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Comparison of pattern recognition techniques for the identification of lactic acid bacteria
Author(s) -
Dalezios I.,
Siebert K.J.
Publication year - 2001
Publication title -
journal of applied microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.889
H-Index - 156
eISSN - 1365-2672
pISSN - 1364-5072
DOI - 10.1046/j.1365-2672.2001.01370.x
Subject(s) - lactococcus , pattern recognition (psychology) , lactic acid , pediococcus , cluster analysis , lactobacillus , biology , artificial intelligence , identification (biology) , bacteria , computational biology , computer science , lactococcus lactis , genetics , botany
Aims: The goal of this study was to evaluate three pattern recognition methods for use in the identification of lactic acid bacteria. Methods and Results: Lactic acid bacteria (21 unknown isolates and 30 well‐characterized strains), including the Lactobacillus , Lactococcus , Streptococcus , Pediococcus and Oenococcus genera, were tested for 49 phenotypic responses (acid production on carbon sources). The results were scored in several ways. Three procedures, k‐nearest neighbour analysis (KNN), k‐means clustering and fuzzy c‐means clustering (FCM), were applied to the data. Conclusions: k‐Nearest neighbour analysis performed better with five‐point‐scaled than with binary data, indicating that intermediate values are helpful to classification. k‐Means clustering performed slightly better than KNN and was best with fuzzified data. The best overall results were obtained with FCM. Genus level classification was best with FCM using an exponent of 1·25. Significance and Impact of the Study: The three pattern recognition methods offer some advantages over other approaches to organism classification.