## 3 Results and Discussion
### Discrimination Analysis
#### 3.2.3 Feature Wavelength Variables  
Figure 5 shows the RMSECV results of numerous optimized models (3315 in total) from 100 runs of the GA and 50 generations per run. These models were trained with different individuals using a number of variables from 117 to 153. All these models resulted in a better collection of the RMSECV value (between 0.1644 and 0.1899) than that of the model based on the full-range wavelength (RMSECV  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modeling** & **Pre-Processing** & **Calibration** & **Cross-Validation** & **Prediction** & **Parameters** \\
**Algorithms** & **Methods** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Parameters** \\ \hline \multirow{4}{*}{KNN} & Raw & 88.0\% & 88.8\% & 90.24\% & K = 5 \\  & Smoothing & 88.0\% & 88.8\% & 90.24\% & K = 5 \\  & Smoothing + MSC & 99.0\% & 99.2\% & 97.56\% & K = 3 \\  & \({}^{\text{a}}\) S-G 1\({}^{\text{st}}\) & 98.4\% & 98.6\% & 97.56\% & K = 7 \\ \hline \multirow{4}{*}{SIMCA} & Raw & 99.2\% & 99.4\% & 98.37\% & \({}^{\text{b}}\) PCs1 = 10, \({}^{\text{c}}\) PCs2 = 9 \\  & Smoothing & 99.4\% & 99.4\% & 98.78\% & PC1 = 9, PCs2 = 7 \\  & Smoothing + MSC & 99.4\% & 99.2\% & 98.78\% & PC1 = 8, PCs2 = 5 \\  & S-G 1\({}^{\text{st}}\) & 96.2\% & 96.0\% & 95.53\% & PC1 = 6, PCs2 = 9 \\ \hline \multirow{4}{*}{PLS-DA} & Raw & 99.4\% & 99.4\% & 97.97\% & LVs = 5 \\  & Smoothing & 99.4\% & 99.4\% & 98.37\% & LVs = 5 \\  & Smoothing + MSC & 99.6\% & 99.4\% & 99.19\% & LVs = 5 \\  & S-G 1\({}^{\text{st}}\) & 99.4\% & 99.2\% & 98.78\% & LVs = 3 \\ \hline \multirow{4}{*}{SVM-DA} & Raw & 100\% & 99.8\% & 99.19\% & Cost = 10\({}^{\text{f}}\), gamma = 10\({}^{\text{-4}}\) \\  & Smoothing & 100\% & 99.8\% & 99.19\% & Cost = 10\({}^{\text{f}}\), gamma = 10\({}^{\text{-4}}\) \\  & Smoothing + MSC & 100\% & 100\% & 99.59\% & Cost = 10\({}^{\text{f}}\), gamma = 10\({}^{\text{-4}}\) \\  & S-G 1\({}^{\text{st}}\) & 99.6\% & 99.2\% & 98.78\% & Cost = 10\({}^{\text{-3}}\), gamma = 10\({}^{\text{-2}}\) \\ \hline \hline \end{tabular}  
* Notes: \({}^{\text{a}}\) S-G 1\({}^{\text{st}}\), Savitzky-Golay first derivative, \({}^{\text{b}}\) PCs1, number of principal components for the cultivar H8 sub-model; \({}^{\text{c}}\) PCs2, number of principal components for the cultivar H168 sub-model.  
\end{table}
Table 2: Comparison of the classification results of four models coupled with different pre-processing methods built on full-range wavelength variables.  
was 0.2651). Taking a closer look at these wavelength variables, the same patterns of wavelengths or wavelength regions were recognized as feature wavelengths by 100 iterations, and they were more distributed in the second overtone region (wavelength number 460-730) and combination bands region (wavelength number 1330-1510). The identified groups around number 452 (1210 nm), 1090 (1724 nm), and 1472 (2310 nm) were significant absorption regions by the -CH function group in carbohydrate, which verified that the carbohydrate content of the two cultivars was different. Hence, to a certain extent, it was reasonable to conclude that the wavelengths identified by the GA were useful for distinguishing sweet corn seed cultivars. Finally, the 126 variables that generated the lowest RMSECV model were selected as the feature wavelengths for further modeling analyses.