Comparison of statistical methods for identification of Streptococcus thermophilus, Enterococcus faecalis, and Enterococcus faecium from randomly amplified polymorphic DNA patterns

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 t wo different primers coupled to hierarchical cluster analysis has proven to be a powerful tool for the classification and typing of Streptococcus ther mophilus, Enterococcus faecalis, 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 inexpen sive method for the identification of thermophilic streptococci, RAPD-PCR p atterns were generated with a single primer (XD9), and the results were ana lyzed using artificial neural networks (Multilayer Perceptron, Radial Basis Function network and Bayesian network) and multivariate statistical techni ques (cluster analysis, linear discriminant analysis, and classification tr ees). Cluster analysis allowed the identification of S, thermophilus but no t of enterococci. A Bayesian network proved to be more effective than a Mul tilayer Perceptron or a Radial Basis Function network for the identificatio n 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 sta tistical techniques (linear discriminant analysis and classification trees) and proved to be less sensitive to the size of the training set and more r obust in the response to patterns belonging to unknown species.