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Developing a detection algorithm to decide the presence or absence of fecal contamination on the surface of poultry carcasses is critical to food safety. The single threshold strategy for a band ratio algorithm has been known to be limited to pixel-basis detection. In an attempt to develop a statistical rule for carcass-basis detection from multispectral images, probability density functions of both contaminated and uncontaminated materials were estimated by parametric and nonparametric 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 fecal material 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 102 birds (56 dirty and 56 clean birds). A preliminary test on the sample birds revealed that the algorithm needed at least 12 contaminated pixels to reach the perfect detection results. The test also showed a false positive rate of less than 5%.