Analyzing Sensory Data with R

Sebastien Le, Thierry Worch, Margot Brard

• Publisher: Chapman and Hall/CRC
• Year: 2018
• Print ISBN: 9781466565722
• eBook ISBN: 9781315360379
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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.

Part II

Qualitative descriptive approaches

Qualitative descriptive approaches

What could justify to bring together, in a same part, free text comments, discrimination test data, and specific approaches such as sorting task and Napping®? One possible answer is that these data are by nature qualitative, in the etymological meaning of the word¹. Another possible answer is that all these approaches can be considered as holistic², although in sensory science, “holistic” mainly refers to sorting task and Napping®. Indeed, the word “holistic” describes methodologies which focus their attention on the entire product, and not on its decomposition into its different sensory characteristics only.

By going back to the different sensory tasks described in this part, it seems obvious that free text comments, which consist in depicting products using one’s own words, are by essence qualitative. With regard to the data provided by discrimination tests, sorting task, or Napping®, it may not be that straightforward. To justify this, let’s go back to the purpose of all these tasks, and the information that can be extracted from them. From a practical point of view, the main purpose of considering products holistically is to evaluate the degree of similarity and/or difference between products. For discrimination tests, the name of the task speaks for itself. Still, let us remind you the purpose of such a task: given a configuration of two different products, typically it consists in indicating which of the products are the same or different. Such tasks seem to have existed forever and are taught in every curriculum in sensory evaluation. On the other hand, sorting task and Napping have experienced a renewed interest lately, in the last couple of decades, and both tasks consist in aggregating or separating products based on their perceived similarities or differences. This justifies the regroupment of these sensory tasks together in this part.

In terms of structure, this second part is organized from the least “constrained” to the most “constrained” methodology, the constraint relying on the number of products involved during one assessment. Free text comments are usually provided when assessing products in monadic sequence. Consequently, as one assessment consists in depicting one product only, the subject doesn’t feel any constraint whatsoever. In discrimination tests, the number of different products to be assessed during one trial is most commonly equal to two. In that case, the subject has to remember two products during each assessment. For sorting and Napping®, however, the number of products is usually higher than two. In the case of Napping®, the subjects have to stand back from the whole product space to provide their own plane representation. From this point of view, the subjects have to remember all the products, so to speak. Such constraint is reduced in a sorting task, as the subjects usually focus on a subset of products to ensure its homogeneity. For these reasons, we decided to present sorting before Napping®. Starting this second part with the word association task is also coherent with the structure of the book, as this methodology is the natural extension of free choice profile (presented at first part of Chapter 3). As we will see, such information is also of utmost importance for the interpretation of sorting and Napping data.

Finally, the order of presenting these chapters within this part is also consistent, from a statistical point of view. Indeed, Chapter 4 introduces the notion of contingency table and presents a very important method through its application to textual data: Correspondence Analysis (CA). Additionally, a very common situation that can only be handled by an extension of Multiple Factor Analysis (MFA) to contingency tables, is also presented in this chapter. Chapter 5 introduces models, such as the Bradley-Terry and the Thurstonian models, which are conceptually more complicated to apprehend than the ANOVA models presented in Chapter 1. Chapter 6 presents the Multiple Correspondence Analysis (MCA), the reference method for exploring multivariate qualitative data. This method can be seen as a natural extension of Correspondence Analysis (CA), and as some sort of Principal Component Analysis (PCA) for qualitative data, but with its own interpretation rules (due to the nature of the data). In particular, the distance between individuals in MCA is more complicated to understand than the one in PCA. Chapter 7 finally presents a special case of MFA, in which groups of variables are composed of two variables only and are not standardized: this case study is a very good complement to Chapter 3.

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¹ Qualitative is derived from the Latin word qualitas (“way of being,” “kind,” “character,” “nature”), a word formed from the word qualis meaning “as such.”

² Holistic comes from the Greek word holos meaning “whole.” Holism is the idea that all the properties of a given system cannot be determined or explained by its component parts alone. Instead, the system as a whole determines in an important way how the parts behave (source: http://en.wikipedia.org/wiki/Holism).

4

When products are depicted by comments

CONTENTS

4.1 Data, sensory issues, and notations

4.2 In practice

4.2.1 How can I appro...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Foreword
• Preface
• Acknowledgments
• I Quantitative descriptive approaches
• II Qualitative descriptive approaches
• III Affective descriptive approaches
• A The R survival guide
• Index