The consumption of food infected with food-borne pathogens has become a global public health problem. Therefore, it is monitor food-borne infections to avoid health and financial consequences. The rapid detection and differentiation of bacteria for biomedical and food safety applications continues to be a significant challenge. Herein, we present a label-free surface-enhanced Raman scattering approach for separating harmful bacteria from food. The method relies on the ascorbic acid reduction method to synthesize silver nanoparticles (AgNPs) and a polydimethylsiloxane (PDMS) multi-hole filter membrane chip (AgNPs@PDMS multi-hole filter membrane chip). Surface-enhanced Raman spectroscopy (SERS) was used, followed by multivariate statistical analysis to differentiate five important food-borne pathogens, including Staphylococcus aureus, Salmonella typhimurium, Listeria monocytogenes, Clostridium difficiles and Clostridium perfringens. The results demonstrated that compared to normal Raman signals, the intensity of the SERS signal was greatly enhanced with an analytical enhancement factor of 5.2 × 103. The spectral ranges of 400-1800 cm-1 were analyzed using principal component analysis (PCA) and stepwise linear discriminant analysis (SWLDA) were used to determine the optimal parameters for the discrimination of food-borne pathogens. The first three principal components (PC1, PC2, and PC3) accounted for 87.3% of the total variance in the spectra. The established SWLDA model had 100% accuracy and cross-validation accuracy, which accurately distinguished the SERS spectra of the five species. In conclusion, the SERS technology based on the AgNPs@PDMS multi-hole filter membrane chip was useful for the rapid identification of food-borne pathogens and can be employed for food quality management.
Keywords: AgNPs sol; Classification; Food-borne pathogens; Multi-hole filter membrane chip; Multivariate statistical analysis; Rapid identification; SERS.
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