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“Free sorting”, in which subjects are asked to sort a set of items into groups of “most similar” items, is increasingly popular as a technique for profiling sets of foods. However, free sorting implies an unrealistic model of sample similarity: that similarity is purely binary (is/is not similar) and that similarity is fully transitive (similarities {A, B} and {B, C} imply {A, C}). This paper proposes a new method of rapid similarity testing—the “free-linking” task—that solves both problems: in free linking, subjects draw a similarity graph in which they connect pairs of samples with a line if they are similar, according to the subject’s individual criteria. This simple task provides a more realistic model of similarity which allows degrees of similarity through the graph distance metric and does not require transitive similarity. In two pilot studies with spice blends (10 samples, 58 subjects) and chocolate bars (10 samples, 63 subjects), free linking and free sorting are evaluated and compared using DISTATIS, RVb , and the graph parameters degree, transitivity, and connectivity; subjects also indicated their preferences and ease-of-use for the tasks. In both studies, the first two dimensions of the DISTATIS consensus were highly comparable across tasks; however, free linking provided more discrimination in dimensions three and four. RVb stability was equivalent for the two methods. Graph statistics indicated that free linking had greater discrimination power: on average subjects made similarity groupings with lower degree, lower transitivity, and higher connectivity for free linking in both studies. However, subjects did overall find free sorting easier and liked it more, indicating a higher cognitive difficulty of free linking. The free-linking task, therefore, provides more robust, realistic similarity maps at the cost of higher panelist effort, and should prove a valuable alternative for rapid sensory assessment of product sets.

International audience; In the field of clustering techniques, little attention has been paid to the recovery of a set of clusters from the structure of an additive tree. To bridge this gap, this work presents an original partitioning technique which aims to reveal clusters from an additive tree that represents a large set of objects. Specifically, an algorithm that splits a tree into successive subtrees was developed, based on a ratio of the lengths of edges. The stability of the clusters obtained with this technique was then evaluated using measurements of cohesion and isolation that were generated using a bootstrapping strategy. Finally, the degree of association of each object to clusters was analyzed to gain insight into their internal structure. This analysis was performed on the results of a sorting task conducted by 156 subjects, who were asked to sort 96 terms associated with the odor of wine. The methodology developed in this paper represents an innovative way to highlight groups of terms within a large set of wine odor attributes, with the ultimate goal being to improve the structure of the lexicon.

International audience; Aromatic characterization is a key element of enhancing one's knowledge of wine. While several studies have investigated the importance of wine expertise in the ability to perform odor-related sensory tasks, little attention has been paid to the influence of expertise on the semantic categorization of wine odors. To bridge this gap, this study aimed to explore the influence of a subject's expertise on the semantic representation of wine odors by means of a free sorting task. For this purpose, 156 subjects were recruited. Their level of expertise was measured using a questionnaire and the data analysis revealed four clusters of subjects with a gradual level of expertise. Subjects also performed a sorting task on 96 odor terms. From the number and the size of odor groups formed, as well as the additive tree representation and the consensus partition between the terms for each expertise level, we observed that all subjects, regardless of their experience, had largely the same semantic categorization of wine-odor attributes, which was mainly shaped by the sources of the odorants. It appeared that level of wine expertise played a minor role in creating the semantic representation of wine odors, affecting mainly the knowledge of specialized terms.

International audience; A method of clustering a collection of datasets measured on the same individuals, called CLUSTATIS, was introduced and applied to the segmentation of the subjects participating in a projective mapping experiment or a free sorting task. A refinement of this method of clustering is proposed. It consists in segmenting the subjects while discarding those subjects who can be considered as atypical because they do not fit to the pattern of any cluster. This strategy of analysis requires the determination of a threshold parameter that delineates the boundary between the main clusters and the noise cluster that contains the atypical subjects. An appropriate selection of this parameter is proposed. The general strategy of analysis is illustrated on the basis of a simulation study and data from projective mapping and free sorting experiments.

International audience; Several statistical procedures have been proposed for the analysis of the data from a free sorting task. A straightforward strategy of analysis based on correspondence analysis and cluster analysis performed on the co-occurrence matrix is proposed herein. More specifically, two situations are considered depending on whether the aim is to depict the relationships among the stimuli or to investigate the agreement among the subjects. The approach of analysis is illustrated on the basis of free sorting data characterizing chocolate products.

Free sorting is a quick and reliable means to assess similarities among a set of stimuli by a panel of subjects. In this procedure, subjects form as many groups of stimuli as they consider necessary, with the understanding that each group is formed with stimuli that are perceived as similar. However, no information is available about the structure of the groups formed by each subject. In practice, these groups are considered as equally distant from each other. This is a questionable assumption as it may not reflect the perception of the subjects towards the stimuli. Taxonomic free sorting is designed to cope with this problem. Once the subjects have achieved the free sorting of the stimuli, they are instructed to organize, step by step, the stimuli into a hierarchical structure thus giving more insight into the similarities among products. Statistical treatments of the data thus obtained are discussed and the procedure of evaluation is illustrated on the basis of a case study. (C) 2011 Elsevier Ltd. All rights reserved.

International audience; Aesthetics is one of the major parameters for consumers when buying a rose bush. Therefore, managing this quality is important for agronomists. Tools are needed to assess visual characteristics and to find links with architectural plant parameters. Sensory analyses were developed using real plants and photographs as stimuli. With technology and modeling improvements, using virtual plants could presents numerous advantages. This study demonstrated the feasibility of using rotating virtual rose bush videos as stimuli for a labeled sorting task. The virtual rose bush reflected a natural within-crop variability of one cultivar based on bud breaks location and axes length. Two panels of subjects closely linked to the horticulture sector sorted and described 40 rotating virtual rose bush videos. Non-metric Multidimensional Scaling (MDS) results for both panels were similar and allowed us to highlight five groups of virtual rose bushes with their specific sensory characteristics and their own most representative products using a combination of the paragons and the most typical products. This approach revealed that subjects detected high visual differences between products, and that by using rotation, they were able to integrate 3D properties about variations around plant facets. Finally, a labeled sorting task is a powerful method for preliminary exploration of the visual aspect of virtual plants.

International audience; The purpose of this study is to investigate the problem of clustering subjects in a free sorting task. We compare different measures of agreement between partitions. From a simulation study, we advocate using the Adjusted Rand index. On the basis of this index, we propose a technique for determining a con. sensus partition as a summary of the initial partitions given by the subjects after a categorization task. Thereafter, the problem of clustering the subjects is explored. For this purpose, a method combining hierarchical clustering and a partitioning algorithm is described. These techniques are applied to a case study of the perception of wine aromas by a panel of subjects. (C) 2013 Elsevier Ltd. All rights reserved.

International audience; The degree of knowledge of a panel of consumers about the stimuli under study is assessed by means of a questionnaire. From the scores obtained by means of a Rasch measurement model, the consumers were segmented into three groups. In order to assess the impact of the consumers' knowledge and experience onto their perception of wine glasses, a free sorting experiment was undergone. The group of consumers identified as connoisseurs sorted the glasses into six categories based on usage and technical properties. The group of consumers identified as non connoisseurs showed less agreement in their sorting of the glasses and mainly based their assessment on descriptive properties related to the design and the shape of the glasses. Thus, the perceptual representations of the glasses and their descriptions are different between the groups of consumers and are impacted by the previous knowledge and the experience of the subjects and their involvement toward the products. (C) 2012 Elsevier Ltd. All rights reserved.