## 3 Results and Discussion
### Calibration Models Using Full Spectra  
Partial least squares (PLS), extreme learning machine (ELM), and least-squares support vector machine (LS-SVM) models were built using full spectra to evaluate the performance of determination of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid. Leave-one-out cross validation was used to optimize the three models. The results of fresh and dry flowers are shown in Tables 2 and 3, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Sample Status**} & \multirow{2}{*}{**Compositions**} & \multicolumn{4}{c}{**Calibration**} & \multicolumn{4}{c}{**Prediction**} \\ \cline{3-8}  & & **Range** & **Mean** & **SD** & **Range** & **Mean** & **SD** 1  \\ \hline \multirow{3}{*}{Fresh} & chlorogenic acid & 0.33–0.59 & 0.48 & 0.067 & 0.34–0.58 & 0.48 & 0.067 \\  & luteolin-7-O-glucose & 0.21–0.42 & 0.32 & 0.046 & 0.22–0.40 & 0.32 & 0.046 \\  & 3,5-O-dicaffeoylquinic acid & 0.79–1.29 & 1.07 & 0.13 & 0.81–1.29 & 1.07 & 0.13 \\ \hline \multirow{3}{*}{Dry} & chlorogenic acid & 0.33–0.61 & 0.48 & 0.067 & 0.34–0.60 & 0.48 & 0.067 \\  & luteolin-7-O-glucose & 0.22–0.42 & 0.33 & 0.046 & 0.23–0.41 & 0.33 & 0.046 \\  & 3,5-O-dicaffeoylquinic acid & 0.79–1.29 & 1.07 & 0.13 & 0.79–1.27 & 1.07 & 0.13 \\ \hline \end{tabular}
\end{table} 
Table 1: Statistical summary of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content in the calibration set and the prediction set (unit: %DW, DW means dry weight). Figure 1: Average spectrum with standard deviation of fresh and dry _Chrysanthemum morifolium_ flowers. Bold lines are average spectra and vertical lines are standard deviation of each wavelength. As shown in Table 2, calibration models for determination of the three compositions in fresh flowers all obtained good results. The F-test showed that determination of coefficient (R\({}^{2}\)) of all models was significant at the significance level of 0.01. The biases of different models were all small. Different models for each composition obtained quite close results. For chlorogenic acid, the ELM model performed best with highest R\({}^{2}\) and RPD, and the bias was also quite small. The PLS model obtained relatively worse results. For luteolin-7-O-glucose, ELM obtained the best performance, and the bias of prediction was higher than the other two models. LS-SVM performed worst with R\({}^{2}_{\rm p}\) lower than 0.80. For 3,5-O-dicaffeoylquinic acid, results of the three models were quite close, and the LS-SVM model was the best model considering all evaluation parameters. As shown in Table 3, calibration models for determination of the three compositions in dry flowers all obtained good results. The F-test showed that determination of coefficient (R\({}^{2}\)) of all models was also significant at the significance level of 0.01. Moreover, the biases of different models were all small. For chlorogenic acid, LS-SVM obtained the highest R\({}^{2}_{\rm c}\), and lowest R\({}^{2}_{\rm p}\) and RPD. As a whole, the ELM model performed best while the PLS model performed worst. For luteolin-7-O-glucose, the ELM model performed best with the highest R\({}^{2}\) and RPD, and the bias was quite small. The PLS and LS-SVM models obtained quite close but worse results. For 3,5-O-dicaffeoylquinic acid, the three models obtained good and quite close results, indicating the effectiveness of these three models. A comparison could be made between models for fresh and dry flowers. Models for chlorogenic acid in fresh flowers performed better than the corresponding models for chlorogenic acid in dry flowers. Models for luteolin-7-O-glucose in fresh and dry flowers showed similar phenomena as