Sub-species discrimination, using pyrolysis mass spectrometry and self-organising neural networks, of Propionibacterium acnes isolated from normal human skin.
Curie-point pyrolysis mass spectra were obtained from 30 Propionibacterium 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 discriminate between the P.acnes isolates from different individual hosts. The classification of the isolates by KANNs was compared with the more classical multivariate techniques of canonical variates analysis and hierarchical cluster analysis and found to give similar groupings. The combination of pyrolysis mass spectrometry with these numerical methods also showed that more than one strain of P.acnes had been isolated from three of the human hosts.
Goodacre
R
R
Institute of Biological Sciences, University of Wales, Aberystwyth, Dyfed, UK. rrg@aber.ac.uk
Howell
S A
SA
Noble
W C
WC
Neal
M J
MJ
D000328:Adult; D016908:Gram-Positive Bacterial Infections / Q000382:microbiology* / Q000473:pathology; D006801:Humans; D013058:Mass Spectrometry; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D011425:Propionibacterium acnes / Q000145:classification* / Q000302:isolation & purification; D012867:Skin / Q000382:microbiology / Q000473:pathology