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DIFFERENTIATION OF FOOD PATHOGENS USING FTIR AND ARTIFICIAL NEURAL NETWORKS

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASAE. 48(5): 1889-1892. (doi: 10.13031/2013.19978) @2005
Authors:   M. J. Gupta, J. M. Irudayaraj, C. Debroy, Z. Schmilovitch, A. Mizrach
Keywords:   ANN, Differentiation, Food pathogens, FTIR spectroscopy

FTIR absorbance spectra in conjunction with artificial neural networks (ANNs) were used to differentiate selected microorganisms at the generic and serogroup levels. The ANN consisted of three layers with 595 input nodes, 50 nodes at the hidden layer, and 5 output nodes (one for each microorganism or strain). Ten replications of each experiment were conducted, and 70% of the data was used for training and 30% for validation of the network. Results indicated that differentiation could be achieved at an accuracy of 80% to 100% at the generic level and 90% to 100% at the serogroup level at 103 CFU/mL concentration.

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