Handbook of Indices of Food Quality and Authenticity (Woodhead Publishing Series in Food Science, Technology and Nutrition)

Contents Preface

7

Chapter 1 The Development of the Concept of Food Quality, Safety and Authenticity 9 1.1 Diversity of composition 10 1.2 Food contaminants 10 1.3 Food quality 11 1.4 Nutritive quality 12 1.5 Food safety 12 1.6 Natural toxicants 13 1.7 Problem of chemical residues 13 1.8 Problem of food adulteration 14 1.9 Changes associated with processing 14 15 1.10 Conservation of excess produce 16 1.11 Evolution of food legislation 17 1.12 Current methods of food analysis 19 1.13 New techniques for food analysis 1.14 Validation and approval of alternative methods of microbial analysis 29 1.15 Quality management systems 29 1.16 Clean food campaigns 30 30 1.17 Current issues in food regulations in the EU and USA References 31 Chapter 2 Food Grains 2.1 Introduction 2.2 Contaminants in grains 2.3 Interspecies and intervarietal wheat admixtures 2.4 Intervarietal rice admixtures 2.5 Cereal/cereal and cereal/legume blends 2.6 Indices for processing quality of wheat and other grains 2.7 Indices for microbial quality of cereals and cereal-based products 2.8 Indices of insect infestation of grains 2.9 Detection of damaged grains in sound grains 2.10 Other grains References

35 36 38 38 42 44 46 58 63 67 67 68

4

Handbook of indices of food quality and authenticity

Chapter 3 Fruit and Vegetable Products

77

3.1

Introduction

78

3.2

Quality indices of fruit and vegetable juices

80

3.3

Organic acids and other additives

84

3.4

Peel homogenates in citrus juices

88

3.5

Dilution

89

3.6 3.7

Juice blends Maturity and ripeness indices of fruits and vegetables

108

3.8

Non-microbial

114

of fruit juices with water

methods for determining

microbial quality

98

References

119

Chapter 4 Milk and Milk Products

131

4.1

Introduction

133

4.2

Milk of different origins

133

4.3

Whey or buttermilk

143

4.4

Reconstituted milk

149

4.5

Adulteration

151

4.6

Other fats in milk fat, butter or ghee

153

4.7

Dilution

168

4.8

Indices of microbial quality of dairy products

177

4.9

Indices of aesthetic quality of dairy products

193

in milk

in milk and other dairy products

of milk with water

4.10 Qualityofcheese

194

References

195

Chapter 5 Meat. Fish and Poultry

209

5.1

Introduction

211

5.2

Identification

5.3

Freshness indicators

231

5.4

Eating quality of fleshy foods

253

5.5

Evaluation of the age of the animal carcass

259

5.6

Contaminants

260

5.7

Quality of comminuted

meats

267

5.8

Meat additives and adulterants

268

5.9

Egg: quality criteria

271

References

of meat species

in flesh foods

212

278

Contents

Chapter 6 Edible Oils and Fats 6.1 Introduction 6.2 Indicators of storage changes 6.3 Indicators of quality of heated oils 6.4 Toxic contaminants and adulterants 6.5 Indices of admixtures, blends, contaminants and adulterants one fat in another 6.6 Sensory quality of oils References

5

300 302 306 309 311 320 345 347

358

Chapter 7 Honey: Quality Criteria 7.1 Introduction 7.2 Adulteration of honey 7.3 Honey obtained from sugar-fed bees 7.4 Identifying the botanical/geographical origin of authentic honey 7.5 Contaminants of honey References

359 362 370 371 378 379

Chapter 8 Spices, Flavourants and Condiments 8.1 Introduction 8.2 Spices as flavourants 8.3 Essential oils 8.4 Adulteration of spice essential oils 8.5 Citrus essential oils 8.6 Vanilla extract 8.7 Mint flavours 8.8 Saffron 8.9 Almond oil 8.10 Oil of sassafras 8.11 Vinegar 8.12 Miscellaneous References

387 394 423 426 429 434 437 438 440 441 442 446 447

Chapter 9 Tea, Coffee and Cocoa 9.1 Introduction 9.2 Tea 9.3 Coffee 9.4 Cocoa and cocoa products References

457 458 458 467 476 483

386

6 Handbook of indices of food quality and authenticity

489

Chapter 10 Indicators of Processing of Foods 10.1 Introduction 10.2 Thermal processing 10.3 Indicators of processing quality of beans 10.4 Fresh versus frozen-thawed foods 10.5 Indicators of storage quality of foods 10.6 Indicators of irradiationof foods References

49 1 49 1 505 507 508 510 526

Index

538

Chapter 1

The Development of the Concept of Food Qua Safety and Authenticity 1.1 Diversity of composition 1.2 Food contaminants 1.3 Food quality 1.4 Nutritive quality 1.5 Food safety 1.6 Natural toxicants 1.7 Problem of chemical residues 1.8 Problem of food adulteration 1.9 Changes associated with processing 1 .I 0 Conservation of excess produce 1 .I 1 Evolution of food legislation 1.I2 Current methods of food analysis 1.I3 New techniques for food analysis 1.13.1 Enzymes as indicators of food quality 1.I32 Biosensors in food analysis 1.13.3 Immunochemical techniques 1.13.4 DNA probes 1.13.5 Polymerase chain reaction 1.13.6Rapid methods for microbiological analysis of foods 1.13.7 Authentication of foods using isotopic methods 1.13.8 RSK values 1.13.9 Identification of fish species in seafoods 1.14 Validation and approval of alternative methods of microbial analysis of foods 1 .I 5 Quality management systems 1 .I6 Clean food campaigns 1 .I7 Current issues in food regulations in the EU and USA References

Chapter 2

Food Grains 2.1 2.2 2.3 2.4 2.5 2.6

Introduction Contaminants in grains lnterspecies and intervarietal wheat admixtures Intervarietal rice admixtures Cerealhereal and cereal/legume blends Indices for processing quality of wheat and other grains 2.6.1 Baking quality of wheat flour 2.6.1.1 Maturity indicators of wheat and their relation to bread quality 2.6.2 Flour quality for chemically leavened baked products 2.6.3 Indicators of cooking and eating quality of extruded products 2.6.4 Pancakes 2.6.5 Indicators of malting quality of barley 2.6.6 Cooking quality of rice 2.7 Indices for microbial quality of cereals and cereal-based products 2.7.1 Ergosterol content 2.7.2 Volatile compounds as indicators of microbial growth 2.7.3 Physical properties of metabolites as indicators of fungal contamination 2.7.4 Other methods 2.7.5 Ergotism 2.8 Indices of insect infestation of grains 2.8.1 Physicochemical methods 2.8.2 Staining methods based on the cell wall constituents of the insects 2.8.3 Methods based on the estimation of non-protein nitrogen especially uric acid 2.8.4 Enzymic methods to detect insect infestation in grains 2.8.5 Detection of insect eggs in stored grains 2.9 Detection of damaged grains in sound grains 2.10 Other grains References

Food Grains

61

Table 2.6 Volatiles in headspace gases (in ng I’of air) produced by different fungi during 6 days of cultivation with continuous air flow Fungus/ Volatile compound ~~

1

Days 2

11 2 7 7

4 1 6 2

3

4

5

6

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

~~

Control 2-Methylfuran 2-Methyl-1-propanol 2-Pentanone 3-Methyl-1-butanol Aspergillus amstelodemr

2-Methylfuran Methylbutenol’

22

10

13

19

39

58

-

-

-

-

-

-

2-Methyl-1-propanol 2-Pentanone 2-Methyl-1-butanol 3-Octen-2-01 3-Octen-3-01

4 10 41 2 1

6 15 81 1 3

5 28 9 6 10

6 19 2 6 10

5 17 1

6 10

-

6 10

5 7

20 4 16

11 24 86

12 12 29

42 48 8

89 84 5

119 76 2

20 3 6 2

51 30 2 4

33 89 7 24

72 145 8 25

132 20 1 15 36

143 202 17 34

-

64 5 35 6 6 2

37 84 20 47 36 3

83 200 17 48 45 6

92 140 9 16 28 7

100 120 7 12 21 5

Aspergillusflavus 2-Methylfuran

2-Methyl-1-propanol 3-Methyl- 1-butanol Fusarium culmorum

Ethyl acetate 2-Methyl-1-propanol Monoterpenes Sesquiterpenes Fusarium cyclopiumb

2-Methylfuran 2-Methyl-1-propanol 3-Methyl-1-butanol 3-0cten-Zd 1-Octen-3-01 Sesquiterpenes

-

‘Could not be separated from 2-methylfuran. Mixture of 2-methyl-3-buten-2-01 and 3-methyl-2-buten-l01.

hThisconcentration of volatiles on day 1 was not measured for this fungus. Source: Borjesson et al., 1989 (reproduced with permission).

Chapter 3

Fruit and Vegetable Products 3.1 Introduction 3.2 Quality indices of fruit and vegetable juices 3.3 Organic acids and other additives 3.3.1 Organic acid profile 3.3.2 Anthocyanin patterns 3.3.3 Microbiological methods 3.3.4 Miscellaneous compounds 3.4 Peel homogenates in citrus juices 3.5 Dilution of fruit juices with water 3.5.1 Inorganic indicators 3.5.2 Organic components 3.5.2.1 Amino acids 3.5.2.2 Vitamins 3.5.3 Stable isotope ratio analysis 3.6 Juice blends 3.6.1 Carbohydrate analysis 3.6.2 Phenolic constituents 3.6.3 Organic acids 3.6.4 Amino acids 3.6.5 Pigments 3.6.6 Miscellaneous constituents 3.6.6.1 Proteins 3.6.6.2 Lipid constituents 3.6.6.3 Histological features 3.6.6.4 Carotenoids 3.6.6.5 Aroma constituents 3.6.6.6 Biogenic amines 3.7 Maturity and ripeness indices of fruits and vegetables 3.7.1 Instrumental techniques 3.7.2 Chemical indicators 3.8 Non-microbial methods for determining microbial quality References

0

0

0

0

0

0

0

2

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5

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8

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m

0

-

100 Handbook of indices of food quality and authenticity

, , 0

0

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Fruit and Vegetable Products

W

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a-

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-

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2

101

Fruit and Vegetable Products

125

Miyake, M., Inaba, N., Maeda, H. and Ifuku, Y. (1990b). J Antibacterial Antifungal Agents, 3pn 18(6):273-278. Miyazato, M., Ishiguro, E. and Danno, A. (1981). Bull. Faculty Agriculture, Kagoshima University No. 31:149-156. Moller, D. and Herrmann, K. (1982).J Chromatogr. 241:371-379. Money, R.W. (1966).J Assoc. Public Anal. 4:41-44. Moreno, J., Caro, J. and Prestamo, G. (1976). Specific electrical conductivity as a possible method for quality evaluation and for detection of adulteration of citrus juices (cited from Food Sci. Technol. Abs. 9:SH 1402). Morgan, R.H. (1954). Food 23:28&287. Mosel, H.D. and Herrmann, K. (1974a). Z. Lebensm. Unters. Forsch. 154324-327. Mosel, H.D. and Herrmann, K. (1974b).J Sci. FoodAgric. 25951-256. Murdock, D.I. Troy, V.S. and Folinazzo, J.E (1952). Food Technol. 6:127-129. Nagy, S. (1977). Lipids: identification, distribution and importance. In Citrus Science and Technology, Vol. I , eds S. Nagy, P.E. Shaw and M.K. Veldhius, AVI, Westport, Connecticut, pp. 266301. Nagy, S. and Nordby, H.E. (1971). In Abstracts of Papers 1971 Conference on Citrus Chemistry Utilization USDA, Winter Haven, Florida. Navarro, J.L., Aristoy, M. and Izquierdo, L.( 1984). Rev. Agroquim. Tecnol. Aliment. 24 (1): 48-58. Niedmann, P.D. (1976a). Chem. Mikrobiol. Technol. Lebensm. 4(5):132-135. Niedmann, P.D. (1976b). Deut. Lebensm. Rundschau 72(4):119-126. Nielsen, J.P. and Gleason, P. (1945). Ind. Eng. Chem. Anal. Edn. 17:131-134. Nielsen, J.P., Campbell, H., Bohart, C.S. and Masure, M.P. (1947a). Food Ind. 19:432436, 479482,580. Nielsen, J.P., Campbell, H., Bohart, C.S. and Masure, M.P. (1947b). Food Ind. 19:305-308, 432436,4794182,580. Nijssen, L.M. and Maarse, H. (1986). Flavor Frag. 3 . 194(5):143-148. Nikdel, S. (1991). In 4Znd Annual Citrus Processor’s Meeting, Citrus Education and Research Centre, Lake Alfred, F1, USA, pp. 23. Nikdel, S., Nagy, S. and Attaway, J.A. (1988). In Adulteration of Fruit Juice Beverages, eds S.Nagy, J.A. Attaway and M.E. Rhodes, Marcel Dekker, New York, pp. 81-105. Nissenbaum, A. and Feld, M. (1980). Znt.J Appl. Radiat. Isotope 31:127-128. Nissenbaum, A,, Lifshitz, A. and Stepek, Y. (1974). Lebensm. Wiss. Technol. 7(3):152-154. Ohta, H., Shimizu, Y., Kawano, S., Hayakawa, A., Watanabe, A. and Kimura, S. (1980).J Jpn. Sol. Food Sci. Technol. 27(7):35+357. Oke, M.S. and Shrikhande, A.J. (1977).J FoodSci. Technol., India 14(6):280-281. Oke, M.S. and Shrikhande, A.J. (1979). In Proceedings of the First Indian Convention of Food Scientists and Technologists, Association of Food Scientists and Technologists (India), Mysore, India No. 5.2, pp. 52-53. Ooghe, W. (1980). Voedingsmiddlentechnologie 13(15):11-15; (17):lS-17. Ooghe, W. and Kastelyn, H. (1985a). Voedingsmiddelentechnologie 18(23):13-15. Ooghe, W. and Waele, A.de (1982a). Voedzngsmzddelentechnologie15(4):28-33; (9)64-68. Ooghe, W. and Waele, A.de (1982b). Flussiges Obst. 49( 11):618-636. Ooghe, W., Kasteleyn, H., Temmerman, I. and Sandra, P. (1984). J High Resolut. Chromatogr. Chromatogr. Commun. 7(5):28+285. Oszmianski, J. and Lee, C.Y. (1991).J. Agrzc. Food Chem. 39:105&1052. Otteneder, H. (1975). Indust. Obst. Gemuseverwertung 60(7):173-178.

Chapter 4

Milk and Milk Products 4.1 Introduction 4.2 Milk of different origins 4.2.1 Ewe's, goat and cow milk 4.2.2 Cow milk and buffalo milk 4.2.3 Human milk 4.2.4 Soy milk in cow milk 4.3 Whey or buttermilk in milk 4.3.1 Whey proteins in milk products 4.4 Reconstituted milk 4.5 Adulteration in milk and other dairy products 4.6 Other fats in milk fat, butter or ghee 4.6.1 Vegetable fats 4.6.2 Fats of animal or marine origin 4.6.2.1 Method based on the solubility of ghee 4.6.2.2 Grossfield number 4.6.2.3 Critical temperature of dissolution 4.6.2.4 Urea fractionation 4.6.2.5 Fluorescence in ghee 4.6.2.6 Methods based on hydroxamic acid index 4.6.2.7 Chromatographic techniques 4.6.3 Other adulterants 4.7 Dilution of milk with water 4.7.1 Other indices for detecting added water in milk 4.8 Indices of microbial quality of dairy products 4.8.1 Methods based on the measurement of metabolic activity 4.8.1.1 Dye reduction tests 4.8.1.2 Electrical methods 4.8.1.3 Microcalorimetry 4.8.1.4 Flow cytometry 4.8.1.5 Fluorescence 4.8.1.6 Enzymic methods 4.8.2 Methods based on the measurement of metabolic intermediates and by-products 4.8.2.1 Pyruvate

132 Handbook of indices of food quality and authenticity 4.8.2.2 Endotoxins by the Limulus amoebocyte lysate test 4.8.2.3 Carbon dioxide by radiometry 4.8.2.4 ATP determination by bioluminescence 4.8.2.5 D-Amino acids 4.9 Indices of aesthetic quality of dairy products 4.9.1 Sediment 4.9.2 Decomposition 4.9.3 Mastitis 4.10 Quality of cheese References

Milk and Milk Products

171

Correlation between freezing point depression and content of fat (F), density (d) and conductivity (c) has been observed after analysis of more than 250 samples (Welboren and Velden, 1974). T h e equation proposed is: Freezing point depression=0.0101F+ 10.815d+0.0312c-10.7694

[4.12]

Freezing point of milk can be affected by various factors (Macdonald, 1950; Unger et al., 1984; Rohm et al., 1992; Buchberger, 1992) such as breed (Zee, 1977), 1977), lactation stage and mastitis; geographical area (Henningson, 1959; Dahlberg et al., 1953), season, feeding and management regimes (Schroppel, 1992; Mohammedi et al., 1992);

water intake, spontaneous change in milk souring and natural deaeration; treatment of milk like cooling, freezing and heating and addition of preservatives (Belgian Standard, 1977). The effect of vacuum pasteurization on the freezing point value (Henningson and Lazar, 1959) must be considered in the case of applicable retail milks. Freezing point increases by 0.006-0.009 °C during pasteurization (Staub and Krahenbuhl, 1954; Buchberger, 1986) and by 0.023 °C for UHT milk. Therefore although freezing point may detect added water in raw milk, it is not always a reliable indicator in heat treated milk (Buchberger, 1986). Also, the results for samples having acidity >0.18 gg lactic acid/100 ml are not representative of the original milk (French Standard N F V 04-205, 04-205, 1990). Freezing point differences seem to be connected to the temperature of the environment, a lower freezing point being recorded at the milking following exposure of the animal to high temperatures. No significant correlation between milk yield and freezing point has been observed, but a low positive correlation between solids-not-fat and freezing point seems to exist. Small seasonal changes in freezing point, particularly when the cows have access to lush spring pasture have been reported. Age and state of lactation, as well as the time of milking, that is morning or evening on any particular day are not known significantly to influence the freezing point (Aschaffenburg and Veinoglou, Veinoglou, 1944). The chlorine/lactose ratio, an indicator of mastitis milk, has not shown any strict relation to the freezing point (Jasinska, Uasinska, 1953). Potassium dichromate is sometimes added as a preservative to samples of milk held as evidence in judicial proceedings. This is known to lower the freezing point. Addition of just 1% 1% soybean milk can increase the freezing point by as much as 0.003 °C (Huh, 1971), and 2% added brine can reduce the freezing point by 0.054 °C on an average (Huh, 1971). A minimum freezing point depression standard, based on area data and administered in a manner similar to a minimum butter fat standard appears to be the most feasible way of utilizing the cryoscopic method for the determination of added water in milk (Henningson, 1961). Freezing point determination is not sensitive enough to detect dilution of buttermilk with water because of great variation in the freezing point of the serum. Refractive index and specific gravity are suggested for routine control work and also require less preparation. If the buttermilk is not fresh, ash determination is the only

2 L4

°C

°H

172 Handbook of indices of food quality and authenticity

&

a

a

-

Y)

3

w

.b-

0

c

L

? Y ._

m N

c m ._

c1

t ._

c

U m

g

v) v)

0 c ._

-

I

c

m

c v)

._

c

m

n

U

m

U

?2

c

Table 4.15 Use of goat milk freezing point depression (FPD) to estimate added water

Milk and Milk Products

173

suitable method for detecting added water, even though there is a large possible error involved (Kiermeier and Pirner, 1956). The normal range of freezing point accepted is -0.528 °C to -0.561 °C (Atherton and Newlander, 1977). There is scanty literature on the freezing point of caprine milk. A mean of -0.582 °C has been reported by some Italian workers (Princivalle, 1948). An overall mean freezing point of -0.5527° C has been considered to be representative of caprine milk in Ontario. In the case of caprine milk, relationships for freezing point depression (FPD) in terms of °H or °C and added water are reported. Table 4.15 shows the use of goat milk freezing point depression (FPD) to estimate added water. Equations [4.13] [4.13] and [4.14] [4.14] giving the percentage of added water in goat milk from the freezing point depression are as follows: O/O

% Added water= 100 (-0.552 "C-FPD)/-0.552 °C - FPD)/ - 0.552

[4.13] [4.13]

O/O

% Added water=100 (-0.572°H - FPD)/ - 0.572

[4.14]

It has been reported that the degree of hydrolysis of lactose in milk is directly related al., 1980). When all the lactose in to the depression of the freezing point (Nijpels et a/., milk is hydrolysed to monosaccharides, the freezing point is known to decrease by -0.274 °C. Figure 4.3 (Jeon (Jeon and Bassette, 1982) shows the regression line that relates percentage lactose hydrolysis in milk to the depression of freezing point. Superimposed upon that line are the freezing points of standard sugar solutions representing

20

40 60 80 Lactose hydrolysis (%)

100

Figure 4.3 Linear relationship between the depression of freezing point and percentage hydrolysis of lactose. 0,Percentage lactose hydrolysis determined by chemical analysis of milk. 0, O Standard sugar solutions containing glucose, galactose and lactose prepared at molar concentrations equivalent to a 5% solution of lactose hydrolysed at 0%. 25%. 50%. 70% and 100%. (Source: Jeon and Bassette, 1982, reproduced with permission)

174 Handbook of indices of food quality and authenticity Table 4.16 Effect of adding water to lactose-hydrolysed milk on freezing point and lactometer readings Lactose-hydrolysed milk Lactose hydrolysis

Water added to lactose-hydrolysedmilk

Freezing point (°H)

Water added to milk

(O/O)

Calculateda'

Achievedb

By ratio

By percent

0 22 35 50 67 85

-0.543 -0.603 -0.639 -0.680 -0.726 -0.775

-0.543 -0.604

0 0.110

0 9.9

-0.641

0.175 0.250 0.335 0.425

14.9 20.0 25.1 29.8

-0.685 -0.726 -0.775

Resultant Quevenne freezing reading point (°H) -0.543 -0.540 -0.538 -0.537 -0.536 -0.535

32.5 29.2 27.5 26.3 24.5 22.3

aCalculated

from the freezing point depression curve (Fig. 4.3). by mixing the lactose-hydrolysedmilk with control (unheated milk). Source: Jeon and Bassette, 1982 (reproduced with permission).

bAchieved

0, 25, 50, 75 and 100% lactose hydrolysis. Table 4.16 shows the effect of adding water to lactose hydrolysed milk on the freezing point and lactometer readings. By careful manipulation, a considerable amount of water can be added to lactosehydrolysed milk and still maintain the freezing point within the normal range for milk. In a study, a taste panel also failed to distinguish between control undiluted milk and the hydrolysed milk diluted with water to near its original freezing point.

4.7.1 Other indices for detecting added water in milk Adulteration of pasteurized buffalo milk with 5-20% 5-20°/o water decreases the electrical conductivity progressively (Montefredine, 1942; Grasshoff, 1988). This can be used as an analytical parameter to check fraud (El-Shabrawy and Haggag, 1980). Correlations between the freezing point of milk and lactose content, and the electrical conductivity at 37 °C "C and 0 °C "C to detect added water have been attempted, but are not found to be statistically significant (Peters et al., al., 1959). This could be because of the great variation in lactose which is dependent on the state of pregnancy of the cow (decreasing with progressing pregnancy); on the feed, which is low with grass; on the acidity, diseases etc. Lactose content however does not vary with the age of the animals, the stage of lactation or the hour of milking (Giuseppe, 1952). Two formulae for checking that milk is not adulterated with water are: (2 X SNF) -(protein + lactose)>9.1

[4.15]

and SNF>lactose + protein + 0.7

(Panero, 1975)

[4.16]ctose)>9.1 [4.16]

Analysis of 2624 milk samples in Spain has shown surface tension and viscosity as

Milk and Milk Products

175

suitable indices for detecting >10% added water to milk (Goded, 1951). Trypsin digestion of the protein in buffalo milk samples followed by precipitation of the undigested protein with trichloroacetic acid and further measuring the decrease of absorbance of the supernatent at 280 nm has been correlated with the degree of dilution. This method gives simple reproducible results compared with those obtained using a cryoscope for freezing point determination (Ali and Hasnain, 1987). Another practical method recommended for testing diluted milk in factories is based on the comparison of percent fat in milk and in the dry substance (Madsen, (Madsen, 1948). The Olivari constant, CSD=Q+3.85C, where Q i s the specific gravity of acetic acid milk serum at 15 °C in °Quevenne, and CCis the chloride in acetic acid milk serum in gg sodium chloride/l can also indicate water in milk. The values of CSD for normal milk range between 34 and 36. Addition of sodium chloride tends to raise the CSD to >36. Dilution with 1% sodium bicarbonate is not detected by the freezing point but can be detected from the Olivari constant, CSD (Chioffi, 1977). Analysis of nitrate in milk could also be used as a clue to detect water addition (Maksimets et al., 1988; Tomeo Ibarra and Bergeret, 1959). Tests based on specific gravity, using especially designed lactometers (Hostettler, 1956) are considered by some authors to be more suitable for detecting added water in milk in developing countries (Dahlberg, 1955). Some formulae for calculating water addition and/or removal of butter fat are also reported (Siegenthaler and Schultess, 1977). Addition of 1% water is known to decrease the specific gravity by 0.0034 (Huh, 1971). Slide rules, based on the differences between.the density of milk and a national or regional standard have been developed to determine the amount of water added to milk (Kapianidze, 1984). The refractive index of the serum obtained from normal milk (6.3-6.7 °SH acidity) by acidification with acetic acid (Anselmi, 1941), 1941), or by boiling milk with copper sulphate (Anas and Noya, 1949; Hoffmann, 1951), potassium ferrocyanide or calcium chloride can detect only above 10% water additions (Slanovec and Arsov, 1977). It is advised that samples in the 37-38 refractometer number range should be regarded as very suspect, those in 33-37 range as diluted and those in 30-355 range as heavily watered (Taborsak and Abramovic, 1978). Ultracentrifugation of human milk followed by refractometry on ultrafiltrate can be used to detect added water in human milk. Deviations below normal readings of 44-46 indicate dilution with water (Sager, 1952). A refractive constant given by K=[(n 2 -1)]/ (n2+ 2)d] is known to be an effective indicator of watering in milk. The terms n and dd denote refractive index of the serum and density as determined with Quevenne's lactometer (Venkatasubramanian and Ramakrishnan, 1951). A high degree of correlation (>0.99) (Rohm, 1986) has been reported between freezing point determination using a thermistor cryoscope and a vapour pressure osmometer under optimum conditions (temperature raised to 20 °C before testing) in milk samples with known amounts of water added. Added water can therefore be calibrated in terms of the osmometer, which has the advantages of small sample size,

176 Handbook of indices of food quality and authenticity Table 4.17 Percent added water in adulterated milk samples as calculated from the vapour pressure measurement (correlation coefficient. 0.997) Actual water added (%) 0 1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 25 30 40 50 a Percent

Osmometer reading (mosmol)

Calculated added water (%)a

0 1.2 1.9 2.8 3.6 4.7 6.1 7.2 8.0 9.2 9.7 10.9 11.4 12.7 13.5 14.7 20.8 25.1 30.5 40.1 45.2

265.0 261.3 259.3 256.7 254.3 250.7 246.7 243.3 241.0 237.3 235.7 232.3 230.7 226.7 224.3 220.7 202.3 189.3 173.3 144.3 129.0

added water was calculated from milliosmolal reading using the

formula: % O/oAdded water

=

(R- S)X100

R where R= osmometer reading for milk known to be free of added water (in mosmol) and S=reading S= reading of the sample (in mosmol). Source: Mitchell, 1977 (reproduced with permission).

ease of calibration and the fact that the instrument requires no operator attention once the sample has been inserted (Mitchell, 1977). Table 4.17 shows percent added water in adulterated milk samples as calculated from the vapour pressure measurement. T h e major sources of error in the use of the osmometer are variation in sample size and contamination of the thermocouple with milk residue. T h e vapour pressure osmometer method for quantitating added water in milk has been adopted as official al., 1978). first action (Richardson et al.,

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4.8 Indices of microbial quality of dairy products Interest in bacteriological testing of milk stems from the fact that bacteria in milk can cause spoilage as well as disease. From a public health standpoint the importance of bacteriological examination was quickly recognized and has gradually become a regular practice. In Europe, the International Dairy Federation has been active in standardizing methods for the examination of dairy products. With marked advances in the eradication of bovine tuberculosis and brucellosis together with widespread pasteurization of milk, interest in the bacteriological testing of raw milk has largely shifted from the disease aspect. In present times, examinations are more concerned with obtaining an estimate of the degree of care taken in the production and handling of milk on the farm. Quality assurance of dairy products entails applications of hazard analysis and critical control points (HACCP) backed up by laboratory analysis of in-line samples and finished products. Two main questions arise in monitoring the microbiological quality of foods: ‘what kinds of microorganisms are present?’ and ‘how many microorganisms are present?’ The number and variety of tests required depend on factors such as the final use of the product, consumer specification and the nature of the process. The standard plate count (SPC) is important as an indication of sanitary conditions of production and handling (American Public Health Association, 1953). Although generally conceded to be most precise for assessing the bacterial population, SPC is not without limitations. No one medium incubated for a short time at a given temperature will bring out all the bacterial types present. Furthermore, colonies may represent single organisms or clumps of several, indicating a considerable inherent error (Wilson, 1935). It should also be emphasized that low SPC in a fresh sample is no guarantee of adequate keeping quality (Johns, 1959; Olson et al., 1953). Acidity monitoring is sometimes done to get a check on quality (Guillermo, 1951). Direct microscopic count (DMC) furnishes a bacteriological estimate within a few minutes. It also enables counts of body cells such as leucocytes, lymphocytes, etc. to be made, a feature especially valuable in indicating mastitis. Somatic cell counts have been correlated best to the percentage of lactose (correlation coefficient, r = -0.398) and a slight positive correlation to the protein content (r = 0.101) (Packard and Ginn, 1991). While DMC has been recommended in place of SPC for the control of pasteurized milk (Mickle and Bolman, 1943), it has not been generally adopted for this purpose. However, it has been used in the control of skim milk powder (Forest and Small, 1959), where it gives valuable evidence of past history of the product not obtainable by the viable count. A modification of the DMC for making counts of thermoduric bacteria has been proposed (Mallmann et al.,1941), but has not been adopted, possibly due to poor agreement with the plate count method (Fischer and Johns, 1942). Thermoduric bacteria are sufficiently heat resistant to survive pasteurizing temperature and thus may be responsible for counts in excess of the legal limits of the pasteurized products. They enter milk chiefly from the surfaces of inadequately cleaned milking and

178 Handbook of indices of food quality and authenticity handling equipment and thus are indication of unsanitary conditions. Thermoduric count is believed by some sanitarians to be more useful than SPC (Barnum, 1959). Thermophilic bacteria capable of growing at holder pasteurization temperature were a serious problem when batch pasteurization was common. With the trend towards higher temperatures with high temperature short time (HTST) and UHT pasteurization, the interest has diminished. These organisms can be detected by incubating plates at 55 °C for 48 h, by direct microscopic examination of the smears or by the dye reduction test (methylene blue or resazurin) incubated at 62-63 °C (Kay et al., 1953). Positive clearance of product before dispatch is often necessary and may result in lengthy 'holding times'. Contamination of pasteurized milk with unpasteurized milk or unpasteurized cow milk is often implicated in outbreaks of salmonellosis (Ryan et al., 1987; Rowe et al., 1987) and campylobacter enteritis (Jones et al., 1981; Barrett, 1986). Enumeration of Escherichia coli seems to have value as an indicator of faecal contamination and thus potential hazard in raw milk (Humphery and Hart, 1986; 1988). The presence of E.coli is also indicative of a likely contamination by Campylobacter jejuni. For instance, it is reported that the mean number of E. coli/ml in campylobacter positive milk is 212.7±105.6, while that of the negative sample is 39.17±20.2. Experience suggests that human infections with campylobacter are more common than salmonellosis (Potter et al., 1984), a fact substantiated from results from various parts of the world (Humphery and Beckett, 1987; Doyle and Roman, 1982; Oosterom et al., 1982; Lovett et al., 1983; De Boer et al.,1984; Waterman et al., 1984). Psychrophilic bacteria growing actively at 7.2 °C (45 °F) should not be overlooked. With milk being held longer at refrigeration temperatures from cow to the consumer, opportunities for the growth of psychrophiles have increased greatly. Many of them are lipolytic and proteolytic, and are capable of inducing flavour changes and other defects in milk and milk products on refrigerated storage. The degree of lipolysis is in fact a quality index of cream and butter (Vyshemirskii et al., 1982). Spoilage in pasteurized milk, cream and cottage cheese is generally due to psychrophilic growth. Pasteurization destroys these organisms, and therefore their presence indicates recontamination.The enumeration of psychrotropic bacteria count (PBC) obtained on the dry petrifilm medium culture plates with triphenyltetrazolium chloride also serves as an indicator of the quality of milk (Bishop and Juan, 1988). In pasteurized products, the use of the coliform test to detect recontamination has been more generally acoepted. Stemming largely from the work of McCrady and his coworkers (McCrady and Langevin, 1932) the value of this test, to both sanitarians and management has steadily received wider recognition. When applied to products containing other sugars in addition to lactose, for example, ice cream, positive results must be confirmed to avoid misleading conclusions. False positive results have been reported where sweetened and unsweetened fresh fruits are added to the mix (Barber and Fram, 1955). Enterococci are also more useful in detecting faecal contamination of cheeses (Brooks, 1974). Proper pasteurization of milk or cream destroys yeasts and moulds and therefore

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their presence in a product indicates recontamination. Mould and yeast counts are employed by cheese manufacturers and sanitarians as indices of plant sanitation. When cream is stored on the farm at unsuitable temperatures for too long periods, growth of Geotrichum candidum takes place and on pasteurization, dead mycelia pass into the butter in appreciable numbers. Their detection is frequently employed by food and drug officials as the basis for seizure and confiscation of butter. Organisms capable of proteolysing casein are frequently responsible for undesirable flavours in dairy products. Surface taint of butter caused by Pseudomonas putrefaciens (Derby and Hammer, 1931) is an example. Organisms attacking fats are often also proteolytic and psychrophiles. This makes them particularly important in butter, cream and cottage cheese, which are frequently held refrigerated for extended periods. Interest has been aroused by food poisoning outbreaks attributed to the presence of Staphylococcus eneterotoxin in non-fat milk solids and in cheddar cheese and its modifications. While toxin production in raw milk itself rarely presents a hazard to health due to repression of the growth of staphylococci by other types, some growth may take place both before and during the cheese making process (Takahashi and Johns, 1959). Cheese with excessive numbers of coagulase-positive staphylococci must therefore be regarded with suspicion. The presence of antibiotics in milk, either residual from therapy or by deliberate addition, can influence the results of bacteriological examination (Foley and Byrne, 1950; Johns and Katznelson, 1949; Wilkowske and Krienke, 1951), 1951),in addition to antibiotics that cause problems in the manufacture of products dependent upon lactic fermentation and the possible hazard to those individuals acutely sensitive to penicillin. Tests have been reported to detect antibiotics in milk, based upon interference with bacterial growth and activity. One of the simplest is the starter activity test (Silverman and Kosikowski, 1952), patterned after that introduced by Horrall and Elliker (1947). Here, the extent of acid development when inoculated with a lactic starter and incubated for several hours is compared with that of a control. Care must, however, be taken to exclude the action of naturally occurring inhibitory substances. Greater sensitivity can be obtained by using Streptococcus thermophilus in place of the common starter streptococci. Another form of test utilizes a redox indicator to reflect interference with bacterial growth when incubated at a suitable temperature; triphenyl-tetrazolium chloride is the indicator commonly recommended (Neal and Calbert, 1956), 1956), although methylene blue (Galesloot, 1955; Schipper and Petersen, 1951) and resazurin have been used. The disk assay method has also been studied extensively. In its standard form, it is most useful for detecting the presence of penicillin; the test organism, Bacillus subtilis subtilis,is less sensitive to other antibiotics (Johns and Berzins, 1955). An interesting modification of this method has been described (Shahani and Badami, 1958), 1958), wherein the agar layer is flushed with resazurin; the completed test takes considerably less time than the standard disk assay method. Apart from antibiotics, sulphonamides are frequently used in combination with certifiable antibiotics for the treatment of mastitis in dairy cows. The sulphonamides

180 Handbook of indices of food quality and authenticity appear in milk immediately after the drug is infused into the udder and may persist in subsequent milkings over a period of several days. Such milk is not considered suitable for food use and must be withheld from the channels of commerce. Methods to detect these sulphonamides have been proposed (Selzer and Banes, 1963). The rapid, accurate and reliable evaluation of total viable cell counts is very important in the efficient monitoring of microbiological quality, especially in raw and ready-to-eat foods. Indirect or non-microbiological methods offer the potential for rapid monitoring of microbial load in terms of metabolic intermediates or end products. Methods such as DEFT (direct epifluorescent filter technique) (Pettipher et al., 1980), polymerase chain reaction, especially for Listeria monocytogenes (Starbuck et al., 1992) and Bactosan are in vogue today.

4.8.1 Methods based on the measurement of metabolic activity 4.8.1.I Dye reduction tests The dye reduction test (Smith and Zall, 1977) is based on the observation of changes brought about in the medium by the metabolic activities of viable microorganisms. Bacterial dehydrogenases transfer hydrogen from a substrate to a redox dye, which undergoes a colour change. The number of organisms present in the sample is correlated with the rate of colour change reaction. A number of dyes, including methylene blue, resazurin and tetrazolium have been used. The methylene blue reduction test, introduced in Denmark and Sweden around 1912 is probably the most extensively used bacterial test. The SPC at 32 "C °C is less likely to disagree with the dye reduction test (Harris et al., 1956). Although as a measure of mean keeping quality methylene blue was slightly better than resazurin, standard deviations have shown a wide scatter of keeping quality for specified standards by either dye test (Anderson and Wilson,

1945). Several factors have tended to distort the relationship between counts and dye reduction times. More productive media and lower incubation temperatures have increased the levels of plate counts. The proportion of thermoduric bacteria influence the reduction, since they are slow reducers. Antibiotics in milk tend to slow down the reduction. Psychrophiles, which sometimes comprise a high percentage of the flora (Johns and Berzins, 1959), 1959), fail to grow at 35-37 °C. Finally, with more efficient cooling, some bacteria are extremely dormant at the start of the test and substances inhibiting bacterial growth are conserved. The resulting prolonged lag phase delays reduction in such a manner that some high count milks escape detection. The creaming error, caused by sweeping varying proportions of the bacterial population on to the surface with rising fat globule (Wilson, 1935) has also been referred to. This is, however, overcome by inversion of the tube every half an hour. The methylene blue reduction test is also sensitive to levels of Cu, added sodium sulphate, and is affected by pH (being least at pH 8.0-8.5) and agitation (Maeno and Asahida, 1954). Several

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workers have reported that the grading based on dye reduction tests is too lenient. In the winter months, a high percentage of samples with high bacterial counts are not detected (Malcolm and Leitch, 1936; Thomas and Tudor, 1937). T h e methylene blue reduction time in milk is an index of bacteriophagic activity against true lactic acid bacteria. Compared to other methods, the reduction in milk is more sensitive and shorter. T h e test also allows the interaction between phage and bacteria to be followed colorimetrically (Miklik, 1951). This technique has been primarily used for the examination of milk, although it has also been adapted for the examination of other foods. In Europe, dairies use the methylene blue reduction test as an index of keeping quality of pasteurized products (Olsen, 1956). A rapid dye reduction test can sometimes be due to aerobic spore formers which survive pasteurization (Olsen, 1956). This test is, however, considered to be inadequate in assessing the suitability of milk for cheesemaking from the viewpoint of bacterial contamination (Gudkov et al., 1979). Use of the dye resazurin as a redox indicator offered the advantage of earlier indication of change. This is affected by the presence of excessive numbers of leucocytes, etc. and thus could indicate the presence of abnormal milk (mastitis or late lactation) (Ramsdell et al., 1935). Well-cooled milks containing excessive numbers of dormant bacteria often escape detection (Hempler, 1953). In the farm bulk tanks the organisms are so dormant that reduction is delayed appreciably. A preliminary incubation is most useful in overcoming this dormancy and also in encouraging the growth of saprophytic organisms (Johns and Berzins, 1959). Another oxidation-reduction indicator, triphenyltetrazolium chloride (Mustakallio et al., 1955) is unfortunately extremely sensitive to light. Its usefulness appears to be confined largely to heavily contaminated milks, although it has been advocated for use in the detection of antibiotics and other inhibitory agents in milk (Neal and Calbert, 1956), and in a keeping quality test for pasteurized milk (Day and Doan, 1946; Broitman et al., 1958) and condensed milk (Luk'yantseva et al., 1978).

4.8.1.2 Electrical methods Impedance is the resistance to the flow of an alternating current through a conducting medium. Impedimetry can be used to monitor the changes in the electrical properties of a culture medium that are brought about by the growth of microorganisms in the medium, as nutrients are converted into metabolic products. Complex uncharged molecules such as carbohydrates are catabolized to smaller charged molecules such as bicarbonate and organic acids. As the microorganisms grow, this process leads to a decrease in the overall impedance of the medium. Thus, measurement of the changes in the electrical impedance of microbial cultures provides a means of detecting microbial proliferation (Gnan and Luedecke, 1982; O'Connor, 1979). T h e technique can detect as few as 102-103 cells ml 1 within 2 h, depending upon the sensitivity of the instrument used. It therefore reduces the holding time needed for microbiological screening (Wood et al., 1978; Firstenberg-Eden, 1983). In marginal samples, where the

182 Handbook of indices of food quality and authenticity Table 4.18 Comparison of shelf life, impedance response detection time, standard plate count and psychrotropic count for 10 milk samples Shelf life (days)ays)

Detection timea (hours)

Mesophilic Psychrotropic plate countb countc -1 (cfu ml ) ml-') (cfu ml-1) mi-')

9 9 10 10 10 10 14 14 14

9.4 12.2 9.6 9.4 10.4 11.1 10.9 11.5 11.4 10.3

400 7000 400 200 200 300 400 100 100 200

15 15

aEarliest detection of duplicate vials at 32 °C. bIncubation at 32 °C for 48 h. cIncubation at 7 °C for 7 days.

20 30 10 100

10 100

30 10 100

"C.

Source: Cady et al., 1978 (reproduced with permission).

total number of coliforms are low (e.g. positive in 1 g, but negative in 0.1 g), sample variation might be expected to cause variable results, even with duplicate samples tested by the same method. Overall, the impedance method gives more positives in themarginal samples than the standard method, suggesting that low coliforms are more likely to be detected by the impedance method (Fryer and Forde, 1989). Large numbers of bacteria generally require less time to reach the threshold level and produce an impedance change. In Table 4.18 are shown the mesophilic plate count, the psychrotropic count and the impedance response detection times for 10 samples with varied shelf lives. In general, the detection times appear to reflect the values of the shelf life. T h e detection time, in fact, seems to correlate better with the shelf life than do the standard plate count and psychrotropic count, and shows promise of being useful in predicting keeping quality (Cady et al., 1978). Analysis of 243 samples with counts varying from 3 X 103- 6 X106 cfu ml -1 has shown a coefficient of correlation of -0.657 and a standard deviation of 0.441 between SPC and impedance detection time. T h e method of sample agitation, that is, standard or violent does not affect the results (Piton and Dasen, 1988). Impedance methods for other groups of organisms such as Salmonella are also available (Easter and Gibson, 1985). A three-way classification scheme has been explored whereby samples producing impedance changes prior to 7 h were classified as having >l0 4 organisms ml-1, those with impedance changes between 7 and 9 h as having between 2000 and l0 4 organisms ml-1 and those with impedance changes >9 h as having less than 2000 organisms ml -1.This scheme could correctly classify 85% of samples tested. These results can be

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Table 4.19 Comparison of the results obtained by the Malthus system and by the standard plate count method Sample no.

1 2 3 4 5 6 7 8 9 10 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 28 29 30 31

Standard plate count (cfu ml-1)

190 000 94 000 220 000 190 000 8 2000 1000 000< 1000 000< 1000 000< 1000 000< 5 200 1000 000< 1000 000< 1000 000< 1000 000< 1000 000< < 10 1000 000< 1000 000< 1000 000< 14 000 240 000 1000 000< 24 000 100 1000 000< 1000 000< 1000 000< 8. H >8. T h e examination of milk in filtered ultraviolet light is a suitable preliminary test for diseased udders. When a deviation from normal yellow fluorescence is found, it is necessary to make a more detailed microscopic and bacteriological investigation (Schonberg, 1943).

4.8.1.6 Enzymic methods Enzymes such as catalase serve as markers for detecting the postpasteurization contamination of milk with gram negative bacteria. Preincubation of pasteurized milk for 24 h at 30 30"C, °C, after adding penicillin and bile salts at 5 I U (1 IU=0.6 IU= µg pg benzylpenicillin sodium) and 1 mg ml-', ml -1 , respectively raises the numbers and catalytic activity of gram negative recontaminant bacteria to a point where they can be easily detected by oxygen release from hydrogen peroxide (>10 000 cfu ml-1), while suppressing growth of thermoduric gram-positive bacteria. T h e limit of detection is reported to be approximately 3 -44 bacteria /100 ml (Spillmann et al., 1988). Milk characterized by an abnormally high chloride and catalase content, by appreciable sediment and by the presence of many leucocytes and streptococci is an almost certain diagnosis of mastitis (Zollikofer, 1941). In raw milk also, bovine catalase can be separated from microbial catalase using the fact that atppH>9, H bovine catalase activity decreases, but that of bacterial origin peaks at ppH H 11. Under these conditions, counts as low as 103 ml-1 can be easily measured (Doi et al., 1992). Oxidase activity is not related to total bacterial count of raw milk, but is related to psychrotropic count except in samples with very high total counts. Oxidase negative milk is known to show a better keeping quality after heat treatment (65 °C for 30 min) compared with oxidase positive milks (Kyla-Siurela and Antila, 1974). In a study on milk samples from various farms, 75% of samples with less than 200 psychrotrophs ml-l were found to be oxidase negative, while all samples with more than 200 000 psychrotrophs ml-1 were found to be oxidase positive. T h e cytochrome oxidase test is known to give the same overall distribution of samples among grades as the methylene blue test, although results are sometimes known to differ among individual samples. A linear relationship between cytochrome oxidase activity and bacterial concentration in the range of 103 -108 has been reported (Rongvaux-Gaida and Piton-Malleret, 1992).

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Table 4.20 Some methods and tecnniques used for assessing shelf life of pasteurized milk and cream Method

Correlated to

Microbial counting Without preincubation Flavour related shelf life (a) (FRSL) at 7 °C skim milk whole milk

Correlation coefficient

Principle/performances

Time to complete (days)

Initial psychrotrophie bacteria count

2

Bacterial count related shelf life (BCRSL)=time to reach count of >=log 7.5 ml-1 -0.72 at 6 °C at 10°C -0.62

Initial psychrotrophic bacteria count

2

FRSL

-0.77

Incubation of milk at 7 °C/5-7 days then plate count after 48 h at 32 °C

9

(d) Moseley count

FRSL

-0.84

(e) Moseley count

Impedance detection time of preincubated samples

(b)

With preincubation (c) Moseley count

(f) Preincubation FRSL at 7 °C with selective medium with agents at one benalkonium chloride temperature or with crystal violet

(g) Preincubation BCRSL at 6 °C, shelf life= with selective time to reach count agents at one log 7 ml-1 temperature milk cream (h) Preincubation Correlation at 2-14 °C in milk, count between BCRSL and To on milk agar with selective agents to predict shelf life at any temperature up to 1 day accuracy Non-enumerative methods Related to microbial growth and/or microbial activity

-0.61 -0.70

7

0.71

-0.89 -0.88

-0.82 -0.76 -0.6-0.8

Preincubation in selective media 18 h/21 °C, plate count after 25 h/21 °C

2

Preincubation 21 °C/25 h 2 in milk containing crystal violet, nisin and penicillin plate count after 25 h/21 °C Preincubation at 12 °C, 2.2 15°C, 18°C and 21°Cin milk, count on 3 different selective milk agar media. Depends on temperature, for 37-89% of samples stored at 2-14 °C shelf life predicted with 15 °C

208 Handbook of indices of food quality and authenticity Vanoni, M.C., Colombini, M and Amelotti, G. (1979). Riv. Ital. Sost. Grasse 56(12):468-471. Vedanayakam, A.R., Krishnasamy, S. and Narasimhan, R. (1972). Indian Vet.3.49:123&1243. Venkatachalam, V. (1937). Analyst 62:732-733. Venkatasubramanian, T.A. and Ramakrishnan, C.V. (195 1). Sci. and Culture 17:26&261. Villaneuva, S.B., Fernandez de la Reguera, B.P. and Pinto, C.M. (1988). Agro Sur 16 (1):47-52. Vitagliano, M. and D’Ambrosio, A. (1957). Latte 31:15-26. Vitagliano, M. and D’Ambrosio, A. (1956/1957). Ann. Fac. Agar. Univ. Napoli Portici (Naples) 22:35-68. Voss, E. and Moltzen, B. (1973). Milchwissenschaft 28(5):282-284. Vyshernirkii, EA., Lyzhenkova, I.I., Poyarkova, G.S. and Chuzhova, Z.P. (1982). Molochnaya Promyshlennost 9: 17-19. Wagner, A., Demko, L. and Merenyi, I. (1984). Ejipar 33( 1):21-22. Waterman, S.C., Park, D.W.A. and Bramley, A.J. (1984). J Hyg. 93:333-337. Welboren, J.T. and Velden, H. Van der. (1974). Mitt. Gebiete Lebensm. Hygiene 65(1):151-156. West, D.W. (1986)._7.Dairy Res. 53: 333-352. Wilkowske, H.H. and Krienke, W.A. (1951).J Milk Food Technol. 14 92-94. Wilson, G.S. (1935). The Bacteriological Grading o f Milk. Medical Research Council, Special Report, 206. Windham, E.S. (1957).J Assoc. OBc. Agric. Chem. 40(2):522-531. Wolfschoon-Pombo, A.E and Furtado, M.A.M. (1989). Z. Lebensm. Unters. Forsch. 188(1):1 6 2 1. Wolfschoon-Pombo, A X and Klostermeyer, H. (1986). Z. Lebensm. Unters. Forsch. 182:103-106. Wolfschoon-Pombo, A.E and Pinto, A.P.E. de F (1985). VCienc. Tecnol. Aliment. 5: 11 1-1 15. Wood, J.M., Lach, V.H. and Jarvis, B. (1978). Evaluation of Impedimetric Methodsfor the Rapid Estimation of Bacterial Populations in Foods. Leatherhead Food Research Association Report No. 289. Wood, R.W. (1950). Science 11236. Younes, N.A. and Soliman, M.A. (1986). Grasasy Aceites 37:20&203. Younes, N.A. and Soliman, M.A. (1987). Grasasy Aceites 38(6):372-374. Younes, N.A. and Soliman, M.A. (1988). Grasasy Aceites 39(2):69-71. Youssef, M.K.E. and Rashwan, M.R.A. (1987). Proc. European Meeting ofMeat Res. WorkersNo. 33, Vol. I1 8: 4, pp. 373-376. Zalazar, C.A., Meinardi, C.A. and Palma, S. (1992). Rev. Argentina Lactologia 4(6):57-62. Zeder, E (1984). Alimenta 23(4):109-112. Zee, B. (1977). Zuivelzicht 69(23):522-524 Zollikofer, E. (1941). Schweiz Milchztg 67:45.

Chapter 5

Meat, Fish and Poultry 5.1 Introduction 5.2 Identification of meat species 5.2.1 Electrophoretic techniques 5.2.1.1 Polyacrylamide gel electrophoresis 5.2.1.2 Polyacrylamide gel isoelectric focusing 5.2.1.3 Polyacrylamide gel electrophoresis - sodium dodecyl sulphate 5.2.2 Immunological techniques 5.2.2.1 Precipitin reaction 5.2.2.2 Enzyme linked immunosorbent assay 5.2.2.3 Enzyme immunoassay 5.2.2.4 Counter Immunoelectrophoresis 5.2.3 Other techniques 5.2.3.1 Acid phosphatase test as a probe 5.2.3.2 Pentoses and pentosans 5.2.3.3 Specific peptide analysis 5.2.3.4 Fat analysis 5.2.3.5 Mineral analysis 5.2.3.6 Histological examination 5.2.3.7 Differential scanning calorimetry 5.2.3.8 Biochemical indices 5.2.3.9 DNA hybridization 5.3 Freshness indicators 5.3.1 Protein breakdown products 5.3.1.1 Total volatile bases 5.3.1.2 Amino nitrogen 5.3.1.3 Amino acids 5.3.1.4 Amines 5.3.1.5hdole 5.3.2 Fat breakdown products 5.3.2.1 Free fatty acids 5.3.2.2 Peroxide value 5.3.2.3 Thiobarbituric acid value 5.3.2.4 Ranco number

210 Handbook of indices of food quality and authenticity 5.3.2.5 Kreiss test 5.3.2.6 Carbonyl compounds 5.3.2.7 Hydrocarbons 5.3.2.8 Chemiluminescence 5.3.3 Nucleic acid breakdown products 5.3.4 General and miscellaneous techniques 5.3.4.1 Colour and pH value 5.3.4.2 Volatile acidity 5.3.4.3 Volatile reducing substance 5.3.4.4 Water holding capacity 5.3.4.5 Volatile metabolites of microorganisms 5.3.4.6 Minerals 5.3.4.7 Degradation products of creatine 5.3.5 Instrumental analysis of meat/fish quality 5.4 Eating quality of fleshy foods 5.5 Evaluation of the age of the animal carcass 5.6 Contaminants in flesh foods 5.6.1 Chemical contaminants 5.6.1.1 Hydrocarbons 5.6.1.2 Heavy metals 5.6.2 Indicators of microbial quality 5.5.2.1 Staining procedures 5.6.2.2 Electrical properties 5.6.3 Indicators of hygienic quality 5.7 Quality of comminuted meats 5.8 Meat additives and adulterants 5.8.1 Artificial colour in sausages 5.8.2 Fillers in sausages 5.8.3 Chickpea flour in sausages 5.8.4 Gelatin in smoked meat products 5.8.5 Blood added to hamburgers 5.8.6 Spleen added to ground beef 5.8.7 Vegetable proteins and other non-meat proteins in meat products 5.8.8 lnterspecies meat adulteration 5.9 Egg: quality criteria 5.9.1 Detection of cracks in whole eggs 5.9.2 Sensory quality of eggs 5.9.3 Microbial quality of eggs 5.9.4 Adulteration in egg products 5.9.5 Egg discoloration References

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It is difficult to make any definite statements about the relationship of tenderness, collagen and free amino acids since there is generally little difference in tenderness, and percent collagen nitrogen fails to change significantly from the raw to the cooked product. In raw pork, leucine is the only amino acid which has been found to be significantly correlated to the tenderness as well as to collagen content. Concentrations of free leucine could probably be used as an indicator of tenderness, but more experimental evidence is required (Usborne et al., 1968). Proteolytic reactions which occur postmortem are responsible for decreases in myosin and in sarcoplasmic proteins (Lawrie, 1966) and these decreases are reflected by increases in free amino acids (Colombo and Gervasini, 1956). As postmortem ageing of muscle increases, tenderness and flavour of cooked meat improves (Wilson, 1960). T h e greater increase in the tenderness in the longissimus than in the biceps femoris during ageing has been attributed to greater amounts of connective tissue in the latter rather than to the changes in free amino acids (Field et al., 1971). Differences in free amino acids due to sex and line of cattle have been found, for example steers containing a slightly higher proportion of free amino acids than bulls give a more tender meat (Field and Chang, 1969). Calcium activated neutral proteinases, calpains I and 11, and cathepsins B, D and L degrade myofibrillar and cytoskeletal proteins (Dayton et al., 1981; Ouali et al., 1987; Mikami et al., 1987), but their importance to tenderization is only inferred. A recent report has confirmed the link between rate of tenderness and rate of proteolysis by calpain I (Dransfield et al., 1992a) and suggested that first order tenderization begins at a muscle p H of 6.1 (Dransfield et al., 1992b). Calpain I becomes activated when the muscle p H falls to about 6.1. This enzyme is autolysed slowly reducing its concentration and the rate of tenderization. Parameters governing activity of calpain have been derived and can predict 68% of the variation in muscle toughness (Dransfield, 1992). Collagen content is important to the structure and meat quality and to the functional properties of emulsion products, and is the major protein in skin, bone, tendon and cartilage (Lawrie, 1979). T h e elastin content of connective tissue is very low and is of little practical importance (Fey, 1977). In Japanese abalone, kuro-awabi (Haliotzsdiscus), collagen content has been correlated to muscle toughness; the higher the collagen content, the tougher the muscle (Olaechea et al., 1993). Collagen can be estimated by the Waring blendor method (Hartley and Hall, 1949), in which the tissue is homogenized with water in a Waring blendor, the p H is adjusted to the apparent isoelectric p H 5.0 and the precipitate washed with water by centrifugation. This method generally gives high values for collagen. It gives reproducible results with raw meat, but not with cooked meat. An enzymic method uses proteolytic enzymes, inactive towards collagen, which break open complex connective tissue structures by hydrolysis of the simple proteins so that soluble nitrogenous proteins can be washed off by centrifugation. Collagen, obtained by the enzymic method has a better relationship to shear values and tenderness scores than by the Waring blendor method (Adams et al., 1960). Collagen can also be determined by near infrared spectroscopy (Chevalier et

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Chapter 6

Edible Oils and Fats 6.1 Introduction 6.2 Indicators of storage changes 6.2.1 Chemical methods 6.2.2Physical methods 6.2.2.1 Thermal methods 6.2.2.2 Chemiluminescence 6.3 Indicators of quality of heated oils 6.4 Toxic Contaminants and adulterants 6.4.1 Contamination by weed seeds 6.4.1 .I Cocklebur 6.4.1.2 Crotolaria spp. 6.4.1.3 Cowcockle and corncockle 6.4.1.4 Morning glory 6.4.1.5 Castor seed 6.4.1.6 Nightshade 6.4.1.7 Jimsonweed 6.4.1.8 Contamination with Karanja (Pongamia glabraloil 6.4.1.9 Contamination with argemone oil 6.4.1. I O Contamination with jatropha oil 6.4.1. I 1 Contamination with kusum oil 6.4.1.I2 Contamination with taramira oil 6.4.1 .I3 Other contaminants of edible oils 6.4.2 contamination due to faulty storage 6.4.3 Spanish Toxic Oil Syndrome 6.4.4 Contamination due to tricresyl phosphate 6.5 Indices of admixtures, blends, contaminants and adulterants - one fat in another 6.5.1 Admixture of vegetable oils with other vegetable oils 6.5.1.I Fatty acid composition 6.5.1.2 Triglyceride analysis 6.5.1.3 Unsaponifiable fraction of oil 6.5.1.3.1 Sterol analysis 6.5.1.3.2 Tocopherol analysis 6.5.1.3.3 Phenolics and alcohols

Edible Oils and Fats 6.5.2 Blends of vegetable and marine/animal fats 6.5.2.1 Fatty acid composition 6.5.2.2 Unsaponifiable fraction 6.5.3 Other adulterants in fats and oils 6.5.4 Constituents specific to or characteristic of an oil 6.5.4.1 Fitelson's reagent 6.5.4.2 Linseed oil in mustard oil 6.5.4.3 Villavachia-Fabris and Pavalini-lsidoro reactions 6.5.4.4 Determination of castor oil 6.5.4.5 Mustard oil determination 6.5.4.6 Nigerseed oil 6.5.4.7 Determination of tung oil 6.5.4.8 Rice bran oil 6.5.5 Detections based on physical properties 6.5.5.1 Atomic absorption spectrophotometry 6.5.5.2 Detection of stearin in palm oil 6.5.5.3 Four-temperature test 6.5.5.4 The Bellier test 6.5.5.5 Molecular refraction 6.5.5.6 Ultrasonic interferometer 6.5.5.7 Differential scanning calorimetry 6.5.5.8 Refractive index 6.5.5.9 UV methods 6.5.5.10 Pyrolysis mass spectrometry 6.5.6 Detections of mixtures of animal fats 6.6 Sensory quality of oils References

301

Edible Oils and Fats

303

d'origine' wines. Hence there is a need to establish methods of determining the trademarks of such products. T h e fact that this can be proved by scientific procedures (Boskou, 1990) is under discussion by the European Union. Chemometrics has been made the basis of such classifications (Forina and Tiscornia, 1982; Forina et al., 1983a, 1983b; Derde et al., 1984; Eddib and Nickless, 1987; Leardi and Paganuzzi, 1987; Tsimidou et al., 1987; Forcadell et al., 1988; Alberghina et al., 1991). Graphic, parametric and non-parametric pattern recognition methods have been performed on data sets of fatty acid composition and/or sterol or triglyceride composition to produce visual or numerical estimates of origin (Aparicio et al., 1987, 1991; Derde et al., 1987; Aparicio, 1988). It is believed that non-parametric discriminant analysis after proper transformation of the data seems to be a suitable approach for characterizing the oils according to their geographical origin and may produce a scientific basis for the assignment of an 'appellation d' origine' trademark (Tsimidou and Karakostas, 1993). Tables 6.1 to Table 6.4 summarize the physical and chemical characteristics and fatty acid composition of oils and fats of commercial importance. It may be noted that the oil content has been correlated to specific gravity of the oilseed kernels, as in the case of groundnuts (Misra et al., 1993). T h e fatty acid composition can be affected by maturation of oilseeds (El-Shami et al., 1994), infection in the plant from which the oil is derived (Conte et al., 1989) and also varies as a function of geographical origin and harvesting time (Parcerisa et al., 1994, 1995). Table 6.1 Physical properties of some fats and oils of commerce Oil

Coconut Cottonseed Linseed Palm Peanut Olive oil Rapeseed Soybean Sunflower Beef tallow

Smoke point ("C)

Flash point ("C)

Fire point ("C)

Specific heat

194-209 185-223

288-316 29C322

329-341 342

-

-

-

-

223 160-207

314 290-333

-

U/d

Surface Interfacial tension tension (80 "C, mN/m)(70 "C, mN/m) -

341 342-363

28.4 2.200 (at 90 "C) 31.3 2.050 (at 70.7 "C) 2.400 (at 140 "C)

-

-

29.9

-

-

2.300 (at 110 "C)

218 213

317 317

344 342

-

30.6

209 2 6 6 3 16

316 344

341

Source: Thomas, 1987.

2.060 (at 80.4 "C) 2.000 (at 60 "C) 2.500 (at 175 "C)

29.8

Edible Oils and Fats

331

Sunflower, soyabean or tomato seed oil

I E u m n chromatography

I

I hydrocarbons etc

free sterols etc.

I

Argentation T P

Waxes

Steryl esters KOH CZHSOH

KOH CZHSOH Alcohols soaps

Sterols soaps

Sterols

I

Fatty acids

Alcohols

Fatty acids

BF3 CH3OH

BF3 CH30H

Methyl esters

Methyl esters

G LC

G LC

Table 6.12 Steryl ester and wax fatty acids of sunflower, soybean and tomato seed oil (% of total fatty acids)

+ 18:2

18:O

20:o

220

Sunflower

A' Bh

12.0 9.7

39.6 5.6

4.0 8.5

41.4 73.4

Soybean

A B A B

9.8 7.0 5.2 8.0

24.0 5.0 79.6 5.8

2.5 2.3 3.0 2.0 trace 3.6

6.7 6.0 1.4 6.6

56.5 80.1 13.4 26.1

16:O

Oil

Tomato seed

18:l

~~

~~~

' Steryl ester fatty acids.

Wax fatty acids. Source: Kiosseogolu and Boskou, 1990 (reproduced with permission).

Chapter 7

Honey: Quality Criteria 7.1 Introduction 7.1.1 Chemical composition and physical properties 7.1.2 Texture of honey 7.2 Adulteration of honey 7.2.1 Adulteration with acid inverted syrups 7.2.2 Adulteration with corn syrup 7.2.3 Other adulterants 7.3 Honey obtained from sugar-fed bees 7.4 Identifying the botanical/geographical origin of authentic honey 7.5 Contaminants of honey References

Honey: Quality Criteria

375

Table 7.9 (S)-(+)-Dehydrovomifoliol content in heather honey and honeys of other floral origin Content (mg kg-1)

Honey Heather honeysa French French French French Spanish

heather, heather, heather, heather, heather,

Calluna Calluna Calluna Calluna Ericaceae

264 210 208 186 56

Honeys of other varietiesa Australian eucalyptus French chestnut Lime

6.02 5.39 1.67

German Russian

1.51 1.37 0.40 0.19 0.03

Spanish French

rape buckwheat acacia orange blossom sunflower

a

Specified by pollen analysis.

Source: Hausler and Montag, 1989 (reproduced with permission).

56-264 mg kg-1 in heather honeys, whereas the levels in honeys of several other floral origins have been shown to be 33 µg kg-1 to 6 mg kg-1. The wide difference between the (S ) - ( +)-dehydrovomifoliol content of heather honeys and that of other floral origin (Table 7.9) offers the possibility of determining adulteration of heather honey (Hausler and Montag, 1989). Some workers have suggested that methyl anthranilate be used as an indicator compound to distinguish citrus honey from other monofloral or multiflora1non-citrus ones (Deshusses and Gabbai, 1962; Dorrscheidt and Friedrich, 1962; White et al., 1962; Hoopen, 1963; Merz, 1963; Cremer and Reidemann, 1965; White, 1965; Chogovadze et al., 1973; Wootton et al., 1978; Graddon et al., 1979; Bicchi et al., 1983; Serra, 1988). It gives a distinctive and pleasant flavour to citrus honey. While the content of methyl anthranilate ranges from 0.84-4.9 ppm in citrus honey, non-citrus samples averaged 0.07 ppm (Knapp, 1967; White, 1965). A fast and simple reversed phase gradient elution HPLC procedure for simultaneous determination of methyl anthranilate for routine characterization of honey and HMF, as evidence of improper processing and storage or adulteration with invert syrup, was reported recently (Vinas et al., 1992). The recoveries of HMF ranged between 98.7% and 103.5% and of methyl anthranilate between 95.0% and 98.4%. However, methyl anthranilate is a volatile compound and therefore suffers significant changes in concentration with different variables including storage conditions (Serra and Coll, 1995). A novel approach to characterizing citrus honey and detecting adulterations of citrus honey is the

376

Handbook of indices of food quality and authenticity

measurement of the δ13C value of the ethanol produced by alcoholic fermentation. T h e δ13C values so obtained exceed the values obtained from other honeys by 5 ppm (Lindner et al., 1996). Distinguishing between various types of honey such as Brassica, Calluna, and Trifolium repens has been investigated and the pollen composition, sugar composition and electrical conductivity are together proposed as a potential screening method (Ravn et al., 1975). T h e floral source of some unifloral New Zealand honeys was reliably determined from gas chromatographic analysis of the noncarbohydrate organic substances after liquid-liquid extraction with diethyl ether (Tan et al., 1988; 1989a, 1989b; 1990). Manuka (Leptospermum scoparium) honeys are characterized by the presence of high levels of 2-hydroxy-3-phenylpropionic acid and syringic acid (Tan et al., 1988), while degraded carotene like substances are known to occur in heather honeys (Tan et al., 1989a). 2-Methoxybutanedioc acid (0-Methylmalic acid) and 4hydroxy-3-methyl-trans-2-pentenedioc acid are proposed as markers of New Zealand rewarewa (Knzghtea excelsa) honey (Wilkins et al., 1995). This has been confirmed after examination of more than 200 samples of rewarewa honey. Nodding thistle (Carduus nutans) honey has shown the presence of 15-87 µ g g-1 (average 43 µ g g-1) of linalool derivatives. T h e 16 compounds isolated in this case have been proposed as suitable marker compounds (Wilkins et al., 1993). T h e aroma compounds hexanal and heptanal in lavender; acetone in fir; diketones, sulphur compounds and alkanes in eucalyptus and some identified compounds in dandelion and rape have been suggested as indicators. This approach needs to be studied further (Bouseta et al., 1992). Recent discoveries of certain animal sterols in plant tissues are most intriguing. One of the most remarkable is of the animal estrogen, estrone in pollen. Analysis of estrone may indicate the authenticity of honey, but screening of honeys from different botanical and geographical origins must be conducted for experimental evidence. Cyanogenesis has been detected in at least 750 plant species representing about 60 families and 250 genera. It is known to vary within populations of plant species such as clover or Trzyolium repens (Daday, 1954; Conn and Butler, 1969). Blossom honeys can be differentiated from honeydew honey on the basis of citrate concentration. Honeydew honeys have about six times higher citrate concentration than blossom honey. However, honeys produced by bees partially fed sugar syrup cannot be differentiated from blossom honey on the basis of citrate content. Electrical conductivity can be used to differentiate honeydew honey from blossom honey, but the sensitivity is lower than that obtained by analysing citrate, which is a reliable index for differentiation of the two honeys (Talpay, 1988). Formate values in most honey types -1 are