Partial Least Squares Regression of Hyperspectral Images for Contaminant Detection on Poultry Carcasses

Abstract

The US Department of Agriculture has developed multispectral and hyperspectral imaging systems to detect faecal contaminants. Until recently, the hyperspectral imaging system has been used as a research tool to detect a few optimum wavelengths for use in a multispectral imaging system. However, with the development of complementary metal oxide semiconductor cameras, discrete wavelengths or subsets of the full-detector range can be used to greatly increase the speed of hyperspectral imaging systems. This paper reports on the use of a broad-spectrum multivariate statistical analysis technique for detecting contaminants with hyperspectral imaging. Partial least squares regression was used for model development. Calibration models from spatially averaged region of interest data were developed with and without smoothing, with and without scatter correction and with and without first derivative (difference) pre-processing. Results indicate that using the full spectral range with scatter correction was needed for good model development. Furthermore, validation of the various calibration models indicated that pre-processing with scatter correction, nine-point boxcar smoothing and first derivative pre-processing resulted in the best validation with about 95% of the over 400 contaminants detected with only 26 false positives (errors of commission). About one-third of the false positives were from bruised wingtips which would not be visible during in-plant commercial processing.