## 2 Results and Discussion
### Patterns of Floral Scent Volatiles in Accessions of Chrysanthemum and Its Relatives  
To simplify the multidimensional dataset (193 volatile compounds), we subjected the peak area data to a principle components analysis (PCA) (Table S1). PC1, PC2, and PC3 explained 33%, 11%, and 10%, respectively, of the total variance in the percentages of volatile peak areas. Together, the first three PCs accounted for 54% of the total variance in the percentage of volatile peak areas. Eigenvector \begin{table}
\begin{tabular}{c c c c c} \hline
**Quantity** & **10–20** & **20–29** & **30–39** & \(\geq\)**40** \\ \hline Plant accessions & 9 & 19 & 8 & 2 \\ Percent & 24\% & 50\% & 21\% & 5\% \\ \hline \end{tabular}
\end{table} 
Table 2: Quantity of floral aroma volatiles in 39 accessions of _Chrysanthemum_. values for each sample in the first two dimensions were plotted on a PCA score plot (Figure 2). The pompon type _Chrysanthemum_ cultivars clustered together in the PCA based on their scent, while the scents of other flower types and _Chrysanthemum_ wild relatives showed wide diversity. We created a 2D loading plot (Figure 3) to further explore the influence of the floral amore volatiles on the differentiation of the 39 accessions. The components that explain more variance in the data have higher loading values, and those explaining less variance or no variance have low or zero loading values, respectively. Ten compounds with the highest loading values are annotated (Figure 3). As follows: Camphor (0.91 in PC1 and \(-\)0.28 in PC2; highest loading value for PC1); \(\alpha\)-pinene (0.02 in PC1 and 0.58 in PC2; highest loading value for PC2); chrysanthenone (\(-\)0.23, \(-\)0.39); safranal (\(-\)0.18, \(-\)0.33); myrcene (0.02, 0.24); eucalypt (0.12, 0.19); 2,4,5,6,7,7 ab-hexahydro-1\(H\)-indene (\(-\)0.10, \(-\)0.16); verbenone (\(-\)0.09, \(-\)0.15); \(\beta\)-phellandrene (0.02, 0.16); and camphene (0.16, 0.01). To compare volatile compounds compositions among the 39 accessions, we performed a hierarchical cluster analysis based on the relative contents of the 193 aromatic volatile compounds. We used [PERSON]'s method for between-groups linkage, and the squared Euclidean distance between clusters as a proximity measurement. The 39 accessions formed six clusters in the dendrogram (Figure 4). Five accessions were in cluster I, in which camphor (50.56%-61.36%) and camphene (6.23%-10.39%) were the major components. Seven accessions were in cluster II, in which camphor (24.48%-42.62%) was also a major component, but at lower concentrations than in cluster I, with eucalypt (0%-30.21%) as the other major component. _Chrysanthemum_ are used as TCM for a long time, and its active compounds are eucalypt and camphor [32]. An analysis of the chemical composition and antimicrobial activity of _C. indicum_ essential oils showed that those obtained from processed flowers had a higher percentage of camphor and greater bacteriatic activity than those obtained from air-dried flowers [11]. Our results suggest that the accessions in clusters I and II are suitable for TCM because of their high camphor content. Cluster III consisted of eight accessions, with \(\alpha\)-gurjunene (0%-21.43%) and 3-tert-butylphenol (0%-23.95%) being the major components. Four accessions were in cluster IV, in which \(\alpha\)-pinene (1.14%-39.14%) was the major component. There were 13 accessions in cluster V, but there was no consistent major compound among their floral scent volatiles, and they were not grouped based on plant taxonomy or flower shape. Figure 4: Hierarchical cluster dendrogram of 39 plant materials. _Chrysanthemum_ cultivar \"Zhe jiang 12\" formed its own cluster, with thujone (65.51%) and \(\alpha\)-thujone (19.31%) as the major compounds. Because the compound identification was based solely on mass spectrometric data comparison, stereochemical structure of these major compound were not clearly identified yet. It is necessary to making the stereochemical structure clearly in our ongoing study.