E
El Mostafa Qannari
Researcher at Institut national de la recherche agronomique
Publications - 102
Citations - 2282
El Mostafa Qannari is an academic researcher from Institut national de la recherche agronomique. The author has contributed to research in topics: Partial least squares regression & Cluster analysis. The author has an hindex of 25, co-authored 102 publications receiving 2004 citations. Previous affiliations of El Mostafa Qannari include Université Nantes Angers Le Mans & National Autonomous University of Mexico.
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Clustering of Variables Around Latent Components
TL;DR: Clustering of variables around latent components is investigated as a means to organize multivariate data into meaningful structures and the strategy basically consists in performing a hierarchical cluster analysis, followed by a partitioning algorithm.
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Defining the underlying sensory dimensions
TL;DR: The algorithm which determines the parameters of the third model has been improved and a the new version of the algorithm is presented and illustrated using the potatoes data proposed by the organisers of the workshop.
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Discrimination on latent components with respect to patterns. Application to multicollinear data
TL;DR: A new presentation of discriminant analysis consists in setting up patterns associated to the various groups and deriving latent variables in such a way that scores in each group are as highly clustered about their pattern as possible.
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Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration
TL;DR: In this paper, the authors compared the performance of ridge regression and principal component regression (PCR) on spectral data with OLS and partial least squares (PLS) on the basis of two data sets.
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Common components and specific weights analysis: A chemometric method for dealing with complexity of food products
TL;DR: It is shown in this work that the investigation of the relationships among data tables collected on the same samples can be a powerful approach in food engineering and reverse engineering and the global characterization of food products.