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E-grāmata: Sensory Discrimination Tests and Measurements: Sensometrics in Sensory Evaluation

  • Formāts: PDF+DRM
  • Izdošanas datums: 19-Aug-2015
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118994856
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Sensory Discrimination Tests and Measurements: Sensometrics in Sensory EvaluationPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 19-Aug-2015
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118994856
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Sensory testing and measurement are the main functions of sensory analysis. In recent years, the sensory and consumer field has evolved to include both difference testing and similarity testing, and new sensory discrimination methods such as the tetrads have received more attention in the literature.

This second edition of Sensory Discrimination Tests and Measurements is updated throughout and responds to these changes and includes:





A wide range of sensory measurements:

Measurements of sensory effect (d', R-index and Gini-index); Measurements of performance of trained sensory panel (Intraclass correlation coefficients and Cronbachs coefficient alpha); Measurements of relative importance of correlated sensory and consumer attributes (drivers of consumer liking or purchase intent); Measurements of consumer emotions and psychographics; Measurements of time-intensity; Measurements of sensory thresholds; Measurements of sensory risk with negative sensory effects (Benchmark Dose, BMD, methodology) Measurements of sensory shelf life (SSL).



A balanced introduction of sensory discrimination tests including difference tests and similarity tests. Bayesian approach to sensory discrimination tests. Modified and multiple-sample discrimination tests. Replicated discrimination tests using the beta-binomial (BB), corrected beta-binomial (CBB), and Dirichlet-multinomial (DM) models. Sensory discrimination methods including the tetrads and the M+N. R and S-Plus codes for all the measurements and tests introduced in the book.

Mainly intended for researchers and practitioners in the sensory and consumer field, the book is a useful reference for modern sensory analysis and consumer research, especially for sensometrics.
Preface xiii
Acknowledgements xv
About the companion website xvii
1 Introduction 1(7)
1.1 Sensometrics
1(1)
1.2 Sensory tests and measurements
1(1)
1.3 A brief review of sensory analysis methodologies
2(1)
1.4 Method, test, and measurement
3(1)
1.5 Commonly used discrimination methods
3(2)
1.6 Classification of sensory discrimination methods
5(3)
2 Measurements of sensory difference/similarity: Thurstonian discriminal distance 8(34)
2.1 Measurement of sensory difference/similarity
8(1)
2.2 Thurstonian discriminal distance, δ or d'
9(5)
2.3 Variance of d'
14(3)
2.4 Tables and R/S-Plus codes for d' and variance of d'
17(8)
2.5 Computer-intensive approach to Thurstonian models of the "M + N" test
25(6)
2.6 Estimates of population and group d'
31(11)
3 Measurements of sensory difference/similarity: area under ROC curve in Signal Detection Theory 42(18)
3.1 Area measure of sensory difference/similarity
42(3)
3.2 ROC curve functions
45(2)
3.3 Estimations of the parameters of ROC curves
47(2)
3.4 Estimations of variances of estimators
49(4)
3.5 R/S-Plus codes for estimations of parameters for the three ratings methods
53(5)
3.6 Estimates of population R-index in replicated ratings
58(2)
4 Difference testing 60(38)
4.1 Binomial model for difference testing
60(1)
4.2 Difference tests using forced-choice methods
61(4)
4.3 Power analysis for tests for one proportion
65(5)
4.4 Discrimination tests using methods with response bias
70(9)
4.5 Power analysis of tests for two proportions
79(8)
4.6 Efficiency comparisons of difference tests
87(5)
4.7 Difference tests for d' and R-index
92(6)
5 Similarity (equivalence) testing 98(41)
5.1 Introduction
98(1)
5.2 Similarity tests using the Two-Alternative Forced Choice (2-AFC) method
99(6)
5.3 Similarity testing using forced-choice methods
105(9)
5.4 Similarity tests using methods with response bias
114(5)
5.5 Similarity tests using ratings of the A-Not A, Same-Different, and A-Not AR
119(3)
5.6 Similarity tests for continuous data
122(5)
5.7 Similarity tests for correlated data
127(5)
5.8 Confidence interval for similarity evaluation
132(4)
5.9 Controversy over similarity (equivalence) tests in statistical and sensory literature
136(3)
6 Bayesian approach to discrimination tests 139(23)
6.1 Introduction
139(1)
6.2 One-proportion two-sided tests
140(8)
6.3 One-proportion one-sided tests
148(7)
6.4 Two-proportion tests
155(6)
6.5 Thurstonian d' for Bayesian estimate of proportion
161(1)
7 Modified discrimination tests 162(40)
7.1 Modified Triangular test
162(9)
7.2 Degree of Difference test
171(6)
7.3 Double discrimination tests
177(9)
7.4 Preference tests with a "no preference" option
186(10)
7.5 Discrimination tests with pseudo-correct responses (forgiveness)
196(6)
8 Multiple-sample discrimination tests 202(53)
8.1 Multiple-sample comparison based on proportions
202(6)
8.2 Multiple-sample comparison based on ranks
208(13)
8.3 Multiple-sample comparison based on categories
221(10)
8.4 Multiple-sample comparison based on ratings
231(6)
8.5 Multiple-sample comparison based on paired comparisons
237(18)
9 Replicated discrimination tests: beta-binomial model 255(28)
9.1 Introduction
255(2)
9.2 BB distribution
257(1)
9.3 Estimation of the parameters
258(6)
9.4 Applications of the BB model in replicated tests
264(13)
9.5 Testing power and sample size
277(6)
10 Replicated discrimination tests: corrected beta-binomial model 283(18)
10.1 Introduction
283(1)
10.2 CBB distribution
283(5)
10.3 Estimation of parameters in the CBB model
288(4)
10.4 Statistical testing for parameters in a CBB model
292(3)
10.5 Testing power and sample size
295(3)
10.6 CBB and Thurstonian models for replicated discrimination methods
298(3)
11 Replicated discrimination tests: Dirichlet—multinomial (DM) model 301(28)
11.1 DM distribution
301(3)
11.2 Estimation of the parameters of a DM model
304(2)
11.3 Applications of the DM model in replicated ratings and discrimination tests
306(15)
11.4 Testing power for DM tests
321(3)
11.5 DM model in a meta-analysis for usage and attitudinal (U & A) data
324(5)
12 Measurements of sensory thresholds 329(16)
12.1 Introduction
329(1)
12.2 Standard dose—response model
330(5)
12.3 Model for responses with an independent background effect
335(5)
12.4 Model for overdispersed responses
340(5)
13 Measurements of sensory risk with negative sensory effects 345(20)
13.1 Benchmark dose methodology
345(1)
13.2 Estimation of BMD from quantal data
346(6)
13.3 Estimation of BMD from replicated quantal data
352(4)
13.4 Estimation of BMD from continuous data
356(9)
14 Measurements of time intensity 365(33)
14.1 Introduction
365(1)
14.2 Smoothing and graphical presentation of T-I data
365(4)
14.3 Analysis based on parameters of smoothed T-I curves
369(2)
14.4 Multivariate data analysis for T-I data
371(4)
14.5 Functional data analysis for T-I data
375(23)
15 Measurements of sensory shelf life 398(14)
15.1 Introduction
398(4)
15.2 Determination of SSL using R package and R codes
402(1)
15.3 Numerical examples
403(9)
16 Measurements of the performance of a trained sensory panel and panelists 412(32)
16.1 Criteria for assessing performance
412(3)
16.2 Estimations of ICC from different types of data
415(14)
16.3 Statistical tests for ICCs
429(3)
16.4 Other indices for evaluation of panel data
432(6)
16.5 Assessing the discriminability of trained sensory panelists and panels
438(6)
17 Measurements of consumer emotions and psychographics 444(19)
17.1 Introduction
444(1)
17.2 Measurements of consumer positive and negative emotions
444(5)
17.3 Psychographics
449(7)
17.4 Propensity score analysis
456(7)
18 Measurements of the relative importance of attributes 463(23)
18.1 Introduction
463(2)
18.2 Determination of the relative importance of attributes based on averaging over orderings
465(6)
18.3 Determination of the relative importance of attributes based on variable transformation
471(3)
18.4 Determination of the relative importance of attributes based on Breiman's RF
474(2)
18.5 Determination of the relative importance of attributes based on fuzzy measures and the Choquet integral
476(5)
18.6 Meta-analysis of the relative importances of attributes
481(1)
18.7 Adaptive penalty analysis combining both directional effects and the relative importance of JAR attributes to overall liking
482(4)
Appendix A List of R/S-Plus codes, data files, and packages used in the book 486(3)
References 489(28)
Author Index 517(8)
Subject Index 525
Jian Bi is a consultant in sensometrics in the food and consumer product industries. From 1996 to 2000, he served as a Senior Statistician in the Institute for Perception in Richmond, VA, USA. Since 2000, he has, as an independent consultant, provided statistical consulting service and data analysis in sensory and consumer research for multiple companies, including Amway, Wrigley, Campbell Soup Company, Heinz, Kraft Foods, PepsiCo. Asia, and Mondelēz. He is the author or co-author of two books and more than 60 papers.
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