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

R Goodacre; E M Timmins; P J Rooney; J J Rowland; D B Kell

doi:10.1016/0378-1097(96)00186-3

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

R Goodacre·E M Timmins·P J Rooney·J J Rowland·D B Kell

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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.

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Algorithms Bacterial Typing Techniques Enterococcus Evaluation Studies as Topic Humans Neural Networks, Computer Species Specificity Spectroscopy, Fourier Transform Infrared Streptococcus Comparative Study Journal Article Research Support, Non-U.S. Gov't

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R Goodacre, E M Timmins, P J Rooney, J J Rowland, D B Kell. 1996-07-01. Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks.. https://doi.org/10.1016/0378-1097(96)00186-3

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