Classification of Salmonella Serotypes with Hyperspectral Microscope Imagery

Among serious foodborne outbreaks, Salmonella has the most infections and incidence cases. Because Salmonella is a leading cause of foodborne illness and a zoonotic agent capable of causing gastroenteritis and septicemia, Salmonella detection and identification has become an important subject of research for the poultry industry. Based on the numerous culture protocols to characterize Salmonella spp., traditional culture-based methods are still the most reliable and accurate “gold standard” techniques for presumptive-positive pathogen detection. However, they are laborious and time consuming processes. Therefore, rapid detection and identification of pathogenic microorganisms naturally occurring during food processing are important in developing intervention and verification strategies. Since current detection methods for Salmonella are limited for a practical use, a more sensitive, accurate and rapid pathogen detection method is needed to prevent foodborne outbreaks. Non-destructive advanced optical methods, such as hyperspectral imaging for evaluation of foodborne pathogens could enhance the presumptive-positive screening method by reducing labor and increasing detection speed. Among the several different hyperspectral imaging platforms, acousto-optic tunable filter (AOTF)-based hyperspectral imaging method was developed for microscopic imaging of live bacterial cells from microcolony on agar plates. Thus, the objective of this research is to develop a hyperspectral microscopic imaging method to classify Salmonella serotypes with their spectral signatures from the cells. Five Salmonella serotypes including Enteritidis (SE), Typhimurium (ST), Kentucky (SK), Heidelberg (SH) and Infantis (SI) and five different machine learning algorithms including Mahalanobis distance (MD), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) were used for classification method development. The SVM algorithm performed better than other algorithms with average classification accuracy of 93.6% (SE), 97.6% (ST), 90.7% (SK), 93.0% (SH), and 94.2% (SI).

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