The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of some Eubacterium species |
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Authors: | Janet Taylor Royston Goodacre William G Wade Jem J Rowland Douglas B Kell |
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Institution: | Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion SY23 3DD, UK;Oral Microbiology Unit, Department of Oral Medicine Pathology, UMDS, Microbiology and Immunology, Guy's Hospital, London SE1 9RT, UK;Department of Computer Science, University of Wales, Aberystwyth, Ceredigion SY23 3DB, UK |
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Abstract: | Pyrolysis mass spectrometry was used to produce complex biochemical fingerprints of Eubacterium exiguum, E. infirmum, E. tardum and E. timidum. To examine the relationship between these organisms the spectra were clustered by canonical variates analysis, and four clusters, one for each species, were observed. In an earlier study we trained artificial neural networks to identify these clinical isolates successfully; however, the information used by the neural network was not accessible from this so-called `black box' technique. To allow the deconvolution of such complex spectra (in terms of which masses were important for discrimination) it was necessary to develop a system that itself produces `rules' that are readily comprehensible. We here exploit the evolutionary computational technique of genetic programming; this rapidly and automatically produced simple mathematical functions that were also able to classify organisms to each of the four bacterial groups correctly and unambiguously. Since the rules used only a very limited set of masses, from a search space some 50 orders of magnitude greater than the dimensionality actually necessary, visual discrimination of the organisms on the basis of these spectral masses alone was also then possible. |
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Keywords: | Chemometrics Eubacterium Genetic programming Pyrolysis mass spectrometry |
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