Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics - Archive ouverte HAL Access content directly
Journal Articles Food Quality and Preference Year : 2020

Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics

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Abstract

The STATIS method has been successfully applied to the analysis of sensory profiling data and other kinds data in sensometrics. We discuss its use and benefits and compare its outcomes to alternative methods for the analysis of multiblock data arising in situations such as projective mapping and free sorting experiments. More importantly, a method of clustering a collection of datasets measured on the same individuals, called CLUSTATIS, is introduced. It is based on the optimization of a criterion and consists in a hierarchical cluster analysis and a partitioning algorithm akin to the K-means algorithm. The procedure of analysis can be seen as an extension of the cluster analysis of variables around latent components (CLV, Vigneau & Qannari, 2003) to the case of blocks of variables. Alongside the determination of the clusters, a latent configuration is determined by the STATIS method. The interest of CLUSTATIS in sensometrics is discussed and illustrated on the basis of two case studies pertaining to the projective mapping also called Napping and the free sorting tasks, respectively.
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hal-02626436 , version 1 (20-07-2022)

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Attribution - NonCommercial - CC BY 4.0

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Fabien Llobell, Véronique Cariou, Amaury Labenne, El Mostafa Qannari. Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, 2020, 79, ⟨10.1016/j.foodqual.2018.05.013⟩. ⟨hal-02626436⟩

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