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Identification of Clusters of Variables and Underlying Latent Components in Sensory Analysis

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Advances in Latent Variables

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

Abstract

The Clustering of Variables around Latent Variables (CLV) approach aims to identify groups of features in a data set and, at the same time, to identify the prototype, or the latent variable, of each group. The procedure makes it possible to search for local groups or directional groups. Moreover, constraints on the latent variables may be added in order to introduce, if available, additional information about the observations and/or the variables. This approach is illustrated in two different contexts encountered in sensory analysis: (1) the clustering of sensory descriptors by taking into account their redundancy; and (2) the segmentation of a panel of consumers according to their liking, by taking into account external information about the products and the consumers.

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Authors and Affiliations

  1. Nantes-Atlantic College of Veterinary Medicine, Food Science and Engineering (Oniris), Sensometrics and Chemometrics Laboratory, Site de la Géraudière, CS 82225, FR-44322, Nantes Cedex 3, France

    Evelyne Vigneau

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  1. Evelyne Vigneau
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Correspondence to Evelyne Vigneau .

Editor information

Editors and Affiliations

  1. Dept. of Economics and Management, University of Brescia, Brescia, Italy

    Maurizio Carpita

  2. Dept. of Economics and Management, University of Brescia, Brescia, Italy

    Eugenio Brentari

  3. Dept. of Chemometrics and Sensometrics, Oniris Nantes National College, Nantes, France

    El Mostafa Qannari

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Vigneau, E. (2014). Identification of Clusters of Variables and Underlying Latent Components in Sensory Analysis. In: Carpita, M., Brentari, E., Qannari, E. (eds) Advances in Latent Variables. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/10104_2014_19

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