Hyperspectral Image Recovery Using a Color Camera for Detecting Colonies of Foodborne Pathogens on Agar Plate

Purpose Hyperspectral imaging often requires a special camera system to obtain spectral images. The cost for acquisition and process of hyperspectral images is usually much higher than color images. On the contrary, typical consumer-grade digital color cameras are much cheaper to obtain and process spatially high-resolution images than hyperspectral cameras. This paper is concerned with the development of a hyperspectral image recovery technique that can reconstruct hyperspectral images only from color images obtained by a digital color camera. Methods A sparse representation and least squares regression-based classification of foodborne pathogens on agar plates are presented. The target pathogen bacteria were the six representative non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145). The wavelength range for the spectral recovery was from 400 to 1000 nm. Unlike many other studies using color charts with known and noise-free spectra for training their spectral recovery models, we directly used the hyperspectral and color images of real scenes for training the spectral recovery models. Both hyperspectral and color images were calibrated to percent reflectance values and then spatially registered. Two spectral recovery models including polynomial multivariate linear regression (MLR) and partial least squares regression (PLSR) were evaluated and compared by cross-validation and independent test. Results The spectral recovery results showed that the PLSR was more effective than the MLR. The classification accuracy measured with recovered spectra in an independent test set was about 5–10% less than the case of using the true hyperspectral images although the difference in maximum classification accuracy was only about 2%. Conclusion The results suggested the potential of a cost-effective color imaging system using hyperspectral image classification algorithms for differentiating pathogens in agar plates.