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E-raamat: Sensory Shelf Life Estimation of Food Products

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  • Formaat: 264 pages
  • Ilmumisaeg: 25-May-2010
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781040069400
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Sensory Shelf Life Estimation of Food ProductsSuurem pilt
  • Formaat: 264 pages
  • Ilmumisaeg: 25-May-2010
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781040069400
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Complying with food regulations and, more importantly, quality standards, requires practical and reliable methods to estimate a product’s shelf life. Emphasizing the importance of the consumer’s perception of when food has reached the end of its shelf life, Sensory Shelf Life Estimation of Food Products provides a tool for adequately predicting sensory shelf life (SSL).

The book delineates the basics of sensory analysis and how it applies to shelf-life studies and includes discussions of experimental design aspects, survival analysis methodology, and its extensions. It provides detailed instructions and software functions for performing SSL estimations, accompanied by data sets and the R Statistical Package functions that are available for download. The author presents the cut-off point methodology used to estimate SSL when the survival analysis methods get complicated. He includes a chapter on accelerated storage covering kinetics, calculations of prediction confidence intervals and potential pitfalls. He also examines extensions of survival analysis statistics to other areas of food quality such as optimum concentration of ingredients and optimum cooking temperatures.

Microbiologically stable foods, such as biscuits or mayonnaise, will have their shelf-life defined by the changes in their sensory properties. Many fresh foods, such as yogurt or pasta, after relatively prolonged storage may be microbiologically safe to eat but rejected due to changes in their sensory properties. Shelf life in most food products is determined by sensory issues instead of microbiological or chemical concerns. This book offers key techniques for experimental design, storage, consumer testing procedures, and calculations. It includes methods for accelerated storage experiments, thoroughly explains statistical data treatment, and includes practical examples.

Preface xiii
Acknowledgments xv
Author xvii
Chapter 1 Introduction
1(22)
1.1 Sensory shelf life definition
1(1)
1.2 Labeling regulations
2(1)
1.3 Shelf life of foods is sensory shelf life
3(1)
1.4 Importance of the consumer in defining food quality
4(2)
1.5 Books on shelf life of foods
6(17)
1.5.1 Labuza (1982)
6(3)
1.5.2 Gacula (1984)
9(1)
1.5.3 IFST guidelines (1993)
10(1)
1.5.4 Man and Jones (1994)
11(3)
1.5.5 Taub and Singh (1998)
14(2)
1.5.6 Kilcast and Subramaniam (2000a)
16(2)
1.5.7 Eskin and Robinson (2001)
18(1)
1.5.8 Labuza and Szybist (2001)
18(2)
References
20(3)
Chapter 2 Principles of sensory evaluation
23(40)
2.1 Introduction
23(1)
2.2 Definition of sensory evaluation
23(2)
2.2.1 Analyze and interpret
23(1)
2.2.2 Other materials
24(1)
2.2.3 Sight, touch, and hearing
24(1)
2.3 Sensory analysis: Trained panels versus experts
25(3)
2.4 General requirements and conditions for sensory tests
28(6)
2.4.1 Testing area
28(2)
2.4.2 Lighting
30(1)
2.4.3 Time of day
31(1)
2.4.4 Carriers
32(1)
2.4.5 Temperatures of samples
32(1)
2.4.6 Sample size
32(1)
2.4.7 Number of samples
33(1)
2.4.8 Coding and order of presentation
33(1)
2.4.9 Palate cleansers
33(1)
2.5 Physiological factors
34(1)
2.6 Psychological factors
34(5)
2.6.1 Expectation error
34(1)
2.6.2 Error of habituation
35(1)
2.6.3 Stimulus error
36(1)
2.6.4 Logical error
36(1)
2.6.5 Halo effect
36(1)
2.6.6 Positional bias
36(1)
2.6.7 Contrast effect and convergence error
37(1)
2.6.8 Mutual suggestion
37(1)
2.6.9 Lack of motivation
37(1)
2.6.10 Capriciousness versus timidity
38(1)
2.7 Sensory evaluation methods
39(24)
2.7.1 Discrimination tests
39(1)
2.7.1.1 Triangle test
39(1)
2.7.1.2 Example of sensory shelf life (SSL) determined by a triangle test
40(4)
2.7.1.3 Paired comparison test
44(1)
2.7.1.4 Difference from control test
44(3)
2.7.2 Descriptive tests
47(7)
2.7.3 Affective tests
54(1)
2.7.3.1 Selecting consumers
55(3)
2.7.3.2 Test location
58(1)
2.7.3.3 Quantitative affective test methods
59(1)
References
60(3)
Chapter 3 Design of sensory shelf-life experiments
63(20)
3.1 Initial considerations
63(1)
3.2 Approximations of shelf-life values
64(3)
3.2.1 Literature values
64(1)
3.2.2 Values from the Internet
65(1)
3.2.3 Values based on distribution times
66(1)
3.3 Temperatures and storage times
67(16)
3.3.1 Temperatures
67(1)
3.3.2 Maximum storage time
68(1)
3.3.3 Time intervals
69(1)
3.3.4 Critical descriptor
70(1)
3.3.5 Storing fresh samples
71(2)
3.3.6 Basic and reversed storage designs
73(1)
3.3.6.1 Basic design
73(2)
3.3.6.2 Reversed storage design
75(4)
3.3.7 How much sample should be stored for sensory shelf life studies?
79(1)
3.3.7.1 Bananas
79(1)
3.3.7.2 Vegetable oil
80(1)
3.3.7.3 Yogurt
81(1)
References
82(1)
Chapter 4 Survival analysis applied to sensory shelf life
83(30)
4.1 What is survival analysis?
83(1)
4.2 Censoring
84(2)
4.2.1 Right-censoring
84(1)
4.2.2 Left-censoring
84(1)
4.2.3 Interval-censoring
85(1)
4.3 Survival and failure functions
86(2)
4.4 Shelf life centered on the product or on its interaction with the consumer?
88(1)
4.5 Experimental data used to illustrate the methodology
89(1)
4.6 Censoring in shelf-life data
90(3)
4.7 Model to estimate the rejection function
93(3)
4.8 Calculations using the R statistical package
96(7)
4.9 Interpretation of shelf-life calculations
103(2)
4.10 An additional example
105(5)
4.11 Should consumers be informed?
110(1)
4.12 Is there a way to deal with totally new products?
110(3)
References
111(2)
Chapter 5 Survival analysis continued: Number of consumers, currents status data, and covariates
113(34)
5.1 Number of consumers
113(1)
5.2 Current status data
114(10)
5.2.1 Introduction
114(2)
5.2.2 Experimental data
116(1)
5.2.3 Model and data analysis
116(6)
5.2.4 Conclusions on current status data
122(2)
5.3 Introducing covariates in the model
124(23)
5.3.1 Consumer demographics
124(1)
5.3.1.1 Experimental data
124(1)
5.3.1.2 Covariate model
124(2)
5.3.1.3 Calculations using R
126(6)
5.3.2 Product formulations
132(1)
5.3.2.1 Experimental data
133(1)
5.3.2.2 Calculations using R
133(6)
5.3.3 Quantitative covariates and number of covariates
139(1)
5.3.3.1 Experimental data
139(1)
5.3.3.2 Calculations using R
140(5)
5.3.4 Number of covariates
145(1)
References
145(2)
Chapter 6 Cut-off point (COP) methodology
147(22)
6.1 When is the survival statistics methodology difficult to apply?
147(1)
6.2 Basics of the COP methodology
148(1)
6.3 Approaches in establishing a COP
149(2)
6.4 Methodology to measure the COP
151(8)
6.4.1 Critical descriptors
151(1)
6.4.2 Preparation of samples with increasing levels of sensory defects
152(1)
6.4.3 Determination of intensity levels of samples by a trained sensory panel
152(1)
6.4.4 Determination of acceptability levels of the same samples by a consumer panel
153(1)
6.4.5 Calculation of the COP
154(5)
6.5 Introduction to kinetics
159(3)
6.5.1 Zero-order kinetics
159(1)
6.5.2 First-order kinetics
159(1)
6.5.3 Choosing between zero- and first-order kinetics
160(1)
6.5.4 Sensory properties that present a lag phase
161(1)
6.6 Using the COP to estimate shelf life
162(4)
6.6.1 Sample storage and trained sensory panel evaluations
162(1)
6.6.2 Results and calculations
163(3)
6.7 Instrumental COPs
166(1)
6.8 Caveats for using COP methodology
166(3)
References
167(2)
Chapter 7 Accelerated storage
169(32)
7.1 Introduction
169(2)
7.1.1 Acceleration factor fallacy
169(1)
7.1.2 Methods of acceleration
170(1)
7.2 Arrhenius equation and activation energy
171(11)
7.2.1 Arrhenius equation
171(1)
7.2.2 Data for activation energy calculations
172(1)
7.2.3 Simple activation energy calculations
173(3)
7.2.4 Activation energy calculations based on non-linear regression
176(6)
7.3 The use of Q10
182(2)
7.4 Survival analysis accelerated storage model
184(9)
7.4.1 Accelerated storage model
184(2)
7.4.2 Experimental data
186(1)
7.4.3 Calculations using R
187(6)
7.5 Potential pitfalls of accelerated shelf-life testing
193(6)
7.5.1 Pitfall 1: Multiple deterioration modes
194(1)
7.5.2 Pitfall 2: Failure in quantifying uncertainty
194(1)
7.5.3 Pitfall 3: Degradation and rejection affected by unforeseen variables
195(1)
7.5.4 Pitfall 4: Masked rejection mode
195(1)
7.5.5 Pitfall 5: Comparisons that do not hold
196(1)
7.5.6 Pitfall 6: Increasing temperature can cause deceleration
197(1)
7.5.7 Pitfall 7: Drawing conclusions from pilot-plant samples
198(1)
7.6 Conclusion on accelerated testing
199(2)
References
199(2)
Chapter 8 Other applications of survival analysis in food quality
201(38)
8.1 Consumer tolerance limits to a sensory defect
201(7)
8.1.1 Survival analysis model
201(2)
8.1.2 Experimental data used to illustrate the methodology
203(1)
8.1.3 Rejection probability calculations
204(4)
8.1.4 Conclusions
208(1)
8.2 Optimum concentration of ingredients in food products
208(14)
8.2.1 Survival analysis model
209(3)
8.2.2 Experimental data used to illustrate the methodology
212(1)
8.2.3 Optimum color calculations
212(9)
8.2.4 Conclusions on optimum color estimations
221(1)
8.3 Optimum salt level in French bread
222(7)
8.3.1 Experimental data used to illustrate the methodology
222(2)
8.3.2 Survival analysis model
224(1)
8.3.3 Optimum salt concentration calculations
225(3)
8.3.4 Conclusions on optimum salt concentration estimation
228(1)
8.4 Internal cooking temperature of beef
229(3)
8.5 Optimum ripening times of fruits
232(7)
References
236(3)
Index 239
A research scientist with the Commission de Ivestigaciones Cientificas, Guillermo Hough is a renowned academic in sensory/consumer sciences. Dr. Hough conducts his research in the area of sensory science and has become an expert in the field of sensory shelf life, having published 20 refereed articles on this theme. A dynamic speaker at international conferences and symposiums, he also lead the collaborative Ibero-American project (CYTED program), which stated the basics for the estimation and modelization of sensory shelf life. His course on sensory science and shelf life has been very popular in Argentina, Chile, Colombia, Italy, Mexico, New York and Paris. His group is in close contact with local and regional food companies, performing a wide range of sensory tests and panel testing. Dr. Hough works at the Instituto Superior Experimental de Tecnologia Alimentaria in the small town of Nueve de Julio, Provincia de Buenos Aires, Argentina.
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