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Zero tolerance of feces on the surfaces of meat and poultry carcasses during slaughter was established as a standard to minimize the likelihood of microbial pathogens. Microbial pathogens can be transmitted to humans by consumption of contaminated meat and poultry. Compliance with zero tolerance of feces in meat processing establishments is currently verified by visual observation. The objective of this study was to investigate the use of visible, near–infrared reflectance spectroscopy as a method to discriminate between uncontaminated poultry breast skin and feces, and to select key wavelengths for use in a hyperpspectral system. Feces (n = 102), uncontaminated poultry breast skin, and skin contaminated with fecal spots were analyzed from 400 to 950 nm. The spectra were reduced by principal component (PC) analysis. The first four PCs explained 99.8% of the spectral variation. PC 1 was primarily responsible for the separation of uncontaminated skin from feces and for the separation of uncontaminated skin from contaminated skin. A classification model was developed and evaluated to classify fecal–contaminated skin from the spectral data with a success rate of 95%. Key wavelengths were identified by intensity of loading weights at 628 nm for PC 1, 565 nm for PC 2, and 434 and 517 nm for PC 4. Discrimination was dependent on the spectral variation related to fecal color and myoglobin and/or hemoglobin content of the uncontaminated breast skin.

A hyperspectral imaging system including a camera with prism-grating-prism spectrograph, fiber optic line lighting, motorized lens control, and hyperspectral image-processing software was developed for poultry safety inspection, particularly identification of fecal and ingesta contamination on poultry carcasses. Both spectral and spatial image data between 400 and 900 nm with 512 spectral bands were acquired from fecal and ingesta contaminated poultry carcasses. Four dominant wavelengths (434, 517, 565, and 628 nm) were selected by principal component analysis from visible/near-infrared spectroscopy for wavelength selection of hyperspectral images. A calibration model for the hyperspectral imaging system was developed from calibration lighting sources (HgAr, Kr, and lasers) for accurate band selection from the hyperspectral images to identify spatial and spectral characterization of fecal and ingesta contaminants. Hyperspectral image processing algorithms, specifically band ratio of dual-wavelength (565/517) images and histogram stretching, were effective in the identification of fecal and ingesta contamination of poultry carcasses. Test results indicated that the detection accuracy was 97.3% for linear and 100% for non-linear histogram stretching. This article presents the research results that hyperspectral imaging can be used effectively for detecting feces (from the duodenum, ceca, and colon) and ingesta on poultry carcasses and demonstrates the potential application for on-line safety inspection of poultry.