Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 11, 2003
Publication Date: N/A
Interpretive Summary: To ensure a healthy and safe meat supply to consumers, the Food Safety Inspection Service (FSIS) established a zero tolerance standard for feces on the surfaces of animal carcasses during slaughter. The reasoning for the zero tolerance is to minimize the likelihood of contamination of meat and poultry with microbial pathogens. Visual observation currently verifies compliance with zero tolerance in meat processing establishments. We investigated visible near infrared spectroscopy as a potential objective method for discriminating between feces and uncontaminated poultry breast skin. The results of this research show that the visible light region can separate feces from the uncontaminated skin. Important visible wavelengths can be derived from this research and implemented in an imaging system for identification of fecal surface contaminates on poultry carcasses. Results may be incorporated with ongoing research to ultimately design a system to detect fecal contamination on poultry carcasses during commercial processing.
Technical Abstract: 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 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 430 to 950 nm. The spectra were reduced by principal component (PC) analysis and partial least squares (PLS) regression. 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 PLS Classification model was able 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. Visual assessment of loading weights suggests that discrimination was dependent on the spectral variation related to fecal color and myoglobin and/or hemoglobin content of the uncontaminated breast skin.