Machine learning–assisted surface-enhanced Raman spectroscopy for multiple and rapid screening of foodborne pathogenic bacteria
Abstract
To address the challenge of mixed contamination of foodborne pathogenic bacteria in food, in this study, a machine learning (ML) assisted surface-enhanced Raman scattering (SERS) sensing platform was proposed for multiple and rapid screening of foodborne pathogenic bacteria. 4-Mercaptophenylboronic acid-functionalized gold nanoparticles (AuNPs@4-MPBA) was introduced as SERS substrate and the molecular recognition and electromagnetic enhancement mechanisms for target analytes were elucidated by theoretical simulation. The sensing platform achieved efficient identification and differentiation of single and mixed contaminations of Escherichia coli, Salmonella typhimurium, Shigella, Listeria monocytogenes, and Staphylococcus aureus in three tea samples. The ability of the model to generalize across varying conditions was evaluated using a mixed spectral dataset from different tea sample matrices. After spectral preprocessing, the Random Forest (RF) model was used to classify 31 samples, achieving a classification accuracy of 97.34% and an out-of-bag accuracy of 95.98%, demonstrating excellent stability and generalization capability. The proposed approach enabled the rapid and accurate classification of foodborne pathogenic bacteria in complex food matrices, offering a promising strategy for rapid food safety emergency screening.