Transactions of the ASABE
Gupta, M.J., Division of Agricultural Engineering, Indian Agricultural Research Institute, New Delhi, India
Irudayaraj, J.M., Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States, Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907-2093, 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 of four foodborne pathogens suspended in four common food matrices at three different concentrations were used with artificial neural networks (ANNs) for identification and quantification. The classification accuracy of the ANNs was 93.4% for identification and 95.1% for quantification when validated using a subset of the data set. The accuracy of the ANNs when validated for identification of the pathogens studied at four different concentrations using an independent data set had an accuracy range from 60% to 100% and was strongly influenced by background noise. The pathogens could be identified irrespective of the food matrix in which they were suspended, although the classification accuracy was reduced at lower concentrations. More sophisticated background noise filtration techniques are needed to further improve the predictions. © 2006 American Society of Agricultural and Biological Engineers.
פותח על ידי קלירמאש פתרונות בע"מ -
הספר "אוצר וולקני"
אודות
תנאי שימוש
Identification and quantification of foodborne pathogens in different food matrices using FTIR spectroscopy and artificial neural networks
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Gupta, M.J., Division of Agricultural Engineering, Indian Agricultural Research Institute, New Delhi, India
Irudayaraj, J.M., Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States, Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907-2093, 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
Identification and quantification of foodborne pathogens in different food matrices using FTIR spectroscopy and artificial neural networks
FTIR absorbance spectra of four foodborne pathogens suspended in four common food matrices at three different concentrations were used with artificial neural networks (ANNs) for identification and quantification. The classification accuracy of the ANNs was 93.4% for identification and 95.1% for quantification when validated using a subset of the data set. The accuracy of the ANNs when validated for identification of the pathogens studied at four different concentrations using an independent data set had an accuracy range from 60% to 100% and was strongly influenced by background noise. The pathogens could be identified irrespective of the food matrix in which they were suspended, although the classification accuracy was reduced at lower concentrations. More sophisticated background noise filtration techniques are needed to further improve the predictions. © 2006 American Society of Agricultural and Biological Engineers.
Scientific Publication