2026-10-23T13:47:04Zhttps://gumushane-rdlopen.rawdatalibrary.net/oai/request

oai:gumushane-rdlopen.rawdatalibrary.net:gumushane/141972026-10-22T11:36:53Zopenairecom_gumushane_10000col_gumushane_10001com_gumushane_10007col_gumushane_10171

Classification and detection of Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes using Fourier-transform near infrared spectroscopy coupled with machine learningOzturk, SametHuang, LihanHwang, Cheng-AnSheen, ShiowshuhFoodborne pathogensFourier-transform near infrared spectroscopyMachine learningRapid classificationThis 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.Elsevier Ltd2026Articlehttps://rdlopen.rawdatalibrary.net/r/gumushane/14197https://doi.org/10.1016/j.foodres.2026.1184850963-9969enFood Research Internationalinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/articlehttps://gumushane-rdlopen.rawdatalibrary.net/tr/items/gumushane/14197