Submitted to: International Society for Optical Engineering
Publication Type: Proceedings
Publication Acceptance Date: 5/1/2003
Publication Date: 7/8/2003
Citation: Windham, W.R., Lawrence, K.C., Park, B., Smith, D.P., Poole, G. 2003. Analysis of reflectance spectra from hyperspectral images of poultry carcasses for fecal and ingesta detection. International Society for Optical Engineering. Interpretive Summary: Scientist at ARS have designed and developed a transportable imaging system to detect fecal and ingesta contamination on the surface of poultry carcasses. Fecal contamination is a major vehicle for spreading disease-causing microorganisms to raw poultry. Key wavelengths to detect contamination were previously selected from the visible light region of uncontaminated skin and pure feces. In this study we investigated the use of carcass images artificially contaminated with fecal spots and the use of simple single term linear regression to select key wavelengths to detect contamination. Image visible light of fecal spots on carcasses were useful to further test key wavelengths. The regression procedure simplified the search for key detection wavelengths and aided in "fine-tuning" the wavelengths. Fecal detection models, specifically a division of 2 and/or 3 key wavelengths was 100% successful in detecting contamination. These model are the basis for the on-line multispectral imaging system to be used in processing plants to detect contamination.
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 visible/near infrared monochromator spectra and with contaminates from birds fed a corn/soybean meal diet. The objectives of this study were to investigate using regions of interest reflectance spectra from hyperspectral images to determine optimal wavelengths for fecal detection algorithms from images of birds fed corn, 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 interest (ROIs) were defined for fecal and ingesta contaminated and uncontaminated skin (i.e. breast, thigh, and wing). Average reflectance spectra of the ROIs were extracted for analysis. Reflectance spectra of contaminants and uncontaminated skin differed. Spectral data pre-processing treatments with a single-term, linear regression program to select wavelengths for optimum calibration coefficients to detect contamination were developed. Fecal and ingesta detection models, specifically a quotient of 2 and/or 3-wavelengths was 100% successful in classification of contaminates.