## 3 Results and Discussion
### Optimal Waveband Selection Using ANOVA Analysis Results  
ANOVA analysis was performed according to the two types of SS surfaces and fruit juice dilution levels in order to select the optimal wavebands for detecting residue contamination. Figures S1-S6 show the F-value of the one-way ANOVA for classifying two types of SS surfaces and four types of fruit residues using the SW and the TWR. The optimal wavebands and classification results for each algorithm in terms of residue detection using the pixel spectra are shown in Tables 1-4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**\#4-Finished SSS**} \\ \cline{3-10}  & **Dilution** & \multicolumn{2}{c}{**Ratio**} & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Validation**} \\ \cline{3-10}  & & **Waveband** & \multicolumn{1}{c}{**F-Value**} & \multicolumn{2}{c}{**No. of Spectra**} & \multicolumn{1}{c}{**TH**} & \multicolumn{1}{c}{**Accuracy[\%]**} & \multicolumn{1}{c}{**No. of Spectra**} & \multicolumn{1}{c}{**Accuracy [\%]**} \\ \hline \multirow{8}{*}{Honeydew} & original & 508 & 59,703 & 1702 & 1857 & 92.55 & 425 & 92.54 \\  & 1:5 & 488 & 11,384 & 1636 & 688 & 92.25 & 408 & 92.19 \\  & 1:10 & 468 & 6652 & 1531 & 429 & 90.68 & 382 & 91.91 \\  & 1:20 & 484 & 2110 & 2052 & 459 & 90.50 & 512 & 89.88 \\  & 1:50 & 484 & 314 & 1460 & 388 & 69.73 & 365 & 63.42 \\  & 1:100 & 660 & 1318 & 1465 & 483 & 68.70 & 366 & 65.64 \\ \hline \multirow{8}{*}{Orange} & original & 492 & 193,159 & 1844 & 620 & 98.23 & 461 & 98.10 \\  & 1:5 & 464 & 66,281 & 1771 & 1422 & 91.15 & 442 & 91.37 \\  & 1:10 & 464 & 18,748 & 1592 & 1003 & 91.47 & 397 & 91.66 \\  & 1:20 & 464 & 10,785 & 1987 & 454 & 92.98 & 496 & 92.74 \\  & 1:50 & 668 & 15,079 & 1527 & 577 & 92.72 & 381 & 92.85 \\  & 1:100 & 660 & 17,562 & 1775 & 994 & 89.81 & 443 & 89.75 \\ \hline \multirow{8}{*}{Apple} & original & 584 & 9260 & 1672 & 677 & 85.81 & 418 & 85.17 \\  & 1:5 & 480 & 3512 & 1861 & 344 & 82.48 & 465 & 79.47 \\  & 1:10 & 468 & 1509 & 1763 & 338 & 83.55 & 440 & 72.34 \\  & 1:20 & 652 & 1073 & 2076 & 433 & 80.63 & 518 & 77.48 \\  & 1:50 & 656 & 359 & 1570 & 437 & 71.83 & 392 & 70.04 \\  & 1:100 & 672 & 2521 & 1431 & 487 & 70.02 & 357 & 69.06 \\ \hline \multirow{8}{*}{Watermelon} & original & 460 & 26,669 & 2246 & 791 & 89.04 & 561 & 89.22 \\  & 1:5 & 464 & 5731 & 1555 & 434 & 87.71 & 388 & 86.91 \\ \cline{1-1}  & 1:10 & 464 & 2845 & 1551 & 340 & 88.54 & 387 & 86.03 \\ \cline{1-1}  & 1:20 & 460 & 1401 & 1984 & 290 & 83.36 & 495 & 77.82 \\ \cline{1-1}  & 1:50 & 652 & 928 & 1576 & 409 & 77.91 & 394 & 69.30 \\ \cline{1-1}  & 1:100 & 672 & 2414 & 1471 & 486 & 71.74 & 367 & 70.76 \\ \hline \hline \end{tabular} Note: TH = Threshold value.  
\end{table} 
Table 2: Optimal wavebands and discrimination algorithm results using the single-waveband method for #4-finished SSS. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**2B-Finished SSS**} \\ \cline{3-10}  & **Dilution** & \multicolumn{2}{c}{**Ratio**} & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Validation**} \\ \cline{3-10}  & & **Waveband [nm]** & \multicolumn{2}{c}{**F-Value**} & \multicolumn{2}{c}{**No. of Spectra**} & \multicolumn{1}{c}{**TH**} & \multicolumn{1}{c}{**Accuracy [\%]**} & \multicolumn{1}{c}{**No.