CLASSIFICATION OF HYPERSPECTRAL IMAGERY FOR IDENTIFYING FECAL AND INGESTA CONTAMINANTS

Authors: Park, Bosoon; Windham, William; Lawrence, Kurt; Smith, Douglas

Submitted to: International Society for Optical Engineering
Publication Type: Proceedings
Publication Acceptance Date: 10/28/2003
Publication Date: 12/9/2003
Citation: Park, B., Windham, W.R., Lawrence, K.C., Smith, D.P. 2003. Classification of hyperspectral imagery for identifying fecal and ingesta contaminants. Proceedings International Society for Optical Engineering. 5271:118-127.

Interpretive Summary: Food safety has become an important thrust for USDA, because reduction in the potential health risks to consumers from human pathogens in food is the most important food safety issue and public concern. Hyperspectral imaging technique demonstrated potential tools for poultry safety inspection. Classification of hyperspectral imagery was developed to identify the type and sources of various contaminants to improve the performance of Hazard Analysis Critical Control Point (HACCP) for federal poultry safety program. We investigated several different classification methods to determine the optimum method having best performance to classify contaminants. A hyperspectral imaging system with selected classification methods can improve new FSIS's poultry safety inspection program incorporating scientific testing and systematic prevention of contamination.

Technical Abstract: This paper presents the performance of classification methods for hyperspectral poultry imagery to identify fecal and ingesta contaminants on the surface of broiler carcasses. A pushbroom line-scan hyperspectral imager was used to acquire hyperspectral data with 512 narrow bands covered from 400 to 900 nm wavelengths. Three different feces from digestive tracts (duodenum, ceca, colon), and ingesta were used as contaminants. These contaminants were collected from the broiler carcasses fed by corn, milo, and wheat with soybean meals. For the selection of optimum classifier, various widely used supervised classification methods (parallelepiped, minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper, and binary coding) were investigated. The classification accuracies ranged from 62.94% to 92.27%. The highest classification accuracy for identifying contaminants for corn fed carcasses was 92.27% with spectral angle mapper classifier. While, the classification accuracy was 82.02% with maximum likelihood method for milo fed carcasses and 91.16% accuracy was obtained for wheat fed carcasses when same classification method was used. The mean classification accuracy obtained in this study for classifying fecal and ingesta contaminants was 90.21%.