Hyperspectral Imaging for Differentiating Colonies of Non-0157 Shiga-Toxin Producing Escherichia Coli (STEC) Serogroups on Spread Plates of Pure Cultures

Direct plating on solid agar media has been widely used in microbiology laboratories for presumptive-positive pathogen detection, although it is often subjective and labour-intensive. Rainbow agar is a selective and differential chromogenic medium used to isolate pathogenic Escherichia coli (E. coli) strains. However, it is challenging to differentiate colonies of the six representative pathogenic non-0157 Shiga-toxin producing E. coli (STEC) serogroups (026, 045, 0103, 0111, 0121 and 0145) on Rainbow agar due to the phenotypic differences and the presence of background microflora. Therefore, there is a need for a method or technology to objectively, rapidly and accurately perform high-throughput screening of non-0157 STEC colonies on agar plates. In this paper, we report the development of a visible-near infrared hyperspectral imaging technique and prediction model for differentiating colony types of the six non-0157 STEC serogroups in spread plates of pure cultures inoculated on Rainbow agar. The prediction model was based on supervised linear classification of factor scores obtained by principal component analysis (PCA). Both PCA-MD (Mahalanobis distance) and PCA-kNN (k-nearest neighbour) classifiers were used for model development. From the 24 hyperspectral images measured from two replicates, 51,173 spectral samples were collected from 1421 colonies. Chemometric preprocessing methods and other operating parameters, such as scatter correction, first derivative, moving average, sample size and number of principal components (PCs), were compared with a classification and regression tree (CART) method, configured as a classification tree and followed by brute-force searching from candidates selected by the CART. The number of PCs, scatter correction and moving average were selected as the most important elements to consider before selecting a set of candidate models. Cross-validation (CV), such as hold out and k-fold CV, was used to validate the prediction performance of candidate models. Serogroups 0111 and 0121 consistently showed over 99% classification accuracy regardless of the classification algorithms. However, the classification accuracies of serogroups 026, 045, 0103 and 0145 showed varying results from 84% up to 100%, depending on which preprocessing treatment and prediction model were adopted. The best overall mean classification accuracy of 95.06% was achieved with PCA-kNN (k=3), six PCs, five-pixel sample size defined around each colony centre, standard normal variate and detrending, first derivative with 11-point gaps and moving average with 11-point gaps. Future studies will focus on automating colony segmentation, further improving detection accuracy of the developed models, expanding the spectral library to include background microflora from ground beef and developing prediction models to detect the target bacteria in the presence of background microflora.