## 3 Results and Discussion
### Calibration Models Using Optimal Wavelengths  
PLS, ELM, and LS-SVM models were built using selected optimal wavelengths to evaluate the prediction performances for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content determination in fresh and dry flowers. The results are shown in Tables 5 and 6. Table 5 shows the detection results of chemical compositions in fresh flowers using optimal wavelengths. All models had R\({}^{2}\) over 0.8 and RPD over 2.00, and the bias values were all small. The F-test showed that determination of coefficient (R\({}^{2}\)) of all models was significant at the significance level of 0.01. For chlorogenic acid, the three models all performed well and obtained quite close results. Their R\({}^{2}\) and RPD were high while RMSE and bias were small. For luteolin-7-O-glucose, similar phenomena could be found that the differences among different models were quite small, and all the three models performed well. For 3,5-O-dicaffeoylquinic acid, the ELM model performed best, with highest R\({}^{2}\) and RPD. Table 6 shows the determination results of chemical compositions in dry flowers using optimal wavelengths. The F-test showed that determination of coefficient (R\({}^{2}\)) of all models was significant at the significance level of 0.01. For chlorogenic acid, the ELM model performed best, and its bias \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Status** & **Compositions** & **Number** & **Wavelength (nm)** \\ \hline \multirow{3}{*}{Fresh} & chlorogenic acid & 8 & 1463, 1082, 1419, 1615, 1399, 1005, 1164, 1325 \\  & luteolin-7-O-glucose & 7 & 1025, 1082, 992, 1429, 1646, 1281, 1406 \\  & 3,5-O-dicaffeoylquinic acid & 8 & 1046, 1126, 1005, 1436, 1615, 975, 1164, 1288 \\ \hline \multirow{3}{*}{Dry} & chlorogenic acid & 8 & 1470, 1076, 1419, 1315, 988, 1396, 1227, 1646 \\  & luteolin-7-O-glucose & 5 & 1072, 1612, 1419, 1318, 1646 \\  & 3,5-O-dicaffeoylquinic acid & 10 & 1126, 1180, 1029, 1210, 1227, 1463, 975, 995, 1646, 1389 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal wavelengths selected by SPA for chlorogenic acid, luteolin-7-O-glucose, and. 3,5-O-dicaffeoylquinic acid prediction. of prediction was higher than that of the PLS model. The LS-SVM model obtained better results than the PLS model in the calibration set, and obtained worse results in the prediction set. For luteolin-7-O-glucoside, the ELM model performed best, followed by PLS and LS-SVM. The results of the three models were not good enough, and the R\({}^{2}\)p of all models was lower than 0.80. For 3,5-O-dicaffeoylquinic acid, good performances were obtained; all models had R\({}^{2}\) over 0.8 and RPD over 2.00. Quite close results were obtained by the three models. For fresh and dry flowers, models for chlorogenic acid and luteolin-7-O-glucose in fresh flowers performed better than the corresponding models for chlorogenic acid and luteolin-7-O-glucose in dry flowers. Models for 3,5-O-dicaffeoylquinic acid in dry flowers performed better than the corresponding models for 3,5-O-dicaffeoylquinic acid in fresh flowers. The overall results indicated that it was feasible to use optimal wavelengths to determine chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content in fresh and dry flowers. As shown in Table 2, Table 3, Table 5, and Table 6, most calibration models using full spectra outperformed those using optimal wavelengths, but their differences were small. Some models using full spectra and optimal wavelengths obtained close results. Another fact to be addressed was that the number of input wavelengths reduced at least 95% compared with full spectra. However, although calibration models using optimal wavelengths obtained good results, much more work should be done to develop models with better robustness and accuracy. For both full spectra situation and optimal wavelength situation, ELM models could be considered as the best models, followed by LS-SVM and PLS models.