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Applied and Environmental Microbiology, May 2001, p. 2156-2166, Vol. 67, No. 5
0099-2240/01/$04.00+0   DOI: 10.1128/AEM.67.5.2156-2166.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.

Comparison of Statistical Methods for Identification of Streptococcus thermophilus, Enterococcus faecalis, and Enterococcus faecium from Randomly Amplified Polymorphic DNA Patterns

Giancarlo Moschetti,1 Giuseppe Blaiotta,1 Francesco Villani,1 Salvatore Coppola,1 and Eugenio Parente2,*

Dipartimento di Scienza degli Alimenti, Università degli Studi di Napoli "Federico II," 80055 Portici,1 and Dipartimento di Biologia, Difesa, e Biotecnologie Agro-Forestali, Università degli Studi della Basilicata, 85100 Potenza,2 Italy

Received 29 September 2000/Accepted 18 February 2001

Thermophilic streptococci play an important role in the manufacture of many European cheeses, and a rapid and reliable method for their identification is needed. Randomly amplified polymorphic DNA (RAPD) PCR (RAPD-PCR) with two different primers coupled to hierarchical cluster analysis has proven to be a powerful tool for the classification and typing of Streptococcus thermophilus, Enterococcus faecium, and Enterococcus faecalis (G. Moschetti, G. Blaiotta, M. Aponte, P. Catzeddu, F. Villani, P. Deiana, and S. Coppola, J. Appl. Microbiol. 85:25-36, 1998). In order to develop a fast and inexpensive method for the identification of thermophilic streptococci, RAPD-PCR patterns were generated with a single primer (XD9), and the results were analyzed using artificial neural networks (Multilayer Perceptron, Radial Basis Function network, and Bayesian network) and multivariate statistical techniques (cluster analysis, linear discriminant analysis, and classification trees). Cluster analysis allowed the identification of S. thermophilus but not of enterococci. A Bayesian network proved to be more effective than a Multilayer Perceptron or a Radial Basis Function network for the identification of S. thermophilus, E. faecium, and E. faecalis using simplified RAPD-PCR patterns (obtained by summing the bands in selected areas of the patterns). The Bayesian network also significantly outperformed two multivariate statistical techniques (linear discriminant analysis and classification trees) and proved to be less sensitive to the size of the training set and more robust in the response to patterns belonging to unknown species.

* Corresponding author. Mailing address: Dipartimento di Biologia, Difesa, e Biotecnologie Agro-Forestali, Università degli Studi della Basilicata, Campus di Macchia Romana, 85100 Potenza, Italy. Phone: 39-0971-205561. Fax: 39-0971-205561. E-mail: parente{at}unibas.it.

Applied and Environmental Microbiology, May 2001, p. 2156-2166, Vol. 67, No. 5
0099-2240/01/$04.00+0   DOI: 10.1128/AEM.67.5.2156-2166.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.


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