Abstract 073
Comparison of three pattern recognition techniques for classification and identification of lactic acid bacteria
J. Appl. Micro. 91 (2): 225-236, 2001
I. Dalezios and K.J. Siebert
Aims: The goal of this study was to evaluate three pattern recognition methods for use in identification of lactic acid bacteria.
Methods & Results: Lactic acid bacteria (21 unknown isolates and 30 well-characterized strains), including 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 Neighbor Analysis (KNN), k-Means Clustering (KMC) and Fuzzy c-Means Clustering (FCM) were applied to the data.
Conclusions: KNN performed better with 5-point scaled than with binary data, indicating that intermediate values are helpful to classification. KMC 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.
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