Hyperspectral imaging using RGB color for foodborne pathogen detection

Abstract. 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 ∼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.