Differentiation of Corynebacterium amycolatum, C. minutissimum, C. striatum and related species by pyrolysis-gas-liquid chromatography with atomic emission detection.

We report here the application of pyrolysis-gas chromatography followed by atomic emission detection (AED) for the characterisation of Corynebacterium amycolatum and related species (i.e., C. striatum, C. minutissimum, C. xerosis and the recently described C. freneyi). This phenotypic method, which analyses the whole chemical composition of bacteria, clearly separates C. amycolatum from other species. Moreover, this C. amycolatum group is subdivided into two distinct subgroups. We cannot differentiate the C. minutissimum strains from those of C. striatum. On the other hand, C. freneyi and C. xerosis are clearly distinct from the other species.

Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks.

In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification.