Unsupervised classification methods in food sciences: discussion and outlook

✍ Scribed by Marcin Kozak; Christine H Scaman

• Publisher: John Wiley and Sons
• Year: 2008
• Tongue: English
• Weight: 173 KB
• Volume: 88
• Category: Article
• ISSN: 0022-5142
• DOI: 10.1002/jsfa.3215

✦ Synopsis

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