# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 3 Results and Discussion
### Effective Wavelengths Extraction  
The usage of full spectra may bring the risk of overfitting, noise and nonlinearities that result in models with low accuracy [17]. Thus, effective wavelength selection from the full spectra (301 bands) was carried out to search for the wavelengths carrying feature information for the identification. In this study, SPA and CARS algorithms were individually used, and the results are shown in Figure 4. For SPA method, the minimum and the maximum numbers were individually set to 5 and 30 based on experiences [42]. The RMSE plot is presented in Figure 4a, and the RMSE reduced to 0.2948 when the number of variables reached 10. As a result, by comprehensively considering the number of variables and RMSE values, 10 wavelengths including 960, 930, 994, 472, 790, 906, 404, 402, 416, and 588 nm were retained in order of importance. In further steps, these 10 variables were applied as inputs to establish SPA-PLS-DA model. As indicated in Figure 4b, a total of 15 sensitive wavelengths selected by CARS had particular importance for the variety identification. Specifically, in the first subplot in Figure 4b, the number of sampled variables decreased as the sampling runs increased. It could be observed that the number began with the largest drop, and then tapered off gradually. Redundant variables in the full spectra were gradually eliminated in this step. In the second subplot, the RMSE value in cross-validation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Preprocessings**} & \multicolumn{4}{c}{**Corrected Classification Rate (\%)**} & \multirow{2}{*}{**Parameters**} \\ \cline{3-4} \cline{6-7}  & & \multirow{2}{*}{**Calibration Set**} & \multicolumn{2}{c}{**Cross-Validation Set**} & & \\  & & & & **Set** & & \\ \hline \multirow{8}{*}{PLS-DA} & None & 97.33 & 95.33 & 92.00 & LVs = 13 \\  & SNV & 98.67 & 96.00 & 91.00 & LVs = 12 \\  & SNV + detrend & 98.00 & 96.00 & 92.00 & LVs = 11 \\  & Normalization & 96.33 & 93.00 & 90.00 & LVs = 13 \\  & 1 st derivative & 97.67 & 96.00 & 92.00 & LVs = 8 \\  & 2 nd derivative & 96.33 & 88.00 & 86.00 & LVs = 5 \\ \hline \multirow{8}{*}{KNN} & None & 78.67 & 77.00 & 72.00 & K = 6 \\  & SNV & 81.67 & 76.67 & 70.00 & K = 3 \\  & SNV + detrend & 90.00 & 86.67 & 79.00 & K = 4 \\  & Normalization & 76.67 & 73.33 & 69.00 & K = 7 \\  & 1 st derivative & 87.67 & 84.33 & 70.00 & K = 5 \\  & 2 nd derivative & 72.67 & 69.67 & 62.00 & K = 6 \\ \hline \multirow{8}{*}{RBF-SVM} & None & 99.67 & 90.00 & 92.00 & / \\  & SNV & 99.33 & 89.67 & 92.00 & / \\ \cline{1-1}  & SNV + detrend & 100.00 & 89.33 & 89.00 & / \\ \cline{1-1}  & Normalization & 99.67 & 89.33 & 89.00 & / \\ \cline{1-1}  & 1 st derivative & 99.33 & 95.00 & 91.00 & / \\ \cline{1-1}  & 2 nd derivative & 99.00 & 89.67 & 89.00 & / \\ \hline \hline \end{tabular} PLS-DA: partial least squares-discriminant analysis; KNN: k-nearest neighbor; RBF-SVM: radial basis function-support vector machine; SNV: standard normal variate; LVs: latent variables.  
\end{table} 
Table 1: Performance of established classification models developed by full spectra with or without preprocessings.