Submitted to: Journal of Biosystems Engineering
Publication Type: Peer reviewed journal
Publication Acceptance Date: 9/26/2012
Publication Date: 10/1/2012
Citation: Kim, T., Lee, H., Kim, M.S., Cho, B. 2012. Optimal optical filters of fluorescence excitation and emission for poultry fecal detection. Journal of Biosystems Engineering. 37(4):265-270. Interpretive Summary: Fecal contamination on poultry carcasses is a serious health risk. Various nondestructive sensing technologies such as reflectance and fluorescence methods have been evaluated for detection of fecal contamination on agricultural products. In this research, a mathematical model was used to determine optimal lighting and detection wavelengths for fluorescence-image-based fecal inspection techniques for poultry. Fluorescence characteristics of samples including poultry carcasses, poultry feces, and organic materials, were evaluated. Results demonstrated over 98% accuracy for detection of fecal contamination on poultry carcasses. These findings provide insightful information to food technologists and engineers for developing nondestructive fecal detection methods for use on meat products. The technique should be beneficial to meat processing industries.
Technical Abstract: Purpose: An analytic method to design excitation and emission filters of a multispectral fluorescence imaging system is proposed and was demonstrated in an application to poultry fecal inspection. Methods: A mathematical model of a multispectral imaging system is proposed and its system parameters, such as excitation and emission filters, were optimally determined by linear discriminant analysis (LDA). An alternating scheme was proposed for numerical implementation. Fluorescence characteristics of organic materials and feces of poultry carcasses are analyzed by LDA to design the optimal excitation and emission filters for poultry fecal inspection. Results: The most appropriate excitation filter was UV-A (about 360nm) and blue light source (about 460nm) and band-pass filter was 660-670nm. The classification accuracy and false positive are 98.4% and 2.5%, respectively. Conclusions: The proposed method is applicable to other agricultural products which are distinguishable by their spectral properties.