Rapid Detection of Foodborne Pathogenic Bacteria in Beef Using Surface-Enhanced Raman Spectroscopy

Foodborne pathogenic bacteria in meat pose a serious threat to human health. Traditional detection methods for these bacteria are often time-consuming and labor-intensive. In this study, we applied surface-enhanced Raman scattering (SERS) combined with portable Raman spectroscopy as a rapid and convenient detection technique. SERS is a sensitive and fast method that enhances light scattering on rough metal surfaces. Silver nanoparticles (AgNPs) were used as SERS substrates to identify and analyze four pathogenic bacteria, including Escherichia coli (E. coli) O157:H7, Salmonella typhimurium (S. typhimurium), Staphylococcus aureus (S. aureus), and Listeria monocytogenes (L. monocytogenes), in beef. We optimized the detection conditions of AgNPs and established the limit of detection (LOD) for these four pathogenic bacteria in both pure culture and beef samples. The LODs were as low as 4-23 CFU/mL in beef samples, indicating high detection sensitivity. Linear discriminant analysis (LDA) was used to analyze the SERS spectra, yielding an accuracy of 91.7-97.3%. This study not only provides a rapid and portable detection method for pathogenic bacteria in beef but also overcomes the limitations of traditional methods that are often time-consuming and not suitable for on-site detection. However, the current study is limited to the detection of the four specific pathogenic bacteria, and further research is needed to expand the range of detectable pathogens and to improve the robustness of the detection models for more complex meat samples. Overall, this research demonstrates the potential of SERS combined with portable Raman spectroscopy as a powerful tool for the rapid detection of pathogenic bacteria in meat products, which could significantly enhance food safety monitoring and control.