Submitted to: Agricultural and Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 20, 2004
Publication Date: December 20, 2004
Citation: Kang, S., Kim, M.S., and Kim, I. 2004. Chicken Disease Characterization by Fluorescence Spectroscopy. Journal of Agri. and Biosys. Eng. 5(1):25-29.
Interpretive Summary: Chickens consumed by U.S. consumers are required by law to be inspected by the United States Department of Agriculture/Food Safety and Inspection Service (USDA/FSIS) inspectors for wholesomeness. Current inspection for chicken carcass wholesomeness is based on visual methods. Under the current Hazard Analysis and Critical Control Point (HACCP)-based Inspection Models Project (HIMP), FSIS requires that any poultry showing evidence of septicemia to be condemned. The Instrumentation and Sensing Laboratory has been involved in the development of spectroscopic sensor systems which can provide an alternative means to inspect poultry carcasses with a high degree of accuracy on a consistent basis. The current ISL inspection system relies on the use of reflectance techniques. As a complementary approach to the reflectance techniques, we examined fluorescence spectroscopy to classify poultry disease categories. Four chicken carcass categories, normal, airsacculitis, cadaver, and septicemia, were classified correctly with 97.9% accuracy. The research results are critical to scientists (e.g., engineers) who are developing automated machine-based poultry carcass inspection systems. This investigation is also beneficial to the poultry processing industry.
Fluorescence spectroscopy was used to characterize chicken carcass diseases. Spectral signatures of three different disease categories of poultry carcasses, airsacculitis, cadaver and septicemia, were obtained from fluorescence emission measurements in the wavelength range of 360 to 600 nm with 330 nm excitation. Principal Component Analysis (PCA) was used to select the most significant wavelength regions for the classification of poultry carcasses. These wavelengths were analyzed for pathologic correlation of poultry diseases. Using a Soft Independent Modeling of Class Analogy (SIMCA) of principal components with a Mahalanobis distance metric, poultry carcasses were individually classified into correct disease categories with 97.9% accuracy.