Development of multispectral image processing algorithms for identification of wholesome, septicemic, and inflammatory process chickens

A multispectral imaging system and image processing algorithms for food safety inspection of poultry carcasses were demonstrated. Three key wavelengths of 460, 540, and 700 nm, previously identified using a visible/near-infrared spectrophotometer, were implemented in a common-aperture multispectral imaging system, and images were collected for 174 wholesome, 75 inflammatory process, and 170 septicemic chickens. Principal component analysis was used to develop an algorithm for separating septicemic chickens from wholesome and 1P chickens based on average intensity of first component images. A threshold value of 105 was able to correctly separate 95.6% of septicemic chickens. To differentiate inflammatory process chickens, a region of interest was defined from which spectral features were determined. The algorithm was able to correctly identify 100% of inflammatory process chickens by detecting pixels that satisfied the spectral feature conditions. A decision tree model was created to classify the three chicken conditions using inputs from the two image processing algorithms. The results showed that 89.6% of wholesome, 92.3% of inflammatory process, and 94.4% of septicemic chickens were correctly classified. Published by Elsevier Ltd.