Open AccessArtificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains
Author(s) -
Chun J.,
Atalan E.,
Ward A.C.,
Goodfellow M.
Publication year - 1993
Publication title -
fems microbiology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.899
H-Index - 151
eISSN - 1574-6968
pISSN - 0378-1097
DOI - 10.1111/j.1574-6968.1993.tb06051.x
Subject(s) - artificial neural network , isolation (microbiology) , identification (biology) , data set , artificial intelligence , set (abstract data type) , test set , test data , computer science , pattern recognition (psychology) , biological system , chromatography , biology , chemistry , microbiology and biotechnology , ecology , programming language
Abstract Sixteen representatives of three morphologically distinct groups of streptomycetes were recovered from soil using selective isolation procedures. Duplicated batches of the test strains were examined by Curie‐point pyrolysis mass spectrometry and the first data set used for conventional multivariate statistical analyses and as a training set for an artificial neural network. The second set of data was used for ‘operational fingerprinting’ and for testing the artificial neural network. All of the test strains were correctly identified using the artificial neural network whereas only fifteen of the sixteen strains were assigned to the correct group using the conventional operational fingerprinting procedure. Artificial neural network analysis of pyrolysis mass spectrometric data provides a rapid, cost‐effective and reproducible way of identifying and typing large numbers of microorganisms.