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In the United States, fecal contamination of produce is a food safety issue associated with pathogens such as Escherichia coli and Salmonella that can easily pollute agricultural products via animal and human fecal matters. Outbreaks of foodborne illnesses associated with consuming raw fruits and vegetables in the US have occurred more frequently in recent years. This problem not only threatens public health, but also results in a huge amount of unnecessary economic losses every year. Therefore, developing optical sensing technologies for the detection of contaminants on fresh produce is urgent and essential. Among fruits, strawberry is one high-potential vector of fecal contamination and foodborne illnesses since the fruit is often consumed raw and with minimal processing. In the present study, line-scan LED-induced fluorescence imaging techniques were applied for inspection of fecal material on strawberries, and the spectral characteristics and specific wavebands of strawberries were determined by detection algorithms. The results indicated that the combination of two-waveband intensity ratios, such as 680 nm / 688 nm and 680 nm / 704 nm, can successfully distinguish fecal contamination, uncontaminated surfaces, and leaves. The binary images showed that the algorithm could successfully detect all of the fecal contamination spots that were applied to the strawberry surfaces. The results of this study would improve the safety and quality of produce consumed by the public.