Skip to main content
ARS Home » Research » Publications at this Location » Publication #183952


item Park, Bosoon
item Yoon, Seung-Chul
item Lawrence, Kurt
item Windham, William

Submitted to: American Society of Agricultural Engineers Meetings Papers
Publication Type: Proceedings
Publication Acceptance Date: 7/18/2005
Publication Date: 8/2/2005
Citation: Park, B., Yoon, S.C., Lawrence, K.C., Windham, W.R. 2005. Dynamic thresholding method for improving contaminant detection accuracy with hyperspectralimages. St. Joseph, Michigan, July 17-20, 2005, Tampa, Florida.American Society of Agricultural Engineers Meetings Papers. Paper No. 053071.

Interpretive Summary: Recent trends in food production, processing, distribution and preparation are increasing demand for food safety research in order to ensure a safer food supply. Along with food safety issues, the Food Safety and Inspection Service (FSIS) in USDA are charged with protecting consumers by ensuring that poultry and poultry products are safe and wholesome. FSIS is pursuing a broad and long-term science-based strategy to improve the safety for protecting public health. For science-based inspection system, ARS has developed hyperspectral and multispectral imaging systems. For better system accuracy, the development of image processing software is crucial. For our research accomplishment, image processing software, specifically dynamic thresholding method to determine optimum threshold values to identify fecal contaminants on broiler carcasses was developed. This new image processing software can improve the FSIS poultry safety inspection program by incorporating scientific testing and systematic detection of fecal contamination during poultry processing.

Technical Abstract: Detection of fecal contamination in the visceral cavity of broiler carcasses is important for food safety to protect consumers from food pathogens. The simple ratio of reflectance values of 565-nm image to 517-nm image was effective for fecal detection in the visceral cavity. Since the accuracy of detection algorithms for identifying cecal contaminants varied with fecal threshold values, determination of optimum threshold was crucial for detecting fecal contaminants during poultry processing. The dynamic threshold method using Fisher’s linear discriminant analysis (FLDA), along with simple multispectral image ratio with Gaussian window averaging, performed better (98.9% accuracy with 1.06% omission error) than static threshold method to identify cecal contaminants. The mean and standard deviation of dynamic threshold were 1.025 and 0.027, respectively. Since the accuracy of fecal threshold was a trade off and sacrificed with missed contaminants and false positives, the dynamic thresholding method using FLDA was useful for cecal contaminant detection. Also, FLDA can be implemented to determine and update fecal threshold values for on-line inspection at poultry processing plants.