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United States Department of Agriculture

Agricultural Research Service

Title: Analysis of Reflectance Spectra from Hyperspectral Images of Poultry Carcasses for Fecal and Ingesta Detection

Authors
item Windham, William
item Park, Bosoon - UGA
item Lawrence, Kurt
item Smith, Douglas
item Poole, Gavin - USDA

Submitted to: International Society for Optical Engineering
Publication Type: Abstract Only
Publication Acceptance Date: July 8, 2002
Publication Date: July 8, 2002
Citation: Windham, W.R., Park, B., Lawrence, K.C., Smith, D.P., Poole, G. 2002. Analysis of reflectance spectra from hyperspectral images of poultry carcasses for fecal and ingesta detection [abstract]. International Society for Optical Engineering.

Technical Abstract: Identification and separation of poultry carcasses contaminated by feces and/or crop ingesta are very important to protect the consumer from a potential source of food poisoning. A transportable hyperspectral imaging system was developed to detect fecal and ingesta contamination on the surface of poultry carcasses. Detection algorithms used with the imaging system were developed from birds fed a corn/soybean meal diet. The objectives of this study were to investigate the differences in reflectance spectra and the optimal wavelengths for the detection algorithms from images of birds fed wheat and milo diets. Spectral and spatial data between 400 and 900 nm with a 1.0 nm spectral resolution were acquired from uncontaminated and fecal and ingesta contaminated poultry carcasses. Regions of interests (ROIs) were defined for fecal and ingesta contaminated and uncontaminated skin (i.e. breast, drum stick, and wing). Average reflectance spectra of the ROIs were extracted for analysis. Reflectance spectra of contaminants and uncontaminated skin differed due to diet. Spectral data pre-processing treatments with a single-term, linear regression program to select wavelengths for optimum calibration coefficients to detect contamination will be discussed. Fecal and ingesta detection algorithms, specifically a band ratio of 2 and/or 3-wavelength images were successful in detecting contamination.

Last Modified: 7/23/2014
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