence of groupings among objects or variables, by means of a similarity measurement (Vandeginste et al., 1998). The similarity among two objects is defined as an inverse function of their distance: the more two objects are distant, the less they are similar. Several metrics may be used to evaluate the distance D between two objects i and j in a n-dimensional space. The most common are vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u V uX 2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi (7) xi xj Þ0 xi xj xi;v xj;v ¼ Euclidean : Di;j ¼ t v¼1

Mahalanobis : Di;j ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi xi xj Þ0 V1 xi xj

(8)

In every case, the similarity value S between the objects i and j is computed as Si;j ¼ 1

Di;j Dmax

(9)

where Dmax is the maximum distance between all the possible pairs of objects in the data set. It follows that the values of the parameter S may vary between two limits: 0 when the two objects considered are those with maximum interdistance and 1 if the two objects are coincident. There are two main types of clustering techniques: hierarchical and nonhierarchical. Hierarchical cluster analysis may follow either an agglomerative or a divisive scheme: agglomerative techniques start with as many clusters as objects and, by means of repeated similarity-based fusion steps, they reach a final situation with a unique cluster containing all of the objects. Divisive methods follow exactly the opposite procedure: they start from an all-inclusive cluster and then perform a number of consecutive partitions until there is a bijective correspondence between clusters and objects (see Fig. 2.12). In both cases, the number of clusters is defined by the similarity level selected. The results can be visualized in a plot called a dendrogram, in which the similarity or distance values corresponding to each fusion/partition step are represented. One of the two axes corresponds to the similarity,


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Agglomerative methods

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FIGURE 2.12 Scheme of agglomerative and divisive clustering approaches illustrated with five objects described by two variables.

while the second one reports the objects following an arbitrary order without any physical implication. Such plots may be very useful for identifying the similarity cut-off level and thus the number of clusters.

D. Classification and class-modeling Classification and class-modeling techniques may provide qualitative answers to many problems of interest in the field of electronic tonguebased studies. They build mathematical rules or models able to characterize a sample with respect to a qualitative property, that is the class to which it belongs. For example, in the case of food problems, they may be useful in order to determine whether a product is genuine or adulterated, whether its quality is acceptable or poor, the identification of its geographical origin, the identity of the cultivar of vegetable ingredients used, which manufacturing technologies have been employed, and so on. In fact, a class (or category) is defined as a grouping of samples characterized by the same value of discrete variables or by contiguous values of continuous variables. Frequently, such variables are qualitative factors whose values cannot be determined experimentally, so that they have to be estimated from the values of some experimentally measurable predictors, by way of suitable mathematical tools. More in detail, classification techniques are able to determine to which class a sample more probably belongs, among a number of predefined classes. They work by building a delimiter between the classes. Then, each new object is always assigned to the category to which it more probably belongs, even in the case of objects which are not pertinent to any class studied. Instead, class-modeling techniques verify whether a sample is compatible or not with the characteristics of a given class of interest. In fact,

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they provide an answer to the general question: ‘‘Is sample X, claimed to belong to class A, really compatible with the class A model?’’. This is essentially the question to be answered in most of the real qualitative problems studied by means of electronic tongues. In addition, such an approach is capable of detecting outliers. Nevertheless, in most of the electronic tongue applications found in the literature, classification techniques like linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) have been used in place of more appropriate class-modeling methods. Moreover, in the few cases in which a class-modeling technique such as soft independent modeling of class analogy (SIMCA) is applied, attention is frequently focused only on its classification performance (e.g., correct classification rate). Use of such a restricted focus considerably underutilizes the significant characteristics of the class-modeling approach. A class model is characterized by two distinctive parameters: sensitivity and specificity. Sensitivity is defined as the percentage of objects belonging to the modeled class which are rightly accepted by the model. Specificity is the percentage of objects not belonging to the modeled class which are rightly rejected by the model. A class-modeling technique builds a class space around a mathematical class model, with a wideness corresponding to the confidence interval, at a preselected confidence level, for the class objects: sensitivity is an experimental measure of this confidence level. A decrease in the confidence level for the modeled class generally reduces the sensitivity and increases the specificity of the model. Frequently, in order to evaluate the model performance, taking into account both these features, an efficiency parameter is computed as the mean (better, the geometric mean) of the sensitivity and specificity values. The results of class-modeling analyses can be visualized by way of the Coomans’ plots (Coomans et al., 1984) which, as in the example of Fig. 2.13, represent the objects of the data set studied in relation to the models of two classes, A and B. The two Cartesian axes correspond to the distances from the class A model and from the class B model, respectively, while two straight lines, parallel to the axes, describe the corresponding class spaces at the predetermined confidence level. The plot area is divided into four regions, which contain respectively: the objects accepted by the class A model (upper left rectangle), the objects accepted by the class B model (lower right rectangle), the objects accepted by both the models (lower left square), and the objects rejected by both the models (upper right square). Classification and class-modeling techniques belong to three main families: distance-based techniques probabilistic techniques experience-based techniques.


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Class A

Distance from the class B model

Class B

Rejected by both the models

Accepted by both the models Accepted by the class B model Distance from the class A model

FIGURE 2.13

1. k-Nearest neighbors

Example of Coomans’ plot.

k-Nearest neighbors (k-NN) is one of the simplest approaches for classification. It is a distance-based technique, which predicts the class membership of a sample on the basis of the class of the k sample(s) nearest to it in the multidimensional space (Vandeginste et al., 1998). To classify a new sample, k-NN computes its distances (usually, the multivariate Euclidean distances, see Eq. 7) from each of the samples of a training set, whose class membership is known. The k nearest samples are then taken into consideration to perform the classification: generally, a majority vote is employed, meaning that the new object is classified into the class mostly represented within the k selected objects. Being a distance-based method, it is sensitive to the measurement units and to the scaling procedures applied. The method provides a delimiter between categories, which is typically nonlinear, generally expressible as a piecewise linear function (see Fig. 2.14). The delimiter usually becomes more smoothed for elevated

86


10

Variable 2

8

6

4

2

0 0

FIGURE 2.14

2

4 6 Variable 1

8

10

Example of k-NN class delimiter for k ¼ 1.

values of k. When the parameter k is optimized to obtain the highest prediction ability for a given data set, validation should be performed by way of a three-set procedure (see Section VI.F). k-NN has been shown to perform as well as or better than more complex methods in many applications (Dudoit et al., 2002; Vandeginste et al., 1998). Furthermore, being a nonparametric method, it is free from statistical assumptions such as normality of variable distributions, so that its applicability is much wider than that of parametric probability-based techniques.

2. Linear discriminant analysis LDA is the first classification technique introduced into multivariate analysis by Fisher (1936). It is a probabilistic parametric technique, that is, it is based on the estimation of multivariate probability density functions, which are entirely described by a minimum number of parameters: means, variances, and covariances, like in the case of the well-known univariate normal distribution. LDA is based on the hypotheses that the probability density distributions are multivariate normal and that the dispersion is the same for all the categories. This means that the variance–covariance matrix is the same for all of the categories, while the centroids are different (different location). In the case of two variables, the probability density function is bell-shaped and its elliptic section lines correspond to equal probability density values and to the same Mahalanobis distance from the centroid (see Fig. 2.15A).


A 0.07 0.06

Probability dens

ity

0.05 0.04 0.03 0.02 0.01 10

0.00 –0.01

2

8

ia

Va r

4 6 Varia ble 1

4

ble

6 2

2

8

10

B 10

Variable 2

8

6

4

2

0 0

2

4 6 Variable 1

8

10

FIGURE 2.15 Bivariate probability distributions (A), iso-probability ellipses and LDA delimiter (B).

87

88


The iso-probability region described by the equation of the Mahalanobis distance is an ellipsoid in the case of three variables or a hyperellipsoid in the case of more than three variables. The Mahalanobis distance can be considered as an Euclidean distance modified for taking into account the dispersion and the intercorrelation of all of the variables (see Eqs. 7 and 8). Because of the aforementioned LDA hypotheses, the ellipses of different categories present equal eccentricity and axis orientation: they only differ for their location in the plane. By connecting the intersection points of each couple of corresponding ellipses, a straight line is identified which corresponds to the delimiter between the two classes (see Fig. 2.15B). For this reason, this technique is called linear discriminant analysis. The directions which maximize the separation between classes are called LDA canonical variables.

3. Quadratic discriminant analysis Quadratic discriminant analysis (QDA) is a probabilistic parametric classification technique which represents an evolution of LDA for nonlinear class separations. Also QDA, like LDA, is based on the hypothesis that the probability density distributions are multivariate normal but, in this case, the dispersion is not the same for all of the categories. It follows that the categories differ for the position of their centroid and also for the variance–covariance matrix (different location and dispersion), as it is represented in Fig. 2.16A. Consequently, the ellipses of different categories differ not only for their position in the plane but also for eccentricity and axis orientation (Geisser, 1964). By connecting the intersection points of each couple of corresponding ellipses (at the same Mahalanobis distance from the respective centroids), a parabolic delimiter is identified (see Fig. 2.16B). The name quadratic discriminant analysis is derived from this feature.

4. Unequal class models Unequal class models (UNEQ) is a powerful class-modeling technique, which originated in the work of H. Hotelling (Hotelling, 1947) and was introduced into chemometrics by M.P. Derde and D.L. Massart (Derde and Massart, 1986). This technique, derived from QDA, is based as well on the hypothesis of a multivariate normal distribution in each category studied and on the use of Hotelling’s T2 statistics to define a class space, whose boundary is an ellipse (two variables), an ellipsoid (three variables), or a hyper-ellipsoid (more than three variables). The dispersion of a class space is defined by the critical value of the T2 statistics at a selected confidence level. The eccentricity and the orientation of the ellipse depend on the correlation between the variables and on their dispersion.


89

A 0.07

ity Probability dens

0.06 0.05 0.04 0.03 0.02 0.01 10

0.00 –0.01

2

8

ia

4

Va r

4 Varia 6 ble 1

ble

6

2

2

8

10

B 10

Variable 2

8

6

4

2

0 0

2

4 6 Variable 1

8

10

FIGURE 2.16 Bivariate probability distributions (A), iso-probability ellipses and QDA delimiter (B).

90


LDA and QDA-UNEQ present some restrictions on the number of objects that can be used. From a strictly mathematical point of view, objects have to be one more than the number of variables measured. Nevertheless, in order to obtain reliable results, these techniques should be applied in cases when the ratio between the number of objects in a given category and the number of the variables is at least three. Furthermore, the number of objects in each class should be nearly balanced: it is not advisable to work when ratios between number of objects in different categories are greater than three. In cases involving many variables (such as voltammetric data), it is possible to apply LDA and QDA-UNEQ following a preliminary reduction in the variable number, for instance, by PCA or wavelet compression.

5. Soft independent modeling of class analogy SIMCA (Wold and Sjo¨stro¨m, 1977) was the first class-modeling technique used in chemometrics. Being a modeling technique, each class is modeled independently of the others. The central feature of this method is the application of PCA to the sample category studied, generally after within-class autoscaling or centering. SIMCA models are defined by the range of the sample scores on a selected number of low-order PCs— ideally the significant ones—and models therefore correspond to rectangles (two PCs), parallelepipeds (three PCs), or hyper-parallelepipeds (more than three PCs) referred to as the multidimensional boxes of SIMCA inner space. Conversely, the PCs not used to describe the model define the outer space, considered as uninformative space. The score range can be enlarged or reduced, mainly depending on the number of samples, to avoid the possibility of under- or overestimation of the true variability (Forina and Lanteri, 1984). The standard deviation of the distance of the objects in the training set from the model corresponds to the class standard deviation. The boundaries of SIMCA space around the model are determined by a critical distance, which is obtained by means of Fisher statistics, so that the shape of the class space is not exactly that of a parallelepiped. There is no specific hypothesis other than that this distance should be normally distributed. However, the distribution of samples in the inner space should be more or less uniform; otherwise, regions in the inner space lacking objects from the modeled class would be incorrectly considered as a part of the model. SIMCA is a very flexible technique since it allows variation in a large number of parameters such as scaling or weighting of the original variables, number of components, expanded or contracted score range, and confidence level applied. Furthermore, SIMCA computes a number of useful parameters, such as the modeling power of the variables (their contribution to the inner space), the discriminating power of the variables (when more than one


91

category is studied), and the distance between the categories. Variables with both small modeling and discriminating power may be eliminated, in order to obtain refined models.

6. Artificial neural networks Artificial neural networks (ANNs) have been widely applied in the electronic tongue literature both for classification and multivariate regression problems: almost one-third of the papers on electronic tongues examined for this review show ANN applications (see Fig. 2.10). ANNs offer some advantages. For instance, they are generally well suited for nonlinear problems, and the related software is easily available. However, a number of important drawbacks should limit ANN use only to the cases in which other techniques fail and a great number of samples are available. Multilayer feed-forward neural networks (MLF) represent the type of ANNs most widely applied to electronic tongue data. Their scheme is shown in Fig. 2.17. MLF are composed of a number of computational elements, called neurons, generally organized in three layers (Marini, 2009). In the first one, the input layer, there are usually N neurons which correspond to the original predictor. The predictors are scaled (generally range scaled). When the number of original predictors is very high, the PCs may be used, in order to reduce the data amount and the computational time. The first layer transmits the value of the predictors to the second— hidden—layer. All the neurons of the input layer are connected to the J neurons of the second layer by means of weight coefficients, meaning that the J elements of the hidden layer receive, as information, a weighted sum S of the values from the input layer. They transform the information received (S) by means of a suitable transfer function, frequently a sigmoid. These neurons transmit information to the third—output—layer, as a weighted combination (Z) of values. The neurons in the output layer correspond to the response variables which, in the case of classification, are the coded class indices. The output neurons transform the information Z, from the hidden layer, by means of a further sigmoid function or a semilinear function. After a first random initialization of the values, a learning procedure modifies the weights wn,j and wj during several optimization cycles, in order to improve the performances of the net. The correction of the weights at each step is proportional to the prediction error of the previous cycle. The optimization of many parameters and the elevated number of learning cycles considerably increase the risk of overfitting and, for this reason, a deep validation is required, with a consistent number of objects.

92


I1

wn,j

Correction

H1

I2

wj O1

S I3

H...

Z O2

S HJ

I... S

Z Output layer

Hidden layer

IN Input layer

FIGURE 2.17

Scheme of multilayer feed-forward neural networks.

Another type of ANNs widely employed is represented by the Kohonen self organizing maps (SOMs), used for unsupervised exploratory analysis, and by the counterpropagation (CP) neural networks, used for nonlinear regression and classification (Marini, 2009). Also, these tools require a considerable number of objects to build reliable models and a severe validation. Nevertheless, in many electronic tongue studies, such constraints are ignored and ANNs are used as the default choice. This choice is also made in cases with very poor data sets and without performing a proper validation. This may be due to the fact that the related computational software is easily available and that many people have a propensity to follow the predominant trends and to use the most potent instruments available, without critical considerations. Furthermore, perhaps, there is a fashionable association of ideas connecting the concepts of artificial tongue and artificial intelligence.

7. State-of-the-art class-modeling techniques Two class-modeling techniques have recently been introduced: multivariate range modeling (MRM) and CAIMAN analogues modelling methods (CAMM).


93

MRM follows a simple and intuitive way of building class models by employing the ranges of the predictors (Forina et al., 2008). In order to take into account intercorrelations in the data set, several new variables are computed as linear combinations of the original predictors and employed to build the models: LDA canonical variables represent a typical example. A basic feature of MRM is the capability of providing, by definition, class models with 100% sensitivity: this property is very important when a model is built for a food protection consortium, the production of whose affiliated producers must all be recognized and accepted by the model. In addition, the eventual presence of noninformative variables does not have a negative influence on the model specificity. A further advantage of MRM can be found in its outcome, which is easily understandable and interpretable also by people with a limited knowledge of multivariate analysis. CAMM is a family of powerful class-modeling techniques which builds models using distances (leverages or Mahalanobis distances from the class centroids) as predictors (Forina et al., 2009). Such new variables may be used separately or in combination with the original predictors and, in many cases, supply models characterized by excellent efficiency values.

E. Regression Regression techniques provide models for quantitative predictions. The ordinary least squares (OLS) method is probably the most used and studied historically. Nevertheless, it presents a number of restrictions which often limit its applicability in the case of artificial tongue data.

1. Ordinary least squares OLS regression, also known as multivariate linear regression (MLR) (Draper and Smith, 1981), searches for the linear function corresponding to the smallest value of the sum of the squared residuals: I X i¼1

yi ^ yi

2

(10)

where yi is the actual value of the response variable for the object i, and ^yi is the value computed by the model, which can be expressed as a mathematical relationship between response and predictors: ^ y ¼ b0 þ b1 x1 þ b2 x2 þ þ bV xV that is, in the matrix notation:

(11)

94


^ ¼ X0 b y

(12)

X is the matrix of the predictors augmented with a column of 1, necessary for the estimation of the intercept values. b is the column vector of the regression coefficients. The regression coefficients are estimated by 1

b ¼ ðX0 XÞ X0 y

(13)

The elements of the vector y are the reference values of the response variable, used for building the model. The uncertainty on the coefficient estimation varies inversely with the determinant of the information matrix (X0 X) which, in the case of a unique predictor, corresponds to its variance. In multivariate cases, the determinant value depends on the variance of the predictors and on their intercorrelation: a high correlation gives a small determinant of the information matrix, which means a big uncertainty on the coefficients, that is, unreliable regression results. Unfortunately, electronic tongue variables are very often considerably intercorrelated: in voltammetric profiles, for instance, currents evaluated at two consecutive potential values frequently carry almost the same information, so that their correlation coefficient is nearly 1. In such cases, standard OLS is absolutely not recommendable. Furthermore, the number of objects required for OLS regression must be at least equal to the number of predictors plus 1, and it is difficult to satisfy such a condition in many practical cases.

2. Principal component regression A very simple strategy to overcome these hurdles is offered by PCA: the PCs may be used as new predictors because they are, by definition, orthogonal, that is, uncorrelated. The response is calculated as a function of a small number of significant PCs, computed from the original variables ( Jolliffe, 1982). This technique, which is very efficient in many cases, is known as principal component regression (PCR). Since the directions that explain the highest variance amount (i.e., the lowest-order PCs) are not always the most important in predicting a response variable, it is possible to follow a refined approach, which performs a step-wise selection of the PCs to be used in the model on the basis of their modeling efficiency.

3. Partial least squares A further better solution is offered by partial least squares (PLS), whose acronym may signify also projections onto latent structures. These latent structures, more frequently called latent variables (LVs) or PLS components, are directions in the space of the predictors with a connotation


95

similar to PCs: while the first PC is the maximum variance direction, the first latent variable is the direction characterized by the maximum covariance with the selected response variable. The information related to the first latent variable is then subtracted from both the original predictors and the response. The second latent variable is orthogonal to the first one, being the direction of maximum covariance between the residuals of the predictors and the residuals of the response. This approach continues for additional LVs. The optimal complexity of the PLS model, that is, the most appropriate number of latent variables, is determined by evaluating, with a proper validation strategy (see Section VI.F), the prediction error corresponding to models with increasing complexity. The parameter considered is usually the standard deviation of the error of calibration (SDEC), if computed with the objects used for building the model, or the standard deviation of the error of prediction (SDEP), if computed with objects not used for building the model (see Section VI.F). vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uN 2 uP u ^ ti¼1 yi yi (14) SDECðPÞ ¼ N where yi is the value of the response variable y for object i, yî the corresponding value computed or predicted by the model, and N is the number of objects used for computing the parameter. In general, the calibration error always decreases when the number of LVs augments, because the fitting increases (toward an overfitting). On the contrary, the prediction error generally decreases until a certain model complexity and then raises: this indicates that the further LVs being introduced are bringing noise, as shown in Fig. 2.18. A simple and practical criterion is the choice of the LV number corresponding to the SDEP absolute minimum or—better—to the first local minimum as the optimal model complexity. When the number of noisy (noninformative) variables is too large, PLS models may also supply rather poor predictive performance. In order to overcome such a matter, a number of techniques for the elimination of noisy variables or the selection of useful predictors have been deployed, such as iterative stepwise elimination (ISE), iterative predictor weighting (IPW), uninformative variable elimination (UVE), and Martens uncertainty test (MUT) (Forina et al., 2007). A number of PLS variants have been deployed, for instance, for developing nonlinear models and for predicting together several response variables (PLS-2). Furthermore, when category indices are taken as response variables, PLS may work as a classification method which is usually called PLS discriminant analysis (PLS-DA).

96


2.4

SDEC SDEP

SD

1.6

0.8

0.0 0

5

10

15 LV number

20

25

FIGURE 2.18 Typical profile of calibration and prediction errors as a function of the PLS model complexity (number of latent variables). The examination of such a plot may be helpful in selecting the optimal model complexity.

F. Validation As claimed in Section VI, models are false, but in some cases, they may be useful. In chemometrics, models are generally aimed at predicting a quantity or a property of interest: the usefulness of a model is thus measured by its reliability in prediction. Prediction ability values should be presented with their confidence interval (Forina et al., 2001, 2007), which depends very much on the number of objects used for the validation. The estimation of predictive ability on new objects—not used for building the models—is a fundamental step in any modeling process, and several procedures have been deployed for this purpose. The most common validation strategies divide the available objects in two subsets: a training (or calibration) set used for calculating the model and an evaluation set used to assess its reliability. A key feature for an honest validation is that the test-objects have to be absolutely extraneous to the model: no information from them can be used in building the model, otherwise the prediction ability may be overestimated. In many modeling techniques, the number of parameters is modified many times looking for a setting that provides the maximum predictive ability for the model. Techniques for variable selection and methods based on artificial neural networks perform an optimization, that is, they search for conditions able to provide the maximum predictive ability possible for a given sample subset.


97

A better validation strategy, in such cases, is to use three sample subsets: a training set, an optimization set, and an evaluation set. The optimization set is used to find the best modeling settings, while the actual reliability of the final model is estimated by way of a real prediction on the third subset, formed by objects that have never influenced the model. The three-set validation procedure should always be used in ANN modeling, which presents a very high risk of overfitting. The evaluation of the predictive ability of a model can be performed in a unique step or many times with different evaluation sets, depending on the strategy adopted.

1. Single evaluation set A single evaluation set is the simplest and most rapid validation scheme. A fraction—usually between 50% and 90% of the total number—of the available samples constitutes the training set, while the remaining objects form the evaluation set. The subdivision may be arbitrary, random, or performed by way of a uniform design, such as the Kennard and Stone and the duplex algorithm (Kennard and Stone, 1969; Snee, 1977), which allows two subsets to be obtained that are uniformly distributed and representative of the total sample variability.

2. Cross-validation Cross-validation (CV) is probably the most common validation procedure. The N objects available are divided into G cancellation groups following a predetermined scheme (e.g., contiguous blocks, or Venetian blinds). The model is computed G times: each time, one of the cancellation groups is used as the evaluation set, while the other groups constitute the training set. At the end of the procedure, each object has been used G-1 times for building a model and once for evaluation. The number of cancellation groups usually ranges from 3 to N. Cross-validation with N cancellation groups is generally known as the leave-one-out procedure (LOO). LOO has the advantage of being unique for a given data set, whereas, when G < N, different orders of the objects and different subdivision schemes generally supply different outcomes. However, especially when the total number of objects is considerable, predictions made on a unique object, although repeated many times, may yield an overly optimistic result. An extensive evaluation strategy consists in performing cross-validation many times, with different numbers of cancellation groups, from 3 up to N. Another possibility is to repeat the validation, for a given number G < N of cancellation groups, each time permuting the order of the objects, thus obtaining a different group composition each time.

98


3. Repeated evaluation set This procedure, also called Monte Carlo validation, computes many models (not rarely 10,000–100,000), each time creating a different evaluation set, with an unfixed number of objects, by random selection. Each object may fall many times, or even no times at all, in the evaluation set. The main drawback of this validation strategy is the longer computational time.

VII. ARTIFICIAL TONGUE APPLICATIONS IN THE FOOD SCIENCE Wine is the food which has been most extensively analyzed by means of electronic tongues. Typical qualitative studies concern the characterization of wine on the basis of vintage and vineyard. Dos Santos et al. (2003) used impedance measurements to differentiate, by means of PCA, Cabernet wine samples on the basis of either the vintage or the vineyard. In another study by the same research group, the same data was processed with clustering techniques, in order to identify groupings, and with ANN classification, testing several algorithm parameter settings (Riul et al., 2004). Rodrı´guez-Meńdez and coworkers applied an electronic tongue based on cyclic voltammetry and square wave voltammetry, with chemically modified electrodes, for discriminating via PCA, as shown in Fig. 2.19, a number of Spanish wines, of the same oenological region (Rioja) and vintage (2003), on the basis of the vineyard: Viura, Garnacha blanca, Tempranillo blanco, Malvasıá, Turrunte´s (Rodriguez-Mendez et al., 2008). Buratti et al. (2004) employed an electronic tongue based on amperometric detection in a flow injection system (FIA), coupled with an electronic nose, to discriminate wines from vineyard Barbera produced in four Italian oenological regions with different denominations: Oltrepo’ Pavese, Piemonte, Asti, and Alba. The chemometric techniques applied were PCA for data exploration, and LDA and CART (classification and regression trees) for classification. Parra and coworkers used an electronic tongue based on cyclic voltammetry and square wave voltammetry to differentiate six Spanish red wines made from the same grape variety, prepared using a similar vinification method, but belonging to three different geographic origins (Rueda, Rioja, and Ribera del Duero) and different aging stages. The pattern recognition technique of PCA was applied (Parra et al., 2004). Also, Wu et al. (2005) proposed a method based on cyclic voltammetry for discriminating among six Chinese yellow wines of two brands (Kuaiji Mount, and Tower) on the basis of the aging, via PCA.


4

Tempranillo (Agoncillo)

Tempranillo (Logroño)

99

Gamacha

PC3

2

0 –2

Malvasia

–4 –6 PC 1

Turruntés

Viura –2 2 6

–4

–2

2

0

4

6

PC2

FIGURE 2.19 PCA 3d score plot (explained variance PC1 ¼ 42%, PC2 ¼ 21%, PC3 ¼ 16%). Discrimination of Spanish red wines on the basis of the vineyard (reproduced from Rodriguez-Mendez et al., 2008, with permission).

A similar study by Verrelli and coworkers, based on PCA of potentiometric data, allowed the differentiation of Verdicchio wines according to the winemaker and the vintage. Furthermore, MLR and PLS models, validated by means of cross-validation, permitted the quantification of a number of oenological parameters, such as SO2, L-malic acid, and the total phenols content (Paolesse et al., 2008; Verrelli et al., 2007). Parra et al. (2006a) developed a hybrid sensor array based on voltammetric electrodes, for monitoring the aging of red wines and for discriminating wine samples aged in oak barrels according to their characteristics (the wood origin and the toasting level). PCA score plots and SIMCA results revealed the high ability of the method proposed, as shown in Fig. 2.20, which represents the Coomans’ plot for three-month and six-month aged wines, showing elevated sensitivity and specificity of the corresponding models. In another work, Parra and coworkers proposed a method based on chemically modified voltammetric electrodes for the identification of adulterations made in wine samples, by addition of a number of forbidden adulterants frequently used in the wine industry to improve the organoleptic characteristics of wines, like, for example, tartaric acid, tannic acid, sucrose, and acetaldehyde (Parra et al., 2006b). The patterns identified via PCA allowed an efficient detection of the wine samples that had been artificially modified. In the same study, PLS regression was applied for a quantitative prediction of the substances added. Model performances were evaluated by means of a cross-validation procedure.

100


Sample distance to model 3 months SIMCA 4

3

Sample of model 6 months Sample of model 3 months Test sample

2

1

0

Significance = 5% 0

1

Sample distance to model 6 months SIMCA 2

3

4

5

FIGURE 2.20 Coomans’ plot of SIMCA models of three-months and six-months aged wine samples (reproduced from Parra et al., 2006a, with permission).

Di Natale et al. (2000) used a potentiometric electronic tongue, compared with an electronic nose, for quantifying via ANN regression 23 parameters of oenological interest: density, effective alcohols, sugar, total alcohols, total extract, net extract, pH, total acids, volatile acids, total SO2, ashes, alkalis, tartaric acid, malic acid, lactic acid, glycerol, polyphenols, antocyans, Ca, K, Mg, Do 420 nm, and Do 520 nm. The PC loading analysis revealed an evident distinction between electronic nose and electronic tongue, according to the assumption that the two systems provide independent information about the samples. Nevertheless, the ANN models presented cannot be considered valid, since the method was applied without a consistent number of samples and without a proper validation. Other typical quantitative applications in the wine sector are related with the prediction of sensorial descriptors. Legin and coworkers used an array of 23 potentiometric sensors for predicting several sensorial attributes (visual, olfactory, and gustatory) in 56 wine samples by means of PLS and ANN regression models (Legin et al., 2003). A test set validation was performed: a given fraction of all the samples was randomly chosen as the test set; all replicas of each sample were correctly included in the same set (calibration or test set). The models presented rather considerable prediction errors, especially for the determination of visual descriptors (nearly the 20%), as might be expected. For the prediction of gustatory attributes, the errors are about 5%, which is an acceptable result, considering that data from sensorial analysis are often characterized by comparable errors. In the same study, PLS models for the quantification of eight oenological parameters were built, obtaining lower prediction errors.


101

Buratti et al. (2006, 2007) employed an amperometric electronic tongue, together with a commercial electronic nose and spectrophotometric methods, to predict sensorial descriptors (total fruits, wood, spicy, sourness, bitterness, astringency, alcohol, body, overall quality) of Italian red dry wines of different denominations of origin. Genetic algorithms were employed to select the variables that maximized the predictive ability of the regression models; validation has been performed by means of a bootstrap procedure, closely related to the Montecarlo validation. Also in this case, the results are not very satisfactory (see Fig. 2.21), due to the intrinsic limits of data from sensorial analysis. As the same authors pointed out, ‘‘Models with a good fitting performance (i.e., high R2 value) do not always have an acceptable predictive performance.’’ Rudnitskaya and coworkers developed PLS models for predicting the aging (from 10 to 35 years) of Port wine obtaining an error of about 1.5 years on the age estimation, which may be acceptable for practical applications. The samples were analyzed by means of an array of 28 potentiometric sensors. Data were preprocessed by OSC (orthogonal signal correction), a typical pretreatment for spectroscopic data; model validation was performed using one-third of the available samples as the external test set (Rudnitskaya et al., 2007). Labrador et al. (2009) developed a technique based on pulse voltammetry, used to predict concentrations of bisulfites, ascorbic acid, and histamine in wine samples, by means of PLS models evaluated via cross-validation. The best prediction results have been obtained for bisulfites. In the first study describing a voltammetric electronic tongue, Winquist and coworkers presented a method for monitoring, by means of PC score projections of large amplitude and small amplitude pulse voltammetry data, the modifications of orange juice and milk after bottle opening during a 20-h period. The scores show a clear trend corresponding to the sampling time sequence and are different for the two foods: the orange juice presented considerable modifications at the beginning and, after about 8 h from bottle opening, reached a steady state. On the contrary, the milk showed a nearly constant trend during the first hours, while noticeable variations start to appear after 10 h (see Fig. 2.22). The authors explained such differences according to two different modification processes: a predominant oxidative alteration in the orange juice, a modest oxidation plus a gradual microbiological alteration in the milk (Winquist et al., 1997). In further studies, the same authors improved the method, building PLS and ANN models for prediction of bacterial growth in milk samples (Winquist et al., 1998). As a supplementary development, they presented a hybrid electronic tongue, based on a combination of pulse voltammetry, potentiometry, and conductivity measurements, able to distinguish, via PCA and ANN classification, six different types

102


8

7

Total fruits aroma 2 R = 82 % 2 Q = 56 % Qboot = 37 %

4

8

4

7

Total fruits flavour 2 R = 91% 2 Q = 77 % 2 Qboot = 66 %

4

4

6

Spicy aroma

2

2

R = 78 % 2 Q = 57 % 2 Qboot = 37 %

R = 80 % 2 Q = 59 % 2 Qboot = 31 %

3

1

9

3

7

4

55 55

2

4

2

6 Wood flavour 2 R = 83 % 2 Q = 72 % 2 Qboot = 66 %

2

6 Body 2 R = 90 % 2 Q = 81 % 2 Qboot = 69 %

4

6

Alcohol 2 R = 88 % 2 Q = 75 % 2 Qboot = 72 %

75

3

7

Astringency 2 R = 79 % 2 Q = 66 % 2 Qboot = 49 %

7

4

6

1

3 6

Wood aroma 2 R = 80 % 2 Q = 56 % 2 Qboot = 32 %

9

3

7

3

6

7 Spicy flavour

7 Bltterness 2 R = 87 % 2 Q = 74 % 2 Qboot = 57 %

2

6

Overall quality 2 R = 87 % 2 Q = 75 % 2 Qboot = 66 %

75

FIGURE 2.21 Scatter plot of the sensorial descriptors and overall quality estimated by e-nose, e-tongue, and spectrophotometric data. Plots show predicted (○) and calculated () versus experimental responses. The R2, Q2 leave-one-out, and Q2 bootstrap values of each model are shown (reproduced from Buratti et al., 2007, with permission).


A

*10-1

B

3.4

103

*10-1

1.0

Comp. −2(12.9%)

Comp. −3(2.7%)

1.7

−0.0

−1.0

−0.0

−1.7 −2.0

−3.0 −1.00

−0.50

0.00

0.50

Comp. −1(64.0%)

1.00

−3.4 −0.37

0.00

0.37

0.74

1.11

Comp. −1(82.6%)

FIGURE 2.22 Aging processes of orange juice (A) and milk (B) monitored by PCA score plots from voltammetric data. The time between two consecutive samples is 30 min (reproduced from Winquist et al., 1997, with permission).

of fermented milk: Filmjolk, A-fil, Kefir, Onaka, A-Yogurt, and Naturell Yogurt (Winquist et al., 2000). In this study, model performances were evaluated on a test set constituted by only a sample for each class, with an elevated risk of overestimation of the actual predictive ability. Artursson and Holmberg (2002) presented an approach for monitoring of water in drinking water production plants. The purpose of the study was to monitor the effect of different filters to determine how the filters work and when a filter starts to malfunction. The measurements are performed with a pulsed voltammetry-based electronic tongue. Data were preprocessed using a wavelet algorithm, with computation of the variance spectrum for the selection of the common best basis. The authors demonstrated that the groupings detectable in the PCA score plot correspond to changes in some chemical parameters (pH, alkalinity, and chemical oxygen demand (COD)), measured by reference instruments. The advantages of the electronic tongue presented in comparison with reference analyses are speed and the possibility of obtaining not only the detection of single analytes but also a qualitative overview of the water. Lvova and coworkers used an electronic tongue composed of several potentiometric sensors and biosensors for distinguishing different types of tea and coffee and provide a quantification of compounds like tannic acid, caffeine, catechines, sugar, and L-arginine. PCA was applied for exploratory analysis, while SIMCA was used for building qualitative models (see the Coomans’ plot in Fig. 2.23), and PCR and PLS were used for building quantitative models. The results presented are very satisfactory (Lvova et al., 2003).

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Sample distance to model 2 (without tannin)

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FIGURE 2.23 Coomans’ plot of SIMCA models of green teas according to the presence of tannic acid (reproduced from Lvova et al., 2003, with permission).

Ciosek et al. (2005) used potentiometric ion-selective sensors for discriminating different brands of mineral waters and apple juices. PCA and ANN classification were used as pattern recognition tools, with a test set validation (Ciosek et al., 2004b). In a subsequent study, the same research group performed the discrimination of five orange juice brands, with the same instrumental device. A variable selection was performed, by means of strategies based on PCA and PLS-DA scores. The validation was correctly performed with an external test set. Gutes and coworkers presented an automated electronic tongue based on sequential injection analysis (SIA) and linear sweep voltammetry, for the simultaneous determination of glucose and ascorbic acid, by means of ANN regression. The models were evaluated with an external test set (Gutes et al., 2006). Olsson et al. (2006) studied the performances of a mechanically selfpolishing electronic tongue based on pulsed voltammetry, for tea analysis. From the PCA scores (see Fig. 2.24), a drift in the measurements is clearly evident. An appropriate row pretreatment of the signals might reduce this effect without loss of useful information. Beullens and coworkers compared two potentiometric electronic tongues—a commercial instrument and a self-assembled device—assessing


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PCA scores Pt electrode in ferrocyanide

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FIGURE 2.24 PCA scores for Pt electrode in ferrocyanide solution. Data represents six series of pulse voltammetry measurements performed with a self polishing device. A reproducible drift pattern is shown (reproduced from Olsson et al., 2006, with permission).

their ability in providing information correlated to taste sensations (sweetness, sourness, saltiness, umami) evaluated by a panel on tomato samples, and in quantifying simultaneously a number of chemicals: glucose, fructose, citric acid, malic acid, glutamic acid, Na, and K. In addition, the tomatoes analyzed were discriminated according to the cultivar. The pattern recognition techniques applied were PCA, LDA, and PLS. A cross-validation approach was followed for evaluation of the model performances (Beullens et al., 2008). Chen et al. (2008) employed a commercial electronic tongue, based on an array of seven sensors, to classify 80 green tea samples on the basis of their taste grade, which is usually assessed by a panel test. PCA was employed as an explorative tool, while k-NN and a back propagation artificial neural network (BP-ANN) were used for supervised classification. Both the techniques provide excellent results, achieving 100% prediction ability on a test set composed of 40 samples (one-half of the total number). In cases like this, when a simple technique, such as k-NN, is able to supply excellent outcomes, the utilization of a complex technique, like BP-ANN, does not appear justified from a practical point of view.

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5 4

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FIGURE 2.25 Score plot on the two LDA discriminant functions, showing the discrimination among honey samples, on the basis of potentiometric data, according to the predominant pollen variety: Er, Erica; Ec, Echium; L, Lavandula (reproduced from Dias et al., 2008, with permission).

Dias and coworkers utilized an array of potentiometric sensors for the classification of honey samples from different Portuguese regions with respect to the predominant pollen type: Erica, Echium, Lavandula. PCA and LDA were employed for the pattern recognition (see Fig. 2.25), after having verified that the variables followed a normal distribution. Cross-validation was applied for evaluating the classification rules, obtaining satisfactory prediction abilities for two classes (about 80%) and poor results for the third one (about 50%) (Dias et al., 2008). In a further study, Dias et al. (2009) studied the deployment of a potentiometric electronic tongue based on an array of 36 sensors, for the recognition of the basic taste sensations and for the detection of fraudulent additions of bovine milk to ovine milk. The signals were processed by means of PCA and LDA (see Fig. 2.26), and the classification rules were evaluated by means of cross-validation. The results presented are excellent for fitting but not very satisfactory for prediction. Paixaõ and coworkers described a voltammetric electronic tongue able to distinguish milk treated by means of different pasteurization processes. Furthermore, the method proved to be useful for the detection of hydrogen peroxide additions to milk. Such a fraudulent practice was discovered in 2007 in Brazil. The practice was aimed at reducing the bacterial growth in milk, thus lessening the degradation processes. But, it is


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Function 2 (14.5 % of variance explained)

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FIGURE 2.26 Score plot on the two LDA discriminant functions, showing the discrimination among skim raw milk samples according to their nature, on the basis of potentiometric data (reproduced from Dias et al., 2009, with permission).

forbidden by law. A simple PCA of the current/potential profiles was able to clearly identify the adulterated milk samples (Paixaõ and Bertotti, 2009). Cosio et al. (2006) used an electronic tongue system based on flow injection analysis (FIA) with two amperometric detectors, together with the use of an electronic nose, in order to classify olive oil samples on the basis of their geographical origin. Counter-propagation maps were used as classification tools. Oliveri et al. (2009) presented the development of an artificial tongue based on cyclic voltammetry at Pt microdisk electrodes for the classification of olive oils according to their geographical origin: the measurements are made directly in the oil samples, previously mixed with a proper quantity of a RTIL (room temperature ionic liquid). The pattern recognition techniques applied were PCA for data exploration and k-NN for classification, validating the results by means of a cross-validation procedure with five cancellation groups. An interesting issue, in the field of nonspecific analysis, is the fusion of data coming from different analytical techniques. As for artificial tongues, potentiometric and voltammetric data have been employed

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jointly (Ivarsson et al., 2001) and also together with conductometric data (Winquist et al., 2000), demonstrating that it is possible to achieve better results than by using the information separately. In other cases, data from electronic tongues and electronic noses have been combined (Winquist et al., 1999), confirming that the two instruments provide complementary information, because data fusion improves the ability of the models. From a chemometric point of view, the only constraint is that an appropriate column preprocessing, such as autoscaling, is required in order to eliminate systematic differences between variables of a different nature. Then, when the original variables are very numerous, it is possible to join the PCs computed separately for each variable block. Anyway, using data from different instruments increases the experimental effort, time, and costs of analysis. For this reason, although fusion is always an elegant data-processing procedure, for practical applications the real benefit/cost balance should be carefully evaluated.

VIII. CONCLUSIONS Artificial tongues represent effective analytical tools able to characterize samples by means of a nonspecific approach. They may provide information useful for many purposes, allowing both qualitative and quantitative applications. In each case, chemometrics plays an essential role in data processing, which corresponds to the brain elaboration of exteroceptive stimuli in mammalian sensory processes. A clever employment of chemometric tools is thus essential for obtaining reliable and valuable results. Some basic guidelines can be drawn: Evaluate carefully the most appropriate data preprocessing tools (sig-

nal corrections, transforms, compression) in order to minimize the amount of unwanted information. Always start pattern recognition by performing a preliminary multivariate data exploration: PCA is a perfect tool for this purpose, being useful to visualize structures (groupings, correlation) within the data and to make decisions about the subsequent processing steps. Use the simplest possible techniques (possibly linear and with few constraints) for data modeling, instead of applying at all costs the most potent tools available: only resort to more complex methods if the data show complex structures and the basic techniques do not


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provide satisfactory results. Anyway, mind that complex methods generally require many constraints to be respected; usually, the first one is the requirement for a considerable number of samples. Perform a correct and extensive validation of models, in order to properly evaluate their prediction ability and thus their actual applicability. In particular, mind that if model building involves optimization steps, a three-set validation strategy should be applied.

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CHAPTER

3 Photodynamic Treatment: A New Efficient Alternative for Surface Sanitation Lubov Brovko1

Contents

Abstract

I. Introduction II. Interaction of Light with Matter and History of PDT III. Mechanisms of Photodynamic Production of Cytotoxic Species IV. Mechanisms of Photodynamic Killing of Bacteria and Viruses A. Interactions of cells with PSs B. Modes of light delivery V. Examples of Photoactive Dyes used for Photodynamic Killing of Microorganisms VI. PDT for Environmental Cleaning and Disinfection VII. Conclusions References

120 121 124 126 126 133 133 138 143 144

A novel and promising technology—photodynamic treatment (PDT), aimed for surface cleaning and sanitation in food industry—is presented. It is based on the treatment of surfaces with nontoxic dyes (photosensitizers), followed by illumination of the surface with regular white light. The method is currently used in the medical field and was proved to have wide specificity against a variety of bacterial and viral pathogens as well as against yeasts and protozoa. An additional advantage of this approach is that development of resistance of microorganisms to PDT was shown to be unlikely.

Canadian Research Institute for Food Safety, University of Guelph, Guelph, Ontario, Canada Corresponding author: Lubov Brovko, E-mail address: [email protected]

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The theoretical basis of light-induced antimicrobial treatment is described, followed by examples of its application for the cleaning and disinfection of surfaces. All available information supports the idea that PDT could offer a very efficient and cost-effective way to combat microbial contamination of foods. The advantages and pitfalls of the technique are discussed. Directions of future research needed for bringing the technology to commercial reality are identified.

I. INTRODUCTION The development of novel cost-effective strategies for minimizing the pathogenic contamination of foods is of significant importance. Major routes for secondary contamination are processing surfaces and utensils. Commonly used sanitizers very often are not effective against bacterial spores and biofilms as well as against viruses. Besides, recent research has indicated that pathogens can acquire resistance to sanitizers, and as a result of such adaptation, cross-resistance to antibiotics has been observed (Romanova et al., 2006). The emergence of multi-antibiotic-resistant pathogens is a risk to animal and human health as well as to the safety of food products. The problem of removing bacteria from food-processing surfaces is compounded by the fact that microorganisms growing in a biofilm secrete extracellular polymeric substances - exopolysaccharides (EPS), which can remain attached to the cell in a capsular form or, alternatively, be released as a slime in which the cells form a complex multicellular structure (Marsh et al., 2003; Wang et al., 2004). Bacteria in biofilms are more resistant to regular sanitizers. Detergents are formulated to remove particular types of soils, for example, proteinaceous, fatty, carbohydrate, or mineral soils, rather than to remove microorganisms. Gibson et al. (1999) reported that detergents did not significantly improve the removal of attached Gram-positive and Gram-negative organisms from food contact surfaces. Many commonly used enzymatic cleaners also fail to reduce the viable bacterial load or remove the bacterial EPS from surfaces (Augustin et al., 2004; Vickery et al., 2004). That is why it is necessary to investigate new approaches for surface decontamination and sanitation that will result in a minimized risk of infections, reduced bacterial loads on ready-to-eat foods, improved food quality, and a decrease in economic losses due to spoilage and recalls. The bactericidal effect of photodynamic treatment (PDT) has been known for a long time (for reviews see Dai et al., 2009; Wainwright, 1998, 2004). The method relies on illumination of microorganisms treated with nontoxic photosensitizers (PSs) by low-power visible (red, blue, or white) light. The interaction of light with PS produces highly active,

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short-lived free radicals that are able to destroy cell components in close vicinity of the dye. This results, in some cases, in a 5–7-log cycle reduction in bacterial counts, indicating the promise of the approach. Development of resistance to antimicrobial PDT has not been reported, and was shown to be very unlikely (Lauro et al., 2002), which makes this approach more attractive for investigation. As visible light can penetrate through thick layers, PDT may also provide a method for eradicating biofilms in situ. Although PDT is gaining increasing acceptance as a novel therapeutic option, its applications for environmental cleaning and disinfection are still in their infancy. The goal of the following review is to summarize the available information on the scientific principles of antimicrobial PDT and present the current state of research and the future possibilities for application of this promising new technology to food scientists and food industry professionals.

II. INTERACTION OF LIGHT WITH MATTER AND HISTORY OF PDT When light encounters matter, several different phenomena are observed, including reflection, transmission, refraction, scattering, and absorption. Among them, absorption is the main method of conversion of the energy of light, which represents a form of electromagnetic radiation, to other forms of energy such as heat and/or chemical energy. The interaction of light and molecules is covered by a rather complex interdisciplinary science which includes the fields of photophysics, molecular spectroscopy, physical organic chemistry, and many others. In the simplest way, it can be divided into two separate topics, one being photophysics of organic compounds, and the other photochemistry of organic compounds. Organic photophysics covers the interactions of light and organic molecules, resulting in net physical changes, but does not involve any net chemical changes. In general, the process can be visualized as the following (Eq. (1)): M þ hn ! M ! M

(1)

where M is an organic molecule in a ground state, that absorbs a quantum of light (hn) whose frequency (n) is specific to the molecule, and M* is an electronically excited state of the molecule M that is formed as a result of light absorption. Subsequently, the electronically excited form M* is converted back to the ground state; sometimes, this process involves the release of the extra energy in the form of light. This phenomenon is called fluorescence or phosphorescence depending on the electronic features of the molecule.

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Organic photochemistry, on the other hand, involves the process presented in Eq. (2): M þ hn ! M ! P

(2)

where M absorbs a photon (hn), and an electronically excited molecule (M*) is formed. But unlike in the photophysical process, M* is converted to the product (or products) P of a photochemical reaction. Thus, the energy of light is converted to chemical energy which in turn can be used in further processes. The goal of this chapter is to describe the possible applications of the highly reactive products of photochemical reactions to destroy and kill harmful bacteria in their vicinity. The first step in a photochemical reaction is the same as in a photophysical reaction and involves the formation of an electronically excited state. Excitation in general proceeds through the promotion of an electron from one of the Highest energy Occupied Molecular Orbitals (HOMOs) to achieve one of the Lowest energy Unoccupied Molecular Orbitals (LUMOs). Unlike the ground-state entity, a molecule in its excited state survives for only a very short time. The short life-time is a consequence of the high energy of the molecule in the excited state, which exceeds that of the ground state, mostly by 150–600 kJ/mol. For comparison, the typical activation energy of a chemical reaction is 30 kJ/mol. The schematics of energy profiles for regular thermal and photochemical reactions are presented in Fig. 3.1. The excess of energy in the excited molecule opens some new reactive channels, and the chemistry of the excited state is usually quite different from that of the parent ground-state species. Many properties of the excited-state molecule differ from those in the ground state, including electronic configuration, electronic density distribution, charge distribution, geometric structure, bond lengths and strengths, redox behavior, acid-base behavior, and magnetic properties. These differences not only make the excited-state entities short-lived, but often their reactivity would be completely unlike that of the ground state. Generally, the initial photochemical products are unstable and undergo secondary thermal and/or photochemical reactions, yielding more stable photoproducts. The main types of photochemical reactions are presented in Fig. 3.2. Among the reactions presented in this figure, Photosensitized reactions are involved in the process of photodynamic killing of live cells. These reactions represent another way to dissipate the extra energy of the excited state, namely, to transfer it to another molecule(s). The species absorbing and transferring the radiant energy of light are called PSs. Both organic and inorganic molecules were shown to be effective PSs, for example, dyes, pigments, aromatic hydrocarbons, and transition metal complexes.


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TS* E*a

R*

IP

E

hn TS Ea

Pph

R Pt Reaction coordinate

FIGURE 3.1 Comparison of the energy profile of thermal and photochemical reaction courses. R and R* are reactants, TS and TS* are transition states, Ea and Ea*, activation energies for the ground and excited states, respectively; IP, intermediate photochemical product; Pt and Pph, products of thermal and photochemical reactions, respectively.

Photosensitized oxidations involving molecular oxygen are especially important in the biological field because of their impact on living organisms. In 1900, it was discovered by chance by a medical student that unicellular protozoa—paramecia were killed when exposed to light in the presence of acridine (a coal tar derivative) (Stables and Ash, 1995). Very soon, it was shown that many other living cells, including tumor cells, microorganisms, and viruses, can be killed in the presence of light, molecular oxygen, and PS. This discovery led to therapeutic applications of the interaction between PS and light to treat tumors. In 1907, the term ‘‘Photodynamic’’ was introduced to name the phenomenon of lightinduced killing of cells in the presence of PS (Hamblin and Mroz, 2008). However, only from the middle of 1980s can the true explosion of interest in Photodynamic Treatment (PDT) be seen. Since then, multiple photodynamic treatments were developed and approved for clinical applications. Despite the evident success of PDT in the medical field, the application of the principles of PDT to control pathogens in the environment and during food processing and handling was practically scarce. Only recently, several articles have been published on the use of PS dyes in combination with light for the removal of pathogens from contact surfaces

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Photochemical reactions

Intramolecular

Intermolecular

Photodissociation Homolytic hn AB Æ A+B Heterolytic hn + AB Æ A + B–

Photosubstitution hn AB + C Æ AC + B Photoaddition hn AB + C Æ ABC Photopolymerization hn nAB Æ (AB)n

Photoionization hn AB Æ AB+ + e– Photoisomerization Structural

Photoredox Photoreduction

Cis-trans Double-bond migration

Photooxidation

Photoenolization Photocyclization

Photooxygenation

Others Photoredox

Photosensitized reactions

FIGURE 3.2 Main types of photochemical reactions (adapted from Stochel et al., 2009).

(Brovko et al., 2005, 2009). This chapter describes the mechanisms underlying PS-mediated killing of microorganisms by incident light and discusses the possible application of this phenomena for surface sanitation, control of pathogens in the environment, and the construction of self-cleaning materials.

III. MECHANISMS OF PHOTODYNAMIC PRODUCTION OF CYTOTOXIC SPECIES In a majority of cases, the environment where live cells exist is oxygenrich. The process involved in the photodynamic destruction of cells is, by its nature, photosensitized oxidation. A simplified illustration of the


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Cytotoxic species

1

Light source

PS Singlet ground state

1 O2* Excited singlet oxygen

e nc

e

sc

re

1

Free radicals, superoxide

Int er cro syst ss em ing

Fluorescence/ heat

Light absorbtion/ excitation

PS* Singlet excited state

o ph

Ph

os

3 PS* Triplet state Sp en in-pe erg rm i yt ran tted sfe r

1

3 O2 Ground state triplet oxygen

PS

FIGURE 3.3 Scheme of photophysical and photochemical processes involved in the production of cytotoxic species during photoexcitation of photosensitizer (PS; adapted from Castano et al., 2004).

processes of light absorption and subsequent energy transfer that lead to the formation of highly reactive species causing cell death is presented in Fig. 3.3. Following the absorption of light by the PS, one of its electrons is boosted into a high-energy orbital keeping its spin orientation antiparallel and thus converting the molecule from the ground singlet state (1PS) to the first excited singlet state (1PS*). Within nanoseconds, this species can lose its energy by emitting light (fluorescence) or by internal conversion into heat. The excited singlet state PS may also undergo the process known as intersystem crossing when the spin of the excited electron inverts to form the relatively long-lived (micro- or milliseconds) excited triplet state (3PS*) that has electrons with parallel spins. The relatively long life-time of the excited triplet state is explained by the fact that the loss of excitation energy of the triplet by the emission of light in this case (phosphorescence) is a ‘‘spin-forbidden’’ direct conversion from a triplet to a singlet state. During this delay time, the PS-excited triplet can undergo two kinds of reactions. In a Type I reaction, 3PS* can react directly with a substrate that is located in its close vicinity and transfer a proton or an electron to form a radical anion or radical cation, respectively. These free radicals may further react with molecular oxygen to produce reactive oxygen species (ROS). Alternatively, in a Type II reaction, the excited 3PS* can transfer its energy directly to oxygen, which is a ‘‘spin-allowed process,’’ as molecular oxygen itself is a triplet in its ground state. As a result, excited state singlet oxygen (1O2*) is formed. Both Type I and Type II reactions can occur simultaneously, and the ratio between these processes depends on the structure of PS, and the availability and concentration of the substrate and oxygen.

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The Type I reaction often results in the formation of superoxide anion via electron transfer from the excited triplet PS to molecular oxygen. Though superoxide is not particularly damaging to the cell, it can react with itself, producing hydrogen peroxide and oxygen in the enzymatic reaction catalyzed by superoxide dismutase, or it can produce highly reactive hydroxyl radical (HO*). These ROS, together with singlet oxygen produced in Type II reaction, are oxidizing agents that can directly react with many biological molecules, such as proteins and nucleic acids, causing their damage and leading to cell death. Because of the high reactivity and short life-time of the ROS produced due to the interaction of light with the PS, only molecules and structures that are in close proximity to the localization of PS are directly affected. Considering that the estimated half-life of singlet oxygen in biological systems is < 40 ns, the affected area would not exceed 20 nm (Moan and Berg, 1991). This feature of photosensitized oxidation has a major effect on the way that the photodynamic process can be applied to be effective in killing live cells and viruses. In order for photooxidative cell damage to occur, all required participants of the reaction must be located in close proximity to each other. Considering that the supply of oxygen is in excess in the regular environment and may not be a limiting factor, the main focus should be on interaction of PS with the target substrate— biomolecule or cell organelle essential to cell integrity and/or metabolic activity. Formation of a tight complex between the PS and target at the moment when the photon of light is absorbed is crucial to the process of photodynamic killing of cells. Another important point to consider is a method of light delivery. The photon of light should be available in the right spot at the right moment for the photodynamic killing to occur. These are the key issues that should be addressed when methods for PS-mediated photokilling of microorganisms are being developed.

IV. MECHANISMS OF PHOTODYNAMIC KILLING OF BACTERIA AND VIRUSES A. Interactions of cells with PSs There are two main routes for PS to interact with the target cell—it could form a tight complex with the surface receptors of the cell wall and/or could be transported inside the cell where it would associate with the molecule/organelle essential for survival. In the first case, the oxidative damage is localized on the cell wall, which could lead to its disintegration and the leakage of intracellular material, resulting in cell death. When the

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PS is inside the cell, it could attach to certain molecules/organelles and, after absorption of light, could cause their damage. This results in the disruption of essential metabolic pathways and subsequently causes the death of the cell. Both processes can proceed simultaneously. The input of each process into the photokilling most probably depends on the type of cell and PS, as well as on the environment. It was observed from early on that there were substantial differences in the susceptibility to PDT between different types of microorganisms— Gram-positive and Gram-negative bacteria, viruses, fungi, and yeasts. It was found that, in general, neutral, anionic, or cationic PS molecules could efficiently kill Gram-positive bacteria, whereas only cationic PS or supplementation of PDT with permeabilizing agents are able to produce a significant kill of Gram-negative species. These differences were further explained by the differences in their physiology. In Fig. 3.4, a schematic of cell-wall structures for Gram-positive bacteria, Gram-negative bacteria, and fungi are presented together with an indication of their relative thickness. In Gram-positive bacteria, a cytoplasmic membrane is surrounded by a thick (20–80 nm) but rather porous layer composed from peptidoglycan and lipoteichoic acid. This allows PS to easily interact with cell-wall components and to cross the cell wall. Positively charged PS could be incorporated into the cell wall through an interaction with negatively charged teichoic and lipotechoic acids similarly to the process of Gram staining of bacteria with crystal violet. When this molecule of PS is excited Gram-negative bacterium

Gram-positive bacterium

Fungus

Cell wall specific polysaccharide

Cell wall teichoic acid

Glycoproteins

20–80 nm

60–80 nm

Membrane lipoteichoic acid

Peptidoglycan Glucan

Pepticloglucan Cytoplasmic membrane

FIGURE 3.4 fungi.

2–5 nm

Chitin

Cytoplasmic membrane

Cytoplasmic membrane

Structures of cell walls for Gram-positive, Gram-negative bacteria and

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A

B

112018 10 KV

3.0 µm

112021 10 KV

3.0 µm

FIGURE 3.5 Electron microphotographs of Bacillus cereus cells before (A) and after (B) photodynamic treatment with Toluidine Blue O (20 mg/ml). Illumination with red light (660 nm, 7.6 mW/cm2) for 1 h (Brovko et al., 2005).

by a photon of light, ROS are generated and photooxdative damage of the cell wall occurs. This in turn leads to formation of pores or to disintegration of the cell and the leakage of intracellular material. In Fig. 3.5, electron photomicrographs of Bacillus cereus are presented before and after PDT with Toluidine Blue O (TBO). It is clear that, in this case, the PDT resulted in the total collapse of cells walls. The morphological changes induced by the photodynamic action of TBO in Staphylococcus aureus and Escherichia coli were studied using atomic force microscopy (Sahu et al., 2009). For Gram-positive S. aureus, the PDT resulted in a light dose-dependent increase in surface bleb formation, suggesting breakage of contact between the cell wall and the membrane with no significant change in the cell dimensions. Photosensitization of Gram-negative E. coli, on the other hand, produced surface indentations, a significant reduction in mean cell height, and flattening of the bacteria. These results indicate damage to the bacterial membrane and a loss of cytoplasmic materials. Such morphological changes are consistent with the previously reported data that, for Gram-negative bacteria, the time course of release of intracellular ATP correlated with the reduction in live cells numbers. For Gram-positive bacteria, there was a delay between the beginning of viability loss and detection of the released intracellular ATP, which was explained by the thickness of the cell wall in Gram-positive bacteria (Romanova et al., 2003).


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The cell wall of Gram-negative bacteria consists of an inner cytoplasmic membrane and an outer membrane consisting of a phospholipid bilayer with incorporated lipopolysaccharides (LPS) directed outside. The two membranes are separated by a thin, though dense, peptidoglycan layer. The total thickness of the cell wall for Gram-negative bacteria is low (2–5 nm); however, it forms an effective permeability barrier between the cell and its environment. Due to the much smaller volume and dense structure of the cell wall in Gram-negative bacteria, the binding of PS is limited. Nevertheless, the net negative charge of the cell surface facilitates the binding of cationic PS providing the basis for photodynamic destruction of Gram-negative bacteria using cationic PS. When the efficiency of photodynamic killing was compared for Gram-positive and Gramnegative strains in similar conditions, it was shown that, in both cases, a multilog reduction in the number of live cells was reached. However, the difference in killing efficiency for different types of bacteria could be significant (Brovko et al., 2009). In Table 3.1, data are presented on the observed reduction in the numbers of bacterial cells in suspension due to photodynamic killing using various dyes. In the majority of cases, the killing effect for Gram-positive bacteria (Listeria monocytogenes and Bacillus sp.) was greater than for Gram-negative species (E. coli and Salmonella Typhimurium).

TABLE 3.1 Killing effect of the photodynamic treatment observed for microorganisms in suspension (adapted from Brovko et al., 2009) Mean log reduction in count after 30 min treatment with the dye Dye, mg/mL EC

AF 5 50 500 RB 5 50 500 MG 5 50 500 PhB 50 500 5000

1.13 >5 >6 0.2 >6 >6 0.09 0.05 0.69 0.16 0.45 0.05

ST

BS

LM

SC

2.0 >6 >6 0.01 0.02 2.39 0.21 0.93 0.06

0.55 >5 >5 >5 >5 >5 0.09 0.02 >5 >6 >6 >6

>6 >6 2.2 >6 >6 >6 0.04 >6 1.93 >6 >6 >6

0.01 1.5 >5 1.01 >6 >6 0.21 0.39 0.68 0.33 1.14 1.15

EC, Escherichia coli; ST, Salmonella Typhimurium; BS, Bacilllus sp.; LM, Listeria monocytogenes; SC, Saccharomyces cerevisiae; AF, Acriflavin Neutral; RB, Rose Bengal; MG, Malachite Green; PhB, Phloxine B.

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Fungal cells and yeasts have a relatively thick cell wall (60–80 nm) comprised of a layer of beta-glycan and chitin that is coated on the outer side by an array of glycoproteins (Fig. 3.4). This creates a permeability barrier intermediate between Gram-positive and Gram-negative bacteria. However, the net charge of the cell wall in yeasts is close to zero (Vergnault et al., 2004), thus making the electrical charge of PS nonessential for the ability to bind to the cell wall. This may explain the significantly lower average efficiency of photodynamic killing observed for Sacccharomyces cerevisiae as compared to bacteria species (Table 3.1). Nevertheless, under some conditions, the observed reduction in the number of yeast cells due to PDT was quite substantial. For example, when S. cerevisiae (107 CFU/ml) were treated with Rose Bengal (50–500 mg/ml), the live cells were practically eliminated from the suspension after 30 min of illumination with white light (Brovko et al., 2009). Some bacteria have the ability to protect themselves from adverse conditions by synthesizing an EPS that forms a 3D structure surrounding cells. These structures are called biofilms and are usually formed on contact surfaces. One of the important features of such environments is increased resistance of bacteria within biofilms to detergents and antibiotics, as the dense extracellular matrix and the outer layer of cells protect the interior of the community. The survival of bacteria in biofilms very often presents a challenge for sanitation processes applied in food industry. Nevertheless, it was shown that PDT is capable of removing biofilms and destroying the bacteria in them (Brovko et al., 2005; Sharma et al., 2008; Zanin et al., 2005). The efficiency of PDT for biofilm destruction was similar or slightly less than for vegetative bacterial cells in suspension (Table 3.2). Another way in nature to protect various forms of life is the formation of spores. Many bacteria, fungi, plants, algae, and protozoan are known to form spores as a strategy to survive for extended periods of time in unfavorable conditions. The high resistance of spores to chemical and physical agents is explained by their multilayered structure (Fig. 3.6). This structure is practically impermeable for cytotoxic chemicals. Besides, endospores have only 20–30% of the water content of vegetative cells TABLE 3.2 Comparison of the efficiency of photodynamic treatments for killing bacteria in vegetative form and in biofilms (Brovko et al., 2005) Log reduction in bacterial cell numbers after photodynamic treatment Salmonella Typhimurium

Listeria monocytogenes

Vegetative cells

Biofilm

Vegetative cells

Biofilm

2.08

1.92

1.66

1.25


131

Exosporium Outer Middle

Coats

Inner Cortex

Protoplast (spore core) Protoplast membrane

FIGURE 3.6 The structure of a bacterial endospore.

and so they are enzymatically dormant and able to resist long periods of desiccation. Some spore-forming bacteria produce special proteins that protect the DNA of the spores. Generally to destroy spores, much harsher conditions are applied when compared with vegetative cells. Among sporicidal treatments, heat (> 121 C), strong hypochlorite solutions, chlorine dioxide, and ionizing or UV radiation are most commonly used. These conditions are not always compatible with a food-processing environment. It was shown recently that the effective destruction of bacterial endospores can be achieved by mild PDT (Brovko et al., 2005; Demidova and Hamblin, 2005b, Oliviera et al., 2009). The susceptibility of spores for PDT was shown to depend on the type of microorganism and on the nature of the PS used. For example, the treatment of B. cereus spores with TBO under red light illumination was shown to effectively decrease the number by more than 4 orders of magnitude. On the other hand, spores of B. megaterium remained intact under the same conditions (Demidova and Hamblin, 2005b). Some cationic porphyrin derivatives were quite efficient in photodynamic killing of bacterial spores, while others provided no effect. Though the presence and number of positive charges in the porphyrinic molecule was shown to be important for photodynamic inactivation, these were not the only factors contributing to the killing efficiency (Oliviera et al., 2009). Unlike in bacteria and fungi, viruses do not have a protective coat that separates essential proteins and nucleic acids from the environment. The majority of viruses consist of nucleic acid polymers (DNA or RNA) enclosed within a protein coat (capsid). Sometimes, viruses pick up a lipid membrane (envelope) from the host cell that surrounds the capsid. The average size of viral particles is in the range 10–300 nm. The most common

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Helical nonenveloped, e.g., Tobacco mosaic virus

Polyhedral nonenveloped, e.g., Adenovirus

Enveloped helical, e.g., Paramyxovirus

Enveloped polyhedral, e.g., Herpesvirus

Complex virus, e.g., bacteriophage T4

Envelope Icosahedral head

Tegument

Long tail fibers

Capsid

RNA

Short tail fibers

Lipid bilayer

DNA core

20 × 300 nm

60 – 90 nm

Tail

150 – 300 nm

100 – 200 nm

Baseplate

30 – 200 nm

FIGURE 3.7 Structures of virus families.

structures of viruses are presented in Fig. 3.7. Viruses do not have the metabolic machinery necessary for growth and propagation. For supporting their life cycle, viruses use the resources of host cells. They cannot naturally reproduce outside of a host cell. Thus, the first step in their life cycle is attachment to the host which is followed by penetration or injection of DNA into the cell. The attachment is mediated by a specific binding between the viral surface proteins and their receptors on the host cellular surface. This specificity determines the host range of a virus. The relatively small size of viral particles, exposure of receptors essential for virus propagation, as well as the lack of protective coat such as a cell wall makes viruses to some extent more susceptible to photodynamic killing than other microorganisms. Virus inactivation in blood products using PDT is a well-established technique approved in many countries since its first introduction in 1992. It was reported that hepatitis B, hepatitis C, and human immunodeficiency viruses were totally inactivated in plasma products by PDT with micromolar concentrations of Methylene Blue (MB; Mohr et al., 1997). Recently, it has been shown that photoactive fullerene derivatives (C60) were very effective in the photodynamic inactivation of bacterial viruses (bacteriophages). The treatment of water with C60-based PS under illumination resulted in a 2-log reduction in the number of bacteriophages in the water within only 2 min (Lee et al., 2009). Despite the variability in susceptibility of microorganisms to PDT, it can be concluded that, in the majority of cases, a PS can be identified that will effectively destroy the target microorganism by PDT.


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B. Modes of light delivery For PDT to be effective, it is important to ensure that light could reach the target substance. Light is either scattered or absorbed when it enters the sample. The extent of both processes depends on the optical properties of the sample and the wavelength of light. Scattering is generally a more important factor in limiting light penetration in most samples. For turbid samples, light intensity could decrease 100–1000 fold per each cm. Thus, the highest efficiency of photodynamic inactivation of microorganisms could be achieved for surfaces and for layers directly underneath the surface with a penetration depth of several centimeters for clear solutions. The absorbance of light by PS itself can also limit light penetration. This phenomenon has been termed ‘‘self-shielding,’’ and is particularly pronounced with PSs that absorb very strongly at the treatment wavelength (Dougherty and Potter, 1991). Thus, for better results, the concentration of PS should be optimized considering not only attachment to the target substance, but also the optical properties of the molecule such as light absorption. The variety of light sources used for PDT includes lasers, photo diodes, and regular mercury or halogen lamps. The intensity of light reported as being sufficient for effective photodynamic killing of microorganisms is in the wide range between 0.5 and 200 mW/cm2. The time of exposure varies from seconds to tens of minutes, depending on the intensity of the used light source. For comparison, according to the World Meteorological Organization, the intensity of direct sunlight is 12 mW/cm2 (Anon, 2008). In general, the light intensities used for PDT are low and do not cause any thermal effects. Thus, it can be concluded, that the illumination required for food-processing areas (220–540 lux) would be sufficient to initiate the photodynamic killing of microorganisms.

V. EXAMPLES OF PHOTOACTIVE DYES USED FOR PHOTODYNAMIC KILLING OF MICROORGANISMS Two major features that govern the choice of PSs for the photodynamic killing of microorganisms are their ability to efficiently form the triplet excited state upon illumination with the light of a specific wavelength, and their high affinity to the life-essential molecules or organelles. The field of application of the PDT is also very important when choosing the proper PS. For medical and therapeutic applications, the PS should have

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minimal dark toxicity, be rapidly excreted by the body, and have strong absorbance with a high extinction coefficient in the 600–800-nm range where light penetration in live tissues at its maximum (Detty et al., 2004). For applications in environmental cleaning, for example, for surface sanitation in food-processing facilities, the most important factors that define the choice of PS besides their photodynamic properties are their low dark toxicity, availability (cost), compatibility with food industry requirements, and minimal effect on food organoleptic qualities (Brovko et al., 2009). Historically, most of the PS tested for their antimicrobial properties were already known to be effective for cancer treatment. They include the following classes of organic dyes: porphyrin-related structures, phthalocyanines, phenothiazinium dyes, xanthylium dyes, and cationic fullerenes (Dai et al., 2009) (Table 3.3). In Fig. 3.8, examples are presented of structures representing the main classes of dyes used for antimicrobial PDT. The most investigated among them are cationic porphyrin derivatives (Alves et al., 2009). They were shown to be effective in the killing of both Gram-positive and Gramnegative bacteria in suspension as well as in biofilms (Demidova and Hamblin, 2004; Di Poto et al., 2009). Yeast cells (Candida albicans) were also shown to be susceptible to photodynamic inactivation when treated with tri- and tetracationic porphyrin derivatives (Cormick et al., 2009). TABLE 3.3 Physicochemical and photochemical properties of the photoactive dyes (adapted from Brovko et al., 2009)

Dye

Rose Bengal (anion)

Malachite Green (cation) Phloxine B (anion)

Absorption wavelength, nm (e, M 1cm 1)

Current applications

525, 540 (7.28 104) Biological stain, eye drops to assess the damage of conjunctiva and corneal cells; treatment of certain cancers 629 (15.0 104) Dye for silk, leather, and paper, biological stain, topical antiseptic, treatment for parasitic, fungal, and bacterial infections in fish Colorant for food, cosmetics and 524 (10.1 104) drugs, biological stain, disinfection and detoxication of waste water, toxicant for fruit fly, bacteriocidal agent in plants

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Porphyrin derivatives

Phtalocyanines

CH3 – N+ I

–

R F

NH N

I + H3C N

F F

N HN

N+ CH3

F

F

I

Phenothiazinium dyes

R

N

N

N

N

Zn

N

N

N

N

CH3

CI– +

R

H2N

NCH3

S

CH3

N

–

+

Tri-Py -Me-PF R

Xanthylium dyes

Cationic fullerens

Merocyanines

CI –

CI

CI O

CI

C ONa

Br

O Na S O O

N+I–

Br

+

N O N

O

NaO

O

O

O Br

Br

N O

Phloxin B

FIGURE 3.8

Major classes of photosensitizers used for antimicrobial treatment.

The photodynamic inactivation capacity of porphyrin-based PSs was confirmed for both cells in suspensions and on the surface of agar plates. The complete inactivation of viruses (> 99.9999%) with tetracationic porphyrins under low intensity white light illumination was observed by Costa et al. (2008). The treatment of microbiologically polluted aquaculture waters with submicromolar doses of cationic porphyrins combined with the action of visible light (including sunlight) was shown to be effective in the inactivation of a mixture of bacterial and fungal pathogens (Magaraggia et al., 2006). The conjugation of porphyrins with polymers (Demidova and Hamblin, 2004; Bonnett et al., 2006; Xing et al., 2009) or their incorporation into polymeric films (Funes et al., 2009) or liposomes and micelles (Ferro et al., 2007, 2009; Tsai et al., 2009) resulted in a further enhancement of the photokilling effect. In general, porphyrin derivatives demonstrated a wide specificity and high efficiency in photokilling. One disadvantage of this class of PSs is the high cost and limited availability of certain porphyrin derivatives. Another way of initiation of porphyrin-mediated photodynamic killing of microorganisms is to use endogenous porphyrins synthesized by some bacteria. It was observed that the addition of 5-aminolevulinic acid (ALA)—the precursor on porphyrin biosynthesis, resulted in the accumulation of sufficient amounts of intracellular porphyrins to initiate the photodynamic killing of microorganisms upon illumination (Hamblin

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and Hasan, 2004). Recently, it was shown that the important food-borne pathogen B. cereus can effectively produce endogenous PS from exogenously applied ALA even at very low concentrations (3 mM) (Luksiene et al., 2009). Subsequent illumination of the cell suspension with blue light (20 mW/cm2) for 15–20 min resulted in a 6.3-log reduction in the number of vegetative cells and a 3.1-log reduction in the number of spores. Another group of compounds often used for the photodynamic killing of microorganisms is phenothiazinium dyes. These include such PSs as TBO, MB, 1,9-dimethyl-methylene blue (DMMB), and new methylene blue (NMB). TBO is probably the most frequently used member of this class of PSs for the photokilling of bacteria and fungi (Tseng et al., 2009; Usacheva et al., 2001; Wainwright et al., 1998). A significant inactivation of biofilms was observed when staphylococcal biofilms were exposed to TBO and lasers simultaneously (Sharma et al., 2008). The most potent form of TBO was reported by Gil-Thomas et al. (2007). They conjugated TBO with tioponin gold nanoparticles. The light-activated antimicrobial activity of this conjugate was at least four times higher when compared with free TBO at the same concentration. The improved performance was explained by the enhanced extinction coefficient of the conjugate which facilitates the formation of the excited TBO and thus of the cytotoxic oxygen species. MB also demonstrated very wide photosensitizing activity (Peloi et al., 2008; Prates et al., 2009). It was reported to be effective against bacteria and fungi. A methylene blue based PDT was shown to eradicate Pseudomonas aeruginosa both in planktonic and biofilm cultures (Street et al., 2009). A strong photokilling effect was reported when cationic Zn(II) pyridyloxyphthalocyanine derivatives were used as PSs (Kussovski et al., 2009; Scalise and Durantini, 2005; Spesia and Durantini, 2008). It was shown that both noncharged and cationic derivatives of phthalocyanine were readily bound to E. coli cells. But only cationic derivatives produced a significant photoinactivation with a 2.5–4.5-log reduction in the number of live cells after 30 min of illumination. However, the variety of microorganisms tested for their susceptibility to photodynamic killing by phthalocyanine-based PSs is very limited. It can be partially explained by the fact that they are not readily available commercially. A group of newly emerging PSs is based on supramolecular carbon nanostructures called fullerenes. Fullerenes are ball-shaped molecules composed entirely of dozens of carbon atoms. The first fullerene (C60) was discovered in 1985 and consisted of 60 carbon atoms arranged as 12 pentagons and 20 hexagons exactly as in a soccer ball (Kroto et al., 1985). From the very beginning, fullerene-type structures attracted a lot of attention of researchers from all different fields of science not only for the beauty of their design but also for their remarkable properties. Among them, the significant absorbance of visible light that results in the


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formation of a long-lived excited triplet state allowed fullerenes to act as PSs. Later, various functionalized fullerene derivatives have been produced that were water soluble and thus compatible with biological applications. Fullerenes have been used for the photoinactivation of viruses (Kassermann and Kempf, 1997), as well as different types of bacteria and fungi (Tegos et al., 2005). Among six fullerene derivatives tested, the bisand tris-cationic fullerenes in combination with white light produced a 4– 6-log reduction in the numbers of Gram-positive, Gram-negative bacteria and fungi. It was shown that these compounds performed significantly better than a widely used PS, TBO. Despite being rather exotic compounds, the high selectivity and efficacy exhibited by these PSs definitely deserve further investigation. Among other PSs used for antimicrobial treatment, Rose Bengal, Malachite Green, and Phloxine B are attracting significant interest for application in the food industry. These compounds belong to the class of xanthylium dyes. This class of dye is widely used as fluorescent stains in biology and they are readily available at a reasonable cost (Table 3.2). Phloxine B, in particular, has negligible toxicity and is approved for use in food and cosmetic products as a colorant. Two of these dyes (Rose Bengal and Phloxine B) are weak acids and thus carry a negative charge. Malachite Green, on the other hand, is positively charged under normal conditions. Their efficiency as PSs was investigated for the photodynamic killing of a variety of microorganisms. Malachite Green demonstrated successful photoinactivation ( 3-log reduction) of Gram-negative Actinobacillus actinomycetemcomitans upon illumination with a low-power red laser for 5 min (Prates et al., 2007). In another publication (Brovko et al., 2009), Malachite Green was shown to be more active against Gram-positive bacteria (L. monocytogenes and Bacillus sp.) and did not produce significant photokilling of Gram-negative bacteria (E. coli and Salmonella). This probably can be explained by the different illumination conditions; red laser was used in the first case and white light from a halogen lamp was used in the latter case. Both Rose Bengal and Phloxine B, though known to effectively produce the triplet excited state under illumination, carry a negative charge and thus are not the obvious candidates for being effective PSs in the photodynamic killing of bacteria. Nevertheless, both dyes were shown to be able to produce a significant photodynamic killing of various bacteria (Brovko et al., 2009; Decraene et al., 2006; Demidova and Hamblin, 2005a, Rasooly and Weisz, 2002; Schafer et al., 2000). It was shown that these dyes actually did not bind to bacterial cells, as simple washing did remove all dye from the sample. However, concentration levels of 50–500 mg/ml of Rose Bengal were sufficient to produce a > 6-log reduction in the number of E. coli and L. monocytogenes cells due to PDT (Table 3.1). Phloxine B was effective as PS only for Gram-positive bacteria—L. monocytogenes and Bacillus sp.

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TABLE 3.4 Photokilling effect of dye-poly (vinyl amine) (PVAm) conjugates Mean log reduction in count after 30 min treatment with the dye conjugate Conjugate

E. coli

PVAm-Rose > 6 Bengal PVAm>6 Phloxine B

Salmonella Typhimurium Bacillus sp. L. monocytogenes S. cerevisiae

2.31

>4

>5

0.08

1.33

>4

>5

0.46

Rose Bengal and Phloxine B at concentrations of 4.6% and 4.8% (wt/v), respectively (adapted from Brovko et al., 2009).

The conjugation of Rose Bengal and Phloxine B with the cationic polymer poly (vinyl amine) produced much more powerful photosensitizers (Brovko et al., 2009). The photokilling effect of these conjugates was much more pronounced than it was for comparable concentrations of the respective dyes in solution (Table 3.4). This was explained by the enhanced interaction of the positively charged polymer-dye conjugates with the negatively charged bacteria cell wall which probably brought PS closer to the target cell and thus facilitated photodestruction. This theory was consistent with the fact that both of these dyes in solution as well as in the form of conjugates did not produce any significant photokilling effect of yeast cells.

VI. PDT FOR ENVIRONMENTAL CLEANING AND DISINFECTION The majority of currently accepted applications of PDT are in the medical area. MB has been widely used by several European blood transfusion services for the decontamination of blood plasma. Ready-to-use reagents and automatic systems are commercially available for photodynamic plasma disinfection from Baxter Healthcare and Maco Pharma (UK). It has been shown previously that the PDT of plasma is particularly effective in the inactivation of enveloped viruses such as HIV, influenza, herpes simplex, West Nile virus, and others (Williamson et al., 2003). Several papers have been published on the photodynamic inactivation of microorganisms in waste water treatment (Acher and Juven, 1977; Gerba et al., 1977a,b, Kussovski et al., 2001; Martin and Perez-Cruet, 1987). Despite the fact that the effectiveness of photodynamic disinfection


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and the low cost of the procedure were demonstrated long ago, the procedure was not yet applied in practice for environmental cleaning and disinfection. It may partly be explained by the undesirable presence of photodynamic dyes in the treated water, which required additional steps to remove the residual PS prior to the release of the water. To avoid this problem, recently it was proposed to use immobilized photoactive dyes for water photodisinfection (Bonnett et al., 2006). Zinc(II) phthalocyanide tetrasulfonic acid as a PS was covalently immobilized on a chitosan membrane reinforced with a nylon net. This membrane was placed into the flow photoreactor system with circulating water containing a bacterial pathogen (E. coli). The schematics of the apparatus are shown in Fig. 3.9. Effective photokilling of E. coli was observed in the system, providing a 2.5-log reduction in the number of live cells within 2 h.

d

e

f k

I

g

I

g

h

Flow direction

b

c

a j Filter water in

I

g

I

g

Port for sampling

FIGURE 3.9 Circulating water photoreactor system for determination of photomicrobiocidal activity under water flow conditions. a, reinforced membrane used in the study; b, water jacket, continuous flow, infrared filter; c, light source; d, air pump; e, bacterial air filter; f, 3-way tap/pressure release; g, 2-way taps; h, frit for aeration; j, peristaltic pump; k, reservoir; l, ground glass joints for ease of cleaning and sterilization (Bonnett et al., 2006).

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6

Ig (surviving cells)

5.5 5 4.5 4 3.5 3 2.5 0

20

40

60

80 100 Time/min

120

140

160

180

FIGURE 3.10 Photomicrobiocidal activity of the reinforced zinc phthalocyanide/chitosan membrane on E. coli suspensions in log survival scale. ♦, freshly prepared membrane; ■, the same membrane after 9 months (Bonnett et al., 2006).

The membrane remained active at a slightly reduced level after 9 months storage in the dark (Fig. 3.10). The proposed approach can be used to lower microbial levels in water flow systems and also might have applications to water detoxification. The photodynamic approach has been applied for the cleaning and disinfection of artificial surfaces, especially for the destruction and inactivation of biofilms. In the majority of cases, it was proposed for the cleaning of surfaces in hospitals (Decraene et al., 2008a,b) and the disinfection of medical devices such as implants (Sharma et al., 2008). Only a few papers on the application of PDT targeted to the needs of the food industry have been published. In 2001, Kreitner et al. (2001) published the results of their study where food-grade PSs were tested for their efficiency in nonthermal food surface pasteurization as a new possibility for protecting foods from microbial spoilage. The two PSs used in this study were sodium chlorophyllin and heamatoporphyrin—natural constituents of food. Chlorophyllin represented negatively charged PSs, while heamatoporphyrin was neutral or positively charged depending on the environment. To mimic bacteria contaminating the ‘‘dry’’ food surfaces, where they have reduced mobility, the cells were placed on agar plates together with an appropriate PS (10 mM). Plates were illuminated for 1 h with a halogen lamp (1000 W) placed at the distance of 25 cm. All of the studied bacteria and yeasts were susceptible to photodynamic inactivation to various degrees (Table 3.5). On average, yeast cells demonstrated a lower susceptibility to photoinactivation, with R. mucilaginosa being the most resistant. This low susceptibility of R. mucilaginosa to photodynamic inactivation was explained by its ability to


TABLE 3.5

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PS-mediated inactivation of tested bacteria and yeasts (Kreitner et al., 2001) Log reduction (CFU/ml)

Microorganism

Heamatoporphyrin

Chlorophyllin

S. aureus B. cereus B. subtilis R. mucilaginosa S. cerevisiae K. javanica

3.9 3.2 4.7 1.7 2.3 3.3

3.1 3.1 4.2 0.3 2.5 3.3

TABLE 3.6 Log reduction of E. coli (EC), Salmonella Typhimurium (ST), Bacillus subtilis (BS), Listeria monocytogenes (LM), and Saccharomyces cerevisiae (SC) after incubation for 30 min with the acriflavin at a concentration of 50 mg/ml on plastic (polystyrene) and stainless steel surfaces; illumination with white light 0.4 mW/cm2 (Tiwana, 2006) Plastic

Stainless steel

Organism

PS þ light

PS only

PS þ light

PS only

EC ST BS LM SC

>2 >4 >5 >2 >1

1 0.01 0.11 0.21 >1

>3 >3 >2 >2 >2

>3 0.23 >2 0.49 0.69

synthesize deep pink pigments on wort agar, which may compete with chlorophyllin for the light absorption and thus interfere with the production of cytotoxic oxygen species. Probably, the selection of another PS that absorbs light at a different wavelength would produce better results. It was shown that the treatment of bacteria on stainless steel surfaces and on polystyrene for 30 min with acriflavin (50 mg/ml) combined with white light illumination resulted in a substantial reduction in live cell numbers for different bacteria and yeast (Table 3.6; Tiwana, 2006). In control samples that were kept in the dark, almost 100% of the applied cells survived, thus confirming the light-induced mechanism of killing. At the studied concentrations, acriflavin is water soluble, so, after treatment, the residual dye can be easily removed by rinsing the surface with water. These data provide evidence that PDT can be used for the cleaning and sanitation of such food-processing and food-handling surfaces. A similar approach was used to inactivate pathogens on the surface of food packaging material (Luksiene et al., 2009). Yellow packaging trays

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were soaked in a suspension of B. cereus (107 CFU/ml) for 30 min prior to the experiment. Afterward, trays were dried for 30 min for further bacteria adhesion. Then, samples were incubated in the dark with a 3–7.5-mM concentration of ALA to induce the production of endogenous porphyrin-based PSs by bacteria. After treatment with ALA, dried samples were illuminated with a light-emitting diode (LED, 400 nm, 20 mW/ cm2) for 5–20 min. The level of decontamination of the packaging material from adhered B. cereus after ALA-based photosensitization reached 4 logs for vegetative cells and 2.7 logs for bacterial spores. These data support the idea that PDT can, in the future, be developed into a completely safe, nonthermal surface sanitation and food preservation technique. The incorporation of PSs into materials was shown to be effective for the construction of surfaces with ‘‘self-cleaning’’ ability when illuminated by white light. TBO and Rose Bengal were incorporated in cellulose acetate film by casting the film from an acetone solution of a cellulose acetate-PS mixture (Decraene et al., 2006). Aliquots of microbial suspensions were placed onto these films and illuminated with a 25-W compact fluorescent lamp. The number of survivors was assessed by a plate counting technique. The obtained data are presented in Table 3.7. Consistent with previous observations, it was shown that Gram-negative bacteria were more resistant to the PDT. However, after 16 h of illumination, 88–100% of the cells present were killed by the contact with the constructed material. One of the possible problems associated with such coating is ‘‘photobleaching’’ of PSs which could result from selfdestruction of the dyes by the generated singlet oxygen radicals. However, when these coatings were exposed to seven cycles of alternating light and dark periods (16 h light and 8 h of darkness), no reduction in its TABLE 3.7 Effects on viable counts of contact with a cellulose acetate coating containing toluidine blue and rose bengal and exposed to light from a 25-W fluorescent lamp (adapted from Decraene et al., 2006) Organism

Staphylococcus aureus Staphylococcus aureus Methicillin-resistant Staphylococcus aureus Clostridium difficile Candida albicans Bacteriophage X174 Escherichia coli Escherichia coli

Light exposure time (h)

% Reduction in viable count

Log10 reduction in viable count

2 6 6

99.6 100 100

2.4 6.3 6.4

4 16 16 6 16

100 88 91 24 100

6.7 0.9 1.1 0.1 6.3


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TABLE 3.8 Photodynamic inactivation of microorganisms on the surface of paper treated with conjugates of Rose Bengal (RB) and Phloxine B (PhB) with poly (vinyl amine) (PVAm) (adapted from Brovko et al., 2009) Mean log reduction of the number of cells after 30 min illumination Dye conjugate

E. coli

Bacillus sp.

L. monocytogenes

S. cerevisiae

PVAm-RB PVAm-PhB

>2 0.64

>1 >2

>2 >2

0.7 < 0.01

photo-inducible bactericidal activity was detectable. These findings suggested that photobleaching was not a problem at least in the short term. The constructed light-activated antimicrobial coatings were further tested in hospital environments and were proved to provide a simple, cost-effective means of reducing the microbial load on surfaces in real-life conditions (Decraene et al., 2008a,b). Paper-based ‘‘self-cleaning’’ materials were constructed using conjugates of Rose Bengal and Ploxine B with poly (vinyl amine) (Brovko et al., 2009). Poly (vinyl amine) is used as a strengthening agent in paper manufacturing (Lorencak et al., 2000). Positively charged polymer chains help to keep together negatively charged cellulose fibers. In the described study, regular filter paper (Whatman No. 1) was impregnated with the solution of PS-conjugate and dried before the experiment. Suspensions of a variety of microorganisms were placed onto this paper and illuminated with regular white light (halogen lamp) for 30 min. The numbers of surviving bacteria were assessed by a plate-counting technique and compared with the initial number of cells in the sample. The obtained data are presented in Table 3.8. For all bacteria, a significant reduction in the number of live cells was observed. In the majority of cases, there was no growth visible on the plates. Yeast cells, as can be expected, were slightly more resistant to photodynamic inactivation, but nevertheless, contact with RB-PVAm conjugate-treated paper killed around 95% of Saccharomyces cerevisiae cells after 30 min of illumination.

VII. CONCLUSIONS The vast amount of scientific data accumulated so far strongly suggest that PDT and novel ‘‘self-cleaning’’ materials based on the photodynamic effect deserve the very close attention of researchers. In the current age of emerging ‘‘superbugs,’’ PDT could offer a very efficient and cost-effective

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way to combat the microbial contamination of foods that can lead to disastrous social and economic consequences. This chapter was written with the hope to encourage future research that is needed to bring this new technology to reality.

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CHAPTER

4 Microoxidation in Wine Production Paul A. Kilmartin

Contents

Abstract

I. Introduction II. Microoxygenation in Industry A. MOX technology: Microbullage delivery B. Alternative oxygenation procedures using polymer membranes C. Oxygen spatial considerations III. Oxidation Processes in Wine A. Oxygen in wine B. Polyphenol-mediated oxidation processes C. Oxidation of wine aromas IV. Microoxygenation Research Findings A. Generation of acetaldehyde B. Influence of SO2 and wine antioxidants C. Effects on red wine color and polyphenol development D. Effects on aromas E. Effects on mouthfeel F. Microbiological considerations V. Final Comments References

150 151 151 152 153 154 154 155 158 159 159 162 164 172 177 179 181 182

Microoxygenation (MOX) is now widely applied for the maturation of red wines as an alternative to barrel aging. The proposed improvements in wine quality arising from MOX include color stabilization, removal of unwanted off-odors, and improvements

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in wine mouthfeel. In this review, an outline is provided of oxygenation systems, particularly microbullage and polymer membrane delivery, and of the current understanding of wine oxidation processes. A summary of the results from published studies into red wine MOX is then provided, beginning with observations on O2 and acetaldehyde accumulation, and the moderating effect of added sulfur dioxide. Effects upon red wine color, particularly the more rapid formation of polymeric pigments and higher color retention, have been consistently demonstrated in MOX studies, along with further effects on specific polyphenol compounds. A few reports have recently examined the effect of MOX on red wine aromas, but these have yet to identify compounds that consistently change in a manner that would explain sensory observations regarding a lowering of herbaceous and reductive odors. Likewise, tannin analyses have been undertaken in several studies, but explanations of the decline in wine astringency remain to be developed. The accelerated growth of unwanted microorganisms has also been examined in a limited number of studies, but no major problems have been identified in this area.

I. INTRODUCTION Microoxygenation (MOX) has been widely applied as a technique for the aging of red wines since its introduction in the mid-1990s (Dykes and Kilmartin, 2007; Oenodev, 2009; Parish et al., 2000; Paul, 2000). The aims of MOX match the benefits expected for red wine exposed to oxygen during barrel aging, including improvements in mouthfeel characteristics, stabilization of wine color, and the removal of unwanted reductive or herbaceous odors. These aspects of red wine maturation can be linked to changes in polyphenol content, particularly involving the anthocyanins that give red wine its color, and flavanol oligomers and polymers associated with astringency. Changes in the structures and concentrations of these compounds during red wine aging have been the subject of recent research developments (Alcalde-Eon et al., 2006; Garcia-Puente Rivas et al., 2006). The number of scientific studies published on red wine MOX was very limited prior to 2006, but over the last 3 years, several research groups have published results that confirm a number of observations made from practical winery experience. In this review, MOX systems currently available to industry will be briefly described, centered upon oxygen delivery methods and oxygen spatial considerations within large tanks. An overview of the role of oxygen in red wine maturation will then be presented, prior to a review of published studies on MOX. These studies will be grouped according to the generation of aldehydes and the loss of sulfur

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dioxide from the wine, effects on red wine color, aromas, and mouthfeel, and finally microbiological considerations.

II. MICROOXYGENATION IN INDUSTRY A. MOX technology: Microbullage delivery The addition of tiny bubbles to introduce O2 into wine at a controlled rate, as an alternative to barrel aging, was developed initially in France in the early 1990s (Lemaire, 2000). It was the slow, metered additions of oxygen that differentiated the technique of MOX from traditional approaches such as periodic racking or more intense oxygen sparging in stainless steel tanks, while providing cost savings versus the purchase of oak barrels. The generation of microbubbles from a porous diffuser (microbullage) is the most widely used technique in commercial applications, with several systems and units currently available (KauriWine, 2009; Oenodev, 2009; Vinovation, 2009). Porous ceramic diffusers with 2–4-mm pore diameters are known to generate microbubbles with diameters around 400 mm (Devatine and Mietton-Peuchot, 2009). The oxygen is then supplied to the wine at dosages of several milliliter of O2 per liter of wine per month (mL/L/month), with some systems calibrated to deliver a certain volume of oxygen, and others based upon the mass of O2 delivered (note 10 mL/L/month ¼ 14.3 mg/L/month at 25 C). A minimum tank height of about 2 m is required to ensure that bubbles coming from the diffuser are fully dissolved into the wine and do not escape the wine surface and strip out wine volatile components. MOX can be undertaken at a number of stages in a wine’s development, from directly after alcoholic fermentation through to post-malolactic fermentation (post-MLF). The choice of O2 dosage rate and the length of time to apply MOX are still very much determined by experience and ongoing wine tastings. Decisions relating to MOX depend upon the initial tannin and anthocyanin content of the wine, aroma profiles (such as the presence of reductive odors), along with SO2 content, pH, and temperature, which can be controlled. A wine that is lower in polyphenol content, or whose existing astringency may want to be retained in the final wine, might not be a good candidate for MOX. Some of the early descriptions of the effect of MOX on red wine included an interesting cycling effect where certain organoleptic aspects of wine quality appear to get worse before the continued application of MOX leads to the desired improvements (Dykes and Kilmartin, 2007; Parish et al., 2000; Vinovation, 2009). In the first ‘‘structuring’’ phase (for several days to weeks), the wine tannins are said to become more aggressive and the varietal aromas decrease, after which the tannins soften and

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aroma complexity develops in the ‘‘harmonization’’ phase (for several weeks to months). The optimum in wine quality can be exceeded should MOX continue for too long, in which case an ‘‘over-oxygenation’’ phase is reached with an increase in astringency and oxidized aromas, and is to be avoided.

B. Alternative oxygenation procedures using polymer membranes An alternative means of introducing oxygen into a fluid, besides the supply of O2 bubbles, is through a permeable membrane, such as plastic tube or vessel (Kelly and Wollan, 2003; Paul, 2000). In this case, the oxygen will dissolve directly into the wine in a ‘‘bubbleless’’ procedure. An example of this approach is the ‘‘O2mate’’ technology developed in Australia, which makes use of gas diffusion through a permeable membrane in the form of a silicone rubber tube for oxygen delivery (O2mate, 2009). Units have been designed for both small- (e.g., barrels) and large-sized vessels, and allow quite small volumes of wine to be oxygenated. Oxygen delivery by diffusion through a dense polymer membrane made out of fluorinated ethylene–propylene has been investigated in detail for use with specially designed research scale (15 L) tanks (Dykes, 2007). The tanks were fitted with a 0.188-m sealed-end polymer tube that was calibrated by measuring O2 accumulation in water/ethanol solutions over a period of 10 days. The rise in dissolved oxygen (DO) was found to be quite linear during this time, and was used to establish the diffuser feed pressure required to deliver O2 at 10, 17, 23, 30, and 36 mg/L/month (namely 200, 300, 400, 500, and 600 kPa). In this process, oxygen is absorbed into the polymer on the gas side and transported by diffusion to the liquid side where desorption of oxygen into the wine occurs. The O2 mass transfer resistance is much larger on the liquid side, due to the presence of a large boundary layer, than on the gas side, which is an important consideration in modeling the system. This method of O2 delivery has been applied to a number of MOX trials at the University of Auckland (Dykes and Kilmartin, 2007; Tao et al., 2007), and allows for replicated trials on small volumes of wine, without concern over the loss of volatile components in the gas bubbles that can escape from small tanks when microbullage delivery is used. A related approach has been the introduction of maturation vessels constructed out of high density polyethylene (HDPE), in which the tank walls, of a certain thickness (typically 4 mm), are themselves used as the membrane to introduce O2 into the wine (Flecknoe-Brown, 2006). The term ‘‘permeation’’ is used in this context, rather than ‘‘diffusion,’’ when the movement is controlled by a pressure difference and described by Darcy’s law. ‘‘Flextank’’ maturation vessels have been designed to

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allow oxygen permeation at rates around 20 mg/L O2 per year, similar to those achieved in wooden barrels. The vessels are claimed to lead to very low aroma losses (through either adsorption or permeation), and not to suffer from the clogging of pores that occurs over time with oak barrels.

C. Oxygen spatial considerations A limited amount of research has been undertaken on issues to do with oxygen transfer rates from bubbles, and the spatial gradients that can develop when O2 is supplied from a fixed bubbler at the bottom of a large tank. Initial modeling of the oxygen distribution has shown how the DO is present at a high concentration around the bubble plume with little initial mixing due to the small fluid velocities involved (Fig. 4.1C). Simulations of this sort can show how certain volumes of the wine could be exposed to higher O2 concentrations during the MOX process, affecting subsequent oxidation processes, and well above the much lower average DO levels in the remainder of a large commercial wine tank. A further consideration that has been highlighted in recent research is the effect of dissolved CO2 on lowering the efficiency of O2 transfer (Devatine et al., 2007). Using engineering principles governing oxygen mass transfer kinetics and tests undertaken in 3-L tanks, an increase in bubble diameter with CO2 desorption has been cited as a reason to explain the decrease in O2 transfer fluxes in wines with a higher CO2 content. Through modeling of oxygen transfer from a rising bubble, the A

B

C

× 10

4

12 10 8 6 4 2

FIGURE 4.1 Application of computational fluid dynamics to simulate: (A) bubble position after 120 s during O2 dosage at 26 mg/L/month and 670-mm bubble size; (B) represents a diagonal slice of the wine phase velocity field, and (C) the dissolved oxygen concentration in milligram per liter. Reprinted with permission from Dykes and Kilmartin (2007). Copyright 2007 Winetitles Pty Ltd.

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influence of CO2 was further confirmed, along with a recognition of the importance of column height relative to diffuser pore diameter (Devatine and Mietton-Peuchot, 2009). These studies illustrate how a number of physical factors such as diffuser position and size, and additional wine gases, can influence the exposure that the wine will receive from added O2, which cannot be assumed to be uniform or equal for all wines.

III. OXIDATION PROCESSES IN WINE A. Oxygen in wine There are several stages in red winemaking in which the wine is exposed to oxygen as a part of regular wine aging and development. These include quite large aerations during pump-overs, in which case the concentration of DO can reach a saturation value of 6 mL/L (8.6 mg/L) at room temperature, and higher values at lower wine temperatures. Much slower rates of O2 ingress occur in the barrel, but this small rate of oxygenation is considered an essential aspect of barrel maturation. A wine saturated with O2 will typically take 1–2 weeks to consume the oxygen, which is taken up by reactions involving polyphenols (Singleton, 1987) and by the yeast lees, when present. The later process occurs through a mild oxidation of yeast membrane lipids (Salmon et al., 2000), which can provide a rapid initial O2 consumption (Salmon et al., 2002). In a comparison of oxygen uptake by a Cabernet Sauvignon wine, a saturation level of 9 mg/L of O2 was removed within 2 days for the wine still on yeast lees, but after filtering and removal of the lees, it took 6 days for the oxygen to be depleted (Dykes, 2007; Dykes and Kilmartin, 2007). Among the impacts of periodic saturations of a wine with oxygen is the loss of protective SO2, important for the microbial stability of the wine (Castellari et al., 2000). However, the concentration of DO in the wine is typically well below the saturation level and can be monitored at a Clark electrode (Laurie et al., 2008; Vidal et al., 2004a), or using alternatives such as photoluminescencebased systems (Nevares and del Alamo, 2008). During several winemaking operations, the following mean DO values were recorded (Castellari et al., 2004): 0.37 mg/L for racking at 15–20 C (but around 1 mg/L for racking at 10 C); 1.75 mg/L for mixing wines from different casks; less than 0.3 mg/L during filtration; up to 1.2 mg/L for centrifugation; 1.27 mg/L for cold stabilization at 5 C; but little increase as a result of batonnage during barrel aging. Historical estimates of the amount of oxygen that enters a wine during barrel aging are of the order of 20–30 mL/L/year (Nevares and del Alamo, 2008; Paul, 2000; Vivas et al., 2004). The case of controlled MOX will be considered separately below.


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There is also considerable interest in the amount of O2 that enters a wine during bottling, which can vary considerably depending upon the technology applied, with concentrations of around 0.8 mg/L typically seen (Castellari et al., 2004). The oxygen that remains in the headspace above the wine (ullage) can pass into the wine over a period of some months and sustain wine oxidation processes at a slow rate (Vidal and Moutounet, 2006). This can add a few milligram per liter of O2 to the wine and match the expected oxygen addition of several months of mass transfer across the wine closure (Kontoudakis et al., 2008), which has been determined to be less than 1 mL of O2 a day for storage under good quality cork and screw-cap closures (Lopes et al., 2006, 2007). The additional O2 supplied for a large ullage under screw cap (64 vs. 4 mL) in a Cabernet Sauvignon wine trial, was found to lead to greater losses of SO2 after bottling, more oxidized and less flint/rubber aromas, and a greater change in the colored anthocyanins over a 3-year period (Kwiatkowski et al., 2007). When a wine is stored under synthetic plastic closures, the rate of O2 ingress and wine oxidation processes are greater. With the use of polyethyleneterephtalate (PET) bottles for wine storage, a slow oxygenation will occur unless an oxygen scavenger is incorporated into the PET bottle, in which case rates of SO2 and anthocyanin loss can be less than for storage in glass containers, due to the removal of O2 already present in the wine at bottling (Giovanelli and Brenna, 2007).

B. Polyphenol-mediated oxidation processes The current understanding of wine oxidation processes centers around polyphenols as the main initial substrate of wine oxidation, with crucial roles for catalytic metals in facilitating the reactions (Danilewicz, 2003). The amount of oxygen that can be taken up by a particular wine has been found to be proportional to the polyphenol content, an uptake that proceeds more rapidly at a higher pH where the phenolate anion forms of the polyphenols are more abundant (Singleton, 1987). Oxygen in its initial triplet state first needs to be activated, and can react with catechol-containing polyphenols via cycling with Fe2þ (Fig. 4.2), which is typically present in wine at a few milligram per liter. Copper ions will further increase the rate of polyphenol oxidation through the redox cycling of iron (Danilewicz, 2007). As O2 is reduced (by up to four electrons before water is produced), it creates a range of reactive species that progressively include the hydroperoxyl radical (HO2), hydrogen peroxide (H2O2), and the very reactive hydroxyl radical (OH) through the Fenton reaction. The hydroxyl radical will react quickly with most organic molecules and will oxidize alcohols to aldehydes (Waterhouse and Laurie, 2006). Alongside acetaldehyde (CH3CHO) production from ethanol, further aldehydes such as

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OH Fe3+ R

R

O O

O2• –/HO2•

• H2O2 HO

OH

CH3CH2OH •

Fe2+

O2

CH3CHOH Fe2+

Fe3+

Semiquinone H2 O

CH3CHO

FIGURE 4.2 Wine oxidation processes; adapted from Danilewicz (2003); R ¼ further organic groups such as the three-ring structure in the flavonoids, and further groups in nonflavonoid polyphenols.

glyceraldehyde can be produced through the action of the hydroxyl radical on the likes of glycerol (Laurie and Waterhouse, 2006b). In the absence of polyphenols, ethanol and tartaric acid are quite stable against oxidation (Wildenradt and Singleton, 1974). Two of the major products of the polyphenol oxidation process important for further wine reactions are thus polyphenol quinones and aldehydes. The quinones can react with various sulfur-containing species, including glutathione naturally present in wine, and sulfur-containing aroma compounds (see below). The quinones can also combine with additional polyphenols to create new products that can oxidize further, and through their more extended conjugation, take on a brown color (Monagas et al., 2005a). The reactivity of polyphenol quinones can be inhibited by the presence of free SO2 in the wine, which has been shown to rapidly reduce certain quinones back to their original polyphenol forms (Makhotkina and Kilmartin, 2009). Aldehydes such as acetaldehyde can play important roles in wineaging reactions and can create covalent links between polyphenols, including anthocyanins and wine tannins (Fig. 4.3). Glyceraldehyde additions to (epi)catechin and malvidin-3-glucoside illustrate the sorts of condensed products that can arise (Laurie and Waterhouse, 2006a). These processes occur alongside other linkage reactions involving small molecules such as pyruvate generated from yeast activity to form the colored visitins, or with direct anthocyanin–tannin linkages (Fig. 4.3; Fulcrand et al., 2006). These processes create a range of pigment classes that form and degrade during wine aging with impacts on red wine color (Alcalde-Eon et al., 2006; Garcia-Puente Rivas et al., 2006; Monagas et al., 2005a,b). Many of the new compounds are resistant to sulfite bleaching, resulting in an increase in the measured ‘‘sulfite-resistant pigments’’ at the expense of the monomeric anthocyanins. An increase in red polymeric pigments was observed to be a consequence of periodic saturations of red


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OCH3 OH + O

HO

OCH3

B

OH

OCH3

C

A

+ O

HO

O-glucose

A

OH

B OCH3

C

Malvidin-3-glucoside

O-glucose O

OH

Malvidin-3-glucoside-4-vinyl (Vitisin B)

OH HO

B

O A

HO

C

OH

OH OH

A

Catechin HO

C O

OH B

OH CHCH3

OH HO

OH

OH OH HO

+ O A

+ O

HO

OCH3

C

OH OH

B

O A

OCH3

A

OCH3

C

O-glucose

B OCH3

C

B

OH Malvidin-3-glucoside-ethyl-catechin

O-glucose OH Malvidin-3-glucoside-catechin

FIGURE 4.3 Representative wine polyphenol structures.

wine with oxygen (Castellari et al., 2000), and to be a general trend for the aging of red wines in the bottle (Saenz-Lopez et al., 2004). As a result of these condensation processes, concentrations of methoxy-containing malvidin anthocyanins decline more quickly in red wines than hydroxycinnamic acids such as caffeic acid with easily oxidizable catechol groups (De Beer et al., 2008). This is particularly the case when higher levels of SO2 are maintained (Tao et al., 2007), an observation that can also be linked to the ability of free SO2 to readily regenerate the original caffeic acid form (Makhotkina and Kilmartin, 2009).

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C. Oxidation of wine aromas The impact of oxygen on wine aroma is likely to involve several oxidation mechanisms. One pathway involves polyphenol quinones, particularly in the case of the removal of unwanted sulfur-containing off-odors (RSH; Mestres et al., 2000), as illustrated in Fig. 4.4A. In addition to reactions of quinones with unwanted ‘‘reductive’’ or rubbery sulfur-containing compounds (e.g., mercaptans), there has been some indication that quinones can react with desirable varietal aromas such as 3-mercaptohexanol (3MH), which imparts passion fruit or berrytype aromas in wines (Blanchard et al., 2004; Danilewicz et al., 2008). On the other hand, the aroma loss might also proceed via reaction with H2O2 or other reactive oxygen species produced during wine oxidation, affecting a wide range of aroma classes. Sulfur-containing compounds acting as nucleophiles can also combine with the carbocation formed as proanthocyanidin polyphenols are hydrolyzed, which has been the basis of a measure of the mean degree of polymerization (MDP) of tannin fractions (Prieur et al., 1994). There is also an expectation that thiols can be directly oxidized through to disulfides (RSSR in Fig. 4.4B) (Mestres et al., 2000; Rauhut et al., 1996), a mechanism also suggested for the case of 3MH (Murat et al., 2003) where a protective effect from anthocyanins present in the wine was noted. In one study, the concentrations of both ethanethiol and the related oxidized form of diethyl disulfide in a red wine were found to decrease over a 60-day period, and at a greater rate under aeration (Majcenovic et al., 2002). However, in a survey of wines over five vintages, the older wines were shown to contain higher concentrations of diethyl disulfide, and lower concentrations of ethanethiol (Fedrizzi et al., 2007).

A

O

OH O2 OH

R2

OH RSH

O

R2

OH

R2 SR

B

Fe3+

RSSR• –

Fe2+

RSSR

O2

HO2•

H2O2

RS– RSH

RS

•

Fe3+

Fe2+

Fe3+

FIGURE 4.4 (A) Polyphenol-mediated oxidation; and (B) metal-catalyzed thiol oxidation mechanisms by which sulfur-containing compounds in wine can be removed; adapted from Danilewicz et al. (2008).


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It has been suggested that the disulfides may subsequently be reduced back to the volatile thiols (with lower perception thresholds) by SO2 present in the wine during bottle storage under low oxygen concentrations (Bobet et al., 1990), or be released by hydrolysis from thioacetate esters (Rauhut et al., 1996). Wine oxidation has also been associated with the formation of unwanted aromas (du Toit et al., 2006b), including 3-(methylthio)propionaldehyde (methional) with a ‘‘farm-feed’’ descriptor, phenylacetaldehyde described as ‘‘honey-like,’’ 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) with a ‘‘kerosene’’ odor, and 4,5-dimethyl-3-hydroxy-2(5-H)-furanone (sotolon) (Silva Ferreira et al., 2003). Increases in the concentrations of methional and TDN were observed in accelerated aging studies of wines at 45 C and saturated with O2, in which case varietal compounds with floral aromas, the terpenes, and norisoprenoids, decreased in concentration (Silva Ferreira et al., 2002). A number of these aldehydes were found at higher concentrations in aged versus younger red wines and above their odor activity values, particularly methional and phenylacetaldehyde (Cullere et al., 2007). Further aldehydes produced during wine oxidation are also expected to contribute to the oxidized aroma, although many of these compounds have yet to be identified (Escudero et al., 2002).

IV. MICROOXYGENATION RESEARCH FINDINGS A. Generation of acetaldehyde The increase in DO in wines undergoing MOX is meant to remain low, with the aim of ensuring that the wine will take up the oxygen without O2 reaching higher levels where negative impacts may result, such as microbial spoilage (see below). Early descriptions of the operation indicated that with an oxygen dosage rate of up to 3 mL/L/month, the concentration of DO did not exceed 0.05 mg/L (Moutounet et al., 1996). In one study, an O2 concentration of 0.04 mg/L was observed for MOX at a rate of 5 mL/L/month, similar to wines in the barrel, while higher values were seen during the cooler months of the year (Castellari et al., 2004). A further report also for red wine MOX at 5 mL/L/month for several months determined values of DO at 0.1–0.25 mg/L, with no significant effect of having oak present (Laurie et al., 2008; Waterhouse and Laurie, 2006). Higher rates of MOX at 30–60 mL/L/month for a few days led to higher O2 concentrations that could exceed 2 mg/L. These levels dropped back to around 0.025 mg/L when the MOX rate was just 1 mL/L/month post-MLF, whereas the control wines without O2 additions remained at about 0.01 mg/L or less. These results indicated that even when very small MOX rates are applied that an increase in DO content can be

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detected, meaning that the rate of oxygen consumption is slower than the rate of oxygen dissolution (Laurie et al., 2008). Similar low O2 concentrations largely in the range of 0.01–0.06 mg/L were recorded for wines undergoing MOX at 1.5–2.5 mL/L/month, with similar levels seen for wines stored in barrels (Nevares and del Alamo, 2008). Measurements of acetaldehyde accumulation were reported in some of the earliest descriptions of the MOX process, where 5 months of MOX at 3 mL/L/month was found to raise the acetaldehyde concentration to 33 mg/L, compared to a control wine at 13 mg/L (Moutounet et al., 1996). Further trials at Oenodev for a Syrah wine in 300-L tanks, and subject to elevated O2 delivery rates of 30, 60, or 90 mL/L/month for 3 weeks, have shown that acetaldehyde will progressively accumulate to be perceived by a tasting panel from an early stage. Increased concentrations of acetaldehyde by GC were confirmed for all treatments over the control by the end of the trial, with very high concentrations (50 mg/L) seen in the 90mL/L/month MOX treatment (Oenodev, 2009). In the presence of free SO2, it can be expected that acetaldehyde will bind with the SO2, and thus the acetaldehyde build up might be seen through an increase in bound SO2. An increase in bound SO2 was seen in one study on Pinot noir wine subject to MOX at 3 mL/L/month for 2 months, at 6 versus 2 mg/L bound SO2 in the control wine (Lesica and Kosmerl, 2006). A buildup of bound SO2 was observed in another study using an alternative microoxidation approach at glassy carbon rods, where the acetaldehyde production was quite marked due to the electrochemical oxidation of ethanol, but it was only toward the end of the 12week trial that the measured acetaldehyde increased significantly (Fig. 4.5; Fell et al., 2007). However, a decrease in both free and bound SO2 might instead be observed due to oxidative losses of SO2 outweighing the binding of SO2 with acetaldehyde, as was seen in related periodic oxygenation experiments in the same trial. Throughout the course of MOX trials with commercial scale Monastrell wines, the concentration of acetaldehyde was not found to increase over a 5-month period (Cano-Lopez et al., 2006), even though more colored compounds involving acetaldehyde were found to form in the MOX wines (see below). However, in similar trials, the acetaldehyde concentration was higher with the MOX treatments after a final MOX phase at 3 mL/L/month post-MLF by up to an additional 13 mg/L; both free and total SO2 were depleted in the MOX wines by the end of the trial (Cano-Lopez et al., 2008). In these trials, the MOX operation was discontinued during MLF for a period of 1–2 months, on the expectation that the bacteria will consume the acetaldehyde produced during this period. In a further trial involving a high O2 dosage rate (76 mg/L/month in a pilot trial with 141-L tanks), binding of acetaldehyde with anthocyanins and flavanols was suggested as a reason for low concentrations of


161

A 30

Control O2 ELMOX

Free SO2 (mg/L)

25 20 15 10 5 0 6

8

10

12

6

8

10

12

6

8

10

12

B

Bound SO2 (mg/L)

20 15 10 5 0 C

Acetaldehyde (mg/L)

10 8 6 4 2 0 Time/weeks

FIGURE 4.5 Changes in the concentrations of (A) free SO2, (B) bound SO2, and (C) acetaldehyde during the final 6 weeks of a trial using periodic oxygen additions (O2) and electrochemical microoxidation (ELMOX). Reprinted with permission from Fell et al. (2007). Copyright 2007 American Society for Enology and Viticulture.

(Acetaldehyde)/ppm

(Acetaldehyde)/ppm

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45 40 35 30 25 20 15 10 5 0 0

20

0

20

40

60

80

100

120

330 320 310 300 60 50 40 30 20 10 0 40

60

80

160

180

Time/days

FIGURE 4.6 Development of acetaldehyde concentrations during the microoxygenation of a Merlot wine for (A) a 141-L pilot plant study and (B) a 2400-L study. Reprinted with permission from Carlton et al. (2007). Copyright 2007 American Chemical Society.

acetaldehyde being observed for several weeks prior to a marked increase in measured acetaldehyde by about 1 mg/L/day (Fig. 4.6; Carlton et al., 2007). The point at which excess free acetaldehyde begins to be observed (also seen in commercial scale 2400-L tanks at the lower O2 rate of 9.3 mg/ L/month) could then be used as a marker for monitoring the progress of the MOX, and may indicate when oxygenation needs to be decreased to avoid elevated levels of aldehydes leading to overoxidized characters in the wine.

B. Influence of SO2 and wine antioxidants It has been widely recognized in the practical application of MOX, that the concentration of free SO2 in the wine has a major impact upon the oxygenation process (Paul, 2000; Vinovation, 2009). Rather than reacting directly with DO, free SO2 has been found to have an antioxidant effect through a fast scavenging of hydrogen peroxide (Danilewicz, 2003), and to play a role in binding up the acetaldehyde produced in wine oxidation processes (Danilewicz et al., 2008). SO2 has a further influence on


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polyphenol oxidation processes through a rapid reduction and regeneration of certain oxidized polyphenols (Cheynier et al., 1989, 1993; Saucier and Waterhouse, 1999). In a recent examination of the interaction of polyphenol quinones with wine antioxidants, the rate of reduction was most rapid in the case of caffeic acid, a representative hydroxycinnamic acid, and also with the flavan-3-ol catechin, but very little reduction was evident with the flavonol quercetin (Makhotkina and Kilmartin, 2009). The removal of hydrogen peroxide, acetaldehyde, and quinones by free SO2 will all alter chemical processes when a wine is exposed to oxygen. Similar quinone reduction processes have been described for the natural grape and wine antioxidant glutathione, of well-known importance in limiting the effects of enzymatic oxidation upon caftaric acid in grape musts (Singleton et al., 1985). Glutathione has been ascribed a similar protective role in wines due to its ability to react with quinones in preference to varietal aroma compounds such as 3MH (Dubourdieu et al., 2000). The rapid reaction of glutathione with polyphenol quinones, in a similar manner to free SO2, has also been recently confirmed (Makhotkina and Kilmartin, 2009). While a similar role in reducing quinones has been ascribed to ascorbic acid (Danilewicz, 2003; Peng et al., 1998; Singleton, 1987), this reaction has been more difficult to confirm in model solution studies with typical wine polyphenols (Makhotkina and Kilmartin, 2009). However, the direct scavenging of oxygen, catalyzed by iron species, remains of interest in the case of ascorbic acid (Danilewicz, 2003), although the ascorbic acid present naturally in grapes can be lost quite quickly in the must upon exposure to oxygen (Singleton, 1987). The influence of SO2 on a Merlot wine undergoing MOX for 16 weeks was examined in 15-L research vessels with O2 supplied through fluorinated ethylene–propylene tubing at 10 mg/L/month (Tao et al., 2007). The use of a polymer membrane for the oxygen supply allows low flow rates and multiple small-scale research tanks to be employed to address effects in a reproducible manner. The control wine had very low levels of measured SO2, and was compared to further treatments with additions of 50, 100, and 200 mg/L of SO2. These additions had an immediate bleaching effect on the wine pigments leading to a decrease in the 520-nm absorbance, by up to half in the case of the 200 mg/L SO2 additions (Fig. 4.7). Throughout the course of the trial, the SO2 concentrations declined progressively, and a corresponding reversal of the bleaching effect was also seen; wines stored in bottles from the beginning of the trial showed only a small decline in SO2 during the 16-week trial. The SO2 content had a significant moderating effect on wine-aging processes. The content of monomeric anthocyanins declined and sulfite-resistant pigments increased quite rapidly over the first 10 weeks of the trial in the lower SO2 wines (and after 7 weeks in the wine that received an initial 100 mg/L SO2 addition, when the free SO2 content dropped below

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C

A No O2

AHCl520 − ASO2520

Total SO2/mg/Lz

250 200 150 100 50 0

0 mg/L 50 mg/L 100 mg/L 200 mg/L (SO2 added)

8 6 4 2

No O2

0

D 5 4 SO2 520

4 3

3

A

Red wine color (A520)

B

No O2

2 0

20

40

60 Time/days

80

100

120

No O2

2

0

20

40

60

80

100

120

Time/days

FIGURE 4.7 Concentration of (A) total SO2, (B) red wine color given by 520-nm absorbance, (C) monomeric anthocyanins given by the subtractive spectrophotometric measure (A520HCl A520SO2), and (D) sulfite-resistant pigments (A520SO2); for a Merlot wine undergoing MOX with different initial SO2 additions (n ¼ 3). Values for wines stored in bottles until the end of the trial with ‘‘no O2’’ are shown on the right. Reprinted with permission from Tao et al. (2007). Copyright 2007 American Chemical Society.

25 mg/L). The 200 mg/L SO2 wine, on the other hand, showed little increase in sulfite-resistant pigments over the course of the trial. Further effects on the polyphenol content of the wines were seen in this trial (Tao et al., 2007), including greater losses of catechin and malvidin-3-glucoside under low SO2 conditions; by contrast, more quercetin was lost in the MOX wines under a higher SO2 content, which can be potentially related to the limited ability of SO2 to reduce quercetin quinones (Makhotkina and Kilmartin, 2009), along with further reaction products arising from the interaction of quercetin with SO2. By contrast, hydroxycinnamic acids such as caftaric acid and caffeic acid were largely unaffected by MOX while adequate concentrations of free SO2 were maintained in the wines.

C. Effects on red wine color and polyphenol development The trends in wine pigment development seen above for the SO2 study have been consistently reported in MOX trials. These include a greater formation of sulfite-resistant pigments, a more rapid loss of monomeric anthocyanins, and a greater retention of wine color. Higher color intensity was one of the findings reported in early research work on the MOX


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technology for wines subject to O2 additions at 1 and 3 mL/L/month for 5 months (Moutounet et al., 1996). Further research at Oenodev using higher O2 rates of 30–90 mL/L/month for 3 weeks also produced color intensities up to 50% higher under MOX (Oenodev, 2009). The application of MOX to a Barbera wine for 45 days at 1.7–2.5 mL/L/month in 50-L tanks led to higher color intensity and the quicker development of sulfiteresistant pigments (Bosso et al., 2000). An early trial on Pinot noir wine subject to MOX at 2 mL/L/month for 7 months in a 1000-L tank also showed that MOX accelerated reactions in which anthocyanins combine with tannins (Castel et al., 2001). In a detailed survey of the pigment composition of a Cabernet Sauvignon wine subject to MOX at 5 mL/L/month for 7 months, several spectroscopic and LC measures were compared (Table 4.1; Atanasova et al., 2002). The MOX wine showed a larger loss of free anthocyanins (determined by LC), and greater development of sulfite-resistant pigments (A520SO2), than the control wine, which also showed pigment development in this direction. The loss of wine color density (A620 þ A520 þ A420) that was evident in the control wine was also lessened as a result of the MOX operation, despite the greater loss of free anthocyanins. On the other hand, the increase in 420-nm absorbance associated with aged wines was no greater in the case of the MOX wine than the control. Importantly, among the individual pigment compounds quantified using LC, MOX led to a greater formation of compounds involving acetaldehyde, including the pyranoanthocyanins and ethyl-bridged compounds. In a commercial scale trial using MOX at 5–10 mL/L/month on a Cabernet Sauvignon wine in 11,000–45,000-L tanks, the 420-nm and 520nm absorbances were greater in wines subject to MOX, including tanks in which toasted oak staves or segments were added at 3 g/L (McCord, 2003). HPLC analyses also showed a larger polymeric anthocyanin peak in the MOX wines (and more so with added oak segments), and lower concentrations of the monomer malvidin-3-glucoside. Some further differences were seen in the concentrations of individual polyphenols, with lower epicatechin and quercetin levels in the final MOX wines.

TABLE 4.1 Changes in wine color parameters for a Cabernet Sauvignon wine subject to MOX for 7 months at 5 mL/L/month; data from Atanasova et al. (2002) Color parameter

Initial

Control

MOX

Free anthocyanins (abs. units) A620 þ A520 þ A420 A520SO2 A420/A520

23.20 0.70 21.8 0.2 4.03 0.06 0.2 0

14.92 0.27 18.5 0.1 4.73 0.12 0.6 0

12.62 0.27 21.1 0.1 5.33 0.06 0.6 0

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Fraction of the total color

In a further trial involving large commercial wine tanks, and a control tank of similar size, MOX rates of 1.5–4 mg/L/month were applied to various red wines post-MLF with free SO2 maintained at 25–35 mg/L throughout (du Toit et al., 2006a). MOX was more effective at increasing the color density of younger red wines, where more sulfite-resistant pigments developed alongside a greater loss of catechin monomers. These trends also translated into a greater proportion of polymeric pigments in some of the MOX wines at the expense of free anthocyanins (Fig. 4.8). It was also recognized that difficulties in conducting replicate experiments on a commercial scale were a problem that might be overcome through new systems to accurately deliver lower rates of O2 into smaller tanks (du Toit et al., 2006a). An extended series of studies on Monastrell wines was undertaken using 17,500-L tanks, and two oxygenation levels set initially at 5 mL/L/ month (T1) and 10 mL/L/month (T2) for 3 weeks prior to MLF, during which the MOX operation was stopped, before being restarted at lower rates post-MLF for further 2 months, finishing at rates of 1.5 and 2.5 mL/ L/month, respectively (Cano-Lopez et al., 2006). A number of color properties were examined in detail, and these confirmed that monomeric anthocyanins such as malvidin-3-glucoside (malv-3-gluc) decreased more rapidly, and more sulfite-resistant pigments were formed at higher O2 exposures (Table 4.2). In addition, the concentrations of various ethyllinked compounds (e.g., malv-3-gluc-ethyl-cat) and pyranoanthocyanins

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

PP FA CP

C

C

0

2

M

C

M

C

M

2

6

6

10

10

Treatment and time in weeks

FIGURE 4.8 Development of wine color properties for wines undergoing MOX at 3 mg/L/month (M), versus control wines (C); with the fraction of color due to the polymeric fraction (PP), free anthocyanins (FA), and copigmentation (CP) reported. Reprinted with permission from du Toit et al. (2006a). Copyright 2006 South African Society for Viticulture and Enology.


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TABLE 4.2 Changes in selected wine color parameters for a Monastrell wine subject to MOX, initially at 5 mL/L/month (T1) and 10 mL/L/month (T2), at the end of MOX and after 6 months in the bottle; data from Cano-Lopez et al. (2006, 2007) Initial

Malv-3-gluc (mg/L) Malv-3-gluc-ethyl-cat (mg/L) Malv-3-glu-4-vinyl (mg/L) Malv-3-glu-cat (mg/L) HPLC polymeric peak (mg/L) A620 þ A520 þ A420 A520SO2 A420/A520

Control

T1

T2

157.9 1.5 109.6 1.0 105.6 3.3 94.1 8.0 1082 33 1215 99 1343 110 978 29

1271 52 1446 108 1281 110 1331 268

800 108 19.7 0.2

900 125 21.6 2.3

900 110 19.2 0.7

800 148 23.4 1.1

12.7 0.1 11.6 0.1 13.7 0.1 14.0 0.2 1.9 0.1 2.3 0.1 2.7 0.1 2.9 0.1 0.59 0.01 0.61 0.01 0.60 0.01 0.61 0.01

After 6 months in the bottle A620 þ A520 þ A420 A520SO2 S Ethyl-linked compounds (mg/L) S Pyranoanthocyanins (mg/L)

10.8 0.7 12.4 0.3 12.4 0.5 2.5 0.2 3.4 0.1 3.5 0.1 6.69 0.23 6.65 0.55 7.04 0.39 6.03 0.67 9.23 1.05 9.40 0.72

(e.g., malv-3-glu-4-vinyl; Fig. 4.3) were higher in the MOX wines versus the control, whereas concentrations of direct adducts (e.g., malv-3-glucat) were not different. Further, there was no extra increase in the hue value (A420/A520) in the MOX wines. The wines were tested again after 6 months in the bottle (Cano-Lopez et al., 2007), and a number of the color characteristics which differentiated the MOX from the control wines continued to hold. These included more sulfite-resistant pigments, which continued to increase in all of the wines (alongside a decrease in monomeric anthocyanins) (Table 4.2). There were also higher wine color density, ethyl-linked compounds, and pyranoanthocyanins in the MOX wines, indicating that the benefits of the MOX step had persisted, despite overall decreases in these measures across the wine samples (contrasted by an increase in color density for wines stored in oak barrels rather than in bottles for 6 months, to a value of A620 þ A520 þ A420 ¼ 14.2 for previously MOXed wines). Many of these trends were confirmed for trials undertaken on three additional Monastrell wines from a later vintage, starting initially at 10 mL/L/ month O2 delivery (Cano-Lopez et al., 2008). In particular, monomeric

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anthocyanins were lost and ethyl-linked compounds were formed more rapidly in the MOX wines, along with more sulfite-resistant pigments. Among further MOX trials, it has been shown that wine color density and polymeric pigments increased, leading to more stable color resistant to sulfite bleaching, for a Pinot noir wine subject to MOX at 3 mL/L/month for 2 months in 30-L tanks (Lesica and Kosmerl, 2006). The issue of the combined effect of MOX and added oak chips at 2 g/L was examined in a trial on a Sangiovese wine with MOX applied at 3 mL/L/month for 90 days in 50-L tanks post-MLF with 50 mg/L total sulfites, or at 9 mL/L/month when 90 mg/L yeast lees were also included (Sartini et al., 2007). The color intensity (A520 þ A420) was again higher in the MOX wines (even against a slight loss of color suggested for the absorbing capacity of the chips), linked to the greater formation of red polymeric color and associated loss of anthocyanins (Table 4.3). Further results included a greater loss of small polyphenols such as quercetin and catechin in the MOX wines (but some protection for caftaric acid afforded with added lees), but somewhat surprisingly a decline in wine hue (A420/A520), which may have reflected the color of the particular polymeric pigment species formed in this trial. The effect of added lees in addition to MOX was difficult to evaluate given the different MOX rates employed. The subsequent storage of these wines in

TABLE 4.3 Changes in selected wine color parameters for a Sangiovese wine subject to MOX with and without added oak chips and yeast lees; data from Sartini et al. (2007)

A520 þ A420 Total anthocyanins (mg/L) by HPLC Red polymeric color A420/A520 Quercetin (mg/L) Catechin (mg/L) Caftaric acid (mg/L)

Chips þ MOX (3 mL/L/ month)

Chips þ Lees þ MOX (9 mL/L/ month)

Control

Chips

7.7 0.010 315 0.8

7.645 0.006 7.788 0.009 7.84 0.003 316 0.2 305 1.8 301 1.6

2.51 0.005 2.548 0.007 2.613 0.007 2.665 0.013 0.54 0.002 0.541 0.003 0.506 0.002 0.507 0.004 8.36 0.173 5.61 0.074 4.16 0.293 5.1 0.030 45.3 0.36

44.5 0.16

39.5 0.18

38.9 0.13

45.4 0.82

45.7 0.07

43.3 0.32

45.6 0.05

169


the bottle for 5 months showed that the MOX wines were more stable in terms of many of the measures employed. The development of a Cabernet Sauvignon wine, undergoing MOX at different oxygen dosage rates, was undertaken at the University of Auckland in 15-L research tanks with O2 supplied through a previously calibrated dense polymer membrane (Dykes, 2007; Dykes and Kilmartin, 2007). Using a PCA projection of the complete HPLC output for chromatograms at 280, 320, 265, and 520 nm (targeting polyphenols generally, hydroxycinnamic acids, flavonols, and anthocyanins, respectively, and using peak alignment software), the wines were seen to follow a similar trajectory across the 15-week trial, but the wines subject to MOX at the highest rates advanced most rapidly (Fig. 4.9). The slowest development was seen in the control wine without added O2. These results illustrate how the oxygen dosage can affect the rate of wine-aging processes, which are accelerated as a result of MOX.

250 Wk 15 200 Wk 13 Wk 11

Principal component 2 (3%)

150 100 50

Wk 15 Wk 13 Wk 11 Wk 15

Wk 3

Wk 9 Wk 7

Wk 3 Wk 5 Wk 3 Wk 3 Wk 1 Wk 1

Wk 11 0 Wk 13 Wk 7

Wk 5

–50

Wk 9 Wk 7

–100

9 11 Wk 15Wk Wk

Wk 1 Wk 1

–150 –200

Wk 13

Wk 9 Wk 5

0 mg/L/mth 10 mg/L/mth 23 mg/L/mth 36 mg/L/mth

Wk 7 Wk 5

–250 –600

–400

–200

0

200

400

600

800

1000

1200

Principal component 1 (84%)

FIGURE 4.9 PCA projection of the combined results for HPLC chromatograms taken at 280, 320, 365, and 520 nm, for a Cabernet Sauvignon wine undergoing MOX at various oxygen dosage rates during 15-week trial, with sampling every 2 weeks (Wk1, Wk3, etc.). Reprinted with permission from Dykes and Kilmartin (2007). Copyright 2007 Winetitles Pty Ltd.

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AOC

A series of MOX studies were undertaken on Mencia, Tinta de Toro, Tinta del Pais, and Tempranillo wines, in which effects on colored compounds and polyphenol antioxidant measures were examined (PerezMagarino et al., 2007, 2009; Rivero-Perez et al., 2008). Trials on each of the four wines were conducted in 2000-L tanks across 3 consecutive years, during which 23–42 mL of O2 was added over a 3-week period pre-MLF. The rate and duration of MOX was determined by individual factors for each of the 12 wines, such as the presence of any reductive characters, and MOX was stopped when tasters considered that vegetal characters had been lost and green tannins had evolved into hard tannins (PerezMagarino et al., 2007). Across the 12 trials, a higher percentage of polymeric anthocyanins and greater loss of monomeric anthocyanins were seen in the MOX wines. A higher color intensity was seen in several cases and an increase in blue tonalities. In a survey of 162 wines made from these four Spanish varieties, the changes in phenolic composition after MOX did not lead to differences in the results obtained for antioxidant scavenging-based assays (Fig. 4.10), which was related to the maintenance of a similar number of hydroxyl groups after condensation reactions had occurred (Rivero-Perez et al., 2008). The assays where some differences were observed were one in which DNA damage was monitored (where MOX wines were more effective), and a lipid peroxide assay (ABAP-LP) (where a lower activity was observed with the MOX wines). It was suggested that a greater proportion of polymeric phenols in the MOX wines could have been more effective in protecting DNA against damage, but were less able to be incorporated into microsomal membranes as required for the lipid peroxidation assay. As well as providing some

100 90 80 70 60 50 40 30 20 10 0

a a

a a

b

a a

a

a

a

a

a

a a

a a

ABTS

b

a

DPPH

DMPD

ORAC*1000

FRAP

Control

HRSA

SRSA

ABAP-LP

DNAdamage*10-2

Mo

FIGURE 4.10 Average responses in antioxidant assays for microoxygenated (mo) and control wines, for 4 replicates of 162 Spanish wines (n ¼ 648). Values that are significantly different (LSD test, p ¼ 0.05) are given different letters. Reprinted with permission from Rivero-Perez et al. (2008). Copyright 2008 Elsevier.


171

interesting insights into the methodology of the different antioxidant assays, this extensive study also showed how a number of polyphenol properties can remain largely unchanged as a result of the MOX process. In further trials on Mencia and Tinta del Paris wines, the presence of French or American oak chips at 4 g/L during MOX led to very few observable differences in terms of polyphenol and color parameters (Perez-Magarino et al., 2009). Some influence of oak toasting degree was related to the ability of compounds released from the chips to combine with anthocyanins to form new pigments, and to a different extent with the two wines examined, but these effects were outweighed by the impact of MOX itself on phenolic composition and wine color. A range of wine parameters were also investigated for discrete oxygen doses at monthly intervals to supply 2.5 and 5.0 mg/L/month for a Pinotage wine in 20-L tanks for up to 6 months with free SO2 maintained at 25 mg/L, and a second trial with O2 applied every 2 weeks for 2 months at a target addition of 1.0 mg/L/month (De Beer et al., 2008); air was supplied through a gas diffuser until the DO reached the required concentration. In the first trial, a decline was observed in the free radical scavenging capacity of the oxygenated wines as measured using the ABTS assay, which was seen as a negative outcome. These changes were matched by a decrease in total phenol content (using the FolinCiocalteu reagent) and total monomer content (by HPLC) in the oxygenated wines, with more pronounced decreases in individual flavonols, flavan-3-ols, and monomeric anthocyanins and an associated increase in polymeric anthocyanins and changes in L*a*b* color parameters (to lower L* and a* values); the hydroxycinnamic acids such as caftaric acid, on the other hand, remained largely unchanged. The phenolic composition was not significantly altered during the second trial by the lower 1.0 mg/L/ month O2 additions. While this study was not an MOX procedure as such, but rather a study in periodic oxygen additions chosen for the smaller research scale vessels, the results did show polyphenol and color trends consistent with previous MOX studies, and provided useful information on sensory aroma and mouthfeel measures (see below). Changes in the polyphenols present in a diethyl ether extract of wines subject to MOX at 4 mL/L/month for 5 weeks have been observed, alongside molecular changes revealed through the application of 1H NMR spectroscopy, which pointed to an increase in oxygen-containing compounds and an increase in acetylated sugars (Conte, 2008; Piccolo et al., 2009). A further analytical methodology that has been recently applied to characterize microoxygenated wines is the electronic tongue based upon potentiometric chemical sensors (Rudnitskaya et al., 2009). Shiraz wines from three successive vintages were treated with MOX post-MLF in 300-L tanks at a rate of 2 mL/L/month, with and without oak chips added at 14 g/L, and with free SO2 maintained at 30–35 mg/L.

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The wines were then bottled and stored for 8–32 months before a wide range of chemical and instrumental analyses were undertaken at the same time. Using a total of 28 physicochemical parameters, the wines were clearly separated by vintage, and according to the treatments applied in some cases. Separation by vintage was also largely achieved using the electronic tongue data. The MOX wines were found to be lower in ionized anthocyanins, but higher in CIE-Lab coordinates (except a*). These two studies show the potential for new instrumental methodologies to be applied in the study of red wine aging and the effects of MOX treatments.

D. Effects on aromas The increase in aldehydes leading to oxidized characters in wines subject to high O2 dosages or prolonged MOX was noted above, and their appearance has been associated with excessive oxygenation and a detrimental effect on the wine. Prior to this point, benefits of lowering vegetative or reductive odors, and increasing fruity or varietal aromas, have been ascribed to MOX (Moutounet et al., 1996; Parish et al., 2000). The chemical changes affected by wine oxygenation that are responsible for these sensory observations have yet to be determined, although recent studies have measured aroma concentrations and/or undertaken sensory trials and have shown that certain compounds are not greatly affected by the addition of O2, or provided an indication of the aroma compounds that can change in MOX wines. The impact of MOX upon reductive odors was included in the study of McCord (2003) for MOX at 5–10 mL/L/month over 5 months on a Cabernet Sauvignon wine in commercial scale tanks. Lower concentrations of methyl mercaptan and ethyl mercaptan were observed in the oxygenated wines, but no impact was seen upon disulfides, in spite of the suggestion that concentrations of the disulfides could increase due to direct oxidation of sulfides. Dimethyl sulfide concentrations were not affected, except that lower concentrations were seen in wines with added toasted oak staves or segments, with or without MOX. The concentrations of various oak extracted compounds were also measured in this study, with similar levels seen with and without MOX alongside appreciable increases due to the presence of the oak staves or segments; in some cases (e.g., lactones and vanillin), oxygenation appeared to enhance aroma extraction. In an initial survey at the University of Auckland in 2004 on Cabernet Sauvignon, Merlot and Malbec wines, subject to MOX in 2000-L tanks at 4–8 mg/L/month for 12 weeks post-MLF, no changes were seen in the aroma profiles of the wines (vs. controls) for a wide range of aroma compounds, including herbaceous methoxypyrazines and C6 alcohols, floral terpenes and b-ionone, or for fruity esters and higher alcohols (Rowdon, 2005). Likewise, the concentrations of the varietal thiol 3MH,


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in the range of 300–600 mg/L, were the same in the MOX and control wines providing no obvious candidates for diminished vegetal characters and enhanced fruitiness in wines treated by MOX. For the MOX trials undertaken with four South African red wines on a commercial scale at 1.5–4 mg/L/month, sensory evaluations were undertaken to provide data on aroma and tasting intensities (du Toit et al., 2006a). Triangle tests confirmed that a sensory panel could distinguish MOX from control wines after a suitable treatment period, although mean scores for several sensory attributes, such as fruitiness, spiciness, vanilla/ butterscotch, and oak/coconut (the last two were higher for wines stored in barrels), were not significantly different between MOX and control wines. The oxidized/aged character was scored higher in one of the 3 mg/L/month MOX wines after 6 months compared to the control or the lower 1.5 mg/L/month dosage wine, which was seen as an indication that the wine had become overaged, reflected in a lower quality rating for this wine. In the monthly oxygenation of Pinotage wines in 20-L tanks (at 2.5 and 5.0 mg/L/month via periodic O2 additions), scores for the berry/ plum intensity and for overall wine quality both fell in the oxygenated wines (De Beer et al., 2008). On the other hand, in a second trial undertaken using 1.0 mg/L/month O2 additions, a decline in the berry/plum intensity was still observed, but without a loss in the overall quality score, suggesting that care needs to be taken with the level of O2 input so as not to overoxidize the wine. At the same time, we can note that periodic O2 additions need to be monitored carefully to ensure that volatile compounds are not stripped along with air bubbles that escape from the wine during this type of oxygenation. Surveys of several classes of aroma compounds were also included in MOX studies undertaken on Mencia and Tinta de Toro Spanish wines, with the inclusion of the impact of oak barrels or wood chips (OrtegaHeras et al., 2008; Rodriguez-Bencomo et al., 2008). The wines in one of the studies received O2 doses of 50–60 mL/L/month for 10 days pre-MLF, said to be necessary to eliminate the reductive compounds that develop after fermentation and allow improved fruit expression through the removal of vegetal properties, prior to a lower dose of 20–30 mL/L/ month applied for up to 10 days (Ortega-Heras et al., 2008). At the end of MOX, all of the wines were racked off and aged for 12 months in oak barrels. Aroma analyses were undertaken at the end of alcoholic fermentation (point A), at the end of the MOX treatment (point B), at the end of MLF (point C), upon transfer to barrels (0MB), and after 4, 8, and 12 months in barrel (4MB, 8MB, 12MB), included in the profile of esters presented in Fig. 4.11. In the case of the esters, considered responsible for the fruity aromas of young wines, there was no consistent difference brought about by the oxygenation procedure between the MOX (dotted lines) and control (solid

A

1.50

AB

B 2.50 C 0 MB

A BC 2.00 4 MB

1.00

8 MB

0 MB

4 MB

12 MB

12 MB

8 MB

mg/l

mg/l

1.50

1.00 0.50 0.50

0.00

0.00 0

3 ET.C.03 AA.C.03

6

9 Months

ET.MO.03 AA.MO.03

12

15

0

3

6

9

12

15

Months ET.C.02 AA.C.02

ET.MO.02 AA.MO.02

ET.C.03 AA.C.03

ET.MO.03 AA.MO.03

ET.C.02 AA.C.02

ET.MO.02 AA.MO.02

FIGURE 4.11 Evolution of ethyl esters (ET) and alcohol acetate esters (AA) for (A) Mencia and (B) Tinta de Toro wines, and for microoxygenated (MO) and control (C) wines from 2002 (02) to 2003 (03). Reprinted with permission from Ortega-Heras et al. (2008). Copyright 2008 Springer-Verlag.


175

lines) wines, against a background of hydrolysis losses, especially for the acetate esters. Little influence of MOX was found with the concentrations of C6 alcohols and terpene compounds, and only small increases in short chain fatty acids and higher alcohols in some of the MOX wines. Once again, the perception of a lowering of vegetal aromas could not be linked to changes in the concentrations of the aroma compounds examined in this study. On the other hand, there was a clear barrel influence upon certain volatile compounds, with increases seen in furfural, eugenol, and vanillin, for example, during barrel aging. What was observed was fewer aldehydes in the MOX wines, linked to aldehydes extracted from wood being taken up in polyphenol-linking reactions. In a second study, MOX was undertaken on a Mencia wine both before (44 mL/L/month for 17 days in 1500-L tanks) and after MLF in the presence of different oak chips (2 mL/L/month for 28 days in 300-L tanks) (Rodriguez-Bencomo et al., 2008). Again, the evolution of oak-derived aroma compounds with time was clearly seen, and the highest concentrations were typically reached after about 21 days, including furfural, whiskey lactone, eugenol, and vanillin, among other compounds, with differences seen according to the type of oak chip applied. On the other hand, there was practically no effect of MOX upon the extraction of the volatile compounds, with some minor differences noted in the concentrations of vanillin and syringaldehyde at a few time points. A further extensive aroma survey was undertaken with Cabernet Sauvignon and Tempranillo wines subject to MOX at 60 mL/L/month for 15 days pre-MLF, and for subsequent storage in oak barrels or stainless steel tanks, with and without MLF, and with and without additional MOX (Hernandez-Orte et al., 2009). Samples were taken pre-MLF, after MLF (when a free SO2 concentration of 32 mg/L was established), and after 4 and 8 months of maturation. In this study, a number of differences, usually small, between MOX and control wines were noted, but the two wines responded differently, and more compounds were found to be affected by the use of MOX in the Cabernet Sauvignon wine. For example, a lower concentration of acetoin was found in the Tempranillo wines subject to MOX, but a higher concentration in the MOX Cabernet Sauvignon wines (pre-MLF) (Table 4.4). Further differences included higher concentrations of methyl vanillate in the MOX wines, while after MLF, the vanillins were generally at higher concentrations in the non-MOX wines. Volatile phenols such as guaiacol were found at higher concentrations in the non-MOX wines. On the other hand, the terpene content was not consistently affected by MOX, the only differences being a higher initial level of citronellol in the Tempranillo wine, which disappeared after MLF, and more geraniol in the MOX Cabernet Sauvignon wine after 4 months, but this became lower after 8 months of aging. Some higher initial concentrations of ethyl

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TABLE 4.4 Concentrations of selected volatile compounds prior to malo-lactic fermentation in Cabernet Sauvignon and Tempranillo wines; data from Hernandez-Orte et al. (2009)

Acetoin (mg/L) Methyl vanillate (mg/L) Guaiacol (mg/L) Ethyl decanoate (mg/L) Isobutyl acetate (mg/L)

Cabernet Sauvignon

Tempranillo

No MOX

MOX

No MOX

MOX

13.7 9.4 4.38 0.34

12.8 9.5 3.82 0.20

9.76 10 20.0 0.8

37.9 7.55 16.8 2.83

2.78 0.77 13.2 0.73

1.87 0.14 33.6 23.3

1.96 0.94

0.94 0.24

52.9 6.55

70.0 13.7

esters in the MOX Cabernet Sauvignon wine (e.g., ethyl decanoate) and of acetate esters in the MOX Tempranillo wine (e.g., isobutyl acetate), also disappeared post-MLF, again indicating that effects seen immediately after MOX were often undetectable after MLF and aging. Importantly, no differences in the short chain fatty acids or C6 alcohols (hexanol and Z-3-hexenol) were seen between MOX and control wines, again failing to provide support for the hypothesis that a lowering of the herbaceous character as a result of MOX could be linked to a decrease in the concentrations of the C6 alcohols. Further, the intensity ratings for descriptive analyses undertaken by a sensory panel showed no significant differences between MOX and control wines for Tempranillo pre-MLF, while immediately after MLF, the panel scored the wood note higher in the non-MOX wines. Likewise, ‘‘fresh fruit’’ 4 and 8 months after MLF was higher in the non-MOX wines, whereas the currant note was dominant in the MOX wines. In the case of the Cabernet Sauvignon wines, the MOX wines were more herbaceous than the control wines (also seen in the Tempranillo wines before MLF). However, due to a possible association of herbaceousness with green pepper notes (typical of Cabernet Sauvignon), it was suggested that MOX reinforces varietality rather than increasing the herbaceous note (Hernandez-Orte et al., 2009). The impact of MOX upon reductive aromas is being examined in a further research project at the University of Auckland, using SPME GC–MS (Nguyen et al., 2010). The wines subject to MOX, or stored in an O2-permeable Flextank, recorded lower concentrations of compounds such as methanethiol (aroma of cooked cabbage, with a perception threshold of 0.3 mg/L; Mestres et al., 2000), and 3-(methylthio)-1-propanol


177

TABLE 4.5 Concentrations of reductive sulfur compounds and the varietal thiol 3MH (mg/L) in a Cabernet Sauvignon wine in 300-L tanks after 16 weeks of MOX or storage in Flextanks (n ¼ 3); data from Nguyen et al. (2010)

Methanethiol Dimethyl disulfide Dimethyl sulfide Methyl thioacetate Ethyl thioacetate 2-(Methylthio)1-ethanol 3-(Methylthio)1-propanol 3-Mercaptohexanol (3MH)

Control

MOX MOX (5 mg/ (20 mg/L/ L/month) month)

Flextank

0.87 0.09 0.39 0.08 11.2 0.5 3.0 0.3 3.2 0.3 56 8

0.69 0.09 0.31 0.11 11.0 1.6 3.0 0.4 3.0 0.2 42 12

0.60 0.15 0.19 0.02 9.2 0.2 2.9 0.1 3.1 0.2 28 1

0.55 0.15 0.21 0.03 9.6 0.3 2.9 0.2 2.8 0.3 29 4

2057 147 1815 343 1335 89

1324 84

0.45 0.02 0.47 0.03 0.45 0.04 0.41 0.02

(cauliflower, with a perception threshold of 1200 mg/L; Mestres et al., 2000), compounds that exceeded their perception thresholds in this study (Table 4.5). A decline was also observed with dimethyl disulfide (although this compound was present at concentrations well below the perception threshold of 20–45 mg/L; Mestres et al., 2000), despite suggestions that oxidation processes could lead to increases in disulfide formation (Mestres et al., 2000; Rauhut et al., 1996). On the other hand, oxygenation was again found to have little impact on the varietal thiol 3MH. The concentrations of the S-thioacetates were also similar in MOX and control wines.

E. Effects on mouthfeel The further benefit ascribed to MOX, of a decrease in astringency and greater smoothness in oxygenated wines (Moutounet et al., 1996), has also been difficult to relate to measurable changes in tannin content and structure. Longer tannin molecules are known to be more astringent (Vidal et al., 2003), and an increase in the total amount of tannin present size is also expected to lead to a more astringent wine, unless other factors intervene such as the formation of bridged structures or capping of tannin chains with anthocyanins (du Toit et al., 2006b; Vidal et al., 2004b). Various observations and experimental results pertaining to wine astringency and related polyphenol content from reported MOX trials are summarized below.

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In a trial on a Barbera wine subject to MOX at 1.7–2.5 mL/L/month for 45 days in 50-L tanks, the wines were found to be higher in smoothness 4–5 months after the end of the MOX process (Bosso et al., 2000). In the trial on Cabernet Sauvignon wines subject to MOX at 5 mL/L/month for 7 months (Atanasova et al., 2002), a polymeric fraction from a Toyopearl column was analyzed by thiolysis to determine the MDP. After 7 months, the MDP values were similar for the MOX (12.2 0.9) and control (12.6 0.3) wines, and both greater than the initial wine value of 10.1 0.4. On the other hand, it was noted that the total amount of tannins (by LC), originally 1434 50 mg/L, declined further in the MOX wines (1214 39 mg/L) compared to the control (1340 44 mg/L) after 7 months. In the commercial scale trial using MOX at 5–10 mL/L/month on a Cabernet Sauvignon wine (McCord, 2003), the MOX wines showed higher tannin levels according to the Adams tannin assay, while no difference was seen in the total phenols measure provided by the Folin-Ciocalteu assay. However, the increase by around 10% in tannin values over the control wines by the Adams assay might not be sufficient to have an appreciable sensory impact. An attempt was made to monitor wine astringency during commercial scale trials during which MOX rates of 1.5–4 mg/L/month were applied post-MLF (du Toit et al., 2006a). In this case, differences in gelatine index values (sometimes higher, sometimes lower in MOX wines vs. the control) did not correlate with sensory ratings (similar intensity scores were given for astringency and bitterness), pointing to the need for more research in this area. In the trial on a Cabernet Sauvignon wine in 15-L tanks at the University of Auckland using oxygen delivered at 10, 23, and 36 mg/L/month through with a dense polymer membrane for 15 weeks, sensory evaluations were undertaken for various mouthfeel measures (Dykes, 2007; Dykes and Kilmartin, 2007). Using principal response curves to collect together the sensory data, some cycling in the response was seen for each treatment compared to the control, with higher points related to higher mouthfeel and astringency scores being seen earlier with the higher MOX rates prior to a decrease in the measure. Among further chemical analyses undertaken in this trial, the MDP values for a proanthocyanidin extract obtained using Sephadex LH-20 were found to increase from around 11 to 18 over the first 11 weeks of the trial, with little difference between the various O2 dosage rates, followed by a decline in MDP once SO2 was added post-MLF, which appeared to have a greater impact than the oxygen supplied. The impact of SO2 was confirmed in a further experiment using wine stored in 1-L airtight containers (with and without weekly oxygen saturations, and with and without SO2 added at 100 mg/L; Dykes and Kilmartin, 2007). With added SO2, the MDP values were seen to decline from days 7 to 21 irrespective of the presence or


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absence of added O2; without SO2, the MDP values remained steady until day 30, when an increase was seen (and to the same extent with or without added O2), which was not observed in the wines with added SO2. In the subsequent trial described above for the impact SO2 levels have on wine color development using 15-L tanks (Tao et al., 2007), the MDP values remained relatively unchanged for the highest initial SO2 addition of 200 mg/L, while some decline was observed in the wines with less SO2 present (Fig. 4.12C), in this case, in the opposite direction to the smaller 1-L trial. It can be noted that the amount and type of tannins which are collected using various extraction procedures can vary, and in these trials, changes in MDP need to be considered alongside the total amount of tannin extracted, and how well these represent the tannins responsible for mouthfeel properties. In the case of the above trial, the lower SO2 wines were in fact marked by a greater polyphenol content in the proanthocyanidin extract (Fig. 4.12A; Tao et al., 2007). The tannin level, determined using both the methyl cellulose and BSA-based assays, was also lower in the high 200 mg/L initial SO2 wine at the end of the trial. Smaller chain lengths can be associated with a lowering of astringency, whereas more total tannin would likely increase astringency; which will dominate with a particular wine is a further question. In more recent studies, the tannin content measured in terms of BSA protein precipitation remained a little higher in MOX wines from commercial scale trials undertaken on Monastrell wines, starting initially at 10 mL/L/month O2 delivery (Cano-Lopez et al., 2008). At the same time, changes in tannin MDP values were variable across the trials (sometimes higher, sometimes lower in the MOX wines). No change was recorded in the sensory ‘‘astringency’’ measure for the Pinotage wines subject to monthly oxygenation in 20-L tanks (at 2.5 and 5.0 mg/L/month using periodic O2 additions), although ‘‘fullness’’ scores were higher in the oxygenated wines after 6 months (De Beer et al., 2008). It was noted that the optimal oxygenation rate and the time required for sensory quality will vary depending upon the initial tannin and anthocyanin content of the particular wine.

F. Microbiological considerations Oxygen additions also have the potential to promote the growth of unwanted aerobic microorganisms, particularly if DO concentrations become too high (du Toit et al., 2006b). Problem areas could include growth of Brettanomyces yeast, avoided by watching out for residual sugar and maintaining good pH and SO2 control, and increased levels of volatile acidity (VA) due to acetic acid bacteria, although decreases in VA were noted for one trial on South Australian wines (Paul, 2000).

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A 0 mg/L 50 mg/L 100 mg/L 200 mg/L (SO2 added)

Total phenols (AHCl280)

8

6

4

2

No O2

0 B

Red color (A520)

1.5

1.0

0.5 No O2 0.0 C

25 No O2

MDP

20 15 10 5 0

0

20

40

60 Time/days

80

100

120

FIGURE 4.12 Concentrations of (A) total phenols given by 280-nm absorbance, (B) red wine color given by 520-nm absorbance, and (C) mean degree of polymerization (MDP) in a proanthocyanidin extract; for a Merlot wine undergoing MOX at 10 mg/L/month with different initial SO2 additions (n ¼ 3). Values for wines stored in bottles until the end of the trial with ‘‘no O2’’ are shown on the right. Reprinted with permission from Tao et al. (2007). Copyright 2007 American Chemical Society.


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The growth of acetic acid bacteria and Brettanomyces was monitored, by plating and enumerating, during commercial scale MOX trials at 1.5–4 mg/L/month on various South African red wines (du Toit et al., 2006a). Acetic acid bacteria numbers declined in the control tanks, but remained higher in the MOX wines, although an increase in VA was not noted. When the free SO2 concentration fell to 18 mg/L during MOX, cell counts of Brettanomyces increased (along with the sensory barnyard/ medicinal character), but dropped off again after addition of SO2 to 35 mg/L free SO2. While SO2 addition can be used to control Brettanomyces, it was noted that excessive SO2 concentrations could inhibit favorable polymerization reactions (du Toit et al., 2006a), thus negating some of the benefits of applying MOX to the wine. An increase in acetic acid concentration in MOX wines, associated with the activity of acetic bacteria, was also seen in Shiraz wines treated with MOX post-MLF in 300-L tanks at 2 mL/L/month (Rudnitskaya et al., 2009).

V. FINAL COMMENTS The technology of MOX provides winemakers with a lower cost alternative to barrel aging for the development of red wines, while avoiding the effects of higher DO concentrations generated during racking procedures. MOX also provides the researcher with an approach to examine the influence of small doses of O2, in the absence of oak barrels, to gain more insight into the role that oxygen specifically plays in red wine maturation. As summarized above, recent MOX studies have consistently confirmed the more rapid development of polymeric pigments associated with more stable red wine color, seen through a decline in monomeric anthocyanins and an increase in sulfite-resistant pigments. The involvement of acetaldehyde in pigment development, itself generated as a product of polyphenol-mediated oxidation processes, has also been demonstrated, along with the moderating influence of SO2. On the other hand, chemical changes in aroma compounds and in tannin content, relevant to sensory changes in wine aromatic and mouthfeel properties, remain to be determined. Further research avenues that may shed more light on the widely recognized benefits of MOX, namely, a lowering of vegetative and reductive aromas and of astringency, include more studies on the effect of O2 on sulfur-containing compounds, and more in-depth studies of tannin structure as a wine ages. Likewise, the inclusion of synergistic effects, for example, the masking effect of sulfur-containing compounds and newly formed aldehydes require more systematic study, along with the effects of polyphenols on the volatility of aroma

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compounds (Aronson and Ebeler, 2004; Lund et al., 2009), and how this may change as the tannin structure develops as a result of oxygenation. There is also a growing recognition of the need to better understand the spatial gradients of DO within large tanks, and include these effects in modeling wine maturation processes (Devatine and Mietton-Peuchot, 2009; Dykes, 2007; Dykes and Kilmartin, 2007). Considerations of this sort, alongside O2 dosage rate and SO2 adjustment, will allow winemakers to make the most effective use of MOX to improve the quality of specific red wines prior to bottling.

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Rudnitskaya, A., Schmidtke, L. M., Delgadillo, I., Legin, A., and Scollary, G. (2009). Study of the influence of micro-oxygenation and oak chip maceration on wine composition using an electronic tongue and chemical analysis. Anal. Chim. Acta 642, 235–245. Saenz-Lopez, R., Fernandez-Zurbano, P., and Tena Maria, T. (2004). Analysis of aged red wine pigments by capillary zone electrophoresis. J. Chromatogr. A 1052, 191–197. Salmon, J. M., Fornairon-Bonnefond, C., Mazauric, J. P., and Moutounet, M. (2000). Oxygen consumption by wine lees: Impact on lees integrity during wine ageing. Food Chem. 71, 519–528. Salmon, J. M., Fornairon-Bonnefond, C., and Mazauric, J. P. (2002). Interactions between wine lees and polyphenols: Influence on oxygen consumption capacity during simulation of wine aging. J. Food Sci. 67, 1604–1609. Sartini, E., Arfelli, G., Fabiani, A., and Piva, A. (2007). Influence of chips, lees, and microoxygenation during aging on the phenolic composition of a red Sangiovese wine. Food Chem. 104, 1599–1604. Saucier, C. T. and Waterhouse, A. L. (1999). Synergetic activity of catechin and other antioxidants. J. Agric. Food Chem. 47, 4491–4494. Silva Ferreira, A. C., de Pinho Paula, G., Rodrigues, P., and Hogg, T. (2002). Kinetics of oxidative degradation of white wines and how they are affected by selected technological parameters. J. Agric. Food Chem. 50, 5919–5924. Silva Ferreira, A. C., Hogg, T., and Guedes de Pinho, P. (2003). Identification of key odorants related to the typical aroma of oxidation-spoiled white wines. J. Agric. Food Chem. 51, 1377–1381. Singleton, V. L. (1987). Oxygen with phenols and related reactions in musts, wines, and model systems: Observations and practical implications. Am. J. Enol. Vitic. 38, 69–77. Singleton, V. L., Salgues, M., Zaya, J., and Trousdale, E. (1985). Caftaric acid disappearance and conversion to products of enzymic oxidation in grape must and wine. Am. J. Enol. Vitic. 36, 50–56. Tao, J., Dykes, S. I., and Kilmartin, P. A. (2007). Effect of SO2 concentration on polyphenol development during red wine micro-oxygenation. J. Agric. Food Chem. 55, 6104–6109. Vidal, J. C. and Moutounet, M. (2006). Monitoring of oxygen in the gas and liquid phases of bottles of wine at bottling and during storage. J. Int. Sci. Vigne Vin 40, 35–45. Vidal, S., Francis, L., Guyot, S., Marnet, N., Kwiatkowski, M., Gawel, R., Cheynier, V., and Waters, E. J. (2003). The mouth-feel properties of grape and apple proanthocyanidins in a wine-like medium. J. Sci. Food Agric. 83, 564–573. Vidal, J. C., Toitot, C., Boulet, J. C., and Moutounet, M. (2004a). Comparison of methods for measuring oxygen in the headspace of a bottle of wine. J. Int. Sci. Vigne Vin 38, 191–200. Vidal, S., Francis, L., Noble, A., Kwiatkowski, M., Cheynier, V., and Waters, E. (2004b). Taste and mouth-feel properties of different types of tannin-like polyphenolic compounds and anthocyanins in wine. Anal. Chim. Acta 513, 57–65. Vinovation (2009). http://www.vinovation.com/MOxmain.htm. Vivas, N., Debeda, H., Menil, F., De Gaulejac, N. V., and Nonier, M. F. (2004). Demonstration of oxygen transfer through barrel staves by using a novel system to measure wood porosity. Preliminary results. Sci. Aliments 23, 655–678. Waterhouse, A. L. and Laurie, V. F. (2006). Oxidation of wine phenolics: A critical evaluation and hypotheses. Am. J. Enol. Vitic. 57, 306–313. Wildenradt, H. L. and Singleton, V. L. (1974). Production of aldehydes as a result of oxidation of polyphenolic compounds and its relation to wine aging. Am. J. Enol. Viticult. 25, 119–126.

CHAPTER

5 The Morama Bean (Tylosema esculentum): A Potential Crop for Southern Africa Jose C. Jackson,*,1 Kwaku G. Duodu,† Mette Holse,‡ Margarida D. Lima de Faria,§ Danie Jordaan,k Walter Chingwaru,} Aase Hansen,‡ Avrelija Cencic,} Martha Kandawa-Schultz,# Selalelo M. Mpotokwane,** Percy Chimwamurombe,# Henrietta L. de Kock,† and Amanda Minnaar†

Contents

I. Introduction II. Agronomic Characteristics A. Current distribution and description of morama bean B. Seed morphology, seedling development, growing stages, diseases, and pests C. Classification and variety identification D. Characteristics of soils from morama bean- and nonmorama bean-growing areas

189 190 190 192 193 193

* Centre for Scientific Research, Indigenous Knowledge and Innovation (CESRIKI), University of Botswana,

Botswana Department of Food Science, University of Pretoria, Pretoria, South Africa Department of Food Science, Quality and Technology Section, University of Copenhagen, Frederiksberg C, Denmark } Department of Human Sciences, Programme for Global Development, Tropical Research Institute–IICT, Rua da Junqueira, Lisbon, Portugal k Market Matters Inc., South Africa, Ithaca, New York, USA } Department of Microbiology, Biochemistry, Molecular Biology and Biotechnology, University of Maribor, Hocˇe, Slovenia # Department of Biochemistry and Chemistry, and Department of Biological Sciences, University of Namibia, Windhoek, Namibia ** National Food Technology Research Centre, Kanye, Botswana 1 Corresponding author: Jose C. Jackson, E-mail address: [email protected] { {


#


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III. Chemistry, Nutritional, and Health Potential A. Overall chemical composition B. Moisture C. Ash D. Lipids E. Protein F. Carbohydrate/dietary fiber G. Minor chemical components H. Antinutritional factors/toxic constituents I. Phenolic compounds J. Potential health benefits and negative effects associated with components of morama beans K. Conclusions IV. Food-Processing Applications and Utilization A. Morama milk B. Morama oil C. Protein-rich morama flours V. Potential Marketing Strategies for Morama-Processed Products A. Market overview B. Consumer analysis C. Commercialization strategies D. Conclusions VI. Socio-Economic Analysis of Communities Where Morama is Found A. The value of morama as perceived by the communities B. Morama availability C. Morama uses in diets in Botswana, Namibia, and South Africa D. Morama health benefits as perceived by the local people E. Morama: A staple food for very poor populations F. Morama domestication: Problems related to cultivating morama as a crop G. Morama market H. Conclusions and recommendations VII. Challenges and Future Research References

Abstract

195 195 196 196 196 201 202 203 204 205 206 207 213 214 218 218 223 224 225 229 230 230 231 232 233 233 234 235 236 237 238 239

The morama bean is an underutilized leguminous oilseed native to the Kalahari Desert and neighboring sandy regions of Botswana, Namibia, and South Africa (Limpopo, North-West, Gauteng, and Northern Cape provinces), and forms part of the diet of the indigenous population in these countries. It is also known as gemsbok bean,

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189

moramaboontjie, elandboontjie, braaiboonjie, marama, marumana, tsi, tsin, gami, and ombanui. It is reported as an excellent source of good quality protein (29–39%); its oil (24–48%) is rich in mono- and di-unsaturated fatty acids and contains no cholesterol. Morama is a good source of micronutrients such as calcium, iron, zinc, phosphate, magnesium, and B vitamins including folate. It is also reported to be a potential source of phytonutrients including phenolic compounds (e.g., tannins), trypsin inhibitors, phytates, and oligosaccharides, components which have been shown in other foods to contribute to health in particular, prevention of noncommunicable diseases such as cardiovascular diseases, diabetes, and some cancers. From a nutritional and health perspective, the morama bean has potential commercial value as a cash crop and value-added products, particularly in the communities where it is found.

I. INTRODUCTION The morama bean is an underutilized crop native to the arid and semiarid regions of Botswana, Namibia, and South Africa and forms part of the diet of indigenous populations. Botanically, morama beans belong to the family Fabaceae (Leguminosae), subfamily Ceasalpinoidae, and genus Tylosema (Dubois et al., 1995; Hartley et al., 2002). The morama bean is also known as gemsbok bean (English); moramaboontjie, elandboontjie, braaiboonjie (Afrikaans); marama, morama (Tswana); marumana (Thonga); tsi, tsin (!Kung San); gami (Khoi); ombanui (Herero). In Botswana, it is referred to as morama, whereas in Namibia and South Africa, it is referred to as marama. For the purposes of this review, the term ‘‘morama’’ will be used. The economic importance of the genus Tylosema has been known for a very long time. Burchell (1824) emphasized the importance and use of morama seeds and the underground tuber by the indigenous population, especially of the Kalahari Desert. The composition of the edible seeds has been reported by different authors as an excellent source of good quality protein (29–39%) and oil contents ranging from 24% to 48% (Amarteifio and Moholo, 1998; Bower et al., 1988; Dubois et al., 1995; Francis and Campbell, 2003; Holse et al, 2010; Ketshajwang et al., 1998; Mitei et al., 2008). It is also a good source of micronutrients such as calcium, iron, zinc, phosphate, potassium, magnesium, and B vitamins (Wehmeyer et al., 1969). There has been very little research conducted on the morama bean, largely because it is found in the wild and only consumed by a small percentage of the population in the countries where found. However, in these areas, it is used by indigenous communities as a source of food, feed, shelter, and medicine, thus contributing to improve the quality of life in traditional agricultural and forest systems in various ways. The morama has enormous potential value that needs to be exploited for the

190


further benefit of the communities. This review presents a summary of the available literature on its agronomic characteristics, chemistry, nutritional, and functional properties, processing applications, socioeconomic issues in the communities where it is found, consumer demand, and potential strategies for marketing value-added morama bean products.

II. AGRONOMIC CHARACTERISTICS A. Current distribution and description of morama bean Coetzer and Ross (1977) recognized four species in the genus Tylosema: (i) Tylosema esculentum (Burchell) A. Schreiber, (ii) Tylosema fassoglense (Kotschy) Torre & Hillc, (iii) Tylosema argentea (Chiov) Brenan, and (iv) Tylosema humifusa (Pichi-Serm & Roti-Michael) Brena. They are found throughout Africa with the exception of T. esculentum (Burchell) A. Schreiber, which is specific only to the semiarid regions of Southern Africa (Fig. 5.1). The morama bean is presently known only in the wild state. It tolerates the scorching heat and long drought periods of the Kalahari Desert of Botswana and Namibia (Fig. 5.2), where it is an important component of

Tylosema esculentum

Tylosema fassoglense

FIGURE 5.1 Morama bean plants and seeds from Southern Africa (T. esculentum and T. fassoglense) (adapted from Maruatona, 2008 and Kayitesi, 2009).

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191

Katima Mulilo Rundu

Tsumeb Otavi Otavi Outjo

Grootfontein

Tsumkwe

Otjiwarongo

Gum

Otjiwarongo

Snow white Snowwhite

Okakarara Omitjenya village

NAMIBIA

D’Kar Ghanzi

Okahandja Omitara

Swakopmund

Windhoek

Walvis Bay

Rehopoth

Gobabis

Vergenoeg

BOTSWANA

Gobabis

Bep hur

Mariental

Luderitz

D’Kar

Ghanzi

Gaborone

Keetman shoop

Ariamsvlei Noordoewer

FIGURE 5.2 Current geographic distribution of morama bean in Namibia and Botswana (Agricultural Laboratory; Ministry of Agriculture, Water, and Forestry, 2004 as cited by €useler and Scho¨nfeldt, 2006). Mu

the diet of the Khoisan and nomadic Bushmen. It is native to the Kalahari Desert and the neighboring sandy regions of Namibia, Botswana, and South Africa (Northern Cape Province, North-West Province, Limpopo Province, and Gauteng Province), mostly in undulating grassveld or savannas (Bower et al., 1988; National Academy of Sciences, 1979). The word ‘‘esculentum’’ means edible and was given to the plant because not only does it produce seeds and tubers that are eaten but browsing stock and game also consume the tuberous stems. The typical habitat is an undulating grassveld (savanna) with morama sprouting among the native grass and acacia-thorn scrub on sandy vlies (wilderness). A mature morama bean plant (Fig. 5.1) has prostrate vines with numerous herbaceous stems which can be up to 6 m or longer (Vietmeyer, 1986). Morama bean is commonly found creeping over the soil surface with vines carrying double-lobed leaves that are soft and reddish brown when young, but later turn leathery and greyish-green with age (National Academy of Sciences, 1979). Golden yellow flowers develop in midsummer and the fruits ripen in late autumn (National Academy of Sciences, 1979). The vines die in winter, but the plant is maintained by a perennial tuber (Hartley et al., 2002). Storage roots or tubers can attain a weight of up to 12 kg in a few years (Bousquet, 1982) and can contain up to 90%

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water, which serves as an important emergency source of water for Kalahari foragers (Keith and Renew, 1975). Powell (1987) attributes the success of the morama bean as an energy-efficient arid land crop to the survivability of tubers during winter dormancy. Typically, the pods can contain one to two seeds and are encased by a hard, water tight coat. Yellow flowers of about 4 cm across, are produced in clusters of three to nine, mostly on the distal two-thirds of the stem. For every 100 plants from the morama bean population, 77 of them were able to outcross, indicating that the morama bean is predominantly an outcrossing species (Monaghan, 1995). Chromosome counts made from the root tips of morama bean seedlings ranged from 42 to 50 showing a relatively high number of chromosomes, which is consistent with chromosomes count of 52 from its closely related species, T. fassoglense (Monaghan, 1995).

B. Seed morphology, seedling development, growing stages, diseases, and pests Morama bean is a wild tuber producing nonnodulating legume. Generally, the plant is low yielding, producing one or two seeds per pod and in total, a few seeds per plant. The seeds develop in a typical legume pod, which is large and squat in shape. In most cases, there are one to three seeds per pod. Rarely, it is possible to find up to 10 seeds in a single pod (personal observation). In terms of cultivation, there are only a few isolated cultivation efforts ongoing in the countries of interest in Southern Africa. The reason for that is the serious lack of important information on the biology of the plant. However, due to the advent of modern crop development tools, the knowledge gap is closing and the cultivation of morama bean is beginning to gain momentum. The seed’s hard shell means that scarification is necessary in order to increase germination rates. A pH of about 7 is preferred for growth and the plant typically grows in sandy loamy soils where waterlogging will not be a problem. There are only very few places in the world that are propagating morama bean, namely, USA, Australia, Namibia, and Botswana. One of the major hindrances to plant improvement through breeding is the heterostylic property of the flowers. A bee (on its own) has been observed and implicated to be the pollinator for morama. The taxonomic identity of the bee is still being investigated. In terms of diseases, not much has been recorded, except a recent report (unpublished data) on two fungi coinfecting the pods of morama. The two fungi associated with the necrotic lesions were revealed to be Alternaria tenuissima and Phoma spp. using DNA-based and morphological characterization. Insects pest have been observed on the wild stand of morama causing seed damage. However, the identity of the insects is yet to be ascertained.

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C. Classification and variety identification There are no varieties of morama bean that have been developed so far. However, there are advanced crop improvement experiments that are designed to produce a variety of cultivars, namely, early maturing cultivars, using morama that matures after about 18–24 months. Microsatellite markers (Weising et al., 1998) have been developed and used previously. Current microsatellite markers for morama beans have been developed using a modified procedure by Zane et al. (2002). These markers will be used in marker-assisted selection (MAS) for the morama bean.

D. Characteristics of soils from morama bean- and nonmorama bean-growing areas Thomas (2004) reported on the characteristics of morama bean- and nonmorama bean soils collected from different sites in both Namibia (Sandveld Site 1, 2, 3, 4 and Buitepos) and Botswana (Charleshill, Chobokwane, Ghanzi, Groote Laagte Site 1, Makgobokgobo Site 1, Xhoga, and Xanagas). In general, the soils were fine aeolian (waterlogged) sands characterized by the presence of dolomite or limestone concretions in the soil profile (Table 5.1). Morama bean soils from Sandveld Site 1 were brown and sandy with limestone/dolomite concretions (compact, often rounded accumulations of mineral matter that form in the soil) on the surface and subsurface layer, while soils from Sandveld Site 2 and Buitepos had no limestone or dolomite concretions either on the surface or within the soil profile. The soil from Sandveld Site 3 was virtually white sand with carbonate concretions in the profile but not on the surface. The soils from the morama bean-growing sites in Botswana were characterized by brown sand with no dolomite or limestone concretions on the surface or in the soil profile. Of all the sites in Botswana, only Ghanzi was characterized by white sand with dolomite or limestone concretions in the soil profile. Nonmorama soils from Namibia (Sandveld Site 4) were very fine white sands that appeared wet, waterlogged, and clayish, while those from Botswana soils (Groote Laagte Site 2 and Makgobokgobo Site 2) were generally brown sands with no limestone or dolomite concretions in the profile.

1. Soil pH The pH of the morama bean soils (0–120 cm) collected from Namibia ranged from 5.08 0.08 to 6.90 0.26. A significant difference was found between morama bean soils from Sandveld Sites 1 and 2, and Buitepos, and nonmorama bean soils from Sandveld Site 4. The soils from Botswana showed mean pH values of 5.04 0.21 to 7.40 0.25 for morama bean-growing areas and 4.04 0.03 to 4.89 0.16 for

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TABLE 5.1 Description of morama and nonmorama bean soils collected from Namibia and Botswana Site

Soil description

Namibia Morama bean soils Sandveld Site 1 Brown fine aeolian sand with limestone/dolomite concretions on top and subsurface layers Sandveld Site 2 Brown fine aeolian sand with no limestone/dolomite concretions Sandveld Site 3 White fine aeolian sand with limestone/dolomite concretions in soil profile but not on surface Buitepos Brown fine aeolian sand with no limestone/dolomite concretions Nonmorama bean soil Sandveld Site 4 White water-logged very fine sand with limestone/ dolomite concretions in profile but not on surface layer. Appeared a bit clayish Botswana Morama bean soil Charleshill Chobokwane Ghanzi Groote Laagte Site 1 Makgobokgobo Site 1 Xanagas Xhoga

Brown fine aeolian sand with no limestone/dolomite concretions Brown fine aeolian sand with no limestone/dolomite concretions White fine aeolian sand with limestone/dolomite concretions in soil profile Brown fine aeolian sand with no limestone/dolomite concretions Brown fine aeolian sand with no limestone/dolomite concretions Brown fine aeolian sand with no limestone/dolomite concretions Brown fine aeolian sand with no limestone/dolomite concretions

Nonmorama bean soil Makgobokgobo Brown fine aeolian sand with no limestone/dolomite Site 2 concretions Groote Laagte Brown fine aeolian sand with no limestone/dolomite Site 2 concretions

nonmorama bean soils. The pH values of morama bean soils from Ghanzi, Groote Laagte Site 1, Xanagas, and Xhoga were significantly higher than those obtained for control soils from Groote Laagte Site 2 and

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195

Makgobokgobo Site 2 (i.e., soils where morama beans do not grow). Morama bean soils collected from Ghanzi showed the highest pH value of 7.40 0.25. The pH of morama bean soils from Groote Laagte Site 1 was significantly different from that of Groote Laagte Site 2, the control from the same location. No significant differences were found between morama and nonmorama bean soils collected from Makgobokgobo. For both Namibia and Botswana, acidity values below pH 5 were recorded for only nonmorama bean soils. Soil pH values did not change significantly with depth within sites.

2. Soil organic matter The organic matter content of soils collected from Namibia was generally low. The mean organic matter content of morama bean soils (0–120 cm) differed significantly for the four Namibian sites (p < 0.05). Buitepos showed the lowest organic matter content of 0.30 0.02%, followed by Sandveld Sites 1 and 2 (0.38 0.01%) and the highest (0.44 0.02%) at Sandveld Site 3. However, these values were significantly lower than those obtained for nonmorama bean soils from Sandveld Site 4 (0.59 0.03%). The soil organic matter content obtained for morama bean soils from Makgobokgobo Site 1 in Botswana was significantly higher than that of nonmorama bean soils from near the same site. As with pH, the organic matter content also did not change with soil depth within each site. The data showed that soils supporting the growth of morama bean are very poor in nutrients, especially N, which is less than 0.06%, and organic matter below 0.45%. As a result, supplying N and P significantly increased plant growth and tuber yield under both field and glasshouse conditions. This indicates that, despite the species adaptation to low-nutrient conditions, the application of mineral fertilizers to morama bean in an agricultural setting is likely to promote and increase tuber yield. Walvoord et al. (2003) reported that there is a large nitrate pool located deep (> 1 m) beneath desert soils. However, this was not reflected in the results from Thomas (2004). It should be acknowledged, however, that desert subsoil nitrate inventories are spatially highly variable, thereby requiring substantial measurement efforts to reduce uncertainty in global extrapolations.

III. CHEMISTRY, NUTRITIONAL, AND HEALTH POTENTIAL A. Overall chemical composition Morama beans may be eaten when they are still immature green beans, but most of them are eaten as mature beans when the seeds are surrounded by hard woody seed coats, reddish to brownish in color. Therefore, the morama beans need to be decorticated before use.

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A number of studies have been carried out, describing the main chemical composition of the edible part of the decorticated morama bean (T. esculentum) and few samples of T. fassoglense have also been included. A literature survey of the performed proximate analysis is summarized in Table 5.2.

B. Moisture The moisture content of the morama bean is very low as the dry matter (dm) content ranges from 93.4% to 98.7% (Bower et al., 1988; Holse et al., 2010; Wehmeyer et al., 1969). Variations may arise from external factors such as soil composition, climate, harvest time, and maturation state of the beans.

C. Ash The ash content is a measure of the total mineral content in the beans, and this seems to be high as the ash content of the beans varies between 2.5% and 3.7% dm (Amarteifio and Moholo, 1998; Bower et al., 1988; Holse et al., 2010; Wehmeyer et al., 1969). Beans from South Africa have significantly higher ash content than beans from both Namibia and Botswana, which do not differ significantly (Holse et al., 2010). This might be due to a soil richer in minerals in South Africa. The content of the different minerals is discussed further below.

D. Lipids The seed oil is golden yellow, with a nutty odor and a pleasant, although slightly bitter flavor, and has been described as similar to almond oil in consistency and taste (van der Maesen, 2006). The content of lipids in the morama bean has been reported in several studies and large variations were reported. Ranges between 24% and 42% were reported by (Amarteifio and Moholo, 1998; Bower et al., 1988; Dubois et al., 1995; Francis and Campbell, 2003; Holse et al., 2010; Mitei et al., 2008; Wehmeyer et al., 1969). Ketshajwang et al. (1998) reported a lipid content of 48% w/w which is substantially higher than other reported results. The high content of lipids is a great advantage of the beans, especially in regard to producing healthy products that may improve the nutritional status of undernourished people in Southern Africa. The high amount of lipids in morama beans is comparable to the content found in seeds used for production of commercial vegetable oils such as sunflower seed (22–36%) and rapeseed (22–49%) and closely approaches that of peanuts (45–55%) (Belitz et al., 2004; Salunkhe and Kadam, 1989). The amount of lipids is twice that of soybeans

TABLE 5.2

Proximate composition (g/100 g) of the morama bean reported as mean SD or range Literatur e reviewed

Wehmeyer et al. (1969)

Bower et al. (1988)

Dubois et al. (1995)

Ketshajwang et al. (1998)

Amarteifio and Moholo (1998)

Francis and Campbell Mitei et al. (2003) (2008)

One batch of One batch of Two batches of One batch One batch X samples samples T. fassoglense harvested in harvested in harvested in harvested in from sites in Botswana Botswana Botswana Botswana Zaire and during Burundi, 1978–1980 respectively

Moisture Lipid Protein Carbohydrate b Crude fiber Dietary fiber Ash a b

5.2 36.1 31.6 23.2 1.0 – 2.9

3.9 1.0 42.2 1.6 31.8 1.1 18.9 2.2 – – 3.2 0.1

– 24–30 – – – – –

– 48.2 – – – – –

Note that these results are on dry matter basis, while the results from the other studies are as is. Obtained by difference.

– 33.5 0.04 34.1 0.12 24.1 0.02 4.4 0.13 – 3.7 0.14

– 41.7 38.4 – – – –

Holse et al. (2010)a

1 kg harvested 14 batches of in Botswana T. esculentum and two of T. fassoglense harvested in Namibia, Botswana, and South Africa during 1990 and 2005–2008 – 1.3–6.6 38.4 32.0–41.9 – 28.8–38.4 – – – – – 18.7–26.8 – 2.5–3.7

198


¨ pik, 1975), which is the (17–20% dm) (Belitz et al., 2004; Street and O legume that compares best with the high protein content of the morama beans. Most investigations are based on one or two samples, but Holse et al. (2010) compared the chemical composition of 16 samples, 14 samples of morama beans, and two samples of T. fassoglense. Mitei et al. (2008) determined the percentage composition of lipid classes in morama oil and found that the oil is predominantly composed of neutral lipids, with triacylglycerols (TAGs) and fatty acids (82.8%) being the dominant lipid components. However, the oil appeared to contain fair amounts of polar lipids like diacylglycerols (3.4%) and monoacylglycerols (5.9%), which would suggest the occurrence of lipasecatalyzed hydrolysis in the oil. Indeed, the acid value of 2.96 mg KOH/g tends to support this observation (Mitei et al. (2008). In addition, the morama oil contained hydrocarbons (1.4%), sterol esters (1.8%), free sterols (1.9%), glycolipids (1.6%), and phospholipids (1.3%).

1. Triacylglycerols The principal components of oils and fats on which all the physical and chemical properties depend are the TAG molecules. The TAGs are in turn made up of a variety of fatty acids esterified to the hydroxyl groups on glycerol molecules. TAG profiles of morama bean oil has been carried out by Mitei et al. (2008) who found seven major TAG classes, with the dominating classes being C54:4, C54:3, and C52:3. Occupancy of the sn-1 and sn-3 positions is 45.0% saturated, 38.4% oleoyl, and 16.6% linoleoyl. The sn-2 position is occupied by oleoyl (56.4%) and linoleoyl (43.6%).

2. Fatty acids The lipid of morama beans is mainly ( 75%) unsaturated fatty acids, with the principal fatty acid being oleic acid (43%). The beans furthermore contain linoleic (22%) and palmitic acid (13%) as well as stearic, arachidic, linolenic, arachidonic, erucic, behenic, myristic, palmitoleic, and gadoleic acid in lower concentrations (Bousquet, 1982; Bower et al., 1988; Engelter and Wehmeyer, 1970; Francis and Campbell, 2003; Ketshajwang et al., 1998; Mitei et al., 2008). The fatty acid composition resembles that of olive oil (Mitei et al., 2008). A literature review of the fatty acid composition of morama beans is given in Table 5.3. Less than 5% of the fatty acids are present as free acids (Bower et al., 1988; Dubois et al., 1995), which means that the activity of lipases is negligible in dry morama beans.

3. Phytosterols

Mitei et al. (2009) determined the relative percentage composition of phytosterols in morama bean oil and found 4-desmethylsterols to be the most abundant phytosterols in the oil (77% or 149.15 mg/g). The content of 4-desmethylsterols is comparable to the phytosterols content of olive oil

TABLE 5.3

Fatty acid composition (% of total fatty acids) of the morama bean reported as mean SD Literature reviewed

Bousquet (1982)

Bower et al. (1988)


Francis and Campbell (2003)

One batch

x

One mixed batch of samples harvested in Botswana during 1978–1980

One batch harvested in Botswana

x

1 kg harvested in Botswana

– 12.8

– 12.93 0.06

7.3

8.82 0.12 3.31 0.03 1.03 0.02

49.0

47.27 0.43

1.7 13 0.1 7.7 2.9 1.0 1.1 43

0.61 0.00

0.7

Engelter and Wehmeyer (1970)

Myristic Palmitic Margaric Stearic Arachidic Behenic acid Palmitoleic Oleic

(14:0) (16:0) (17:0) (18:0) (20:0) (22:0) (16:1) (18:1)

Trace 14.1 1.3

– 16.9

1.3 0.3 13.8 5.0

6.5 0.3 3.3 0.4

10.0 3.4

9.7 7.0 2.8 1.3

0.7 0.1 47.9 0.9

1.8 34.8

1.7 0.3 48.5 8.0

Gadoleic

(20:1)

1.0 0.2

0.13 12.09 0.09 6.75 2.76 0.38 47.61 (n 9) þ 1.67 (n 7) 0.57

Mitei et al. (2008)

Average

(continued)

TABLE 5.3

(continued) Literature reviewed

Bousquet (1982)

Bower et al. (1988)


Francis and Campbell (2003)

One batch

x

One mixed batch of samples harvested in Botswana during 1978–1980

One batch harvested in Botswana

x

1 kg harvested in Botswana

Average

1.8 0.2 24.6 0.4 Trace

26.3

19.2 9.5

26.43

23.5

2.63 0.01 23.40 0.42

2.1 22

2.3 2.1

2.0 1.5

Engelter and Wehmeyer (1970)

Erucic Linoleic (no common name) a- and g-linolenic Arachidonic Others

(22:1) (18:2) (16:2) (18:3) (20:4) –

1.2 1.0

2.7 1.54

The major fatty acids in the morama bean are in bold. The last column gives the average fatty acid composition (normalized to 100%).

Mitei et al. (2008)

2.2 2.0 1.3

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(100 mg/g). The morama seed oil 4-desmethylsterols consisted of b-sitosterol (84.50 mg/g), stigmasterol (32.07 mg/g), campesterol (21.92 mg/g), 22-dihydrospinasterol (5.26 mg/g), D5-avenasterol (4.45 mg/g), and D7-avenasterol (1.58 mg/g). Morama oil also contained significant amounts of 4,4-dimethylsterols and 4-monomethylsterols (16% in total) as well as unidentified sterols (7%).

E. Protein Keegan and Van Staden (1981), Bower et al. (1988), Amarteifio and Moholo (1998), and Holse et al. (2010) have all investigated the protein content of morama beans and it ranges from 29% to 39% on a dry matter basis. The protein content is comparable to or higher than most other legume seeds such as dry peas, chick peas, lentils, kidney beans, cowpea, and lupin with contents between 20% and 40% dm (Gueguen, 1983; Nassar et al., 2008) and equals that of soybeans (33–46% dm) (Belitz et al., 2004; Gueguen, 1983; Hymowitz et al., 1972). The high content of protein in morama beans gives the beans a great potential both as a nutritive food itself and as a protein-rich food ingredient for supplementation in other food products. A significantly higher amount of protein is found in beans from South Africa compared to beans from Botswana. The amount of protein in beans from Namibia is between the contents from the two other countries (Holse et al., 2010). The variation in protein content might be due to different concentration of nitrogen in the soils. Bower et al. (1988) found that globulins are the most abundant (53%) protein constituents in morama beans. The beans furthermore consist of albumins (23.3%), prolamins (15.5%), alkali soluble glutelins (7.7%), and acid-soluble glutelins (0.5%).

1. Amino acids The amino acid composition of proteins in morama beans is largely dominated by glutamic and aspartic acid as well as tyrosine (Bousquet, 1982; Bower et al., 1988; Dubois et al., 1995; Maruatona et al., 2010). All the essential amino acids are present in the beans. However, the sulfur-containing amino acids, methionine and cysteine, are present in quite low amounts. In general, morama bean protein appears to be comparable to soybean in content of essential amino acid, with methionine as the limiting amino acid (Bower et al., 1988). Morama protein quality is generally superior to most common legume crops and comparable to that of casein or milk protein (Powell, 1987). A literature review of the amino acid composition of morama bean proteins is given in Table 5.4.

202


TABLE 5.4 Amino acid composition (% of total amino acids, normalized to 100%) of the morama bean Amino acid

Bower et al. (1988) Dubois et al. (1995)a Maruatona et al. (2010)

Essential Arginine Cysteine Histidine Isoleucine Leucine Lysine Methionine Phenylalanine Threonine Tyrosine Tryptophan Valine

6.3 0.8 2.4 4.0 5.9 5.5 0.8 4.8 3.0 11.6 1.7 4.4

7.8 1.0 2.8 3.3 5.8 5.5 0.3 4.2 3.3 11.9 ND 3.9

7.3 0.1 2.8 4.5 6.6 5.7 0.8 4.8 3.1 11.2 ND 4.9

Nonessential Alanine Aspartic acid Glutamic acid Glycine Proline Serine Ammonia

3.1 10.8 15.6 5.7 6.9 5.3 1.3

3.1 10.5 17.7 6.0 7.6 5.4 ND

3.6 9.3 15.5 6.3 7.8 5.6 ND

Amino acids present in the highest amounts in morama beans are highlighted in bold. a T. fassoglense.

F. Carbohydrate/dietary fiber Wehmeyer et al. (1969), Bower et al. (1988), and Amarteifio and Moholo (1998) reported the content of carbohydrate to be 23%, 24%, and 19%, respectively. These values have been obtained indirectly as the difference between 100% and the content of proteins, lipids, and minerals. Holse et al. (2010) found that the content of carbohydrate was dominated by total dietary fiber as it varied between 18.7% and 26.8% dm (Table 5.2). The majority of the dietary fiber is insoluble as only about 4% of the dietary fibers are soluble. Comparing the content of total dietary fiber of morama bean with the content of peanut (9% dm) and soybean (10% dm) (U.S. Department of Agriculture, 2007), it appears that the morama bean has a considerably higher level of indigestible carbohydrates. Holse et al. (2010) also reported a very low starch content, which is in contrast to other legumes, in which starch is usually the most abundant carbohydrate

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(22–45%) (Hoover and Sosulski, 1991). Hence, the morama bean is quite unique due to its high content of dietary fiber and low content of starch.

G. Minor chemical components 1. Vitamins

Wehmeyer et al. (1969) published results on the content of B vitamins (thiamine, riboflavin, and nicotinic acid), vitamin C, and b-carotene and found that the morama bean is a good source of both B vitamins and vitamin C, but a poor source of b-carotene. Holse et al. (2010) investigated the content of the eight vitamin E isomers and found that the vitamin E composition in morama beans is dominated by g-tocopherol with 59–234 mg/g, followed by a- and b-tocopherols with 14–48 mg/g and 1.1–3.3 mg/g, respectively. Furthermore, traces of d-tocopherol as well as b- and g-tocotrienols were present in some samples. The remaining two tocotrienols (a- and d-) were not present in the beans. The presence of a-, b-, and g-tocopherols in the morama bean was also found by Mitei et al. (2009) who examined morama oil and by Dubois et al. (1995) who examined two samples of T. fassoglense. The vitamin E components have numerous health benefits. The high content of g-tocopherol is of particular biological relevance as it has shown potential anticarcinogenic and anti-inflammatory activities (Brigelius-Flohe´, 2006; Saldeen and Saldeen, 2005).

2. Minerals Morama is a good source of macroelements such as potassium, phosphate, magnesium, and calcium as well as the trace elements zinc, iron, and copper (Amarteifio and Moholo, 1998; Holse et al., 2010; Wehmeyer et al., 1969), as summarized in Table 5.5. The relatively high level of minerals in the morama bean is quite surprising when taking into account the poor soils in which they grow. The beans actually have a level of macroelements (K, P, Mg, and Ca) similar to that of peanuts and approaching that of soybeans (U.S. Department of Agriculture, 2007) and furthermore contain several trace minerals (Zn, Fe, Cu, and Mn) in amounts that match the content of soybeans and peanuts. Especially the high contents of zinc and iron are important, since large percentages of the population in Africa are deficient or at risk of inadequate intake of zinc and iron (Hotz and Brown, 2004; UNICEF and Micronutrient Initiative, 2004).

3. Phytoestrogens Phytoestrogens are naturally occurring plant-derived phytochemicals, which are precursors of hormone-like compounds found in mammalian systems. They possess a wide range of health promoting effects in

204


TABLE 5.5 Concentrations of minerals in the morama bean (mg/100 g) given as mean SD or range

K P Mg S Ca Al Zn Fe Na Mn B Cu

Amarteifio and Moholo (1998)

Holse et al. (2010)

776 12.8 397 19.7

757–1316 334–554 248–374 191–236 119–133 0.5–8.2 3.3–3.8 1.3–3.7 0.2–2.9 1.5–2.6 1.8–2.9 0.6–1.6

152 4.7 4.9 0.17 4.1 0.29

humans, including potential anticarcinogenic activity (Branca and Lorenzetti, 2005; Kwiatkowska, 2007). The morama bean is an excellent source of lignans (Holse et al., 2010). The content of secoisolariciresinol in morama beans (305–406 mg/100 g) is higher than that of soybean (13–273 mg/100 g) and peanut (333 mg/100 g) (Mazur et al., 1998). Likewise, the level of lariciresinol in morama beans (614–825 mg/100 g) is higher than that of soybeans (287 mg/100 g), while the level of pinoresinol in morama beans (21–23 mg/100 g) is lower than the level in soybeans (446 mg/100 g) (Penalvo et al., 2004). These lignans can be converted by intestinal bacteria into enterolignans, which possess biological activities such as (anti) estrogenic and antioxidant action. Therefore, they may reduce the risk of certain types of cancers as well as cardiovascular diseases (Adlercreutz, 2007). Contrary to soybeans which are rich in isoflavones (Mazur et al., 1998), no isoflavones were found in the morama bean samples. The level of secoisolariciresinol and lariciresinol is retained during roasting, which is an important characteristic since the beans usually are eaten roasted (Holse et al., 2010).

H. Antinutritional factors/toxic constituents 1. Trypsin inhibitor A relatively strong trypsin inhibitor is found in raw morama beans (Bower et al., 1988; Elfant et al., 1985; Maruatona et al., 2010; Powell, 1987). Bower et al. (1988) reported that trypsin inhibitor was about 20%

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205

of the total morama seed protein and that the trypsin inhibitor activity can be destroyed by heat. Baking the defatted meal at 140 C for 30 min decreased the activity by 70%.

2. Cyanogenic glycosides Morama bean samples have been investigated for their potential cyanogenesis, which is the ability of some plants, including grain legumes and nuts, to release cyanide during degradation of cyanogenic glycosides. Upon tissue disruption, the cyanogenic glycosides may be enzymatically hydrolyzed to the respiratory poison hydrogen cyanide (Breiteneder and Radauer, 2004; Poulton, 1990). The analyses have shown that morama beans seem to contain neither cyanogenic glycosides nor the enzymes that break these down to hydrogen cyanide (Holse et al., 2010). The fact that morama beans (T. esculentum) do not seem to be cyanogenic is in accordance with results from Dubois et al. (1995) who found that T. fassoglense seeds did not contain any detectable amounts of cyanogenic glycosides.

3. Allergens Lupin and peanut are potent allergens and especially, peanut is one of the most prevalent allergenic foods in the Western World. Humans suffering from allergic reactions to these legumes may also get an allergic reaction to other legumes with proteins that are able to cross-react with proteins of lupin or peanut. Consequently, the IgE antibodies that have been used to test morama beans are antibodies that react with the major allergens in lupin and peanut. The analyses revealed no presence of proteins crossreacting with the allergenic proteins from lupin and peanut. Hence, there is no reason to suspect that people suffering from lupin or peanut allergy will suffer from allergic reactions when consuming products containing morama beans (Holse et al., 2010).

I. Phenolic compounds Various excellent reviews are available on phenolic compounds, their chemistry and analysis, content in foods and nutritional significance (Bravo, 1998; Dykes and Rooney, 2006; Manach et al., 2004; Naczk and Shahidi, 2006; Robbins, 2003). From a nutritional perspective, phenolic compounds (especially tannins) are regarded as antinutritional factors due to their ability to form complexes with dietary proteins and minerals and digestive enzymes (Bravo, 1998). However, lately there has been increasing focus on the positive aspects of phenolics due to their ability to act as antioxidants which may offer potential health benefits such as prevention of diseases such as cancer and cardiovascular disease.

206


TABLE 5.6 Total phenolic content and antioxidant activity of morama bean seed coat and cotyledon Sample

Total phenolics (mg CEa/100 mg)

Antioxidant activity (mM Trolox equivalents/100 mg)

Morama bean Seed coat Cotyledon

24.6 (0.5)b 2.8 (0.1)

409 (5) 8 (1)

Source: van Zyl, 2007. a CE—catechin equivalents. b Standard deviation in parentheses.

Table 5.6 shows the total phenolic content (determined using the Folin–Ciocalteu assay) and antioxidant activity (ABTS radical scavenging capacity) of extracts from morama bean seed coat and cotyledon prepared with acidified methanol. It is clear that morama bean seed coat and cotyledon have appreciable levels of total phenolics and antioxidant activity. These phenolics are concentrated in the seed coat. It has been reported that the morama bean cotyledon contains high levels of the amino acid tyrosine (Maruatona et al., 2010) which is phenolic in nature and can therefore contribute to the total phenolic content of the cotyledon as determined with the Folin–Ciocalteu assay. Reversed-phase high performance liquid chromatography has been used to determine the types and levels of phenolic acids and flavonoids in morama bean seed coat and cotyledon (Table 5.7). The seed coat has a higher concentration of total phenolic acids and total flavonoids than the cotyledon. On the whole, flavonoids constitute the major class of phenolics in the morama bean. For the phenolic acids, protocatechuic acid and sinapic acid are present in highest concentrations in the cotyledon and seed coat, respectively. For the flavonoids, fisetin and myricetin are present in highest concentrations in the cotyledon and seed coat, respectively. The high levels of myricetin observed may have important contributions to antioxidant activity of the seed coat and may offer potential health benefits. Myricetin, a flavonol, contains structural features that are important for high antioxidant activity of flavonoids. These are an ortho-dihydroxy group on the B ring, a 2,3-double bond on the C ring, and a C4 keto group on the C ring.

J. Potential health benefits and negative effects associated with components of morama beans Tables 5.8 and 5.9 below summarize the potential health benefits and negative effects, respectively, associated with the components of morama beans.

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TABLE 5.7 Types and levels (mg/100 g sample) of phenolic acids and flavonoids in acidified methanol extracts of morama bean seed coat and cotyledon determined using reversed-phase HPLCa (adapted from van Zyl (2007)) Phenolic compound

Seed coat

Cotyledon

Hydroxybenzoates Gallic acid Protocatechuic acid p-Hydroxybenzoic acid Vanillic acid Syringic acid

192 (3)b 69 (2) 157 (7) 388 (53) NDc

23 (1) 153 (9) ND 21 (1) 13 (0)

Hydroxycinnamates Caffeic acid p-Coumaric acid Sinapic acid Ferulic acid Total phenolic acids

293 (45) 119 (17) 543 (80) 277 (37) 2038

39 (0) ND 59 (19) 9 (1) 317

Flavonoids Catechin Rutin Naringin Hesperidin Fisetin Myricetin Quercetin Naringenin Kaempferol Total flavonoids Total

ND 602 (73) 492 (120) 434 (51) 481 (101) 10582 (1067) 331 (21) 18 (1) 98 (9) 13,038 15,076

12 (1) 26 (1) 10 (0) 9 (1) 39 (0) 19 (2) 24 (0) ND 20 (0) 159 476

Column: YMC-Pack ODS AM-303 (250 mm 4.6 mm, 5 mm) (Waters, MA, USA). Solvents: A—0.1% acetic acid in water; B—0.1% acetic acid in acetonitrile. Detector: UV–Vis at 280 nm. Flow rate: 0.8 ml/min. Injection volume: 20 ml. a HPLC conditions. b Standard deviation in parentheses. c ND—not detected.

K. Conclusions While an analysis of individual components in morama beans (above) can give a good lead to their possible beneficial and negative effects, it is the sum total, antagonistic or synergistic effects of the various component combinations which determine their overall effect on health. It is the high content of amino acids, and some fatty acids, complemented with minor

TABLE 5.8

Potential health benefits associated with the components of Tylosema esculentum (morama beans)

Component

Fatty acids Alpha-linoleic acid

Linoleic acid

Myristic acid

Oleic acid Stearic acid Erucic acid Palmitoleic acid Arachidonic acid

Effect

Reference

Inhibits the growth of breast cancer cells Reduces platelet stickiness and protects against coronary heart disease Deficiency can lead to lower levels of docosahexaenoic acid, leading to abnormal brain and eye function Optimizes nutrition, growth, and feeding efficiency of children suffering from cystic fibrosis Attenuates diabetic complications Deficiency can lead to cancer and immune system dysfunction Stabilizes proteins used in the immune system Promotes membrane binding that is essential for proper protein localization or biological function May hinder progression of adrenoleukodystrophy Reduces low-density lipoprotein in the blood Reduces platelet stickiness and protects against coronary heart disease Possibly a signaling molecule which can help fight weight gain; utilized by the key enzymes that control fat oxidation, at extraordinarily high rates Component of phospholipids of membranes of the body’s cells, and is abundant in the brain and muscles Necessary for the repair and growth of skeletal muscle tissue

Kim et al. (2009) Rastogi et al. (2004) Neuringer et al. (1986) van Egmond et al. (1996)

Wright et al. (2010) Wright et al. (2010) Lakshmikuttyamma et al. (2008) Rizzo et al. (1986) Hunter et al. (2009) Rastogi et al. (2004) Power et al. (1997), Zelkowitz (2008)

Wang et al. (2006)

Important for neurological health and protection of the brain from Darios and Davletov (2006) oxidative stress Important for growth and repair of neurons. Its disturbed Rapoport (2008) metabolism is associated with neurological disorders such as Alzheimer’s disease and Bipolar Disorder Amino acids Cysteine

Histidine

Aspartic acid

Reduces risk of breast cancer Precursor to glutathione, a powerful antioxidant and also used to cleanse the body of toxins N-acetyl-cysteine, a synthetic precursor of cysteine, is commonly used as an antidote to paracetamol-induced liver damage N-acetyl-cysteine suppresses induction of autophagy by bacterial endotoxin lipopolysaccharide, hydrogen peroxide, and nitric oxide Metabolic by-products, for example, histamines are involved in regulation of breast cancer growth and progression Mediates clearance of necrotic material Inhibits formation of insoluble immune complexes and enhances their ability to activate complement, resulting in faster clearance Assists in growth and repair of body tissues, and to maintain myelin sheaths that protect nerve cells Modulates physiological processes such as cell adhesion and migration, fibrinolysis, and coagulation Induces apoptosis in colon cancer cells

AACR (2003), Agarwal et al. (2004), Lin et al. (2010), Sun et al. (2010) Kortsalioudaki et al. (2008) Yuan et al. (2009)

Medina et al. (2008) Poon et al. (2010) Manderson et al. (2009) Blank and Shoenfeld (2008) Blank and Shoenfeld (2008) Dadachova (2010), Dia and Mejia (2010) (continued)

TABLE 5.8

(continued)

Component

Effect

Reference

Ariyannur et al. (2010)

Threonine

Needed for stamina, brain, and neural health Assists the liver by removing excess ammonia and other toxins from the bloodstream Promotes thymus growth and activity

Arginine

Lysine

Vitamins and Health Supplements Guide (2005) Blank and Shoenfeld (2008)

Assists in growth and repair of body tissues and maintenance of myelin sheaths protecting nerve cells Necessary for formation of tooth enamel and elastin and collagen Vitamins and Health Supplements Guide (2005) Has a mild glucose-sparing effect and is useful in the stabilization of blood sugar because it can be converted into glucose in the liver by the process of gluconeogenesis Inhibits herpes virus Maggs et al. (2000) Plays an important role in the synthesis of various protein Garcia-Miranda et al. (2010), Brixel molecules (e.g., creatine and insulin) et al. (2010) Important in maintaining erection and treatment of sterility in men Gur et al. (2007), Mathers et al. (2009) Important in reduction of alcohol toxicity effects and wound Debats et al. (2009) healing Reduces accumulation of compounds such as ammonia, lactate, Liu et al. (2009) and other by-products of physical exercise Inhibits platelet aggregation and can also decrease blood pressure Marfella et al. (1996) Has antioxidant properties Kizhakekuttu and Widlansky (2010) Inhibits herpes virus Thorne Research (2007) Lowers cholesterol at a certain ratio with arginine Sanchez et al. (1988), Spielmann et al. (2008)

Methionine

Useful in the treatment of Parkinson’s disease Has significant positive effects on sufferers of chronic fatigue, muscle, and joint pains Helps in breaking down fatty acids and prevents buildup of fat on the artery walls; also involved in the normal detoxification of liver Functions as an antioxidant by supplying sulfur for inactivating free radicals Phenylalanine Decreases pain and inflammation in the treatment of rheumatoid arthritis and osteoarthritis Improves speech and rigidity in the treatment of Parkinson’s disease Useful in the treatment of vitiligo, a skin pigment disorder Tyrosine Improves cognitive function; helps reduce the effects of stress and fatigue on tasks requiring concentration and mental sharpness Tryptophan May enhance relaxation and sleep, causes arterial vessel relaxation, and controls blood pressure Relieves minor premenstrual symptoms Soothes nerves and anxiety Reduces carbohydrate cravings Other components Vitamins Anticancer effects (B vitamins) Important for immune status Vitamins B12 and B6 have preventive effects against age-related chronic diseases, including cardiovascular disease (CVD), stroke, and cognitive decline

Leong et al. (2009) Rutjes et al. (2009) Caballero et al. (2010)

Kim et al. (2010) Choi et al. (2008) Skibo et al. (2010) Szczurko and Boon (2008) Govoni et al. (2010) Wang et al. (2010), Sayegh et al. (1995) Freeman (2004) Wang et al. (2010) Ashley et al. (1985) Buhr and Bales (2010), Huang et al. (2006) Maggini et al. (2007) Buhr and Bales (2010), Huang et al. (2006) (continued)

TABLE 5.8

(continued)

Component

Dietary fiber

Effect

Reference

Can slow absorption of refined carbohydrates and lower glycemic Preuss (2009) index of foods, resulting in reduced effects of diabetes due to lower availability of glucose Phytoestrogens Anticarcinogenic activity Branca and Lorenzetti (2005), Kwiatkowska (2007) Cholesterol lowering effect Jenkins et al. (2003), Rosell et al. (2004) Lignans Anticancer effects Adolphe et al. (2010), TorresSanchez et al. (2008), Fini et al. (2008) Gallic acid Inhibits herpes virus Kratz et al. (2008) Anticancer effects

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TABLE 5.9 Potential negative effects associated with the components of morama beans Component

Fatty acids Erucic acid

Palmitic acid

Amino acids Glutamic acid

Effect

Reference

Can cause thrombocytopenia (the presence of relatively few platelets in the blood) Increases the risk of developing cardiovascular diseases

Crowther et al. (1995); FSA (2004) WHO (2003); Li and Renier (2008)

Pulce et al. (1992) Causes numbness at the back of the neck, gradually radiating to both arms and the back, general weakness, and palpitations (Chinese restaurant syndrome)

Other components Yamanishi et al. Protease Potent allergens. Fortunately, inhibitors these inhibitors can be (2003); destroyed by heat, which tends Shimakura et al. (e.g., Kunitzto reverse their antinutrient (2004) type elastase effects inhibitor)

components as vitamin E, important minerals, lignans, and the absence of allergens and cyanogenic glycosides as potential health risks, which make morama beans an excellent food source with potential health benefits. The relatively high content of amino acids in morama beans [tyrosine ( 11%), aspartic acid (9–10.5%), and glutamic acid (15–17%)] and fatty acids, such as oleic acid (55–80% of fatty acid content), linoleic acid (22–26% of fatty acid content), and palmitic acid (12–17% of fatty acid content), also makes these components significant in human health. More studies are urged on the specific effects of the components of morama beans.

IV. FOOD-PROCESSING APPLICATIONS AND UTILIZATION The morama bean produces edible seeds and tubers that traditionally are eaten boiled or roasted. The tuber (2 years or younger) can be eaten raw, boiled, or baked, while older tubers are used as a source of water, since it

214


TABLE 5.10 Potential uses of the morama bean Source: Jackson (2010) Food

Description

Morama oil

Solvent extracted/ Cooking oil, mechanically pressed of salads, whole morama beans cosmetic oil Milling and grinding of whole Confectionery morama beans to a paste Milling of morama beans to Supplement to fine particles; may include staple cereal defatting first to produce a flours high protein flour Milled flour extruded into Snacks, breakfast various shapes cereals, meat analogues Whole morama dry roasted Roasted nuts, whole or chopped Morama and wheat flour and Baked snack other ingredients baked into foods snack foods Morama flour and other spices Meat analogue baked as a meat-like loaf Morama powder added to mix Frozen snack food for ice-cream Whole morama beans Savory cooked thermally processed in a beans sauce

Morama butter Morama flour

Morama texturized foods Morama nuts

Morama biscuits, cookies, muffins, bread, cake Morama roast Morama ice-cream Canned morama beans in tomato sauce

Uses

contains up to about 90% of water by weight. The roasted bean has a delicious nutty flavor and is cooked with cornmeal to prepare porridge or is ground into a powder that is boiled in water to produce a cocoa-like beverage. More recent research has shown that the bean can be utilized as flour, butter, oil, milk, and meat analogue products as well as a range of snack foods (Table 5.10).

A. Morama milk Morama milk is a creamy white water extract of morama beans that closely resembles dairy milk or soymilk in appearance and composition. The first report of the processing of morama beans into milk has been described by researchers in Botswana (Mpotokwane et al., 2007).

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215

Although not available commercially, morama milk can be consumed as a refreshing and nutritious beverage similar to dairy milk or soymilk. It can be used as an infant supplement providing additional protein, energy, and other nutrients to vulnerable populations where the supply of dairy milk is inadequate. It can also be an intermediate material for other applications such as yoghurt. The chemical composition of morama milk is indicated in Table 5.11 and it is compared with soymilk and dairy milk (Mpotokwane et al., 2007). Morama milk has 6% total solids compared with 10% for soymilk and 12% for dairy milk. The morama milk solids include protein, which is about 1.5%, fat is 3.1%, carbohydrates is 1.1%, and ash is 0.2%. It has high levels of sodium (47.9 mg/100 g) and iron (3.7 mg/100 g) compared with the other two milks but much lower calcium (6.8 mg/1000 g). The proportion of unsaturated fatty acid in morama milk is significantly higher than in soymilk and dairy milk ( Jackson et al., 2009). As with other legumes, the sulfur amino acids, methionine, and cysteine, were limiting in morama milk, which contains 0.28 g/100 g methionine and 0.02 g/100 g cysteine (Table 5.12). Tryptophan was not detected. Of the other essential and semiessential amino acids, leucine, tyrosine, arginine, and lysine were the major amino acids in morama bean milk. Others that were also found in high levels include glutamic acid, aspartic acid, and proline. These findings are consistent with the literature on the amino acid composition of morama bean, which is similar to other legumes like the soybean (Bower et al., 1988; Mmonatau, 2005; Ripperger-Suhler and Longenecker, 1982). Morama milk therefore replicates the amino acid composition of the bean. TABLE 5.11 Composition of morama milk, soymilk, and dairy milk Source: Data adapted from Mpotokwane et al. (2007), Jackson et al. (2009), and Liu (1997) Component (/100 g)

Morama milk

Soymilk

Dairy milk

Calorie (kcal) Protein (g) Ash (g) Minerals (mg) Calcium Sodium Iron Moisture (g) Carbohydrate (g) Fat (g) Saturated fatty acid (%) Unsaturated fatty acid (%)

37.9 1.5 0.2

44.0 3.6 0.5

59.0 2.9 0.7

6.8 47.9 3.7 94.1 1.1 3.1 31.5 68.5

15 2 1.2 90.8 2.9 2.0 40–48 52–60

100 36 0.1 88.6 4.5 3.3 60–70 30–40

216


TABLE 5.12 Amino acid composition (g/100 g milk dry matter basis) of morama bean milk compared with a commercial soybean milk Source: Data adapted from Jackson et al. (2009) and Liu (1997) Amino acid

Morama bean

Morama milk

Soy milk

Aspartic Glutamic Histidine Arginine Lysine Serine Threonine Tyrosine Cysteine Alanine Proline Valine Methionine Isoleucine Phenylalanine Glycine Leucine

10.3 14.9 2.3 6.0 5.2 5.1 2.9 11.1 0.8 3.0 6.6 4.2 0.76–1.32 3.8 4.6 5.4 5.6

2.6 0.7 3.9 0.6 0.8 0.2 2.0 0.6 1.6 0.3 1.6 0.1 1.0 0.03 2.8 0.4 0.02 0.0 1.0 0.2 2.1 0.5 1.4 0.1 0.3 0.2 1.3 0.1 1.1 0.3 1.7 0.3 2.3 0.6

11.8 19.0 2.6 7.8 6.3 5.3 3.9 4.0 4.9 4.4 5.0 4.9 1.4 5.1 5.1 4.3 8.3

The processing of morama milk generally involves a thermal treatment such as blanching and roasting of the beans, cracking, milling, suspending in water, boiling, and filtration to obtain a milk-like phase. Blanching and roasting techniques are believed to improve the flavor by removing the bitter flavor components and aiding in the development of desirable nutty flavor components (Iwuoha and Umunnakwe, 1997). A small-scale method for processing morama beans into milk is outlined in Fig. 5.3. Morama beans were cracked, preprocessed by blanching in a bicarbonate of soda solution, drained, rinsed, and then ground with hot water to form a slurry. The slurry was heat-processed and then filtered successively. The residue, known as morama pulp, was separated and the resultant liquid extract was heated, homogenized, bottled, and pasteurized before serving. Other preprocessing treatments, including (i) soaking then water blanching, (ii) soaking only, (iii) water blanching, (iv) roasting shelled, (v) roasting unshelled, (vi) salt water blanching, and (vii) defatted shelled, were also assessed to process morama milk. However, these were not considered acceptable as the milks produced were too watery, too dark, too bitter, or were not creamy enough (Mpotokwane et al., 2007). While

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217

Shelled beans

Bicarbonate soda blanch

Preprocessing in blender

Pressure-cooking in SoyCow® machine

Filtration

Homogenization

Bottling/pasteurization

Storage

FIGURE 5.3 Flow diagram of processing of morama milk (adapted from Mpotokwane et al., 2007).

the bicarbonate soda blanch pretreatment method was suitable for smallscale processing of morama milk, it does produce milk with a characteristic aftertaste. For consumers who are not accustomed to the flavor and taste, it could be described as an undesirable ‘‘bitter’’ taste. Consequently, unless such flavor and taste are reduced or masked, morama milk may not be readily accepted by some consumers in the population. It can be flavored and colored with natural and artificial flavorants and colors such as strawberry to produce a more acceptable taste that consumers are used to. Fermented morama milk products such as morama yoghurt have been reported by Phuthego et al. (2009). Morama milk yoghurt like soymilk yoghurt, provide economic and nutritional benefits, because they are likely to have higher protein levels at comparable or lower cost than regular fermented milk products (Karleskind et al., 1991). This is because

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legume milks are relatively easy to prepare and serve as a low-cost protein beverage in countries where commercial dairies are not adequate. Morama milk yoghurt is processed similar to the production of yoghurt from soybeans by fermenting morama milk with lactic acid bacteria (Kumar and Mishra, 2004; Phuthego et al., 2009; Yazici et al., 1997).

B. Morama oil Morama oil has been described as resembling almond oil, and being suitable for domestic purposes, having a pleasant nutty flavor, albeit with a slightly bitter taste (FAO, 2010). Chemically, its composition is reported to be similar to that of olive oil, with the exception of the long chain fatty acids (Ketshajwang et al., 1998; Mitei et al., 2008; Yeboah and Moshoeshoe, 2008). It also contains other important compounds, including 4-desmethylsterols (75%) and significant amounts of 4,4-dimethylsterols and 4-monomethylsterols (15.72% total), which may potentially impact its antioxidant potential favorably (Mitei et al., 2009). The oil potentially has both food and nonfood uses, with the latter primarily being in the processing of cosmetics. Extraction can be done using an oil press, or an organic solvent such as hexane as with other oil seeds. A higher yield of oil is obtained with solvent extraction compared to pressing, but there are inherent health risks with solvent extraction; if not, all the solvent is ultimately evaporated off. Ketshajwang et al. (1998) reported extraction rates as high as 48.2% using hexane extraction, while Yeboah and Moshoeshoe (2008) using a 30 kg/cm2 hydraulic press reported yields of 20%. Therefore, oil pressing may provide a safer and more feasible option particularly for small-scale processors. Oil pressing involves either coldpressing or warm-pressing; cold-pressing does not involve the use of heat on the seeds that are being pressed, while warm-pressing uses heat application (Fig. 5.4). More residual oil in the press cake is thus obtained from coldpressing; warm-pressing gives more yield, but compromises oil quality. Tlhong et al. (2009) described the sensory attributes of morama oil as fresh, thick, creamy, and smooth with a grassy and earthy aroma and raw nutty flavor and aftertaste. Compared to both sunflower and olive oils, potato chips fried in morama oil were rated as more acceptable by consumers (Tlhong et al., 2009). Therefore, as a cooking oil, morama oil has great potential in terms of consumer acceptability. However, its acceptability as a salad oil remains unexplored.

C. Protein-rich morama flours The preparation of protein-rich morama flours follows a number of simple unit operations. These include heating, dehulling, defatting (in some cases), and milling (Fig. 5.5). These operations may impact either

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Sorting of morama (in shell)

Shelling

Sorting

Grinding/size reduction

Weighing

Compressing

Oil extraction

Oil

Press cake (by-product) Mixing and compressing

Extraction

Oil

Press cake (by-product)

Clarification by sedimentation for 48 h Packaging and storage

FIGURE 5.4 Process flow diagram for morama oil processing (adapted from Yeboah and Moshoeshoe, 2008).

positively or negatively on nutritional, functional, sensory, and phytochemical quality of resultant flours. The proximate composition of full fat and partially defatted morama flours prepared from heated and unheated morama beans is provided in Table 5.13. Full fat morama flours had similar protein and fat contents to those reported by Amarteifio and Moholo (1998) and Mmonatau (2005). Defatting is required when protein-rich, stable flours are required. Although full fat flours are deemed to be more energy dense than fully

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Morama beans Sorting and cleaning Dry heat treatment/roasting (150 ºC for 20 min) Hulls rich in phenolic compounds

Dehulling Coarse grinding Full fat meal

Fine milling

Solvent oil extraction Full fat flour

Crude oil

Defatted morama meal Fine milling

Defatted morama flour

FIGURE 5.5 Schematic of processing procedure for morama flour (adapted from Maruatona, 2008).

TABLE 5.13 Proximate composition of morama flour (g/100 g) dry basis (adapted from Kayitesi, 2009) Flour

Protein

Full fat flour from unheated beans (FUH) Full fat flour from heated beans (FH) Partially defatted flour from unheated beans (PDUH) Partially defatted flour from heated morama beans (PDH)

34.3a (0.5) 38.1c (0.0) 2.7a (0.0) 24.8b (0.1)

a, b, c, d, e

Fat

Ash

Carbohydrates

34.6a (0.7) 39.1d (0.0) 2.9a (0.0) 23.3a (0.0) 50.0b (0.5) 15.3b (0.1) 4.2b(0.1)

30.5c (0.0)

53.2c (1.5) 11.2a (0.0) 4.7c (0.1) 31.2d (0.0)

Mean values within a column with different letters differ significantly (p < 0.05). Standard deviations are given in parentheses.

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or partially defatted flours, they have lower protein contents and are prone to hydrolytic and oxidative rancidity. Defatting significantly increases the protein contents of resulting flours. Dry heating also results in slightly higher protein contents. Similar findings were reported by Maruatona et al. (2010) for defatted morama flour. This is possibly explained by the disruption of lipid bodies of the morama beans upon heating, allowing the oil to be more readily expelled during the defatting step. Dry heating or roasting is able to effectively inactivate heat labile trypsin inhibitors. Maruatona et al. (2010) reported that dry heating of morama beans at 150 C for 20 min reduced the trypsin inhibitor activity in its defatted flour from 251 TUI/ml extract to 3 TUI/ml extract. Although in vitro digestibility was improved by 2.7%, the lysine content was reduced by 11.9% (Maruatona et al., 2010). Slight reductions in lysine upon roasting or dry heating of morama beans have also been reported by Mmonatau (2005) and Kayitesi (2009) (Table 5.14). This may be attributed to the Maillard reaction. TABLE 5.14 Amino acid composition of morama flours g/100 g flour (dry matter basis) (adapted from Kayitesi (2009) Amino acid

Partially defatted flour from Partially defatted flour from unheated morama beans (PDUH) heated morama beans (PDH)

Essential amino acids Histidine Isoleucine Leucine Lysine Methionine Phenylalanine Threonine Valine Nonessential amino acids Alanine Arginine Aspartic acid Cysteine Glutamic acid Glycine Proline Serine Tyrosine a, b

1.3a 2.2a 3.0a 2.8b 0.4a 2.1a 1.5a 2.2a

1.4a 2.9a 3.3a 2.7a 0.4a 2.3a 1.6a 2.4b

1.6a 3.3a 4.0a 0.1a 7.7a 3.0a 3.6a 2.6a 6.0a

1.7a 3.5b 4.4b 0.1a 8.3b 3.2b 4.0b 2.9b 6.6b

Mean values within a row with different letters differ significantly (p < 0.05), PDUH ¼ partially defatted morama flour from unheated beans, PDH ¼ partially defatted flour from heated beans (150 C for 20 min.

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Sulfur amino acids, methionine and cysteine, are limiting in partially defatted flours prepared from heated and unheated morama beans, containing 0.4 g methionine/100 g flours and 0.1 g cysteine/100 g flours. The major amino acids in morama flours are glutamic acid, aspartic acid, as well as tyrosine. This is in agreement with amino acid composition of morama beans reported by various researchers (Bousquet, 1982; Bower et al., 1988; Dubois et al., 1995; Maruatona et al., 2010). Morama bean flours seem to be comparable in terms of essential amino acids composition to commercial soybean flours (Maruatona et al., 2010). The use of dry heating processes in the preparation of morama flours can also affect their functional and sensory properties. Heating of morama beans prior to decortication reduced protein solubility and emulsifying capacity of resulting defatted morama bean flours (protein contents: 53% and 56% for unheated and heated samples; fat contents: 7% and 1.9% for unheated and heated samples) but improved water absorption capacity significantly (Maruatona et al., 2010). Jideani et al. (2009) also found that roasting increased the water absorption capacity of full-fat morama flour (protein content: 32–33%; fat content: 36–39%) but reported increased protein solubility and emulsifying activity upon heating. Differences in results can probably be attributed to the fact that Maruatona et al. (2010) worked with defatted flours, whereas Jideani et al. (2009) worked with full fat flours. Functional properties of morama flours and its potential application in food systems are summarized in Fig. 5.6. Kayitesi (2009) studied the potential use of morama flours to improve the nutritional quality of sorghum porridge, a staple food to millions in Africa. Compositing sorghum porridge with morama bean flours significantly increased protein and fat contents in porridges. Lysine content of the composite porridges was 3–4 times higher than that of sorghum porridge alone. The total phenolic content and antioxidant activity of composite porridges were also significantly higher than that of sorghum porridge. Porridge composited with full fat morama flour from heated beans was found to be as acceptable as sorghum porridge, but more acceptable than that of composites using defatted flour. A bitter taste and aftertaste were perceived in composite porridges from defatted flours, whereas composite porridges from full fat morama flours were described as buttery/creamy. Existing literature demonstrates several possible morama bean-processing applications, including nutritious milk beverage and yoghurt, high value oil, and protein-rich flours. The physico-chemical and sensory properties suggest the high quality and acceptability of the products. However, research on the market potential of these products is also required to determine their commercial feasibility for small-scale processors in Africa.

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Morama beans

No heating

Dry heating

Defatting

Unheated morama flour: protein 58%, fat 7%

Heated morama flour: protein 61%, fat 2%

Nutritional quality: Lysine retained, lower protein digestibility

Functional properties: higher NSI, EC, OAC and L*; lower WAC and FC

Nutritional quality: 12% loss in lysine, higher protein digestibility

Functional properties: Lower NSI, EC, OAC, FC and L*, higher WAC

Sorghum/maize composite flours, porridges, biscuits

Breads, cakes, doughnuts, sausages, beef patties

Sorghum/maize composite flours, porridges, biscuits

Breads, pancakes, waffles, gravies, soups

FIGURE 5.6 Physico-chemical and protein-related functional properties of defatted morama bean flours and their potential applications in food systems (adapted from Maruatona, 2008).

V. POTENTIAL MARKETING STRATEGIES FOR MORAMA-PROCESSED PRODUCTS A large number of indigenous Southern African plants have, over the ages, been used for nutrition and medicine (Ham et al., 2008). Traditionally, some of these plants have been collected from the wild or obtained in crude form from traditional healers for home consumption and as a result have not been commercialized. Commercialization and value addition of indigenous plants have been mooted as a possible solution to the alleviation of poverty in resource poor communities ( Jordaan et al., 2008). Such initiatives would provide income-generating opportunities for these communities as well as serve as an incentive to value and conserve natural resources more. The first steps toward the commercialization of the morama bean were taken with the European Union-funded project (MARAMA II), which was aimed at developing innovative and healthful morama bean products

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targeting niche markets. The products are targeted at niche markets, initially in Southern Africa (Botswana, Namibia, and South Africa) and then potentially also global niche markets. A range of prototype morama bean products were developed. These included morama milk, a dairy substitute; full fat and defatted morama flours aimed to enhance the nutritional value of cereal staples for example sorghum porridge; morama oil for use in salads, cooking and cosmetics; and roasted morama for use as a snack product. The process used for these products was based on existing technology to manufacture products from similar raw materials, for example, soybeans and peanuts. The market characteristics of and demand for these products were assessed during a pilot study to determine the scope for commercializing these products. Consumers’ perceptions of these products were also tested to identify possible marketing strategies for each of the prototype products.

A. Market overview 1. Market size and characteristics The market for products derived from morama in Botswana, Namibia, and South Africa is potentially significant, with the three countries serving as home to around 50 million people, most of which are concentrated in South Africa (Central Intelligence Agency, 2007; Planet Retail, 2007). However, a very large proportion of this population is relatively poor, with a large section of the wealth within these countries concentrated among a very small section of the population (UNDP, 2009). Consumers in these countries are also quite diverse, with a large number of ethnic groups, religions, and languages making the region a very diverse social and cultural region (Central Intelligence Agency, 2007). Urbanization is also a growing phenomenon (Central Intelligence Agency, 2007; Institute of Security Studies, 2007; Statistics South Africa, 2006), and some of the major health issues include a very high incidence of HIV/AIDS, micronutrient deficiencies, and noncommunicable chronic diseases (NCDs) such as diabetes, high blood pressure, cancer, and cardiovascular diseases (Central Intelligence Agency, 2007). With growing disposable incomes, urbanization, increasingly formalized markets, and growing consumer awareness in terms of health, sustainability, and social responsibility issues in these countries, the market for niche or speciality products has been growing and presents opportunities for new and innovative products. This creates opportunities for the commercialization of new products based on morama.

2. Retail environment The retail environment in Botswana, Namibia, and South Africa is dominated by a number of large retail groups that account for a significant proportion of the retail sales in these countries. It is of note that five major

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retailers account for 86% of the market share in South Africa, 80.5% in Botswana, and 72.6% in Namibia based on the banner sales of the respective retailers (Planet Retail, 2007). This is indicative of high levels of retail concentration in all these three countries, with a small number of companies controlling very large proportions of the retail sales in the respective countries (Planet Retail, 2007). In terms of the size of the retail sectors in Botswana, Namibia, and South Africa, it is evident that the size of the South African retail sector is significantly larger than both Botswana and Namibia’s retail sectors. The value of the South African retail sector is estimated at US$6285 Million for 2007 while that of Botswana is estimated at US$674 Million and that of Namibia at US$237 Million. These differences are attributable to the differences in the sizes of the economies and population (Planet Retail, 2007). Based on the size, differentiation, and value of the South African retail sector, it would appear that it is the most attractive of the three countries for retail of value-added morama products. The South African market hosts a diversity of retail outlets that range from low-cost commodity retailers who offer basic products at relatively low cost to exclusive niche product retailers who offer niche products at higher prices (Planet Retail, 2007). It also receives significantly larger numbers of tourists. However, based on interviews held with consumers, knowledge about morama was higher in Botswana and Namibia which could influence their purchase decision (Tlhong et al., 2009). This would need to be taken into consideration in terms of recommending niche markets to target.

3. Competition An analysis of competing products and potential substitutes revealed that morama products will potentially compete with a number of other, wellestablished products and brands that are already commercialized. These include soy-based products such as soy milk, soybean oil, and high protein soy flour. The direct implication of this is that for morama bean products to compete on price directly with the competing products, it will need to do so based on the health benefits or other novelty value identified by the current Marama II FP-6 project consortium. Without these, it may prove to be very challenging to the feasibility of these products.

B. Consumer analysis Table 5.15 provided the purchasing characteristics of consumers who participated in the market testing through structured questionnaires and in-depth interviews. It indicated that consumers in the three markets were aware of the competitive products, bought them almost on a monthly basis, and spent on an average between US$4–16 monthly.

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TABLE 5.15 Consumer purchasing characteristics and willingness to pay for morama bean-processed products in Botswana, South Africa and Namibia (adapted from Jordaan et al. (2009)) Morama product

Market

Consumer responses

Morama milk

Botswana Familiar with predominantly long

Morama–sorghum composite porridge

South Africa

life dairy milk but not really soy milk. Buy long-life milk products on a weekly or monthly basis. Spend on average US$15.94 per month on long-life milk products and not really aware of the health benefits of soy products. Consumers who were aware of the health benefits were identified as being high in protein and low in cholesterol as the primary benefits. 80% willing to pay the same price or less compared to similar know products. Price, ingredients, and health benefit play a significant role in decision-making.

Familiar with a variety of

nutritionally enhanced porridges, for example, vitamin- and ironfortified maize, especially maize and oats. Buy such porridge products on a monthly basis. Spend on average US$10 per month on porridge products and are not too aware of the health benefits of these products. Consumers who were aware of the health benefits were identified as being high in energy and fiber as the primary benefits. 73% willing to pay the same price or more compared to similar know products.

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TABLE 5.15

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(continued)

Morama product

Market

Consumer responses Price, ingredients, and health

benefit play a significant role in decision-making. Morama roasted nuts

Namibia

Familiar with a variety of snack

products, especially peanuts as well as a variety of luxury nuts such as cashews, almonds, although they rarely bought snack products. Buy snack products on a monthly or 6 monthly basis. Spend on average US$4 per month on snack products. The individuals were quite aware of the health benefits of roasted nut products, including high levels of protein and unsaturated fats. 90% willing to pay the same price or less compared to similar known products. Price, ingredients, and health benefit play a significant role in decision-making.

Consumers in South Africa and Namibia were more aware of the health benefits of the competitive products; almost all of the health benefits they identified were related to the nutritional value. None of them indicated any phytochemical properties or any specific disease that they could prevent. Consumers’ willingness to pay for the morama products in comparison to similar products, for example, soy milk, cereal–protein composite porridges, and roasted peanuts in the same market segment, is also shown in Table 5.15. The reasons for the specific levels of willingness to pay varied between products and consumers. Some consumers experienced morama products as having an unpleasant bitter taste. Due to the relative newness of the products, they also indicated that if the morama products were more expensive than other products, then they would be unwilling to buy them; they suggested that it was better for morama to establish itself first through low prices. They also added that if morama

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products had the same benefits as other products, then they would want to pay the same price. Some consumers however were willing to support indigenous or natural value-added products even if they were a little more expensive. A small number of consumers commented that in their experience more expensive products tended to be always the healthiest in similar commodities but that they would still need to balance this carefully against the limitations of their disposable income.

1. Conjoint analysis

Jordaan et al. (2009) reported on the key product attributes that would trigger consumers’ buying decisions of the morama-processed products. For morama milk, they found that consumers would achieve the greatest utility from similar products that were already known in the market and which offered many benefits at a low price. Conversely, unknown products with no additional benefits at a relatively high price are expected to offer the least utility to consumers. This observation is significant in the light of the commercialization of morama milk. Therefore, they concluded that commercialization would be impeded by marketing messages that offered no significant benefits and products that were relatively expensive ( Jordaan et al., 2009). Their research also showed that with long-life milk products, consumers’ purchasing choices was primarily driven by the health benefits of the product and not the price ( Jordaan et al., 2009). This is usually indicative of the types of products that are considered by consumers to be ‘‘luxury’’ such that there is focus on the attributes of the product rather than of the price ( Jordaan et al., 2009). For the morama–sorghum composite porridge product, consumers highlighted that a good tasting product at a low price will be preferable than a high-priced product with an average taste ( Jordaan et al., 2009). As with the results reported previously on morama milk, the commercialization potential of the porridge will be impeded by a conventional tasting cereal product at a high price. It is noteworthy that consumers’ choices with regard to their purchasing decisions for composite porridges are primarily driven by the price of the product and not the taste of the product. This preference is usually indicative of a staple, necessity-type product where consumers are focused on the price of the product rather than the other product attributes ( Jordaan et al., 2009). This is expected since sorghum porridges are considered to be staple foods in the Southern African diet. From the utility estimates for the roasted morama product, consumers indicated that a more expensive product will generate greater utility than a less expensive version ( Jordaan et al., 2009). This behavior is typical of ‘‘Veblen’’ goods, which are characterized by an increasing preference for the product as the price of the product increases rather than decreases according to the law of demand (Leibenstein, 1950; Wood, 1993). This may

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be applicable to luxury nut products such as cashew nuts where part of the allure of the product is derived from the price rather than the attributes. It is noteworthy that consumers’ choices with regard to their purchasing decisions for luxury nuts are driven by the type of luxury nut, in all likelihood due to strong individual preferences, not only the taste or the price. When roasted morama was considered as a luxury nut, there was a negative correlation between it and consumer utility. This was likely due in part to consumers’ unfamiliarity with morama ( Jordaan et al., 2009).

C. Commercialization strategies Based on the analysis of the morama market and the potential consumers as reported by Jordaan et al. (2009), a number of strategies for commercializing morama products in South Africa, Botswana, and Namibia were possible. Firstly, morama bean products needed to have at least similar product performance to competing products in terms of consumer acceptability. Besides the product performance, the differentiating benefits of morama products needed to be clearly conveyed to consumers. This was especially important since most consumers were currently not even aware of the benefits of consuming soy and some, particularly in South Africa, were unfamiliar with morama. This would require further product research, advertisement, packaging, and promotions. Consumers were also very aware of the balance between the value and the price for products; therefore, morama products would need to reflect their intrinsic and perceived value ( Jordaan et al., 2009). Since consumers of morama products appeared to be price-sensitive, the pricing of morama products would likely need to be similar or less than competing products such as soy milk or soy–sorghum composite porridge products to convince consumers to consider morama products. Jordaan et al. (2009) reported on the marketing strategy for several morama products. They suggested that morama milk could be placed in the market as a nondairy milk supplement with health benefits similar to soy milk. This product could be sold through the various formal and informal retail networks in the respective countries at a pricing level equal to or less expensive than competing products. The promotion of the product should be undertaken through a variety of marketing channels aimed at reaching target consumers and should focus on the unique benefits and characteristics of the product. The sorghum–morama composite porridge is aimed at the fortified sorghum porridge market. Given the high nutritional properties reported by Maruatona et al. (2010), morama flour could be positioned either as a commercial fortified product or as a fortified complimentary food product in feeding programs aimed at improving the nutritional status of

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vulnerable populations including infants, young children, pregnant and lactating women, as well as HIV/AIDS patients. The pricing level should be equal to or cheaper than competing products. If morama–sorghum composite flour was used in a feeding scheme, it should be made available either as a ‘‘recipe’’ to communities that have direct access to morama beans as a method to improve the nutritional value of their traditional sorghum porridges or as a feeding program product available to vulnerable people at very little or no cost. The promotion of the product should be undertaken through a variety of marketing channels aimed at reaching target consumers and should focus on its unique benefits and characteristics, for example, as a healthier, more nutritious porridge which is made from local ingredients and which has an improved, unique taste.

D. Conclusions Notwithstanding the market opportunities available for morama products, the ecological and economic feasibility of these products still need further research. Depending on which products are considered commercially viable, it is recommended that existing commercial manufacturers of products similar to the envisaged morama products in Botswana, Namibia, or South Africa be considered as partners in the commercialization. Such a partnership could involve existing business enterprises and the rural communities from where the raw material is either collected or grown. These businesses should typically already have commercial interests in similar products to the prototype morama products.

VI. SOCIO-ECONOMIC ANALYSIS OF COMMUNITIES WHERE MORAMA IS FOUND A socio-economic study of communities where morama is found was conducted by Lima de Faria et al. (2008) in six rural areas in Botswana, South Africa, and Namibia. The aim was to assess the current uses and benefits of the morama bean and the viability of cultivating and adding value to it. Also, the extent to which adding value could help to improve the well-being of the populations living in the regions in which morama beans are endemic was also evaluated. In Botswana, the areas included the regions of Jwaneng (Sese and Naledi villages) and Ghanzi (East Hanahai, West Hanahai, Grootlaagte Qabo, and Kagakae villages). In South Africa, the areas were Lephalale district (Marapong settlement) and in the Steenbokpan region (Leseding settlement). In Namibia, it was the Gobabis region (Sandveld, Epukiro Post 3, and RC Mission villages)

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and in the Okakarara region (Okatuuo, Okatjau, Ohamueke villages). In all villages, at least one focus group (n ¼ 15–20) was held. In-depth interviews were also conducted with key informants (e.g., village headmen, village development leaders, doctors, nurses, traditional healers, school teachers, street vendors, and local administrators).

A. The value of morama as perceived by the communities Local knowledge of wild edible plants and plant gathering is normally linked to people living in remote or isolated areas, in which local communities display a high cultural homogeneity (Saka et al., 2008). Such was not the case in the areas targeted by the fieldwork, in the three countries, in which multiethnic communities displayed different approaches both to this natural product and to the way they envisaged its uses. In Botswana, the gathering of wild plants and fruits still form an integral part of the traditions of people living in the Kalahari region. Apparently, besides enhancing food security, autonomy, and freedom, gathering wild fruit gives these populations a particular sense of identity. Morama is part of the populations’ collective memory and continues to be a topic they are eager to discuss with outsiders. It still plays an important role in (San people) rituals such as the initiation of girls. However, gathering expeditions are becoming increasingly rare, which is mostly due to the long distances that separate the settlements from the areas where morama grows (Lima de Faria et al., 2008). In South Africa, gathering and eating (roasted) morama is considered to be a longstanding tradition for both the black people (living on farms or in squatter camps) as well as for the white farmers. Both groups expressed how much they appreciated morama. They also shared memories of growing up with morama, gathering them from the wild, thereby in similar ways showing a sense of local belonging (Lima de Faria et al., 2008). However, in South Africa, current gathering of these beans is mostly confined to the plants that grow spontaneously along the roadside. Only the few people who still live and work on farms can freely gather them there. For people living in periurban townships (such as Marapong in the Lephalale district), gathering morama is a risky activity since they are not allowed to enter the perimeters of privately held farms. This explains why the information collected about knowledge and usage of morama was scarce in South Africa. According to this survey, morama does not seem to be associated with specific livelihoods of a single cultural/ethnic group or community in South Africa (Lima de Faria et al., 2008). The groups that were surveyed lived in either (i) black ‘‘townships’’ related to new industrial periurban areas (such as Marapong in Lephalale), that is, new residential areas of black people coming from different origins, culturally heterogeneous, seeking work in the new industrial

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enterprises and on its surrounding economic and urban structures, (ii) (rural) squatter camps occupied by families that were previously integrated in the farming system, and are now expected to restart their lives outside of it (such as Leseding in Steenbokpan) (Sadiki and Ramutsindela, 2002), or (iii) groups of people gathered to participate in the focus group discussion coming from diversified geographic and cultural origins, having in common the fact that they work and live in the same area (Lima de Faria et al., 2008). In Namibia, morama beans are also perceived to be part of local communities’ traditions and food habits. The San in the Namibian Kalahari area, who still gather morama in group expeditions, have inscribed the relation with these natural products in their holistic and spiritual world view—‘‘It was planted by God, so one cannot replant’’ (Lima de Faria et al., 2008). When asked if they needed permission to gather the morama, they replied: ‘‘God gave us this food, why should we ask for permission?’’ (Lima de Faria et al., 2008). The Herero communities in the Okakarara region, who were originally nomadic pastoralists and therefore used to collecting wild plants while on the move, retain a strong relationship with the beans. This relationship linking people and this natural resource was apparent when a Herero informant, when asked about the possibility of cultivating morama, replied that he considered it to be unfeasible as the tuber ‘‘had always been there!’’ (Lima de Faria et al., 2008).

B. Morama availability In the regions surveyed, there was a general agreement that in the past morama was more widespread and abundant. The reduction in availability of morama was mostly ascribed to pervasive drought conditions and important shifts in government land policies. In addition, the changes in the cultural habits of new generations, reinforced by better roads and improved transportation networks, have facilitated access to local food markets. This in turn reduced the practice of gathering food products, a practice left to the elderly and the poorer sections of the population (Lima de Faria et al., 2008). Land redistribution policies have affected the availability of morama. In Botswana, the villages surveyed were built in the late 1980s under the National Program of Resettlement, forming complex interethnic communities with limited access to the ‘‘morama areas’’. Morama beans grow in areas that became inaccessible for local populations since the land in question has mostly been allocated to diamond mining, tourism, and cattle industries. In South Africa, the end of agricultural farming and post-Apartheid, moved ‘‘black’’ people to periurban settlements (Lyne and Darroch, 2003). The latter is also as a result of urbanization. This situation limits

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access to arable land as they are living far away from the areas where morama beans grow. Also, many of the morama-growing areas are now privately owned, fenced-in properties, targeted at tourist (safari and game) activities (Lima de Faria et al., 2008). Only in the Namibian rural areas surveyed, a closer relationship between the human units and the morama beans was recorded. Herero chiefdoms, traditionally acting as land owners and cattle-herders, kept their own lands and have free access to the communal areas where they still freely pursue traditional gathering practices (Lima de Faria et al., 2008). These communities sustain a certain hierarchy combined with political autonomy within the Namibian state that gives them power to organize their economies and share the benefits, while maintaining a measure of economic and social sustainability (Lima de Faria et al., 2008).

C. Morama uses in diets in Botswana, Namibia, and South Africa As a food product, morama beans are mostly recognized for their good taste and high nutrient levels. The most popular way of eating morama is by roasting the dry seeds in hot sand (either in a pot or directly on hot soil by an open fire) (Lima de Faria et al., 2008). Once roasted, they are eaten as a snack (in a similar way as roasted peanuts). No salt is added. Like peanuts, they are valued for being high in calories and for satisfying immediate nutritional needs. They can also be eaten boiled like any other types of beans, when they are fresh. In both Botswana and Namibia, the morama had a variety of alternative applications even though some of the recipes (e.g., those made with the morama tuber) are no longer in use. Oil is extracted from the boiled green beans and used for cooking as well as a body lotion. Morama was generally added to maize porridges (which form part of the daily diet). The tuber was also consumed while young, after being roasted on hot ashes. In Namibia and Botswana, morama is seen as an important and complementary food supplement (which is often compared to meat). However, in South Africa, the uses of morama are limited to the gathering of the dry beans, if and when available, which are usually roasted or very seldom, boiled when fresh. In South Africa, there was no evidence of people using other parts other than the beans of the plant in their diets. No other forms of processing or alternative uses have been found in the areas studied.

D. Morama health benefits as perceived by the local people It was broadly recognized in the communities interviewed that the nutritional qualities of the morama beans had obvious benefits by boosting people’s immune systems thereby protecting them from illness. They

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are used as a supplement for babies and infants, as well as in women’s diets during pregnancy. Morama is seen as an energizer and a sexual booster. Owing to its oily composition, morama is also used for skin care (as an oil, lotion, or scrub) and may be used for the treatment of skinrelated problems such as eczema (Lima de Faria et al., 2008). In one of the settlements surveyed in Namibia (Epukiro Post 3 in the Sandveld area), the San referred to the use of mashed morama beans for the treatment of eye infections. In South Africa, no medical uses of morama plants were identified, although people repeatedly stated that ‘‘before, when there were more morama, people had fewer diseases’’. An old farm worker from Steenbokpan said: ‘‘In the past we used to eat morama regularly and we did not get sick of anything’’. People stated that, after eating morama beans, they felt stronger and more energetic. For this reason, morama beans are also given to breastfeeding women and to old people.

E. Morama: A staple food for very poor populations The possibility of the beans being stored for long periods of time adds to the value of morama. It is regarded as a kind of ‘‘life saver’’ in times of crisis, when other foods are not readily available. Morama beans may even replace an entire meal in emergency situations in which people have little income, are unemployed, and are living in periurban townships (without access to arable land). The beans may also be used as a means of diversifying poor people’s diet, as Hitchcock (2002) puts it: ‘‘There are relatively few San, if any, in Southern Africa today who derive their subsistence and income from hunting and gathering, although a number of communities do depend on foraging as a buffering strategy and as a means of diversifying their diets’’. Thus, there is an obvious link between situations of social stress, defined by high rates of unemployment and associated poverty, and the importance given to these beans. The great value attributed to morama and their increasing scarcity is confirmed by people describing the collection of morama from mice holes. Also in the isolated and very dry regions of the Kalahari in Botswana, morama beans are reutilized by villagers after being digested by animals such as goats and hens. This important link between the cultural value attributed to morama and social and economic deprivation was also supported by local farmers’ testimonies. In Steenbokpan in South Africa, one farmer explained that black people are not gathering as much morama as in the old times because their standards of living have improved, while they have increased their access to more diversified foods richer in proteins (Lima de Faria et al., 2008).

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F. Morama domestication: Problems related to cultivating morama as a crop In the three countries surveyed, there was no evidence of any successful experience regarding morama cultivation. In Botswana, cultivation had only been experimented with on a very small scale in the 1990s in two villages of the Ghanzi region with the help of an NGO called KURU. Morama was also cultivated on a research farm in the Namibian Sandveld region (Sandveld Research Station), but without any tangible results. According to key informants of both countries, the cultivated plots were not properly fenced and supervised (mostly in Botswana), while local people did not understand the reasons for the implementation of these projects and therefore failed to perceive their value (both in Botswana and Namibia). In South Africa, in the regions surveyed, so far no trials of morama cultivation have been carried out and locally, morama is only known as a wild plant. Most informants were quite surprized when asked about this possibility (Lima de Faria et al., 2008). The main problems underlined by rural populations regarding the cultivation of morama as a crop were the (i) soil quality; (ii) labor inputs (man power); (iii) availability of water; availability of land; (iv) slow growth cycle/low yields; and (v) prevalent instant cash/economic culture. In some villages, the soil was considered inadequate for being too sandy. Morama cultivation was also considered to be very labor-intensive since it required continuous weeding. The availability of water was also considered to be a major problem. According to key informants, the establishment of irrigation infrastructure for morama beans needs to be associated with other cultivated crops or plantation projects, such as vegetable gardens. Mostly in Botswana and Namibia, the availability of land was also said to be an important issue owing to the large size of the tuber. Moreover, continuous relocation processes have reduced its availability in the villages’ surrounding areas, increasing the competition for arable land which is used primarily for products with higher yields such as sorghum and maize. The slow growth cycle of the plant and its low yields were also mentioned as a constraint for morama domestication, given that one is dealing with food crops and communities that are generally anxious to see immediate results. Economic reasons were also identified as a constraint to domestication. According to a key informant in Botswana, domestication of morama means that people would be obliged to wait longer to generate income compared with other cash crops. The current prevalence of casual employment replacing long-term work has created a culture in which people expect immediate payment upon the completion of the task at hand. For this reason, morama that grows spontaneously and provides instant cash may be considered more favorably in

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comparison to its domesticated version. Domestication may also cause people to steal morama for quick cash (Lima de Faria et al., 2008).

G. Morama market In the past, similar to other wild berries or fruits, morama was not marketed and only used for individual or household consumption by hunter–gatherer populations. Morama commercialization, within an informal and local market, is therefore a result of the impact of the monetary economy. Together with other food products it became a source of instant cash. The market for morama beans is characterized by (i) the shortage of raw materials due to poor yields; (ii) low prices, poor information on growing; (iii) limited market access; and (iv) limited involvement of retailers (Lima de Faria et al., 2008). Only small quantities of morama are sold in the villages, mostly to neighbors, or in nearby towns by villagers who temporarily act as street vendors. On account of the small quantities gathered, morama is generally channelled into household consumption and food sustainability (as they are easy to store and can be kept for long periods). People only sell morama when there is a surplus. In areas where it is easy to gather, vendors do not make much profit from selling the beans, as most people already have access to them. Although morama is sold in the local market throughout the year, supply significantly increases in the month of April when the gathering season starts. In most cases, there is a direct relationship between gathering and selling morama, without any retailers being involved in the marketing of the beans (Lima de Faria et al., 2008). Only in the Okakarara area of Namibia were there reports of morama gatherers (very poor San people) being paid by morama sellers (Herero farmers) to collect them in large quantities, which were later on sold in nearby markets. The beans are rarely sold fresh. Instead, they are normally sold uncooked in their shells or roasted either with the shell or unshelled (Lima de Faria et al., 2008). Prices vary somewhat between villages as they are determined by the seller, in accordance with their weight (measured in kilograms or by using approximate measures such as a cup or another container). In Botswana and Namibia, prices also depend upon morama being (un) shelled, fresh, raw, or roasted. The price may increase a little if the beans are sold in nearby towns. Small-scale and medium-scale farmers also sell morama to local people (to villagers or to farm workers). In the Okakarara area in Namibia, relations between commercial food stores and the ‘‘kapanas,’’ the traditional street vendors, seem to be good and they appear to act in a complementary fashion. Nomadic vendors sell morama in conjunction with other products such as vegetables or meat. Yet, this type of vending implies considerable costs as a result of poor public

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transportation; thus most vendors rely upon private transport, that is, by hitching a ride. In South Africa, the local (informal) market for morama beans is very weak compared to those observed in the regions surveyed in Botswana and Namibia (in which there were certain linkages between rural and urban areas, given that morama is brought by street vendors from rural villages to the nearest town or even to the capital cities). Thus, only a few cases were recorded of people selling them in South Africa. Apparently, morama beans are not being sold along the main roads. Shop vendors in Steenbokpan in South Africa said that there was no need to sell them since morama beans were plentiful and everyone had access to them (Lima de Faria et al., 2008).

H. Conclusions and recommendations Most communities surveyed showed their readiness to engage in an experimental project regarding morama processing and marketing at the local level. They were quite positive about the prospects of generating profits from such a project. It was nevertheless in Namibia that the correlation between developing small processing industries and an increase in the populations’ well-being appeared to have some measure of sustainability. In terms of looking for local partners to start an experimental project, the Gobabis area and the Sandveld Research Station looked like the most appropriate site for morama cultivation trials involving the local dwellers. They had already cultivated morama and it worked (Lima de Faria et al., 2008). In this region, the village RC Mission would also be a good place for such experiences because of its strong engagement in several organizational activities with the local population. To support this idea, it is important to note that in this village, there is a strong interest to integrate the San into the community, in spite of their nomadic lifestyle and a certain self-image of exclusion. In the Okakarara area, the domination of the Herero is quite apparent. Being cattle herders, they have always had a reliable source of income. Having built a strong traditional leadership structure, they have developed an efficient system of community self-help. These characteristics suggest that they would also be a suitable group to work with in terms of creating a common project that would empower these communities and help build sustainable and fair market chains for morama value-added products. There is a risk that if wild morama seeds are to be commercialized, they could end up being removed from the local communities who instead of collecting the beans in accordance with their own needs while rationally exploring their use, would be forced to buy them in the market at higher prices. This situation should be taken into account once proposals are presented to these communities (Lima de Faria et al., 2008).

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Morama domestication on a certain scale seems to be the sole solution if a market for these means is supposed to reach broader boundaries (such as the national and the international level). Communal land would be best suited for morama domestication, by involving mostly poor populations within their already existent representative and cooperative structures. Processing industries should be developed at the local level and inscribed within existing local infrastructures in order to benefit and employ the poorer sections of the population.

VII. CHALLENGES AND FUTURE RESEARCH The major challenge with the morama bean achieving its potential as a crop for Southern Africa is having a sustainable supply for commercial activities. Morama has not been domesticated and is still only available in the wild. The issue of sustainable supply is always a challenge with wild products, which generally have very intricate requirements for optimal production conditions, rendering them very difficult to propagate and hence hindering domestication efforts. However, its excellent properties and potential value commercially suggests that for the short term, a balance should be established that would allow for sustainable harvesting from the wild while long-term efforts be targeted toward breeding and domestication. The morama bean is one of the many plants native to Africa with great agricultural potential, but which still needs to be developed through plant breeding to improve growth and yield. As reported by Chimwamurombe (2008) and Nepolo et al. (2009), the plant needs to be converted into a crop by developing desirable cultivars that are high yielding and early maturing. With the increasing human population, improvements in droughttolerant crops such as morama bean could be of great benefit to indigenous populations. If not sustainably harvested, the legume will face the problem of overexploitation from local people through overharvesting of seeds as well as defoliation by game and livestock. The morama bean, like other wild underutilized crops, is currently highly underresearched. There have been only two major funded research projects conducted on the morama bean; both of these were funded by the European Union. One focused on production issues and the other on postproduction. While a significant amount of new knowledge about the chemistry, processing applications, and health benefits of morama has been obtained through these projects, particularly using new techniques, there still remains a lot that is unknown. In order to achieve its potential, research is needed to understand the chemistry of the oil, protein, and carbohydrate components on varietal selection and breeding of these leguminous grains to enhance productivity and end-use quality

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as well as the nutritional and health-promoting components and antinutrients of the morama. Emphasis needs to be placed on the development of suitable processing technologies for the morama, for example, dehulling and milling operations to process into value-added products such as infant foods. Finally, appropriate markets need to be identified and developed for these grains as cash crops or value-added products.

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INDEX A Acetaldehyde accumulation, 160 concentrations, 162 free and bound SO2, 160–161 Adulteration, 59, 99 Alcoholic fermentation, 4 Allergens, 205 Amperometric detection, 98 Aroma, wine effects, 172–177 oxidation, 158–159 Artificial effervescence, 27 Artificial neural networks (ANNs), 91–92 Artificial tongue adulteration and food falsification, 59 analytical techniques impedance spectroscopy, 68–69 potentiometry, 67 voltammetry, 68 beverage differentiation, 65 chemometrics classification and class-modeling, 83–93 exploratory analysis, 79–83 multivariate experimental design, 71–73 preprocessing, 73–78 regression techniques, 93–96 validation, 96–98 environmental analyses, 64 food science aging process, 103 amperometric detection, 98 ANN models, 100 biosensors, 103 commercial electronic tongue, 105 cross-validation approach, 104–105 glucose and ascorbic acid determination, 104 hybrid sensor, 99 impedance measurements, 98

pasteurization processes, 106 PCA, 98–99 potentiometric ion-selective sensors, 104 potentiometric sensors, 103, 106 predicting sensorial attribution, 100 pulse voltammetry, 101 sensorial analysis, 100–102 SIMCA models, 99–100 square wave and cyclic voltammetry, 98 tea analysis, 104 water monitoring approaches, 103 wine adulterations, 99 age prediction, 101 characterization, 98 food taste properties, 66 historical aspects, 62–63 liquid food, 64–65 nonspecific analytical responses, 60 pharmaceutical technology studies, 64 production phase, 58 qualitative approach and quantitative applications, 63 research-and-development, 58 sensations, mammals, 60 terminology, 61–62 vanguard analytical strategies, 59 variability amount implications, 58 B Beverages. See Champagne and sparkling beverages Biofilms, 130 Biosensors, 103 Bubble bubbling regimes, 23–24 bursting process avalanches, 51–53 flower-shaped structures, 48–49 hexagonal pattern arrangement, 47–48 high-speed photography, 43–44

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248

Index

Bubble (cont.) schematic transversal representation, 50–51 shear stresses, 49–50 surface active molecules, 45–47 close-up time sequence and mechanism, 25 growth characteristics, 28–29 high-speed photography and strobe lighting, 28 pressure, 30–31 microbubbles, 151 micrometric gas bridge establishment, 26 size carbon dioxide content, 33–34 gravity acceleration, 32–33 pressure, 33 significant difference, 34 temperature dependence, 32 two gas pockets, 26 Bubble nucleation process, champagne artificial effervescence, 27 bubbling instabilities bubbling regimes, 23–24 close-up time sequence and mechanism, 25 micrometric gas bridge establishment, 26 two gas pockets, 26 cellulose fibers bubbling frequency, 20–22 CO2 dissolved concentration, 19–20 conditions, 18 real gas pocket trapping, 18–19 structural levels, 15–16 critical radius, 12–13 natural effervescence, 13–15 C Cabernet Sauvignon wine, 169 Cell intraction, photosensitizers biofilms, 130 cytotoxic species, 125 electron microphotographs, Bacillus cereus, 128 fungal cells and yeasts, 127, 130 gram negative bacteria, 127, 129 gram-positive bacteria, 127–128 killing effects, 129 metabolic pathways, 127 modes of light delivery, 133

photoactive fullerene derivatives, 132 spore formation, 130 viruses and structures, 131–132 Champagne and sparkling beverages bubble growth characteristics, 28–29 high-speed photography and strobe lighting, 28 pressure, 30–31 bubble nucleation process artificial effervescence, 27 bubbling instabilities, 23–27 cellulose fibers, 15–23 critical radius, 12–13 natural effervescence, 13–15 bubbles bursting process avalanches, 51–53 flower-shaped structures, 48–49 hexagonal pattern arrangement, 47–48 high-speed photography, 43–44 schematic transversal representation, 50–51 shear stresses, 49–50 surface active molecules, 45–47 bubble size carbon dioxide content, 33–34 gravity acceleration, 32–33 pressure, 33 significant difference, 34 temperature dependence, 32 chemical composition, 8–9 CO2 dissolved gas molecules blending, 4 first alcoholic fermentation, 4 second alcoholic fermentation, 5–6 flute vs. coupe champagne serving, 36–37 CO2-dissolved concentrations, 37–38 time series data recordings, 39–40 pressure under the cork, 6–8 temperature, role of, 42–43 uncontrolled champagne cork, 9–12 Chemometrics classification and class-modeling artificial neural networks, 91–92 class-modeling techniques, 92–93 k-nearest neighbors, 85–86 linear discriminant analysis, 86–89 quadratic discriminant analysis, 88 soft independent modeling of class analogy, 90–91 unequal class models, 88, 90

Index

column autoscaling, 76–77 column centering, 76 exploratory analysis clustering, 82–83 principal component analysis, 79–82 multivariate experimental design experimental factors, 71 factorial scheme, 71–72 regression techniques ordinary least squares, 93–94 partial least squares, 94–96 row preprocessing effectiveness, 73–74 exploratory analysis, 79–83 first and second order derivation, 75–76 forward and backward currents, 76 signal compression and variable reduction SELECT, 78 wavelet transform, 77–78 signal variations, 75 standard normal variate transform, 75 validation cross validation, 97 repeated evaluation set, 98 single evaluation set, 97 strategy, 96–97 Codex Alimentarius Commission (CODEX), 59 CO2 dissolved gas molecules, beverages blending, 4 bubbling environment, 35 first alcoholic fermentation, 4 flute vs. coupe champagne serving, 36–37 time series data recordings, 39–40 vessel influence, 37–38 second alcoholic fermentation, 5–6 temperature, role of, 42–43 Cross validation (CV), 97 Cyanogenic glycosides, 205 Cytotoxic species photoexcitation, photosensitizer, 125 PS-excited triplet reaction, 125–126 ROS, 126 E Effervescence process artificial effervescence, 27 natural effervescence lumen, 13

249

mechanism, 14 time sequence, 15 visual aspects, 38 Electronic tongue, 103. See also Artificial tongue F Fatty acids, 198–200 Food and Agricultural Organization (FAO), 59 Food falsification, 59 Food processing, maroma milk amino acid composition, 215–216 chemical composition, 215 fermented milk products, 217–218 preprocessing treatment, 216 small-scale method, 216–217 thermal treatment, 216 oil, 218 protein-rich morama flours amino acid composition, 221 dry heating process, 222 physico-chemical and protein-related functional properties, 223 preparation, 218–219 processing procedure, 220 proximate composition, 219–220 uses, 222 G Glucose and ascorbic acid, 104 Glutathione, 163 H Hybrid sensor, 99 K k-Nearest neighbors (k-NN), 85–86 L Linear discriminant analysis (LDA), 86–89 M Malachite green, 137 Methylene blue (MB) European blood transfusion, 138 photoactive dyes, 136–137

250

Index

Microoxygenation (MOX) acetaldehyde accumulation, 160 concentrations, 162 free and bound SO2, 160–161 aroma effects, 172–177 microbiological considerations, 179–181 microbullage delivery, 151–152 mouthfeel effects, 177–179 oxygen spatial considerations, 153–154 polymer membrane, oxygenation procedures, 152–153 red wine color effects and polyphenol development antioxidant assays, 170 Cabernet Sauvignon wine, 169 chemical and instrumental analyses, 172 color intensity, 164 color properties, 166 HPLC analyses, 165 Monastrell wine, 166–167 pigment composition, 165 polymeric pigments and color density, 168 red wine maturation, 158 SO2 influence and wine antioxidants, 162–164 wine oxidation processes oxygen in wine, 154–155 polyphenol-mediated, 155–157 wine aromas, 158–159 Monastrell wine, 166–167 Morama bean (Tylosema esculentum) allergens, 205 ash content, 196 availability, 232–233 carbohydrate/dietary fiber, 202 chemical composition, 195–196 cultivation, 235–236 cyanogenic glycosides, 205 dietary use, 233 economic importance, 189 fatty acids, 198–200 geographic distribution and description, 190–192 health benefits, 206, 208–212, 233–234 lipids, 196, 198 market, 236–237 milk amino acid composition, 215–216

chemical composition, 215 fermented milk products, 217–218 preprocessing treatment, 216 small-scale method, 216–217 thermal treatment, 216 minor chemical components minerals, 203 phytoestrogens, 203–204 vitamins, 203 moisture, 196 oil, 218 pests and diseases, 192 phenolic compounds, 205–207 phytosterols, 198, 201 potential marketing strategies commercialization strategies, 229–230 competition, 225 conjoint analysis, 228–229 consumer purchasing characteristics, 225–227 market size and characteristics, 224 retail environment, 224–225 protein, 201–202 protein-rich morama flours amino acid composition, 221 dry heating processes, 222 physico-chemical and protein-related functional properties, 223 preparation, 218–219 processing procedure, 220 proximate composition, 219–220 uses, 222 seed morphology, seedling development and growing stages, 192 soil organic matter, 195 soil pH, 193–195 staple food, 234 triacylglycerols, 198 trypsin inhibitor, 204–205 values, 231–232 varieties and classification, 193 MOX. See Microoxygenation MRM, 92–93 Multivariate design of experiments (MDOE) chemometrics, 69 experimental factors, 71 factorial scheme, 71–72 Multivariate range modeling (MRM), 92–93

Index

N Natural effervescence lumen, 13 mechanism, 14 time sequence, 15 O Ordinary least squares (OLS), 93 Oxidation processes photosensitized oxidation, 123 wine aldehydes, 156 aromas, 158–159 condensation processes, 157 oxygen, 154–155 polyphenol quinones, 156 P Partial least squares (PLS), 94–96 Pasteurization, 106 PDT. See Photodynamic treatment Pests and diseases, 192 Phenolic compound, 205–207 Phenothiazinium dyes, 136 Phloxine B, 137 Photoactive dyes antimicrobial properties, 134 cationic polymer poly (vinyl amine), 138 cationic porphyrin derivatives, 134 endogenous porphyrins, 135 malachite green, 137 MB, 136–137 medical and therapeutic applications, 133 phenothiazinium dyes, 136 phloxine B, 137 porphyrin derivatives, 135 rose bengal, 137 TBO, 136 Photodynamic treatment (PDT) application and principles, 123 cell intraction, PSs biofilms, 130 electron microphotographs, Bacillus cereus, 128 fungal cells and yeasts, 127, 130 gram negative bacteria, 127, 129 gram-positive bacteria, 127–128 killing effects, 129 metabolic pathways, 127 modes of light delivery, 133

251

photoactive fullerene derivatives, 132 spore formation, 130 viruses and structures, 131–132 cytotoxic species photoexcitation, photosensitizer, 125 PS-excited triplet reaction, 125–126 ROS, 126 effects, 120 evironmental cleaning and disinfection biofilm destruction and inactivation, 140 food-grade PSs, 140 immobilized photoactive dyes, 139 inactivate pathogens, 141 photobleaching, 142–143 self-cleaning materials, 143 virus inactivation, 138 photoactive dyes (see Photoactive dyes) photochemical reaction, 122 photophysical reaction, 121 photosensitized oxidations, 123 photosensitized reactions, 122 Phytosterols, 198, 201 PLS. See Partial least squares Polyphenols oxidation processes aldehydes, 156 condensation processes, 157 polyphenol quinones, 156 wine aroma, 158 red wine maturation, 150 Porphyrin derivatives, 135 Potentiometric ion-selective sensors, 104 Potentiometric sensors, 103, 106 Principal component analysis (PCA) patern recognition tools, 104 red wine, color effects, 169 voltammograms, 80 Protein-rich morama flours amino acid composition, 221 dry heating processes, 222 physico-chemical and protein-related functional properties, 223 preparation, 218–219 processing procedure, 220 proximate composition, 219–220 uses, 222 Q Quadratic discriminant analysis (QDA), 88

252

Index

R Red wine color effects color development, 166 PCA projection, 169 polymeric pigments, 168 polyphenol antioxidant measures, 170 wine color parameters, 165, 167–168 maturation, 158 S Sangiovese wine, 168 Soft independent modeling of class analogy (SIMCA), 90–91 Sulfur dioxide antioxidants free SO2, 162 glutathione, 163 quinone reduction processes, 163 wine-aging processes, 163 free and bound SO2, 160–161 T Triacylglycerols, 198 Trypsin inhibitor, 204–205

U Unequal class models (UNEQ), 88, 90 W Wine adulterations, 99 age prediction, 101 antioxidants free SO2, 162 glutathione, 163 quinone reduction processes, 163 wine-aging processes, 163 characterization, 98 color parameters in Cabernet Sauvignon, 169 in Monastrell wine, 166–167 in Sangiovese wine, 168 oxidation processes aromas, 158–159 oxygen, 154–155 polyphenol-mediated, 155–157 red wine (see Red wine) World Health Organization (WHO), 59

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