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Statistical Methods in Food and Consumer Research 2nd edition [Kietas viršelis]

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(Gacula Associates Consulting, Scottsdale, AZ, USA), (J&J Pharmaceutical R&D, Rantan, NJ, USA), (Temple University, Philadelphia, PA, USA), (Sensometrics Research and Service, Richmond, VA, USA)
  • Formatas: Hardback, 888 pages, aukštis x plotis: 246x189 mm, weight: 1750 g, Approx. 130 illustrations; Illustrations, unspecified
  • Serija: Food Science and Technology
  • Išleidimo metai: 01-Dec-2008
  • Leidėjas: Academic Press Inc
  • ISBN-10: 0123737168
  • ISBN-13: 9780123737168
Kitos knygos pagal šią temą:
Statistical Methods in Food and Consumer Research 2nd editionDidesnis paveikslėlis
  • Formatas: Hardback, 888 pages, aukštis x plotis: 246x189 mm, weight: 1750 g, Approx. 130 illustrations; Illustrations, unspecified
  • Serija: Food Science and Technology
  • Išleidimo metai: 01-Dec-2008
  • Leidėjas: Academic Press Inc
  • ISBN-10: 0123737168
  • ISBN-13: 9780123737168
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780123737168.html
Raktažodžiai:
Statistical Methods in Food and Consumer Research continues to be the only book to focus solely on the statistical techniques used in sensory testing of foods, pharmaceuticals, cosmetics, and other consumer products.

This new edition includes the most receent applications of statistical methods, and features significant updates as well as two new chapters.

Covering the application of techniques including R-index, the Bayesian approach for sensory differences tests, and preference mapping in addition to several other methodologies, this is the comprehensive reference needed by those studying sensory evaluation and applied statistics in agriculture and biological sciences. Research professionals working with food, beverages, healthcare, cosmetics, and other related areas will find the book a valuable guide to the variety of statistical methods available.


Key Features:
* Provides comprehensive coverage of statistical techniques in sensory testing
* Includes data compiled from real-world experiments
* Covers the latest in data interpretation and analysis
* Addresses key methods such as R-index, Thursonian Discriminal Distances, group sequential tests, beta-binomial tests, sensory difference and similarity tests, just-about-right data, signal-to-noise ratio, analysis of cosmetic data, Descriptive Analysis, claims substantiation and preference mapping
Preface xiii
Introduction
1(24)
A Brief Review of Tools for Statistical Inference
1(15)
Notation and Symbolism
2(2)
The Normal Distribution
4(4)
Estimation
8(1)
Testing of Hypotheses
9(7)
Experimental Significance
16(1)
Principles of Experimental Design
16(5)
Randomization
17(1)
Replication
17(1)
Local Control
17(1)
Blinding
18(1)
Planning the Test Program
19(2)
The Role of the Statistician in Research
21(4)
Partnership
21(1)
Client-Consultant Relationship
21(1)
Exercises
22(3)
Statistical Sensory Testing
25(52)
Psychophysical Aspects of Sensory Data
25(5)
Weber's Law
27(1)
Fechner's Law
27(1)
Stevens' Power Law
28(1)
Thurstone's Law of Comparative Judgment
29(1)
Scales of Measurement
30(5)
Hedonic Scale
32(2)
Intensity Rating Scale
34(1)
Rankings
35(1)
Scale Structures
35(2)
Construction of a Scale
37(13)
Edwards' Method of Successive Intervals
38(8)
Scaling of Categories
46(4)
Distribution of Sensory Data
50(7)
Measures of Shape Distribution
50(2)
Statistical Properties of Subjective Data
52(3)
Transformation
55(2)
Selection of Panel Members
57(20)
Criteria Based on Binomial Response
57(6)
Criteria Based on Rating Scale
63(1)
Sequential Analysis
64(5)
Quality Control Chart for Degree of Difference
69(5)
Exercises
74(3)
The Analysis of Variance and Multiple Comparison Tests
77(36)
Analysis of Variance
77(21)
Assumptions in the Analysis of Variance
80(1)
Fixed- and Random-Effects Models
81(17)
Multiple-Comparison Tests
98(9)
Least Significant Difference Test
99(1)
Dunnett's Test for Multiple Comparisons with a Control
100(1)
Tukey's Studentized Range Test
101(2)
Duncan's Multiple-Range Test
103(2)
Choice of Multiple-Comparison Tests
105(2)
Sample Size Estimation
107(6)
Sample Size for Confidence Interval Estimation
109(1)
Sample Size for Dichotomous Responses
110(1)
Exercises
111(2)
Experimental Design
113(56)
Simple Comparative Experiments
113(8)
Group-Comparison Designs
113(2)
Paired Comparison Designs
115(6)
Completely Randomized Designs
121(3)
Randomized Complete Block Designs
124(7)
Randomized Complete Block Designs with More Than One Observation per Experimental Unit
128(3)
Latin Square Designs
131(11)
Replicating LS Designs
134(4)
Partially Replicated Latin Square
138(4)
Cross-Over Designs
142(11)
Split Plot Designs
153(16)
Exercises
166(3)
Incomplete Block Experimental Designs
169(36)
Balanced Incomplete Block Designs
169(13)
Parameters of Balanced Incomplete Block Designs
170(1)
Intrablock Analysis
171(6)
Interblock Analysis
177(1)
Combining Intrablock and Interblock Estimates
177(5)
Balanced Incomplete Block Designs Augmented with a Control
182(5)
Doubly Balanced Incomplete Block Designs
187(7)
Composite Complete-Incomplete Block Designs
194(11)
Intrablock Analysis
198(4)
Exercises
202(3)
Factorial Experiments
205(42)
The 2n Factorial Experiments
206(10)
The 3n Factorial Experiments
216(9)
The p x q and p x q x k Factorial Experiments
225(8)
Simple Confounding and Fractional Factorial Experiments
233(14)
Confounding in 2n Factorial Experiments
234(3)
Fractional Factorial
237(7)
Exercises
244(3)
Response Surface Designs and Analysis
247(64)
General Concepts
247(9)
Construction of Response Surfaces
251(5)
Fitting of Response Surfaces and Some Design Considerations
256(7)
Illustrations of Fittings of First- and Second-Order Models
263(12)
Composite Designs
275(11)
Composite Designs from Fractional Factorials
282(1)
A 1/2 Replicate of 24
282(1)
A 1/4 Replicate of 26
283(3)
Rotatable Designs
286(18)
Composite Rotatable Designs
292(2)
Arrangements of Composite Designs in Blocks
294(10)
A Response Surface Analysis Approach for Sensory Data
304(7)
Exercises
308(3)
Shelf Life Testing Experiments
311(40)
Hazard Function
312(4)
Shelf Life Models
316(15)
Normal Distribution
317(1)
Lognormal Distribution
318(2)
Weibull Distribution
320(8)
Graphical Test for Failure Distributions
328(3)
Regression Analysis
331(20)
Confidence Intervals
340(5)
Nonlinear Relationships
345(3)
Exercises
348(3)
Nonparametric Statistical Methods
351(104)
Some Methods for Binary Data
351(19)
Tests for Independent Proportions
351(8)
Tests for Dependent Proportions
359(5)
Combining 2 x 2 Contingency Tables
364(5)
Measure of Association for 2 x 2 Tables
369(1)
Some Methods Based on Ranks
370(44)
Rank Tests for Single and Paired Samples
371(7)
Rank Tests for Two Independent Samples
378(11)
Rank Tests for Multiple Independent Samples
389(6)
Rank Tests for Multiple Related Samples
395(9)
Measures of Association for Ranking Data
404(10)
Some Methods for Categorical Data
414(24)
Contingency Tables and Chi-square Statistics
414(4)
Goodness of Fit Tests
418(4)
Analysis of r x c Contingency Tables
422(8)
Measures of Association for r x c Contingency Tables
430(3)
Analysis of Square (r x r) Contingency Tables
433(5)
Multidimensional Contingency Tables
438(17)
Introduction
438(1)
Simpson's Paradox
439(2)
Tests for Independence
441(2)
Numerical Example for Tests for Independence
443(6)
Exercises
449(6)
Sensory Difference and Similarity Tests
455(104)
Sensory Difference Tests
455(41)
Triangle Test
455(16)
Duo-Trio Test
471(2)
Pair Difference Test (2-AFC)
473(4)
Three-Alternative Forced-Choice Test (3-AFC)
477(1)
A-Not A Test
478(6)
Same-Different Test
484(1)
Comparisons of Difference Test Designs
485(11)
Sensory Similarity (Equivalence) Tests
496(18)
Introduction
496(1)
Similarity Tests Using Forced-Choice Methods
497(1)
Similarity Tests Using the Paired Comparison Method
498(3)
Similarity Tests Using A-Not A and Same-Different Methods
501(2)
Similarity Tests for Continuous Data
503(3)
Similarity Tests for Correlated Data
506(4)
Hypothesis Test and Confidence Interval for Similarity Evaluation
510(4)
Replicated Difference and Similarity Tests
514(45)
Introduction
514(1)
The Beta-Binomial (BB) Model
515(7)
Tests Based on the BB Model
522(10)
The Corrected Beta-Binomial (CBB) Model
532(4)
Tests Based on the CBB Model
536(4)
The Dirichlet-Multinomial (DM) Model
540(5)
Tests Based on the DM Model
545(7)
Exercises
552(7)
The Method of Paired Comparisons in Sensory Tests and Thurstonian Scaling
559(82)
Paired Comparison Designs
559(12)
Completely Balanced Paired Comparison Designs
559(5)
Incomplete Balanced Paired Comparison Designs
564(7)
Paired Comparison Models
571(36)
The Scheffe Model
571(11)
The Bradley---Terry Model
582(14)
The Thurstone---Mosteller Model
596(9)
Choice of Models
605(2)
Thurstonian Discriminal Distance d'
607(17)
Introduction
607(2)
Estimation of d'
609(5)
Variance of d'
614(6)
Tables and S-PLUS Codes for d' and Variance of d'
620(1)
Confidence Intervals and Tests for d'
621(3)
Area Under ROC Curve and R-Index
624(17)
Introduction
624(1)
Estimating R-Index and Its Variance
625(2)
Difference Testing Using R-Index
627(2)
Similarity Testing Using R-Index
629(1)
Linking R-Index with d'
630(2)
Same-Different Area Theorem
632(3)
Exercises
635(6)
Descriptive Analysis and Perceptual Mapping
641(46)
Descriptive Analysis
641(1)
Consumer Tests
642(2)
In-House Consumer Test
642(1)
Home-Use Consumer Test
643(1)
Central Location Consumer Test
644(1)
Hedonic and Intensity Rating Scales
644(15)
Just-About-Right Rating Scale
645(1)
Signal-to-Noise Ratio
646(7)
Questionnaire Design
653(6)
Perceptual Mapping
659(20)
Factor Analysis
660(14)
Principal Component Analysis
674(5)
Preference Mapping
679(8)
Exercises
685(2)
Sensory Evaluation in Cosmetic Studies
687(34)
Experimental Designs
687(19)
Clinical Study Data
688(3)
Self-Controlled Design
691(7)
Parallel Design
698(3)
Cross-Over Design
701(5)
Regression Analysis
706(15)
Polynomial Regression
706(4)
Nonlinear Regression
710(8)
Fit of Nonlinear Regression Model
718(1)
Exercises
719(2)
Appendix 721(108)
References 829(16)
Index 845(6)
Food Science and Technology: International Series 851