Spectroscopic Quantification of Bacteria Using Artificial Neural Networks

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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.

Document Type: Research Article

Affiliations: 1: Department of Agricultural and Biological Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA 2: The Gastroenteric Disease Center (GDC), The Pennsylvania State University, University Park, Pennsylvania 16802, USA

Publication date: November 1, 2004

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