## 3 Results and Discussion
### Classification Model Construction
#### 3.5.2 Identification Results Based on SPA and VCPA with Classifiers  
To further compare the classification ability of extracted spectral features by NRS algorithms, the selected feature wavelengths by the SPA (12 spectral bands) and VCPA (13 spectral bands) methods were also combined with three classifiers of RF, ELM, and SIMCA to establish different identification models. These were named SPA-RF, SPA-RF, SPA-ELM, VCPA-ELM, SPA-SIMCA, and VCPA-SIMCA, and the classification **accuracies** of the different models are shown in Table 4. The confusion matrix of the classification results of paddy storage status by SPA and VCPA with different classifiers on both the data sets are shown in Figure 9.  
The analysis showed that the whole performances of models based on feature wavelengths by the SPA and VCPA algorithms were inferior to classification models based on the NRS series methods. The classification ability of SPA-RF was higher than that of VCPA-RF and their CCRs exceeded 90% in the calibration set, at 94.73% and 91.05%, respectively. For the test set, their CCRs decreased to 86.31% and 83.15% and were lower by 7.37% and 10.53% than that of the best NRS-a-RF model, respectively. That is to say, the stability of the SPA-RF and VCPA-RF models still needed to be improved. The classification results of SPA-ELM and VCPA-ELM models showed a downward trend; their CCRs were 88.42% and 84.73% in the calibration set. For the test set, the number of misclassified samples increased to 19 and 22 and their CCRs were 80.0% and 76.84%, respectively. For the SPA-SIMCA and VCPA-SIMCA models, their classification results were unsatisfactory. In particular, the CCRs were only 72.63% and 70.52% in the test set, respectively. From the information mentioned above, it can be determined that the SIMCA method is probably not suitable for the classification of paddy storage status based on spectral feature information.  
The best NRS-a-RF model and the SPA-RF model were selected to further compare the identification ability for different paddy samples through the indicators of sensitivity and specificity (Table 5). The analysis showed that the sensitivity and specificity indicators of the NRS-a-RF model were distributed within the range of 0.96-0.99 and the indicators of the SPA-RF model were within the range of 0.92-0.99 in the calibration set. Their performances were relatively ideal and there was no significant difference between them. For the test set, the evaluation indicators of the NRS-a-RF model were distributed between 0.93 and 0.98, which indicated a good stability for it. However, the classification accuracy and stability of the SPA-RF model showed a decreasing trend (a range of 0.83 to 0.95). From this, it could be seen that the overall classification performance of the SPA-RF model for the storage status of paddy samples was also inferior to the best NRS-a-RF model. Therefore, it was feasible to use the nine spectral feature wavelengths of the NRS-a algorithm combined with the RF classifier to establish the NRS-a-RF model for the identification of the storage status of paddy samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Models**} & **Calibration Set** & **Test Set** \\ \cline{2-3}  & **CCR (\%)** & **CCR (\%)** \\ \hline SPA-RF & 94.73 & 86.31 \\ VCPA-RF & 91.05 & 83.15 \\ SPA-ELM & 88.42 & 80.0 \\ VCPA-ELM & 84.73 & 76.84 \\ SPA-SIMCA & 82.63 & 72.63 \\ VCPA-SIMCA & 80.52 & 70.52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracy of paddy storage status by SPA and VCPA with different classifiers.  
Figure 9: The confusion matrix of classification results of paddy storage status by SPA and VCPA with different classifiers. (**a**) Calibration set; (**b**) Test set.