## 3 Results and Discussion
### Classification Results between Normal and Defective White Radishes  
Table 3 summarizes the classification results between normal and defective classes using different wavelengths based on PLS-DA, SVM, ANN, and FLDA models. All the models presented satisfactory \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Wavelengths** & **545 nm** & **564 nm** & **611 nm** & **629 nm** & **642 nm** & **655 nm** & **724 nm** & **730 nm** & **738 nm** & **809 nm** \\ \hline
545 nm & 1.000 & - & - & - & - & - & - & - & - \\
564 nm & 0.998 \(\ast\)-1 & 1.000 & - & - & - & - & - & - & - \\
611 nm & 0.988 \(\ast\) & 0.994 \(\ast\) & 1.000 \(\ast\) & - & - & - & - & - & - \\
629 nm & 0.987 \(\ast\) & 0.993 \(\ast\) & 1.000 \(\ast\) & 1.000 & - & - & - & - & - \\
642 nm & 0.984 \(\ast\) & 0.992 \(\ast\) & 0.999 \(\ast\) & 1.000 \(\ast\) & 1.000 & - & - & - & - \\
655 nm & 0.980 \(\ast\) & 0.989 \(\ast\) & 0.998 \(\ast\) & 0.999 \(\ast\) & 1.000 \(\ast\) & 1.000 & - & - & - \\
724 nm & 0.943 \(\ast\) & 0.951 \(\ast\) & 0.963 \(\ast\) & 0.966 \(\ast\) & 0.969 \(\ast\) & 0.974 \(\ast\) & 1.000 & - & - \\
730 nm & 0.928 \(\ast\) & 0.934 \(\ast\) & 0.945 \(\ast\) & 0.949 \(\ast\) & 0.953 \(\ast\) & 0.958 \(\ast\) & 0.996 \(\ast\) & 1.000 & - & - \\
738 nm & 0.911 \(\ast\) & 0.916 \(\ast\) & 0.926 \(\ast\) & 0.931 \(\ast\) & 0.935 \(\ast\) & 0.941 \(\ast\) & 0.992 \(\ast\) & 0.998 \(\ast\) & 1.000 & - \\
809 nm & 0.843 \(\ast\) & 0.846 \(\ast\) & 0.854 \(\ast\) & 0.860 \(\ast\) & 0.866 \(\ast\) & 0.874 \(\ast\) & 0.957 \(\ast\) & 0.974 \(\ast\) & 0.984 \(\ast\) & 1.000 \\ \hline \hline \end{tabular}  
* Correlation coefficients followed by one asterisk \(\ast\) and two asterisks \(\ast\)\(\ast\) are significant at \(p<\) 0.05 and \(p<\) 0.01, respectively.  
\end{table} 
Table 2: Pearson’s linear correlation coefficients and probability levels among 10 selected wavelengths by SPA (successive projections algorithm). Figure 4: The RMSE (root mean square error) plot (**a**) and selected wavelengths (designated by open and solid circle markers); (**b**) by SPA (successive projections algorithm) with spectral preprocessing using SNV+ MC (mean center). prediction performance with overall accuracies of 95.6%-100% for discriminating black-heart white rablishes. When using the full spectrum of 420 wavelengths, three models including PLS-DA, SVM, and ANN had high overall accuracies with 100% for calibration and 98.9%-100% for prediction. There was one normal radish misclassified as defective for prediction by PLS-DA, and two defective rablishes misclassified as normal for prediction by SVM. Furthermore, ANN had the best results with an overall accuracy of 100% for both calibration and prediction. To verify detection performance based on the selected wavelength for further on-line or real-time inspection systems, spectral responses including ten and four wavelengths were separately evaluated for classification between normal and defective rablishes, based on the PLS-DA, SVM, ANN, and FLDA models. With ten wavelengths as inputs, the models achieved overall accuracies of 99.7%-100% and 98.9%-100% for calibration and prediction, respectively. ANN gave the highest overall accuracy of 100% for both calibration and prediction. SVM had a relatively low overall accuracy due to misclassification by recognizing one defective rablish as normal and one normal radish as defective. PLS-DA and FLDA had the same overall accuracy with 99.7% and 99.4% for calibration and prediction, respectively, although they had slight discriminating differences between normal and defective classes. While using four wavelengths, the overall classification accuracy decreased slightly to 97.2%-98.8% for calibration and 95.6%-97.8% for prediction. Among the four models, FLDA had the best results with overall accuracy of 98.4% for calibration and 97.8% for prediction.