A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid identification of Bacillus spores and classification of Bacillus species |
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Authors: | Elon Correa Royston Goodacre |
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Institution: | (1) School of Chemistry, The University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK;(2) Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK |
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Abstract: | Background The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their
use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS)
have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes
interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming
bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass
and spores were analyzed by Curie-point Py-MS. |
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