Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra |
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Authors: | Jongsik Chun Ekrem Atalan Sam-Bong Kim Hong-Joong Kim Mohamed E Hamid Martha E Trujillo John G Magee Gilson P Manfio Alan C Ward Michael Goodfellow |
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Institution: | Department of Microbiology, The Medical School, University of Newcastle upon Tyne, Newcastle upon Tyne NE2 4HH, UK; Regional Public Health Laboratory, Newcastle upon Tyne, UK |
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Abstract: | Abstract An artificial neural network was trained to distinguish between three putatively novel species of Streptomyces using normalised, scaled pyrolysis mass spectra from three representative strains of each of the taxa, each sampled in triplicate. Once trained, the artificial neural network was challenged with spectral data from the original organisms, the 'training set', from additional members of the putative novel taxa and from over a hundred strains representing six other actinomycete genera. All of the streptomycetes were correctly identified but many of the other actinomycetes were mis-identified. A modified network topology was developed to recognise the mass spectral patterns of the non-streptomycete strains. The resultant neural network correctly identified the streptomycetes, whereas all of the remaining actinomycetes were recognised as unknown organisms. The improved artificial neural network provides a rapid, reliable and cost-effective method of identifying members of the three target streptomycete taxa. |
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Keywords: | Pyrolysis mass spectrometry Artificial neural network Identification Actinomycetes |
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