Spectroscopic quantification of bacteria using artificial neural networks

Mathala J. Gupta, Joseph Irudayaraj, Chitrita Debroy

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Fourier transform-infrared spectroscopy, in conjunction with artificial neural networks, has been used for identification and classification of selected foodborne pathogens. Five bacterial species (Enterococcus faecium, Salmonella Enteritidis, Bacillus cereus, Yersinia enterocolitica, Shigella boydii) and five Escherichia coli strains (O103, O55, O121, O30, O26) suspended in phosphate-buffered saline were enumerated to provide seven different concentrations ranging from 109 to 103 CFU/ ml. The trained artificial neural networks were then validated with an independent subset of samples and compared with the traditional plate count method. It was found that the concentration-based classification of the species was 100% correct and the strain-based classification was 90 to 100% accurate.

Original languageEnglish (US)
Pages (from-to)2550-2554
Number of pages5
JournalJournal of food protection
Volume67
Issue number11
DOIs
StatePublished - Nov 2004

All Science Journal Classification (ASJC) codes

  • Food Science
  • Microbiology

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