Clustering of datasets, applications to sensory analysis

Les données structurées sous forme de tableaux se rapportant aux mêmes individus sont de plus en plus fréquentes dans plusieurs secteurs d’application. C’est en particulier le cas en évaluation sensorielle où plusieurs épreuves conduisent à l’obtention de tableaux multiples ; chaque tableau étant rapporté à un sujet (juge, consommateur, …). L’analyse exploratoire de ce type de données a suscité un vif intérêt durant les trente dernières années. Cependant, la classification de tableaux multiples n’a été que très peu abordée alors que le besoin pour ce type de données est important. Dans ce contexte, une méthode appelée CLUSTATIS permettant de segmenter les tableaux de données est proposée. Au cœur de cette approche se trouve la méthode STATIS, qui est une stratégie d’analyse exploratoire de tableaux multiples. Plusieurs extensions de la méthode de classification CLUSTATIS sont présentées. En particulier, le cas des données issues d’une épreuve dite « Check-All-That-Apply » (CATA) est considéré. Une méthode de classification ad-hoc, nommée CLUSCATA, est discutée. Afin d’améliorer l’homogénéité des classes issues aussi bien de CLUSTATIS que de CLUSCATA, une option consistant à rajouter une classe supplémentaire, appelée « K+1 », est introduite. Cette classe additionnelle a pour vocation de collecter les tableaux de données identifiés comme atypiques. Le choix du nombre de classes est abordé, et des solutions sont proposées. Des applications dans le cadre de l’évaluation sensorielle ainsi que des études de simulation permettent de souligner la pertinence de l’approche de classification. Des implémentations dans le logiciel XLSTAT et dans l’environnement R sont présentées.Multiblock datasets are more and more frequent in several areas of application. This is particularly the case in sensory evaluation where several tests lead to multiblock datasets, each dataset being related to a subject (judge, consumer, ...). The statistical analysis of this type of data has raised an increasing interest over the last thirty years. However, the clustering of multiblock datasets has received little attention, even though there is an important need for this type of data.In this context, a method called CLUSTATIS devoted to the cluster analysis of datasets is proposed. At the heart of this approach is the STATIS method, which is a multiblock datasets analysis strategy. Several extensions of the CLUSTATIS clustering method are presented. In particular, the case of data from the so-called "Check-All-That-Apply" (CATA) task is considered. An ad-hoc clustering method called CLUSCATA is discussed.In order to improve the homogeneity of clusters from both CLUSTATIS and CLUSCATA, an option to add an additional cluster, called "K+1", is introduced. The purpose of this additional cluster is to collect datasets identified as atypical.The choice of the number of clusters is discussed, ans solutions are proposed. Applications in sensory analysis as well as simulation studies highlight the relevance of the clustering approach.Implementations in the XLSTAT software and in the R environment are presented

CLUSTATIS: cluster analysis of blocks of variables

The STATIS method is one of many strategies of analysis devoted to the unsupervised analysis of multiblock data. A new optimization criterion to define this method of analysis is introduced and an extension to the cluster analysis of several blocks of variables is discussed. This consists in a hierarchical cluster analysis and a partitioning algorithm akin to the K-means algorithm. Moreover, in order to improve the cluster analysis outcomes, an additional cluster called noise cluster which contains atypical blocks of variables is introduced. The general strategy of analysis is illustrated by means of two cases studies

Combining hedonic information and CATA description for consumer segmentation.

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Toward a valence × arousal circumplex-inspired emotion questionnaire (CEQ) based on emoji and comparison with the word-pair variant

Product-focused emotion research continues to be in ascendency. Corresponding method development is ongoing, and is the focus of the present research which takes a first step towards developing and testing an emoji variant of the valence x arousal circumplex-inspired emotion questionnaire (CEQ). In Study 1, through a consumer-driven approach to identifying the meaning of candidate emoji, an emoji CEQ variant was developed using emoji with meanings similar to those of the 12 word-pairs. Study 2 then compared the emoji CEQ with the word-based variant in a study with 15 written food stimuli. This was done in an online survey with UK adults using a between-subjects design where participants completed one of the two CEQ variants. The emotion profiles for individual foods elicited by the emoji CEQ fitted expectations and were largely similar to those from the word-based variant. However, for some food names the emotion profiles were less rather than more similar, and these differences were attributed to incomplete alignment between the emoji and emotion word-pairs at specific CEQ positions. Examples of least concordance were "apple ": Active/Alert (19%) vs. warning ( ) (0%), "tuna steak ": Jittery/Nervous (4%) vs. confounded face ( ) (11%), and "blue-vein cheese ": Jittery/Nervous (8%) vs. confounded face ( ) (18%). The presented emoji CEQ is suitable for application although users should be aware of differences relative to the word-based variant. To minimise these, further improvement of the emoji CEQ is suggested, as is applications with other (tasted) stimuli to validate appropriateness of the emoji CEQ in product focused emotion research

Emoji meanings (pleasure-arousal-dominance dimensions) in consumer research : between-country and interpersonal differences

In line with the increasing popularity of emoji, the need for methodological research into these pictorial representations of emotion remains. The present research contributes to this goal by continuing to establish the meaning of emoji and exploring these according to between-country and interpersonal differences. The emoji (n = 12) were selected to span the valence x arousal emotion space, and the PAD model (Pleasure-Arousal-Dominance) was used to establish emoji meaning for the three dimensions, operationalized as measurement on 6 x 3 semantic differentials. Participants in the main study came from three countries-Germany, Singapore, and Malaysia (n = 2465), and a supplementary study included the United Kingdom and New Zealand (n = 600) (subset of four emoji). The results confirmed that emoji meanings according to the PAD model were largely similar between countries (albeit not identical). There were multiple minor significant differences for individual emoji, and where these existed, they often related to the dimension of Arousal, prompting a need for further investigation. Interpersonal differences were examined for gender (men and women), age group (18-45 and 46-69 years old), and frequency of emoji use. Again, significant differences were smaller rather than larger and supported the notion that emoji are generally applicable for multicountry research. However, caution regarding the participants who use emoji infrequently may be warranted. Practical ApplicationThe findings from this research will help academics and practitioners who are interested in using emoji for sensory and consumer research (or are already doing so) with more robust interpretations of their findings. For a set of 12 emoji that provide broad coverage of the valence x arousal emotional space, meanings are provided on the three dimensions of the PAD model. The data is collected in five countries and contributes to increased confidence that emoji meanings are by and large similar in these countries

Consumer Preference Segments for Plant-Based Foods: The Role of Product Category

A survey of willingness to consume (WTC) 5 types of plant-based (PB) food was conducted in USA, Australia, Singapore and India (n = 2494). In addition to WTC, emotional, conceptual and situational use characterizations were obtained. Results showed a number of distinct clusters of consumers with different patterns of WTC for PB foods within different food categories. A large group of consumers did not discriminate among PB foods across the various food categories. Six smaller, but distinct clusters of consumers had specific patterns of WTC across the examined food categories. In general, PB Milk and, to a much lesser extent, PB Cheese had highest WTC ratings. PB Fish had the lowest WTC, and two PB meat products had intermediate WTC. Emotional, conceptual and situational use characterizations exerted significant lifts/penalties on WTC. No penalty or lifts were imparted on WTC by the situational use of ‘moving my diet in a sustainable direction’, whereas uses related to ‘when I want something I like’ and ‘when I want something healthy’ generally imparted WTC lifts across clusters and food categories. The importance of this research for the study of PB foods is its demonstration that consumers are not monolithic in their willingness to consume these foods and that WTC is often a function of the food category of the PB food