Characterization and classification of wines according to geographical origin, vintage and specific variety based on elemental content: a new chemometric approach.

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ID: 100254
2019
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Abstract
A highly informative chemometric approach using elemental data to distinguish and classify wine samples according to different criteria was successfully developed. The robust chemometric methods, such fuzzy principal component analysis (FPCA), FPCA combined with linear discriminant analysis (LDA), namely FPCA-LDA and mainly fuzzy divisive hierarchical associative-clustering (FDHAC), including also classical methods (HCA, PCA and PCA-LDA) were efficaciously applied for characterization and classification of white wines according to the geographical origin, vintage or specific variety. The correct rate of classification applying LDA was 100% in all cases, but more compact groups have been obtained for FPCA scores. A similar separation of samples resulted also when the FDHAC was employed. In addition, FDHAC offers an excellent possibility to associate each fuzzy partition of wine samples to a fuzzy set of specific characteristics, finding in this way very specific elemental contents and fuzzy markers according to the degrees of membership (DOMs).
Reference Key
feher2019characterizationjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Feher, Ioana;Magdas, Dana Alina;Dehelean, Adriana;Sârbu, Costel;
Journal Journal of food science and technology
Year 2019
DOI
10.1007/s13197-019-03991-4
URL
Keywords
chemometrics icp-ms wine elemental data fuzzy clustering

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