Advances in representation and analysis of mono and multi-intake Temporal Dominance of Sensations data

Abstract : Using the concept of dominance, other than intensity, Temporal Dominance of Sensations (TDS) has become a highly used temporal descriptive technique providing information on sequentiality and duration time of dominant sensations. In the present work we propose: (i) a new graphical tool for representing the sequentiality of (multi-intake) TDS data and (ii) an inferential approach to data analysis based on duration of dominance. TDS data are generally presented as TDS curves. However, visual inspection and curve comparison among intakes of a same product, several products or both, can be a cumbersome task. To better show sequentiality of dominant attributes, we propose different TDS band-plots. These representations show whether attributes are dominant or not (at panel level) allowing the follow-up of each descriptor and improving visual comparison of products. Nonetheless, visual assessment is not enough to determine significant differences. Using the total duration of dominance of each attribute, three-way ANOVA's including subject, product and intake as factors, together with their two-way interactions, with subject as random effect can be performed. This test reveals if a certain attribute is dominant for a significantly longer or shorter period of time in some products, as well as its changes over intakes. Interaction between product and intake becomes highly important to see if evolution over intakes is the same for all the products. Further, multidimensional differences can be summarized over attributes by a MANOVA and represented by a Canonical Variate Analysis (CVA), allowing the comparison of different intakes of a same product, or different products, or altogether.
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Food Quality and Preference, Elsevier, 2017, 56, 〈10.1016/j.foodqual.2016.01.011〉
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Mara V. Galmarini, Michel Visalli, Pascal Schlich. Advances in representation and analysis of mono and multi-intake Temporal Dominance of Sensations data. Food Quality and Preference, Elsevier, 2017, 56, 〈10.1016/j.foodqual.2016.01.011〉. 〈hal-01426953〉

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