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FEMS Microbiol Lett. 1996 Jul 1;140(2-3):233-9.

Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks.

Author information

1
Institute of Biological Sciences, University of Wales, Aberystwyth, UK. rrg@aber.ac.uk

Abstract

Diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR) was used to analyse 19 hospital isolates which had been identified by conventional means to one Enterococcus faecalis, E. faecium, Streptococcus bovis, S. mitis, S. pneumoniae, or S. pyogenes. Principal components analysis of the FT-IR spectra showed that this 'unsupervised' learning method failed to form six separable clusters (one of each species) and thus could not be used to identify these bacteria base on their FT-IR spectra. By contrast, artificial neural networks (ANNs) could be trained by 'supervised' learning (using the back-propagation algorithm) with the principal components scores of derivatised spectra to recognise the strains from their FT-IR spectra. These results demonstrate that the combination of FT-IR and ANNs provides a rapid, novel and accurate bacterial identification technique.

PMID:
8764485
DOI:
10.1016/0378-1097(96)00186-3
[Indexed for MEDLINE]

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