Nonparametrics for Sensory Science A More Informative Approach

by Rayner, J. C. W.; Best, D. J.; Brockhoff, Per Bruun; Rayner, G. D.

• ISBN13: 9780813811123
• ISBN10: 0813811120
• Edition: 1st
• Format: Hardcover
• Copyright: 2005-05-09
• Publisher: Wiley-Blackwell

Summary

Sensory evaluation is the perception science of the food industry. Sensory data can be costly to obtain and so gleaning the most information possible from the data is key. Increasingly, value is added to sensory evaluation by the use of statistics, especially to improve the quality of product development and to make the most of market research. Nonparametrics for Sensory Science is written to complement existing parametric methodology. Nonparametric methods are appropriate when facts are only available in nominal or ordinal form, and when the model assumptions necessary for parametric procedures do not hold. Author Rayner and his colleagues consider problems including the most commonly occurring and important experimental designs: the one-sample, k-sample, blocked samples, samples with factorial structure and samples with correlation structure. Innovative new techniques are outlined and complemented with real examples. Techniques described may be applied to data where the traditional, most frequently applied nonparametric tests, such as the Kruskal-Wallis, the Friedman and the Spearman tests, are applied. Those familiar with traditional nonparametric testing will be able to update their knowledge, acquiring powerful new methods. Those without prior knowledge of nonparametric testing will be able to acquire that knowledge through this book. Aimed at sensory scientists and statisticians interested in nonparametrics, the techniques of Nonparametrics for Sensory Science are of broad general interest, but are of particular interest in sensory evaluation applications.

Author Biography

J.C.W. RAYNER, Ph.D. is associate professor in the School of Mathematics and Applied Statistics at the University of Wollongong, Australia. D.J. BEST, Ph.D. is former principal research scientist at CSIRO Division of Food Research and is now principal research fellow in the School of Mathematics and Applied Statistics at the University of Wollongong, Australia. P.B. BROCKHOFF, Ph.D. is professor of Informatics and Mathematical Modelling at the Technical University of Denmark. G.D. RAYNER, Ph.D.is a fellow of the University of Wollongong, Australia.

Table of Contents

Preface	v
Introduction	1	(24)
Preliminaries	1	(3)
Overview of this chapter	1	(1)
Nonparametric and/or parametric analysis?	1	(3)
Concepts and Tools	4	(11)
Statistics, Design and Analysis	4	(1)
Moment Effects	5	(1)
Partitioning Omnibus Statistics into Directional Components Statistics	6	(1)
Orthonormal Functions	7	(2)
Ties	9	(1)
Scores	9	(1)
Pearson's X2/p Statistic	10	(1)
Confidence Circles	11	(1)
Anderson-Darling and Levene Tests	12	(1)
Permutation and Bootstrap Methods	13	(1)
Correspondence Analysis	14	(1)
Market Segmentation	15	(1)
Cigarette Example	15	(7)
Scope and Outline	22	(3)
The Completely Randomized Design	25	(20)
Introduction	25	(1)
Tasters Example	26	(3)
The Untied Analysis	29	(2)
Japanese Chocolate Data Example	31	(2)
The Tied Analysis	33	(3)
Bradley et al. Example	36	(6)
Nonparametric Multiple Comparisons	42	(1)
A Model for the Japanese Chocolate Data	42	(2)
Ordering Effects	44	(1)
The Randomized Block Design	45	(32)
Introduction	45	(1)
Peach Example	46	(5)
Softdrink Can Size Example	51	(1)
The Untied Analysis	52	(4)
Tomato Example	56	(5)
Geese Meat Flavour Example	61	(5)
The Tied Analysis	66	(5)
Further Examples	71	(3)
Ordering Effects	74	(3)
Balanced Incomplete Block Designs	77	(24)
Introduction	77	(1)
Examples	77	(7)
Dried Egg Example	77	(3)
Cleaning Materials Example	80	(3)
Ice Cream Flavour Example	83	(1)
The Untied Analysis	84	(2)
Canned Fruit Example	86	(3)
The Tied Analysis	89	(2)
Ice Cream Examples	91	(5)
Off-Flavour Example	91	(2)
Vanilla Flavour Example	93	(3)
Paired Comparisons	96	(2)
Partially Balanced Incomplete Blocks	98	(3)
Correlation Effects	101	(26)
Introduction	101	(1)
Pearson and Spearman Correlation Coefficients	101	(5)
Generalised Correlation	106	(3)
Lemonade and Geese Meat Examples	109	(3)
Lemonade Example	109	(2)
Geese Meat Example	111	(1)
Page and Umbrella Tests for the Randomized Block Design	112	(4)
Lancaster Decomposition of the Anderson Statistic	112	(3)
The Page Test	115	(1)
An Umbrella Test Statistic	115	(1)
Page and Umbrella Tests for Balanced Incomplete Block Designs	116	(1)
Generalised Correlation in Multiway Tables	116	(4)
Triply Ordered Trivariate Distributions	117	(2)
Doubly Ordered Trivariate Distributions	119	(1)
Happiness Example	120	(2)
Completely Ordered Analysis	120	(1)
Doubly Ordered Analysis	121	(1)
Modelling the Counts in a Two-Way Doubly Ordered Contingency Table	122	(5)
Categorical Data for Randomized Block Designs	127	(16)
Introduction	127	(1)
Binary Data	127	(3)
Ordinal Trinary Data	130	(3)
Lawless and Heymann Example	133	(1)
Hot Chips Example	134	(4)
Cochran-Mantel-Haenszel Analysis	138	(3)
Page Tests for Categorical Data in Randomized Blocks	141	(2)
Goodness of Fit	143	(20)
Introduction	143	(1)
Testing for Goodness of Fit in General	144	(1)
Examination Mark Example	145	(3)
The Smooth Tests of Goodness of Fit	148	(2)
Cordial Drink Preference Example	150	(2)
Smooth Components of the Pearson Statistic	152	(2)
Radioactive Counts Example	154	(3)
Milk Bacteria Example	157	(1)
The Bivariate Normal Distribution	158	(5)
Concluding Remarks	163	(2)
References	165	(6)
Subject Index	171	(2)
Author Index	173	(2)
Examples Index	175