Development of Hyperspectral ImagingTechnique for SalmonellaEnteritidisand Typhimurium on Agar Plates

Abstract

<abstract><title><italic>Abstract. </italic></title> Salmonella is a common cause of foodborne disease resulting from the consumption of contaminated food products. Although a direct plating method is widely used for presumptive positive screening of pathogenic Salmonella colonies on agar plates, it is labor-intensive, time-consuming, and also prone to human errors. This article reports the development of a hyperspectral imaging technique for automated screening of the two common serotypes of Salmonella, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), grown on agar plates and for differentiating them from background microflora often found in poultry carcass rinses. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora on brilliant green sulfa (BGS) and/or xylose lysine tergitol 4 (XLT4) agar plates. Five different machine-learning algorithms including Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) in addition to a multivariate data analysis method, the principal component analysis (PCA), were compared to determine the best classification algorithm in Salmonella detection and classification. When trained on the data from pure cultures of Salmonella and known background microflora, the classification accuracy of each classification algorithm in detecting Salmonella on BGS agar was about 98% on average, although it was difficult to differentiate between SE and ST. The classification accuracy in detecting Salmonella colonies on XLT4 agar was about 88% on average while the detection accuracy for ST colonies were over 99%. The validation of the classification algorithms with independent test samples of chicken carcass rinses spiked with SE and ST showed that the best performance was achieved by QDA with the prediction accuracy of about 99% (Kappa coefficient=0.97).