Hyperspectral imaging of common foodborne pathogens for rapid identification and differentiation

Hyperspectral imaging (HSI) provides both spatial and spectral information of a sample by combining imaging with spectroscopy. The objective of this study was to generate hyperspectral graphs of common foodborne pathogens and to develop and validate prediction models for the classification of these pathogens. Four strains of Cronobacter sakazakii, five strains of Salmonella spp., eight strains of Escherichia coli, and one strain each of Listeria monocytogenes and Staphylococcus aureus were used in the study. Principal component analysis and kNN (k‐nearest neighbor) classifier model were used for the classification of hyperspectra of various bacterial cells, which were then validated using the cross‐validation technique. Classification accuracy of various strains within genera including C. sakazakii, Salmonella spp., and E. coli, respectively, was 100%; except within C. sakazakii, strain BAA‐894, and E. coli, strains O26, O45, and O121 had 66.67% accuracy. When all strains were studied together (irrespective of their genus) for the classification, only C. sakazakii P1, E. coli O104, O111, and O145, S. Montevideo, and L. monocytogenes had 100% classification accuracy, whereas E. coli O45 and S. Tennessee were not classified (classification accuracy of 0%). Lauric arginate treatment of C. sakazakii BAA‐894, E. coli O157, S. Senftenberg, L. monocytogenes, and S. aureus significantly affected their hyperspectral signatures, and treated cells could be differentiated from the healthy, nontreated cells.