# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## 3 Results and Discussion
### Classification Results and Analysis of Discriminant Models Based on Feature Wavelengths  
LDA, SVM, and ELM models were established using feature wavelengths to discriminate different varieties of wheat seeds to explore the influence of feature wavelengths extracted by different methods on classification performance, and the results are shown in Table 4. It could be seen that ELM performance was the best, which achieved an accuracy of 87.74% on the calibration set and 83.24% on the prediction set when combined with RF. The performance of LDA model was inferior to the other two models, which was consistent with the modeling analysis that was based on full wavelengths. For variable selection algorithms, the discriminant models combined with RF was superior to those combined with SPA and PCA loading. This might be because RF algorithm extracted more spectral wavebands' information with fingerprint characteristics approximately double times than the others. The number of feature wavelengths that were selected by SPA was the fewest, which perhaps filter too much useful information, leading to the worst accuracy of less than 75% both on calibration and prediction set. Although the number of feature wavelengths extracted by PCA loading is three more times than that of SPA, it also failed to achieve satisfactory results. The experience and judgement error of human might be the main reason for the unsatisfactory accuracy of PCA loading. In general, RF-ELM acquired the optimal performance among all of the discriminant models based on feature wavelengths. Although the accuracy was still lower than that of discriminant model based on raw full wavelengths displayed in Table 1; Table 2, it could be acceptable to some extent. In other words, the introduction of feature wavelengths helps to develop a multispectral imaging instrument for the classification of large-scale wheat seeds in the modern seed industry. The discrimination performance that was based on the full spectra was also better than that of the feature spectra when different varieties of maize seeds and oat seeds were classified [16; 37]. It could be understandable that the classification result based on feature wavelengths was not as good as that of the raw full wavelengths. As, in the process of extracting feature wavelength, plenty of useful spectral \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extracting Method**} & \multicolumn{2}{c}{**LDA**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**ELM**} \\ \cline{2-7}  & **Cal./\%** & **Pre./\%** & **(\(c\),\(g\))** & **Cal./\%** & **Pre./\%** & **\(n\)** & **Cal./\%** & **Pre./\%** \\ \hline PCA loading & 60.72 & 61.12 & (32,768, 2) & 66.93 & 66.05 & 2600 & 79.39 & 72.0 \\ SPA & 64.89 & 65.54 & (32,768, 8) & 70.46 & 70.62 & 2400 & 70.06 & 70.54 \\ RF & 79.62 & 78.48 & (32,768, 2) & 84.37 & 82.00 & 2400 & 87.74 & 83.24 \\ \hline \hline \end{tabular} Note: Cal. means the accuracy of calibration set; Pre. means the accuracy of prediction set; Penalty coefficient \(c\) and the kernel parameter \(g\) as the parameter of SVM; The optimal number of hidden nodes, \(n\), as the parameter of ELM.  
\end{table} 
Table 4: The performance of discriminant models based on feature wavelengths. Figure 5: The distribution maps of feature wavelengths extracted by different methods: (**a**) The distribution maps of feature wavelengths extracted by SPA; and, (**b**) The distribution maps of feature wavelengths extracted by RF. variables' features for variety classification of wheat seeds might be lost in addition to the elimination of redundant information. However, the best classification performance was the SVM model paired with a variable selection method, SPA, when CRISPR/Cas9-induced rice mutants were identified [38]. That is to say, the methods of feature wavelengths extraction could be able to, but not always, improve the performance of the discriminant model.