Differentiation of big-six non-O157 Shiga-toxin producing Escherichia coli (STEC) on spread plates of mixed cultures using hyperspectral imaging

There have been considerable recent advances in the technology for rapidly detecting foodborne pathogens. However, a traditional culture method is still the “gold standard” for presumptive-positive pathogen screening although it is labor-intensive, ineffective in testing large amount of food samples, and cannot completely prevent unwanted background microflora from growing together with target microorganisms on agar media. We have developed multivariate classification models based on visible and near-infrared hyperspectral imaging for rapid presumptive-positive screening of six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) on agar plates of pure and mixed cultures. The classification models were developed with spread plates of pure cultures. In this study, we evaluated the performance of the classification models with independent validation samples of mixed cultures that were not used during training and found the best classification model for differentiating non-O157 STEC colonies on spread plates of mixed cultures. A validation protocol appropriate to hyperspectral imaging of mixed cultures was developed. An additional independent validation set of 12 spread plates with pure cultures was used as positive controls to help the validation process with the mixed cultures and to affirm the model performance. One imaging experiment with colonies obtained from two serial dilutions was performed. A total of six agar plates of mixed cultures were prepared, where O45, O111 and O121 serogroups that were relatively easy to differentiate were inoculated into all six plates and then each of O26, O103 and O145 serogroups was added into the mixture of the three common bacterial cultures. The number of mixed colonies grown after 24-h incubation was 331 and the number of pixels associated with the grown colonies was 16,379. The best model found from this validation study was based on pre-processing with standard normal variate and detrending, first derivative, spectral smoothing, and k-nearest neighbor classification (kNN, k = 3) of scores in the principal component subspace spanned by 12 principal components. The results showed 95 % overall detection accuracy at pixel level and 97 % at colony level. The developed model was proven to be still valid even for the independent validation samples although the size of a validation set was small and only one experiment was performed. This study was an important first step in validating and updating multivariate classification models for rapid screening of ground beef samples contaminated by non-O157 STEC pathogens using hyperspectral imaging.