The Effect of Regions of Interest and Spectral Pre-Processing on the Detection of Non-0157 Shiga-Toxin Producing Escherichia Coli Serogroups on Agar Media by Hyperspectral Imaging
| Autor: | Jerry W. Heitschmidt, William R. Windham, Neelam Narrang, Scott R. Ladely, Seung-Chul Yoon, William C. Cray, Kurt C. Lawrence, Bosoon Park |
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| Rok vydání: | 2012 |
| Předmět: | |
| Zdroj: | Journal of Near Infrared Spectroscopy. 20:547-558 |
| ISSN: | 1751-6552 0967-0335 |
| Popis: | Foodborne infection caused by Shiga toxin-producing Escherichia coli (STEC) is a major worldwide health concern. The best known and highly virulent STEC serogroup is E. coli 0157:H7, which can be easily identified when cultured on sorbitol-MacConkey (SMAC) agar. Recently, six non-0157 STEC serogroups have been found to cause human illnesses. These non-0157 serogroups ferment sorbital and form pink colonies; therefore SMAC agar cannot be used to differentiate non-0157 serogroups from each other and other flora growing on the plate. This study investigated the potential of visible and near infrared hyperspectral imaging and chemometrics to spectrally differentiate six representative non-0517 STEC serogroups (026, 045, 0103, 0111, 0121 and 0145) grown as spots on Rainbow agar media. Mahalanobis distance classifiers were developed with spectra obtained from ground truth regions of interest (ROIs) of each serogroup colony. The ROIs were selected as a doughnut-like open-ellipse to only include the leading edge of growth and as a closed-ellipse covering the entire colony. For each ROI type, the Mahalanobis distance classifiers were developed with log (1/Reflectance), first derivative and standard normal variate and detrending (SNVD) pre-processing treatments. Serogroups 045 and 0121 were consistently classified over 98% accurate, regard less of the classification algorithm used. The lowest classification accuracies were from classifiers developed with only log (1/ R) ROI spectra. First derivative and SNVD spectra helped to increase the detection accuracies of the other serogroups. The classification accuracy for serogroups 026, 0111, 0103 and 0145 with the closed-ellipse and open-ellipse classification algorithms showed varying results from 8% to 87% and 57% to 100%, respectively. The lower accuracies with closed ellipse spectra were due to greater spectral variation in the centre pixels on a per-pixel basis. Practical implications of this study are the demonstrated potential of hyperspectral imaging for presumptive-positive screening of non-0157 serogroups on Rainbow agar and the extensibility of the developed sampling methods and classification models for future research to identify the target bacteria in the presence of background flora grown on spread plates. |
| Databáze: | OpenAIRE |
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