###### Abstract  
With increasing public demand for ready-to-eat fresh-cut food products, proper sanitation of food-processing equipment surfaces is essential to mitigate potential contamination of these products to ensure safe consumption. This study presents a sanitation monitoring technique using hyperspectral fluorescence images to detect fruit residues on food-processing equipment surfaces. An algorithm to detect residues on the surfaces of 2B-finished and #4-finished stainless-steel, both commonly used in food processing equipment, was developed. Honeydew, orange, apple, and watermelon were selected as representatives since they are mainly used as fresh-cut fruits. Hyperspectral fluorescence images were obtained for stainless steel sheets to which droplets of selected fruit juices at six concentrations were applied and allowed to dry. The most significant wavelengths for detecting juice at each concentration were selected through ANOVA analysis. Algorithms using a single waveband and using a ratio of two wavebands were developed for each sample and for all the samples combined. Results showed that detection accuracies were better for the samples with higher concentrations. The integrated algorithm had a detection accuracy of 100% and above 95%, respectively, for the original juice up to the 1:20 diluted samples and for the more dilute 1:50 to 1:100 samples, respectively. The results of this study establish that using hyperspectral imaging, even a small residual quantity that may exist on the surface of food processing equipment can be detected and that sanitation monitoring and management is possible. f 12 November 2020 30 December 20 the risk of contamination of agricultural products during such processing, it is necessary to develop better sanitation monitoring systems [9]. Sanitation monitoring of processing surfaces in food processing facilities is primarily a manual operation (e.g., visual inspection) conducted by inspectors according to established guidelines [10]. The conventional sanitary inspection method, namely, microbial contamination inspection, involves identifying a potentially contaminated location, collecting samples from the surface of this location, culturing the microorganism(s) for a period of one to three days, and as counting single organisms is very difficult. This microbial culturing process is tedious, requiring laboratory operations. Nucleic-acid-based methods are more sensitive than traditional culture-based methods because they enable detection of the DNA or RNA sequences of specific pathogens; however, they require skilled experts and expensive equipment and are also tedious [11; 12]. These methods can be used to measure different contamination levels in one area corresponding to the sampling location, and the number of samples obtained via these methods is limited by the analysis equipment and the analysis period. Therefore, monitoring techniques, which can enable rapid measurement of the contamination levels without limiting the number of samples and the sampling locations, are required. A system that can rapidly and accurately detect the presence of vectors for pathogen growth on the surfaces of food processing equipment is especially needed to enhance food safety [13]. Various imaging and spectroscopic technologies have been researched for the rapid detection of contamination in foods and food processing facilities [14; 15]. Particularly, hyperspectral imaging technology, which combines imaging and spectroscopy techniques, facilitates nondestructive measurement, involving the acquisition of spatial and spectral information simultaneously for each pixel in a sample image [16]. It can also facilitate mapping of the differences in the physical, chemical, and biological properties of a target object into a spatial distribution, and monitoring of the entire surface of the target object in real time.