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Long-term identification of streptomycetes using pyrolysis mass spectrometry and artificial neural networks

Cited 4 time in Web of Science Cited 8 time in Scopus

Chun, Jong Sik; Ward, Alan C; Kang, Sa Ouk; Hah, Yung Chil; Goodfellow, Michael

Issue Date
Elsevier BV
Zentralblatt für Bakteriologie : international journal of medical microbiology, Vol.285 No.2, pp.258-266
Sixteen reference strains and thirteen fresh isolates of three putatively novel Streptomyces species were examined six times over twenty months using pyrolysis mass spectrometry to examine the long-term reproducibility of the procedure. The reference strains and new isolates were correctly identified using information in each of the datasets and operational fingerprinting, but direct statistical comparison of the datasets for strain identification was unsuccessful between datasets. Artificial neural networks were also used to identify the strains held in the datasets. Neural networks trained with pyrolysis mass spectra from a single dataset were found to successfully identify the reference strains and fresh isolates in that dataset but were unable to identify many of the strains in the other datasets. However, a neural network trained on representative pyrolysis mass spectra from each of the first three datasets were found to identify the reference strains and fresh isolates in those three datasets and in the three subsequent datasets. Therefore, artificial neural network analysis of pyrolysis mass spectrometric data can provide a rapid, cost-effective, accurate and long-term reproducible way of identifying and typing microorganisms.
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