Classification and detection of Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes using Fourier-transform near infrared spectroscopy coupled with machine learning.

This study was conducted to investigate the potential use of Fourier-transform near-infrared (FT-NIR) spectroscopy combined with machine learning (ML) algorithms to accurately identify three foodborne pathogens, including Salmonella spp., Listeria monocytogenes, and Escherichia coli O157:H7. Each bacterial strain (two per pathogen) was individually cultured and purified through sequential washing using ethanol-deionized (DI) water solutions. Each purified culture was transferred into each well of a 96-well cell plate covered with custom-cut filter paper and then vacuum dried at 50 °C under 20 kPa for 1 h. The absorbance spectra of dehydrated bacterial cells were then acquired across the range of 1000-2400 nm using a diffuse reflectance probe connected to a FT-NIR process analyzer. To identify the optimal classification pipeline, the acquired spectra were analyzed using ten different pre-processing methods, three feature selection methods, and supervised algorithms including partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). Furthermore, the accuracy of the developed ML pipelines was further enhanced through under-sampling and boosting steps. The results demonstrated that SVM, RF, ANN and CNN outperformed PLS-DA, where the classification accuracies were above 90%. The findings demonstrate that using Savitzky-Golay first derivative (SG1) filtering to pre-process the full spectra followed by SVM classification yielded the highest accuracy, achieving 95.3% overall accuracy in ML pipeline. This study highlights the powerful capability of FT-NIR spectroscopy coupled with ML algorithms to detect and identify foodborne pathogens on dehydrated surfaces in process environments.