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Gupta, M.J., Department of Agricultural and Biological Engineering, Pennsylvania State University, University Park, PA, United States
Irudayaraj, J.M., Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States, Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, United States
Debroy, C., Gastroenteric Disease Center (GDC), Pennsylvania State University, University Park, PA, United States
Schmilovitch, Z., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Mizrach, A., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
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. © 2005 American Society of Agricultural Engineers.
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Differentiation of food pathogens using ftir and artificial neural networks
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Gupta, M.J., Department of Agricultural and Biological Engineering, Pennsylvania State University, University Park, PA, United States
Irudayaraj, J.M., Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States, Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, United States
Debroy, C., Gastroenteric Disease Center (GDC), Pennsylvania State University, University Park, PA, United States
Schmilovitch, Z., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Mizrach, A., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan, Israel
Differentiation of food pathogens using ftir and artificial neural networks
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. © 2005 American Society of Agricultural Engineers.
Scientific Publication
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