This research developed and evaluated three multispectral algorithms derived from hyperspectral line-scan fluorescence imaging using violet LED excitation for the detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680, 684, 720, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on high-speed apple processing lines. This fast and non-destructive method for detection of fecal contamination can be implemented in the food and agricultural industries to help in risk reduction and food safety assurance for preventing or minimizing the potential foodborne illness. Published by Elsevier Ltd.