Unsupervised classification methods in food sciences: discussion and outlook

Authors

  • Marcin Kozak,

    1. Department of Biometry, Faculty of Agriculture and Biology, Warsaw University of Life Sciences, Nowoursynowska 159, PL-02-776 Warsaw, Poland
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  • Christine H Scaman

    Corresponding author
    1. Food, Nutrition, and Health, Faculty of Land and Food Systems, University of British Columbia, Vancouver, V6T 1Z4, Canada
    • Food, Nutrition, and Health, Faculty of Land and Food Systems, University of British Columbia, Vancouver, V6T 1Z4, Canada
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Abstract

This paper reviews three unsupervised multivariate classification methods: principal component analysis, principal component similarity analysis and heuristic cluster analysis. The theoretical basis of each method is presented in brief, and assumptions inherent to the methods are highlighted. A literature review shows that these methods have sometimes been used inappropriately or without referencing all essential parameters. The paper also brings to the attention of the reader a relatively unknown method: probabilistic or model-based cluster analysis. The goal of this method is to uncover the true classification of objects rather than a convenient classification provided by the other methods. For this reason it is felt that model-based cluster analysis will have broad application in the future. Copyright © 2008 Society of Chemical Industry

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