## 3 Results and Discussion
### Classification Model Construction  
#### 3.5.1 Identification Results Based on NRS with Classifiers  
The selected feature wavelengths by NRS-a (nine spectral bands) and NRS-b (eight spectral bands) were combined with the three methods of RF, ELM, and SIMCA to establish different classification models; these were named NRS-a-RF, NRS-b-RF, NRS-a-ELM, NRS-b-ELM, NRS-a-SIMCA, and NRS-b-SIMCA. The classification accuracies of the different models are shown in Table 3. The confusion matrix of classification results of paddy storage status by NRS with different classifiers on the two data sets is shown in Figure 8. From Table 3 and Figure 8, the classification result of the NRS-a-RF model, with CCR of 96.31%, was slightly higher than that of the NRS-b-RF model (CCR of 94.21%) in the calibration set and only one misjudgment was made for the identification of grade3 paddy samples, severely unsuitable storage. However, there were significant differences in the test set, and the CCR of NRS-b-RF decreased from 94.21% to 86.31%, whereas the NRS-a-RF model only decreased by 2.63%. Therefore, the NRS-a-RF model had an ideal classification result in terms of accuracy and stability.  
Figure 6: The optimal wavelengths and probabilities by SPA.  
Figure 7: The optimal wavelengths and probabilities by VCPA.  
Figure 8: The confusion matrix of classification results of paddy storage status by NRS with different classifiers. (**a**) Calibration set; (**b**) Test set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Models**} & **Calibration Set** & **Test Set** \\ \cline{2-3}  & **CCR (\%)** & **CCR (\%)** \\ \hline NRS-a-RF & 96.31 & 93.68 \\ NRS-b-RF & 94.21 & 86.31 \\ NRS-a-ELM & 93.68 & 85.26 \\ NRS-b-ELM & 91.57 & 84.21 \\ NRS-a-SIMCA & 88.94 & 82.10 \\ NRS-b-SIMCA & 86.31 & 81.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification accuracy of paddy storage status by NRS with different classifiers.  
The CRR of the NRS-a-ELM model in both the calibration set (CRR of 93.68%) and test set (CRR of 85.26%) were higher than that of the NRS-b-ELM model with CCRs of 91.57% and 84.21%, and there was no significant difference in the discrimination results of individual grade samples between them. Furthermore, the whole performance of the NRS-a-ELM and NRS-b-ELM models was inferior to the NRS-a-RF and NRS-b-RF models, which meant that the classification ability of the RF method was better than the ELM method. Based on the SIMCA classifier, the whole identification ability of the NRS-a-SIMCA and NRS-b-SIMCA models was weaker than the NRS-a-ELM and NRS-b-ELM models and even worse than the NRS-a-RF and NRS-b-RF models. The CCRs of NRS-a-SIMCA, with 88.94% and 82.10%, and NRS-b-SIMCA, with 86.31% and 81.05% in both the calibration and test sets, were lower by 11.58% and 12.63% than that of the best NRS-a-RF model in the test set. It is feasible to use the selected feature wavelengths from the NRS algorithm to identify the different storage states of paddy, where the feature wavelength from the NRS-a algorithm was more representative and the performance of the RF classifiers was superior to others. Therefore, the NRS-a-RF model had great potential for detecting the quality and safety status of paddy during storage.