Multi-metal element analysis for the identification of foodborne pathogenic bacteria

Mineral element contents, combined with multivariate analysis, were used for the identification and classification of foodborne pathogens from a common genus (Rhodococcus equi, Staphylococcus spp., Listeria spp., Salmonella spp., Shigella spp., Escherichia coli, Enterobacter sakazakii, Yersinia enterocolitica and Vibrio spp.). 45 macro- and trace mineral elements of 30 foodborne pathogens were determined by a semiquantitative inductively coupled plasma mass spectrometry (SQ-ICP-MS) technique. The elemental analysis identified 10 significant elements (28Si, 43Ca, 57Fe, 47Ti, 52Cr, 55Mn, 66Zn, 88Sr, 137Ba and 208Pb) by ANOVA in different types of pathogens. Principal component analysis (PCA) reduced the 10 variables to 6 principal components which could explain 98.40% of the total variance. The classification models constructed by the Fisher linear discriminant analysis (Fisher LDA) and back-propagation artificial neural network (BP-ANN) achieved correctly classified rates of 86.9% and 91.3%, respectively. The results indicated that the combination of multi-metal element composition determination and multivariate analysis can be used as fingerprint to quickly identify and classify foodborne pathogens.