## 3 Results and Discussion
### Optimal Waveband Selection Using ANOVA Analysis Results
#### 3.2.3 Development of Global Imaging Algorithm for Detecting Residues

Table 6: Optimal two wavebands and discrimination algorithm results for all the samples according to the dilution levels. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Total**} & \multicolumn{4}{c}{**2B-Finished SSS**} & \multicolumn{4}{c}{**\#4-Finished SSS**} \\ \cline{2-9}  & **No of Sample** & \multicolumn{2}{c}{**Single Waveband**} & \multirow{2}{*}{**TH**} & \multirow{2}{*}{**Accuracy [\%]**} & \multirow{2}{*}{**No of Sample**} & \multicolumn{2}{c}{**Single Waveband**} & \multirow{2}{*}{**TH**} & \multirow{2}{*}{**Accuracy [\%]**} \\  & & **[nm]** & & & & & **[nm]** & & \\ \hline original & 7533 & 488 & 482 & 93.37 & 9329 & 460 & 670 & 84.43 \\
1:5 & 8140 & 484 & 670 & 92.42 & 8526 & 464 & 611 & 90.74 \\
1:10 & 7825 & 484 & 545 & 90.91 & 8043 &Figure 6 shows an example of the process in which an image is applied at the selected wavelength using ANOVA analysis. The threshold value determined using the TWRA is applied after acquiring the ratio image with the selected single-wavelength images. The obtained binary image renders it possible to determine whether the residues are detected by expressing the part recognized as detected in white and the part recognized as missing in black. An imaging algorithm to detect fruit residue contamination was developed using the waveband image selected through ANOVA analysis. Figure 6 illustrates the image processing that was performed to detect residues using a two-band ratio image. If a residue is present, the binary image is '1', otherwise it is '0'. Figure 7 shows the determination of residue contamination based on the concentration for four types of fruit. These results establish the possibility of using fluorescence imaging techniques to detect organic residues that may be present in food processing equipment, even after cleaning and sterilization. Figure 6: Image processing for classification of fruit residues on stainless steel: (**a**) The hyperspectral fluorescence images are acquired, (**b**) the single-band images are selected, (**c**) the two-waveband images are obtained, and (**d**) the binary images are acquired by applying a threshold value and the residue is detected. Figure 7: Discrimination image applied global imaging algorithms for juice dilution levels of each fruit (**a**) honeydew, (**b**) orange, (**c**) apple and (**d**) watermelon. Table 7 shows the results of image discrimination applied with the integrated TWRA. The discrimination accuracy of the integrated image model for the 2B-finished SSS was 100%, 99.17%, and 95% for original juices up to 1:20 dilution samples, 1:50 dilution samples, and 1:100 dilution samples, respectively. The discrimination accuracy of the integrated image model for the #4-finished SSS was 100%, 94.17%, 93.89%, and 89.44% for original juices up to 1:10 dilution samples, 1:20 dilution samples, 1:50 dilution samples, and 1:100 dilution samples, respectively. The image-based discrimination result showed better performance than the spectrum-based discrimination result. Also, residue detection performance was better for the 2B-finished SS surface than for the #4-finished SS surface.