Optical EngineeringHyperspectral waveband selection for contaminant detection on poultry carcasses
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We address the important product inspection application of contaminant detection on chicken carcasses. Detection of four contaminant types of interest (duodenum, ceca, colon, and ingesta) from chickens fed with three different feeds (corn, milo, and wheat) is considered. We consider feature selection algorithms for choosing a small set of spectral bands (wavelengths) in hyperspectral (HS) data for online contaminant detection. For cases when an optimal solution is not realistic, we introduce our new improved forward floating selection algorithm; we call it a quasi-optimal (close to optimal) algorithm. Our algorithm is an improvement on the state-of-the-art sequential forward floating selection algorithm. We train our algorithm on a pixel database using only corn-fed chickens and test it on HS images of carcasses with three feeds. Our new algorithm gives an excellent detection rate and performs better than other suboptimal feature selection algorithms on this database.