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The Effect of Pyrolysis and Instrument Parameters on the Results of Various Supervised Learning Techniques

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Abstract:

Four supervised pattern-recognition procedures—linear discriminant analysis, soft-independent models of class analogy, a univariate expert system, and a multivariate expert system—were applied to pyrolysis-mass spectrometry data from five bacterial species. A test set was generated by individually modifying the tuning parameters of the mass spectrometer followed by collection of one spectrum for each bacteria. Five replicate pyrolysis-mass spectra of each bacteria were used as a training set for the classification of the test set. The multivariate expert system consistently scored over 92% correctly identified, while the other procedures resulted in less than 80% correctly identified. Under these harsh conditions, the multivariate expert system was not affected seriously by the mass spectrometer tuning changes.

Keywords: Computer applications; Mass spectroscopy

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/0003702914337885

Affiliations: 1: Department of Chemistry and Geochemistry, Colorado School of Mines, Golden, Colorado 80401 2: Department of Chemistry and Geochemistry, Colorado School of Mines, Golden, Colorado 80401; present address: US Army, CRDEC, Aberdeen, MD 21010 3: Department of Chemistry and Geochemistry, Colorado School of Mines, Golden, Colorado 80401; present address: Department of Chemistry, Ohio University, Athens, OH 45701

Publication date: January 1, 1991

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