## 4 Conclusions  
In this study, a hyperspectral fluorescence imaging technique was developed for the rapid detection of food residues that can remain as contaminants on the surfaces of food processing equipment. A technique was developed for rapidly detecting apple, orange, melon, and watermelon residue that may remain on the surfaces of 2B- and #4-finished stainless steel sheets, which are commonly used for food processing equipment. These two types of stainless-steel surfaces have different fluorescence spectral characteristics. Analyses for the optimal wavelength using ANOVA indicated that the accuracy for 2B-finished SSS was greater than that of #4-finished SSS. Compared to the SWA, the detection accuracy of the TWRA was slightly higher or similar. The discrimination accuracy of the integrated image model to detect the organic residue of all fruits on the 2B-finished SSS was 100% and above 95%, respectively, for original juices up to 1:20 dilution samples and for 1:50-1:100 dilution samples, respectively. By combining these results, global wavebands were selected according to the dilution levels, regardless of the type of fruit. Moreover, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Dilution**} & \multicolumn{4}{c}{**2B-Finished SSS**} & \multicolumn{4}{c}{**\#4-Finished SSS**} \\ \cline{3-8}  & & \multirow{2}{*}{**No. of Droplet**} & \multirow{2}{*}{**No. of Detection**} & \multicolumn{1}{c}{**Accuracy**} & \multirow{2}{*}{**No. of Droplet**} & \multirow{2}{*}{**No. of Detection**} & \multicolumn{1}{c}{**Accuracy**} \\  & & & & & & & [\%] \\ \hline \multirow{6}{*}{Honeydew} & original & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:5 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:10 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:20 & 90 & 90 & 100.0 & 90 & 87 & 96.67 \\  & 1:50 & 90 & 90 & 100.0 & 90 & 86 & 95.56 \\  & 1:100 & 90 & 86 & 95.56 & 90 & 83 & 92.22 \\ \hline \multirow{6}{*}{Orange} & original & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:5 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:10 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:20 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:50 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:100 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\ \hline \multirow{6}{*}{Apple} & original & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:5 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:10 & 90 & 90 & 100.0 & 90 & 78 & 86.67 \\  & 1:50 & 90 & 90 & 100.0 & 90 & 78 & 86.67 \\  & 1:100 & 90 & 85 & 94.44 & 90 & 74 & 82.22 \\ \hline \multirow{6}{*}{Watermelon} & original & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\  & 1:5 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\ \cline{1-1}  & 1:10 & 90 & 90 & 100.0 & 90 & 90 & 100.0 \\ \cline{1-1}  & 1:20 & 90 & 90 & 100.0 & 90 & 84 & 93.33 \\ \cline{1-1}  & 1:50 & 90 & 87 & 96.67 & 90 & 84 & 93.33 \\ \cline{1-1}  & 1:10 & 90 & 81 & 90.00 & 90 & 75 & 83.33 \\ \hline \multirow{6}{*}{Total sample} & original & 360 & 360 & 100.0 & 360 & 360 & 100.0 \\ \cline{1-1}  & 1:5 & 360 & 360 & 100.0 & 360 & 360 & 100.0 \\ \cline{1-1}  & 1:10 & 360 & 360 & 100.0 & 360 & 360 & 100.0 \\ \cline{1-1}  & 1:20 & 360 & 360 & 100.0 & 360 & 339 & 94.17 \\ \cline{1-1}  & 1:50 & 360 & 357 & 99.17 & 360 & 338 & 93.89 \\ \cline{1-1}  & 1:100 & 360 & 342 & 95.00 & 360 & 322 & 89.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Discrimination accuracy from image using global two-band ratio algorithm. the detection accuracy on the 2B-finished surface was higher than that on the #4-finished surface. Based on these results, an algorithm will be developed that can be applied to organic residue of various fresh-cut fruits and vegetables in the future. The results of this study establish the possibility of using fluorescence imaging techniques to detect residues that may be present in food processing equipment, even after cleaning and sterilization [6]. Fluorescence imaging can be applied for testing the presence of organic residues of various agricultural products by detecting microgram quantities.