'American Society of Agricultural and Biological Engineers (ASABE)'
Abstract
Not AvailableFTIR 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.United States-Israel Binational Agricultural Research and Development Fund (Grant No. US-3296-02
