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El. knyga: Analyzing Sensory Data with R

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Choose the Proper Statistical Method for Your Sensory Data Issue

Analyzing Sensory Data with R gives you the foundation to analyze and interpret sensory data. The book helps you find the most appropriate statistical method to tackle your sensory data issue.

Covering quantitative, qualitative, and affective approaches, the book presents the big picture of sensory evaluation. Through an integrated approach that connects the different dimensions of sensory evaluation, you’ll understand:

  • The reasons why sensory data are collected
  • The ways in which the data are collected and analyzed
  • The intrinsic meaning of the data
  • The interpretation of the data analysis results

Each chapter corresponds to one main sensory topic. The chapters start with presenting the nature of the sensory evaluation and its objectives, the sensory particularities related to the sensory evaluation, details about the data set obtained, and the statistical analyses required. Using real examples, the authors then illustrate step by step how the analyses are performed in R. The chapters conclude with variants and extensions of the methods that are related to the sensory task itself, the statistical methodology, or both.

Recenzijos

"Lź and Worch present students, academics, researchers, and statisticians with a quantitative, qualitative, and effective approach to sensory evaluation. The authors have organized the bulk of their text in three sections, devoted to quantitative descriptive approaches, qualitative descriptive approaches, and affective approaches, in turn. Individual chapters within these sections cover a variety of subjects, such as product rating according to a single list of attributes, product rating when depicted by comments, and product rating when solely assessed by liking." Ringgold, Inc. Book News, February 2015 "a comprehensive and practical book written for sensory scientists. The book introduces techniques for the analysis of sensory data with a strong emphasis on multivariate analysis. Throughout the book, real data from a variety of sensory experiments are used to illustrate how to analyze sensory data appropriately using R. The examples used in the book emphasize a good understanding of the objective of the experiment, the nature of the data collected and its statistical notation, how to implement the appropriate method of analysis using R, and how to read the output and carefully interpret the results. Sensometrics is a dynamic and fast-moving field, yet books on how to analyze complicated sensory data are rare. This practical book is a valuable resource for scientists learning how to analyze sensory data appropriately. It can be used as a course textbook to teach new sensory scientists the full suite of existing statistical techniques, or as a reference handbook for the experienced sensory scientist seeking information on how to analyze a specific type of sensory data. well-organized, easy to read, and provides practical instruction in statistical techniques for analyzing sensory data using R." Gui-Shuang Ying, University of Pennsylvania, in Journal of the American Statistical Association, January 2017

"Lź and Worch present students, academics, researchers, and statisticians with a quantitative, qualitative, and effective approach to sensory evaluation. The authors have organized the bulk of their text in three sections, devoted to quantitative descriptive approaches, qualitative descriptive approaches, and affective approaches, in turn. Individual chapters within these sections cover a variety of subjects, such as product rating according to a single list of attributes, product rating when depicted by comments, and product rating when solely assessed by liking." Ringgold, Inc. Book News, February 2015

Foreword xi
Preface xiii
Acknowledgments xvii
I Quantitative descriptive approaches
1(106)
1 When panelists rate products according to a single list of attributes
5(30)
1.1 Data, sensory issues, and notations
5(2)
1.2 In practice
7(17)
1.2.1 What basic information can I draw from the data?
9(3)
1.2.2 How can I assess the performance of my panel?
12(8)
1.2.3 How can I assess the performance of my panelists?
20(4)
1.3 For experienced users: Measuring the impact of the presentation order on the perception of the products?
24(4)
1.4 Exercises
28(4)
1.5 Recommended readings
32(3)
2 When products are rated according to a single list of attributes
35(34)
2.1 Data, sensory issues, and notations
35(1)
2.2 In practice
36(20)
2.2.1 How can I get a list of the sensory attributes that structure the product space?
37(3)
2.2.2 How can I get a sensory profile for each product?
40(4)
2.2.3 How can I represent the product space on a map?
44(8)
2.2.4 How can I get homogeneous clusters of products?
52(4)
2.3 For experienced users: Adding supplementary information to the product space
56(7)
2.3.1 Introduction to supplementary information
57(2)
2.3.2 The panellipse function of the SensoMineR package
59(4)
2.4 Exercises
63(4)
2.5 Recommended readings
67(2)
3 When products are rated according to several lists of attributes
69(38)
3.1 Data, sensory issues, and notations
69(2)
3.2 In practice
71(19)
3.2.1 Why can't I analyze such a table in a classical way?
72(4)
3.2.2 How can I get a representation of the product space based on a consensus?
76(10)
3.2.3 How can I integrate the group structure in my interpretation?
86(4)
3.3 For experienced users: Comparing different panels with hierarchical multiple factor analysis (HMFA)
90(10)
3.4 Exercises
100(2)
3.5 Recommended readings
102(5)
II Qualitative descriptive approaches
107(122)
4 When products are depicted by comments
111(38)
4.1 Data, sensory issues, and notations
111(2)
4.2 In practice
113(24)
4.2.1 How can I approach textual data?
114(5)
4.2.2 How can I get an individual description of each product?
119(5)
4.2.3 How can I graphically represent the product space?
124(11)
4.2.4 How can I summarize the comments?
135(2)
4.3 For experienced users: Comparing free comments from different panels, the Rorschach test revisited
137(5)
4.4 Exercises
142(5)
4.5 Recommended readings
147(2)
5 When two different products are compared in various situations
149(24)
5.1 Data, sensory issues, and notations
149(2)
5.2 In practice
151(10)
5.2.1 How can I measure the distance between two products?
151(4)
5.2.2 How can I measure the inter-distance between products when compared in pairs?
155(6)
5.3 For experienced users: The Thurstonian approach
161(6)
5.4 Exercises
167(3)
5.5 Recommended readings
170(3)
6 When products are grouped into homogeneous clusters
173(28)
6.1 Data, sensory issues, and notations
173(2)
6.2 In practice
175(12)
6.2.1 How can I approach sorting data?
175(2)
6.2.2 How can I get a representation of the product space?
177(5)
6.2.3 How can I fully interpret the product space?
182(4)
6.2.4 How can I understand the data from a panel perspective?
186(1)
6.3 For experienced users: The hierarchical sorting task
187(9)
6.4 Exercises
196(2)
6.5 Recommended readings
198(3)
7 When products are positioned onto a projective map
201(28)
7.1 Data, sensory issues, and notations
201(3)
7.2 In practice
204(16)
7.2.1 How can I approach Napping® data?
204(5)
7.2.2 How can I represent the product space on a map?
209(2)
7.2.3 How can I interpret the product space with the verbalization data?
211(5)
7.2.4 How can I represent the consumers, and how can I explain the product representation through their individual rectangles?
216(4)
7.3 For experienced users: The sorted Napping®
220(3)
7.4 Exercises
223(3)
7.5 Recommended readings
226(3)
III Affective descriptive approaches
229(106)
8 When products are solely assessed by liking
233(38)
8.1 Data, sensory issues, and notations
233(2)
8.2 In practice
235(27)
8.2.1 How can I approach hedonic data?
235(11)
8.2.2 How can I identify the best product?
246(8)
8.2.3 How can I get homogeneous clusters of consumers?
254(8)
8.3 For experienced users: Dealing with multiple hedonic variables and supplementary consumer data
262(5)
8.3.1 Dealing with multiple hedonic variables
263(1)
8.3.2 Dealing with supplementary consumer data
264(3)
8.4 Exercises
267(3)
8.5 Recommended readings
270(1)
9 When products are described by both liking and external information "independently"
271(30)
9.1 Data, sensory issues, and notations
271(2)
9.2 In practice
273(13)
9.2.1 How can I explain the differences in preferences using sensory data?
273(5)
9.2.2 How can I evaluate the relationship between each sensory attribute and the hedonic scores, at different levels?
278(5)
9.2.3 How can I locate an optimum product within the product space?
283(3)
9.3 For experienced users: Finding the best correspondence between the sensory and hedonic matrices, using PrefMFA
286(8)
9.4 Exercises
294(4)
9.5 Recommended readings
298(3)
10 When products are described by a mix of liking and external information
301(34)
10.1 Data, sensory issues, and notations
301(3)
10.2 In practice
304(14)
10.2.1 How can I optimize products based on Just About Right data?
305(7)
10.2.2 How can I optimize products based on Ideal Profile Method data?
312(6)
10.3 For experienced users: Assessing the consistency of ideal data in IPM
318(10)
10.4 Exercises
328(3)
10.5 Recommended readings
331(4)
A The R survival guide
335(18)
A.1 What is R?
335(2)
A.1.1 Introduction to R
335(1)
A.1.2 The R environment
336(1)
A.2 Installing R
337(1)
A.3 Running my first R function
337(5)
A.3.1 Import/export data
337(2)
A.3.2 Objects in R
339(2)
A.3.3 First operations on a data set
341(1)
A.4 Functions in R
342(2)
A.4.1 Help
342(1)
A.4.2 Package
343(1)
A.5 Non-exhaustive list of useful functions in R
344(6)
A.6 Recommended packages
350(1)
A.7 Recommended readings
351(2)
Index 353
Sebastien Le, Thierry Worch