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The U. S. Department of Agriculture, Agricultural Research Service has been developing a method and system to
detect fecal contamination on processed poultry carcasses with hyperspectral and multispectral imaging systems. The
patented method utilizes a three step approach to contaminant detection. Spectra of homogenous samples of feces,
ingesta (undigested food particles), and skin were first collected. Then those spectra were evaluated with multivariate
analysis techniques to identify significant wavelength regions for further analysis. Hyperspectral data were then
collected on contaminated poultry carcasses and information learned from the spectroscopic data was used to aide in
hyperspectral data analysis. Finally, the results of the hyperspectral data were used to identify a few optimum
wavelengths for use in a real-time multispectral imaging system. In this work, two techniques for developing spectral
datasets and algorithms for classifying surface contaminants on poultry carcasses were explored. The first consisted of
a scanning monochrometer that measured the average spectra of uncontaminated breast skin and fecal and ingesta
contaminants. The second technique used regions of interest (ROI) from a hyperspectral image to collect spatially
averaged spectra. Comparison of the spectra from each instrument showed variations in the spectra collected from
similar samples. There was an offset of absorption values between the two instruments and the hyperspectral imaging
system had better resolution at higher absorption wavelengths. Although both systems were calibrated prior to
measuring, there was also a slight shift in absorption peaks between the two systems. Both techniques were able to
classify contaminated skin from uncontaminated skin in a full cross-validated test set with better than 99% accuracy.
However, when the classification model developed from the monochrometer spectra was applied to whole-carcass
hyperspectral images, numerous common carcass features, such as exposed meat and wing-shadowed skin, were
wrongly identified as false positives. Since spectra of entire poultry carcasses were available in the original
hyperspectral dataset, the hyperspectral ROI technique allowed researchers to easily add the spectra of these false
positives to the calibration dataset. New partial least squares regression models with meat and skin shadow spectra
resulted in different principal component loadings and improved classification models. The classification model with
the combined ROI spectra from skin, feces, ingesta, meat, and skin shadows gave a classification accuracy of 99.5%.
When this model was compared to the original model developed from the monochrometer dataset on a few
hyperspectral images of contaminated carcasses, fewer false positives were classified with the hyperspectral ROI
model without sacrificing the accuracy of contaminant detection. Further research must be done to fully characterize
the accuracy of the model.
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