SUBSPECIES DISCRIMINATION, USING PYROLYSIS MASS-SPECTROMETRY AND SELF-ORGANIZING NEURAL NETWORKS, OF PROPIONIBACTERIUM-ACNES ISOLATED FROM NORMAL HUMAN SKIN

Curie-point pyrolysis mass spectra were obtained from 30 Propionibacte rium acnes strains isolated from the foreheads of six healthy humans. Multivariate analyses and Kohonen artificial neural networks (KANNs), employing unsupervised learning, were used successfully to discriminat e between the P. acnes isolates from different individual hosts. The c lassification of the isolates by KANNs was compared with the more clas sical multivariate techniques of canonical variates analysis and hiera rchical cluster analysis and found to give similar groupings. The comb ination of pyrolysis mass spectrometry with these numerical methods al so showed that more than one strain of P. acnes had been isolated from three of the human hoses.