## 3 Results and Discussion
### Calibration Models Using Optimal Wavelengths
The results showed that ELM, LS-SVM, and PLS models could be used to detect chemical compositions in fresh and dry _Chrysanthenum morifolium_, and ELM had the greatest potential. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compositions**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Calibration**} & \multicolumn{4}{c}{**Prediction**} \\ \cline{4-9}  & & & \(\mathbf{R^{2}}_{\mathbf{c}}\) & **RMSEC** & **Biasc** & \(\mathbf{R^{2}}_{\mathbf{p}}\) & **RMSEP** & **RPD** & **Biasp** \\ \hline \multirow{3}{*}{chlorogenic acid} & PLS & 8 & 0.89 ** & 0.022 & \(-7.10\times 10^{-7}\) & 0.84 ** & 0.027 & 2.48 & 0.0032 \\  & ELM & 39 & 0.93 ** & 0.018 & \(-4.77\times 10^{-6}\) & 0.87 ** & 0.025 & 2.68 & 0.0042 \\  & LS-SVM & 146.7564, 9.64007 & 0.92 ** & 0.018 & \(1.27\times 10^{-14}\) & 0.81 ** & 0.030 & 2.23 & 0.0050 \\ \hline \multirow{3}{*}{luteolin-7-O-glucose} & PLS & 5 & 0.78 ** & 0.021 & \(2.30\times 10^{-7}\) & 0.68 ** & 0.026 & 1.77 & 0.0030 \\  & ELM & 18 & 0.83 ** & 0.019 & \(1.65\times 10^{-6}\) & 0.78 ** & 0.022 & 2.09 & 0.0041 \\  & LS-SVM & 16.9896, 0.39888 & 0.90 ** & 0.014 & \(-1.28\times 10^{-16}\) & 0.68 ** & 0.026 & 1.77 & 0.0020 \\ \hline \multirow{3}{*}{3,5-O-dicaffeoylquinic acid} & PLS & 8 & 0.84 ** & 0.051 & \(-3.02\times 10^{-6}\) & 0.83 ** & 0.054 & 2.41 & \(-\)0.0050 \\  & ELM & 20 & 0.85 ** & 0.049 & \(-1.45\times 10^{-6}\) & 0.83 ** & 0.054 & 2.41 & \(-\)0.0013 \\ \cline{1-1}  & LS-SVM & 23.811,479235 & 0.84 ** & 0.050 & \(-7.30\times 10^{-10}\) & 0.84 ** & 0.054 & 2.41 & \(-\)0.0025 \\ \hline \hline \end{tabular} The ** symbols indicate that the R2 is significant at the significance level of 0.01.  
\end{table} 
Table 6: Results of calibration models for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid in dry flowers using optimal wavelengths (unit: % DW, DW means dry weight). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compositions**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Calibration**} & \multicolumn{4}{c}{**Prediction**} \\ \cline{3-10}  & & & \(\mathbf{R^{2}}_{\mathbf{c}}\) & **RMSEC** & **Biasc** & \(\mathbf{R^{2}}_{\mathbf{p}}\) & **RMSEP** & **RPD** & **Biasp** \\ \hline \multirow{3}{*}{chlorogenic acid} & PLS & 7 & 0.90 ** & 0.021 & \(1.87\times 10^{-6}\) & 0.88 ** & 0.023 & 2.91 & 0.0018 \\  & ELM & 10 & 0.91 ** & 0.020 & \(-6.50\times 10^{-10}\) & 0.87 ** & 0.024 & 2.79 & 0.0023 \\  & LS-SVM & 8.6896, 6.2569 & 0.91 & 0.020 & \(2.80\times 10^{-16}\) & 0.87 ** & 0.024 & 2.79 & \(-8.77\times 10^{-5}\) \\ \hline \multirow{3}{*}{luteolin-7-O-glucose} & PLS & 7 & 0.83 ** & 0.019 & \(2.61\times 10^{-6}\) & 0.80 ** & 0.020 & 2.3 & 0.0012 \\  & ELM & 18 & 0.85 ** & 0.018 & \(-9.42\times 10^{-7}\) & 0.82 ** & 0.019 & 2.42 & \(-\)0.0014 \\  & LS-SVM & 4.9733, 0.50549 & 0.87 ** & 0.017 & \(-1.18\times 10^{-16}\) & 0.81 ** & 0.020 & 2.3 & \(-\)0.0019 \\ \hline \multirow{3}{*}{3,5-O-dicaffeoylquinic acid} & PLS & 7 & 0.84 ** & 0.051 & \(-4.57\times 10^{-7}\) & 0.80 ** & 0.062 & 2.10 & 0.0039 \\  & ELM & 19 & 0.87 ** & 0.047 & \(-2.11\times 10^{-6}\) & 0.83 ** & 0.055 & 2.36 & 0.0016 \\ \cline{1-1}  & LS-SVM & 3,119,660,6357 & 0.86 ** & 0.048 & \(6.35\times 10^{-10}\) & 0.81 ** & 0.059 & 2.20 & 0.0035 \\ \hline \hline \end{tabular} The ** symbols indicate that the R2 is significant at the significance level of 0.01.  
\end{table} 
Table 5: Results of calibration models for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid in fresh flowers using optimal wavelengths (unit: % DW, DW means dry weight). Visualization of Chlorogenic Acid, Luteolin-7-O-glucose, and 3,5-O-Dicaffeoylquinic Acid in Chrysanthemum morifolium  
As shown in Tables 2 and 4, calibration models for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content determination obtained good results. These results illustrated the feasibility of using hyperspectral imaging to determine these chemical compositions.