Abstract
Salmonella enterica is a leading cause of foodborne illnesses globally, with significant mortality rates, especially among vulnerable populations. Traditional serotyping methods for Salmonella are accurate but expensive, resource-intensive, and time-consuming, necessitating faster and more reliable alternatives. This study evaluates the IR Biotyper, a Fourier-transform infrared spectroscopy system, in differentiating Salmonella serovars. We assessed 458 isolates of nine Salmonella serovars (Infantis, Enteritidis, Typhimurium, I,4,[5],12:i:-, Montevideo, Agona, Thompson, Panama, and Abony) from diverse sources. The IR Biotyper was used to acquire spectra from these isolates. Machine learning algorithms, including support vector machines, were trained to classify the isolates. The accuracy of classifiers was validated using a validation set to determine sensitivity, specificity, positive predictive value, and negative predictive value. Initial classifiers showed high accuracy for Abony, Agona, Enteritidis, and Infantis serovars, with sensitivities close to 100%. However, classifiers for S. Typhimurium, S. Panama, and S. Montevideo exhibited lower performance. Implementing a hierarchical classification system enhanced the accuracy of serogroup O:4 serovars, demonstrating that this approach offers a robust framework for Salmonella serovar identification. The hierarchical system enables progressive refinement of classification, minimizing misclassifications by focusing on serogroup-specific features, making it adaptable to complex data sets and diverse serovars. The IR Biotyper demonstrates high potential for rapid and accurate Salmonella serovar identification. This study supports its implementation as a cost-effective, high-throughput tool for pathogen typing, enhancing real-time epidemiological surveillance, and guiding treatment strategies for salmonellosis. This method establishes a robust and scalable framework for advancing Salmonella serotyping practices across clinical, industrial, and public health domains by leveraging hierarchical classification.IMPORTANCEEarly and accurate identification of Salmonella serovars is extremely important for epidemiological surveillance, public health, and food safety. Traditional serotyping is very successful but is laborious and costly. In this study, we demonstrate the promise of Fourier-transform infrared spectroscopy together with machine learning as a means for Salmonella serotyping. Using hierarchical classification, we attain optimal serovar identification accuracy, particularly for challenging-to-type serogroups. Our findings recognize the IR Biotyper as a high-throughput, scalable pathogen typing solution that offers real-time data that can enable enhanced outbreak response and prevention of foodborne disease. The approach bridges the gap between traditional microbiological practice and sophisticated analytical technology, the path to more effective, cost-saving interventions in the clinical, industrial, and regulatory settings. Application of these technologies can significantly improve Salmonella surveillance-control and Public Health outcomes.