Hyperspectral imaging using RGB color for foodborne pathogen detection

This paper reports the development of a spectral reconstruction technique for predicting hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for the study. A line-scan pushbroom hyperspectral imaging spectrometer was used to scan full reflectance spectra of pure non-O157 STEC cultures in the visible and near-infrared spectral range from 400 to 1000 nm. RGB color images were generated by simulation from hyperspectral images. Polynomial multivariate least-squares regression analysis was used to reconstruct hyperspectral images from RGB color images. The mean R-squared value for hyperspectral image reconstruction was similar to 0.98 in the spectral range between 400 and 700 nm for linear, quadratic, and cubic polynomial regression models. The accuracy of the hyperspectral image classification algorithm based on k-nearest neighbors algorithm of principal component scores was validated to be 92% with the test set (99% with the original hyperspectral images). The results of the study suggested that color-based hyperspectral imaging would be feasible without much loss of prediction accuracy compared to true hyperspectral imaging. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.