DIFFERENTIATION OF FOOD PATHOGENS USING FTIR AND ARTIFICIAL NEURAL NETWORKS

Abstract

FTIR absorbance spectra in conjunction with artificial neural networks (ANNs) were used to differentiate selectedmicroorganisms at the generic and serogroup levels. The ANN consisted of three layers with 595 input nodes, 50 nodes at thehidden 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 couldbe achieved at an accuracy of 80% to 100% at the generic level and 90% to 100% at the serogroup level at 103 CFU/mLconcentration.