## 3 Results and Discussion
### Prediction and Comparstion of between SVM, LS-SVM and IFOA-LS-SVM Models
To further prove the IFOA-LS-SVM model having a good universality for the prediction of extraction yield, the prediction result of different models on generalization dataset are also listed in Table 3. As we can see, the SVM model obtained a RMSE of 0.1192, an MRE of 3.81%, an MAE of 0.0798 and a TIC of 0.0049; the LS-SVM model gave a RMSE of 0.0903, an MRE of 2.05%, an MAE of 0.0466 and a TIC of 0.0023; the IFOA-LS-SVM model obtained a RMSE of 0.0596, an MRE of 0.97%, an MAE of 0.0224 and a TIC of 0.0011. For quercetin, the SVM model gave a RMSE of 0.1008, an MRE \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytes** & **Models** & **RMSE** & **MRE (\%)** & **MAE** & **TIC** \\ \hline \multirow{3}{*}{Rutin} & SVM & 0.1268 & 3.87 & 0.0998 & 0.0082 \\  & LS-SVM & 0.1035 & 1.88 & 0.0566 & 0.0028 \\  & IFOA-LS-SVM & 0.0540 & 1.05 & 0.0189 & 0.0014 \\ \hline \multirow{3}{*}{Quercetin} & SVM & 0.1204 & 3.99 & 0.0965 & 0.0080 \\  & LS-SVM & 0.0997 & 2.05 & 0.0557 & 0.0030 \\ \cline{1-1}  & IFOA-LS-SVM & 0.0536 & 0.97 & 0.0183 & 0.0013 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction performance of different models on the testing dataset of analytes. of 3.86%, an MAE of 0.0776 and a TIC of 0.0045; the LS-SVM model obtained a RMSE of 0.0889, an MRE of 2.78%, an MAE of 0.0425 and a TIC of 0.0020; the IFOA-LS-SVM model gave a RMSE of 0.0612, an MRE of 0.99%, an MAE of 0.0231 and a TIC of 0.0014. In order to clearly illustrate comparative results, the MRE (%) indicator and rutin are chosen as illustration. As can be seen, comparing with those of the SVM model and LS-SVM model, the predictive errors of IFOA-LS-SVM decreased 74.5% and 52.7%, respectively. Meanwhile, the visual comparison illustrations for the two analytes are shown in Figure 12. As can be seen, the IFOA-LS-SVM model has fewer estimation errors compared to the SVM model and LS-SVM model. Similar to the testing dataset, the comparison result on generalization dataset suggests that the proposed IFOA-LS-SVM model also has good universality ability. In conclusion, the proposed IFOA-LS-SVM model has a good prediction performance for the yield prediction of quercetin and rutin from _Euonymus alatus_ ([PERSON].) [PERSON]. Figure 11: Prediction performance of different models on the test dataset of: rutin (**a**); and quercetin (**b**). Figure 12: Prediction performance of different models on the generalization dataset of: rutin (**a**); and quercetin (**b**). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytes** & **Models** & **RMSE** & **MRE (\%)** & **MAE** & **TIC** \\ \hline \multirow{3}{*}{Rutin} & SVM & 0.1192 & 3.81 & 0.0798 & 0.0049 \\  & LS-SVM & 0.0903 & 2.05 & 0.0466 & 0.0023 \\  & IFOA-LS-SVM & 0.0596 & 0.97 & 0.0224 & 0.0011 \\ \hline \multirow{3}{*}{Quercetin} & SVM & 0.1008 & 3.86 & 0.0776 & 0.0045 \\  & LS-SVM & 0.0889 & 2.78 & 0.0425 & 0.0020 \\ \cline{1-1}  & IFOA-LS-SVM & 0.0612 & 0.99 & 0.0231 & 0.0014 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction performance of different models on the generalization dataset of analytes.