## 3 Results and Discussion
### Discrimination Analysis
#### 3.2.2 Discriminant Models Based on Full-Range Wavelength Variables  
For the total 746 samples, 123 samples were selected from each cultivar at equal intervals and included in an independent testing set. The remainders were used as the training set for building models. More details about the training set and the testing set are shown in Table 1. The number of samples in the training set was purposely kept at 500 to obtain equal size subsets while conducting the ten-fold cross-validation. Table 2 summarizes the results of different models based on the full-range wavelength variables. All models could achieve appropriate accuracies with the optimal parameters, but the accuracies varied according to different pre-processing methods and modeling algorithms. On one hand, the smoothing method slightly increased the accuracy to 98.78% and 98.37% for SIMCA and PLS-DA models, respectively, but it did not improve the results of KNN and SVM-DA models. Further processing with MSC could improve the overall accuracies of these four modelling algorithms, and the main reason for this might be that the data were more uniform and comparable after removing scattering effects by processing with MSC. While processing with the Savitzky-Golay first derivative method, the accuracies increased in most of the models, except the SIMCA algorithm, dropping to 95.53%. Overall, the Savitzky-Golay first derivative did not generate accuracies as high as the MSC pre-processing did. [PERSON] et al., (2015) and [PERSON] et al., (2016) also showed that MSC generated better results than derivative pre-processing while analyzing NIR data [21; 31]. This phenomenon can \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cultivar** & **Training Set** & **Testing Set** & **Total** \\ \hline H8 & 250 & 123 & 373 \\ H168 & 250 & 123 & 373 \\ Total & 500 & 246 & 746 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The final number of seeds (spectra) for each cultivar utilized in the modeling process. Figure 4: The scores plot of the first three principal components calculated from the original spectra of sweet corn seeds in two cultivars (cultivar H8 in red and H168 in blue), where all samples were linked to the arithmetic mean center of their cultivar cluster by a line. be explained by the fact that derivative pre-processing was sensitive to the high-frequency noises, and the accuracies could be affected by this fraction of noise after derivative processing. On the other hand, the SVM-DA model achieved the highest prediction accuracy of 99.59% for the independent testing set, and it was the only algorithm that could classify all training samples in the calibration and cross-validation processes. [PERSON] et al., (2015) and [PERSON] et al., (2018) also reported that the SVM worked better than other algorithms while modeling full-range wavelengths to identify seed varieties [6; 45]. The best accuracy in this study for the prediction set was slightly higher than the results obtained by [PERSON] et al., (2012), [PERSON] et al., (2016), and [PERSON] et al., (2018), while using the short-wave HSI technique to classify maize seed varieties [7; 11; 13]. This may have been because the addition of long-wave features enhanced the classification ability of the model. However, it should be noted that the sample varieties and modeling algorithms might also result in differences in the classification rates [7]. Afterward, the SVM-DA was followed by two linear models, PLS-DA and SIMCA. PLS-DA resulted in an accuracy of 99.19%, which was slightly better than the highest accuracy of 98.78% of the SIMCA algorithm. This might have been due to the fact that the PLS-DA algorithm also considered the variance in the response variable by exacting the five LVs, while SIMCA did not. The KNN model could obtain an accuracy as high as 97.56%, but it was still the lower than those maximums of other modeling algorithms. This result indicated that nonlinear and linear discriminant models can improve the accuracy results of distance-based discriminant models.