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This paper reports the results of a feasibility study for the development of a hyperspectral image recovery
(reconstruction) technique using a RGB color camera and regression analysis in order to detect and classify colonies of
foodborne pathogens. The target bacterial pathogens were the six representative non-O157 Shiga-toxin producing
Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown in Petri dishes of Rainbow agar.
The purpose of the feasibility study was to evaluate whether a DSLR camera (Nikon D700) could be used to predict
hyperspectral images in the wavelength range from 400 to 1,000 nm and even to predict the types of pathogens using a
hyperspectral STEC classification algorithm that was previously developed. Unlike many other studies using color charts
with known and noise-free spectra for training reconstruction models, this work used hyperspectral and color images,
separately measured by a hyperspectral imaging spectrometer and the DSLR color camera. The color images were
calibrated (i.e. normalized) to relative reflectance, subsampled and spatially registered to match with counterpart pixels
in hyperspectral images that were also calibrated to relative reflectance. Polynomial multivariate least-squares regression
(PMLR) was previously developed with simulated color images. In this study, partial least squares regression (PLSR)
was also evaluated as a spectral recovery technique to minimize multicollinearity and overfitting. The two spectral
recovery models (PMLR and PLSR) and their parameters were evaluated by cross-validation. The QR decomposition
was used to find a numerically more stable solution of the regression equation. The preliminary results showed that
PLSR was more effective especially with higher order polynomial regressions than PMLR. The best classification
accuracy measured with an independent test set was about 90%. The results suggest the potential of cost-effective color
imaging using hyperspectral image classification algorithms for rapidly differentiating pathogens in agar plates.
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