title: Discrimination between methicillin-resistant and methicillin-susceptible Staphylococcus aureus using pyrolysis mass spectrometry and artificial neural networks.
abstract: Curie-point pyrolysis mass spectra were obtained from 15 methicillin-resistant and 22 methicillin-susceptible Staphylococcus aureus strains. Cluster analysis showed that the major source of variation between the pyrolysis mass spectra resulted from the phage group of the bacteria, not their resistance or susceptibility to methicillin. By contrast, artificial neural networks could be trained to recognize those aspects of the pyrolysis mass spectra that differentiated methicillin-resistant from methicillin-sensitive strains. The trained neural network could then use pyrolysis mass spectral data to assess whether an unknown strain was resistant to methicillin. These results give the first demonstration that the combination of pyrolysis mass spectrometry with neural networks can provide a very rapid and accurate antibiotic susceptibility testing technique.
authors.affiliation: Institute of Biological Sciences, University of Wales, Aberystwyth, UK. rrg@aber.ac.uk
authors.forename: R
authors.initials: R
authors.lastname: Goodacre
authors.forename: P J
authors.initials: PJ
authors.lastname: Rooney
authors.forename: D B
authors.initials: DB
authors.lastname: Kell
mesh_terms: D016000:Cluster Analysis; D013058:Mass Spectrometry / Q000379:methods*; D008712:Methicillin / Q000494:pharmacology; D016106:Methicillin Resistance; D008826:Microbial Sensitivity Tests / Q000379:methods*; D016571:Neural Networks, Computer; D010406:Penicillins / Q000494:pharmacology; D013211:Staphylococcus aureus / Q000145:classification* / Q000187:drug effects
chemical_list: D010406:Penicillins; D008712:Methicillin