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Developing an algorithm to decide the presence or absence of fecal contamination on the surface of poultry carcasses is critical to food safety. The global threshold strategy for a band-ratio algorithm has been known to be limited to pixel-basis detection. In an attempt to develop a statistical decision rule for carcass-basis detection from multispectral images, probability density functions of both contaminated and uncontaminated materials were estimated by parametric and non-parametric methods. We found that uncontaminated poultry carcasses could be modeled by a Gaussian distribution, whereas contaminated materials were non-Gaussian. A kernel density estimator was used to analyze the non-Gaussian characteristic of the contaminated materials on a transformed projection axis. A linear mixture of the density functions was introduced to model the observations made on the projection axis. A new detection algorithm was designed using the mixture model and tested for 496 birds (248 dirty and 248 clean birds). A test on the sample birds revealed that the algorithm needed at least 12 contaminant pixels to reach the perfect detection results. The test also showed a false-positive rate of less than 5%.