Defining the underlying sensory dimensions

Qannari, El Mostafa; Wakeling, Ian; Courcoux, Philippe; MacFie, H.J.H.

doi:10.1016/s0950-3293(99)00069-5

Cited by 123 publications

(104 citation statements)

References 3 publications

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“…The CCSWA was first applied in sensory profiling analysis where judges assess a set of products using several sensory attributes [11]. It was subsequently applied in chemometrics for combining different kinds of measurements made on food products [12,13].…”

Section: Presentation Of the Two Methodsmentioning

confidence: 99%

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Common components and specific weight analysis and multiple co‐inertia analysis applied to the coupling of several measurement techniques

Hanafi

Mazerolles

Dufour

et al. 2006

Journal of Chemometrics

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The present paper compares two multiblock techniques: the Common Components and Specific Weights Analysis (CCSWA) and the Multiple Co-inertia Analysis (MCoA). Both methods are used to (1) to investigate the relationships among various data tables and (2) to extract latent variables from information of different nature, reflecting different facets of a food product. Our objective is to study the ability of these methods to extract, from a set of data tables, latent characteristics which are representative of the whole modifications brought to a complex system (food product) by a modification of a given process factor. The comparison of these methods is based on the investigation of their conceptual framework by particularly highlighting new properties of CCSWA. Moreover, the two techniques of analysis are compared on the basis of a case study in cheese processing where each cheese sample is described by different kinds of measurements.

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Section: Presentation Of the Two Methodsmentioning

confidence: 99%

“…The computation of the first common component q 1 and the saliences l is realised by the following procedure [11].…”

Section: Appendixmentioning

confidence: 99%

Common components and specific weight analysis and multiple co‐inertia analysis applied to the coupling of several measurement techniques

Hanafi

Mazerolles

Dufour

et al. 2006

Journal of Chemometrics

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“…Focusing on the latter criterion, the goal is to evaluate whether a consensus within the panel exists. However, as pointed out by several authors (see, e.g., Dijksterhuis, 1995;Qannari, Wakeling, Courcoux, & MacFie, 2000), even for trained panels it cannot be ruled out that individual differences are present. This situation may, for example, occur when the attributes are difficult to define or when different assessors attach a different meaning to the same attributes (Dijksterhuis, 1995).…”

Section: Introductionmentioning

confidence: 91%

CLV3W: A clustering around latent variables approach to detect panel disagreement in three-way conventional sensory profiling data

Wilderjans

Cariou

2016

Food Quality and Preference

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a b s t r a c tTo detect panel disagreement, we propose the clustering around latent variables for three-way data (CLV3W) approach which extends the clustering of variables around latent components (CLV) approach to three-way data typically obtained from a conventional sensory profiling procedure (i.e., assessors rating products on various descriptors). The CLV3W method groups the descriptors into Q clusters and estimates for each cluster an associated latent sensory component such that the attributes within each cluster are as much related (i.e., highest squared covariance) as possible with the latent component. Simultaneously, for each latent sensory component separately, a system of weights is estimated that yields information regarding the extent to which an assessor (dis)agrees with the rest of the panel according to the latent sensory component under study. Our new approach is illustrated with a dataset pertaining to Quantitative Descriptive Analysis applied to cider varieties. It is shown that CLV3W, as opposed to related approaches, is able to detect differential panel disagreement on various latent sensory components.

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“…Le problème ici, est de déterminer les axes de coinertie dans ce contexte en vue de caractériser les objets dans les systèmes communs de représentation. Ces axes de co-inertie communsà tous les tableaux peuventêtre assimilés aux composantes communes comme c'était le cas dans le cadre d'un M − multi-tableaux ayant en commun des individus en ligne, se reporterà Qannari et al, (2000, 2001) et Hanafiet Qanari (2008. Ces axes permettent de calculer des coefficients de corrélation au carré entre composantes partielles des sous tableaux de deux multi-tableaux et des poids spécifiques de chaque sous tableau qui sont en réalité des inerties projetées.…”

Section: Introductionunclassified

Proposition d'analyses de co-inertie entre deux multi-tableaux verticaux: les methodes ACIs et ACIOs.

Niére¹,

Kissita²,

Mizère³

2013

afst

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Abstract. The analysis of Co-inertia between two tables was generalized with the simultaneous analysis several couples of triplets of tables in STATICO in the link between two vertical multi-tables. In this article, being given the context of STATICO, we propose an analysis of successive Co-inertia (ACIs). The axes of Co-inertia which are given are common to all the tables. To interpret the variables of each table, we propose the orthogonal version of this analysis of Co-inertia which we call ACIOs. We apply this method to the real data.Résumé. L'analyse de co-inertie entre deux tableaux aété généralisée l'analyse simultanée de plusieurs couples de triplets de tableaux dans STATICO dans le lien entre deux multitableaux verticaux. Dans cet article,étant donné le contexte de STATICO, nous proposons une analyse de co-inertie successive (ACIs). Les axes de co-inertie qui sont déterminés sont communsà tous les tableaux. Pour interpréter les variables de chaque tableau, nous proposons la version orthogonale de cette analyse de co-inertie que nous appelons ACIOs. Nous appliquons cette méthode aux données réelles.

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Defining the underlying sensory dimensions

Cited by 123 publications

References 3 publications

Common components and specific weight analysis and multiple co‐inertia analysis applied to the coupling of several measurement techniques

Common components and specific weight analysis and multiple co‐inertia analysis applied to the coupling of several measurement techniques

CLV3W: A clustering around latent variables approach to detect panel disagreement in three-way conventional sensory profiling data

Proposition d'analyses de co-inertie entre deux multi-tableaux verticaux: les methodes ACIs et ACIOs.

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