## 3 Results
Major SHs identified were zingiberene (16.50% to 39.62%), _ar_-curcumene (11.64% to 26.50%), \(\beta\)-caryophyllene (10.57% to 13.98%), \(\gamma\)-muvrolactone (4.26% to 7.70%), \(\beta\)-sesquiphellandrene 2.02% to 5.94%), \(\alpha\)-copaene (2.04% to 2.86%), \(\alpha\)-humulene (0.64% to 2.19%) and _trans_-\(\alpha\)-bergamotene (1.15% to 2.15%). Monoterpene hydrocarbons (MHs), \(\alpha\)-thujene (1.71% and 1.2%), \(\alpha\)-pinene (1.99% and 1.45%), sabinene (6.49% and 4.23%), and (_E_)-\(\beta\)-ocimene (0.59% and 5.74%) were present mostly in flower buds and open flowers, respectively, while \(\beta\)-pinene (0.43%) was only found in open flowers. Limonene was about 5.5 times more abundant in open flowers than flower buds and new leaf shoots. From the group of oxygenated monoterpenes (OMs), OM linalool and its _cis_- and _trans_-linalool oxides (furanoid) were only detected in open flowers (Table 1). Principal component analysis (PCA) was carried out to identify the grouping pattern among potted (infested) lychee plants (Figure 3) and field-collected (non-infested) samples (Figure 4) based on volatile compounds obtained from GC-MS data (Table 1). For better distinction between the groups, we focused on the proportions of the volatile compounds. Based on the Eigenvectors criterion, principal components with an eigenvalue greater than one are considered important. First, PCA analysis was applied based on the GC-MS data from samples A1 to A5. Principal components F1 and F2 had higher Eigenvectors of 9.58 and 5.85, respectively (Table 2). Two macro principal components accounted for 45.60% (F1) and 27.87% (F2) representing 73.47% of the total variance, indicating that the F1 and F2 had good support for the difference in the plant tissues (Figure 3a). Figure 3: Panel (**a**) shows the Biplot Analysis for Principal Component Analysis (PCA) of volatile compounds emitted from potted lychee plants under different levels of LEM infestation (A1 to A5). Panel (**b**) shows the Agglomerative Hierarchical Cluster (AHC) analysis for LEM infestation samples A1 to A5 based on selected volatile active compounds. Initial infestation (A1), intermediate infestation (A2), heavy infestation (A3), overexploitation (A4), and healthy, uninfested plant (A5). Table 3 shows the percent contribution and squared cosine values (cos\({}^{2}\)) of each principal component. The percent contribution of each variable shows the most important variables. Also, the cos\({}^{2}\) values indicate the potential importance of components for a given observation. Taking the values of cos\({}^{2}\) and the percent contribution, nonanal (7.63%; 0.73), decanal (7.44%; 0.71), _ar_-curcumene (9.57%; 0.92), \(\alpha\)-zingiberene (8.59%; 0.82) and \(\delta\)-cadinene (8.60%; 0.82) showed a strong impact on the F1, while \(\alpha\)-copaene (16.13%; 0.94) showed the strongest impact on the F2. Cos\({}^{2}\) for F1 was the highest impact on the observation of initial infestation A1 (0.758) and overexploitation A4 (0.614); heavy infestation A3 (0.724) was the highest impact on F2. Agglomerative Hierarchical Clustering (AHC) shows the similarity between existing and merged clusters. Different LEM infestation levels were classified into 2 clusters (Figure 3b). Cluster 1 includes initial infestation (A1), heavy infestation (A3), and uninfested plant (A5), and the second cluster included intermediate infestation (A2) and overexploitation (A4). Compounds limonene, nonanal, decanal, \(\beta\)-caryophyllene, _ar_-curcumene showed a strong impact on cluster 1, while compounds \(\alpha\)-pinene, sabinene, limonene, \(\beta\)-caryophyllene, _ar_-curcumene showed a strong impact on the cluster 2 (Table 4). \begin{table}
\begin{tabular}{c c c c} \hline
**Principal Components** & **Eigenvalue** & **Proportion of Total Variance (\%)** & **Cumulative Proportion of Total Variance (\%)** \\ \hline F1 & 9.575 & 45.597 & 45.597 \\ F2 & 5.852 & 27.868 & 73.465 \\ F3 & 3.424 & 16.307 & 89.772 \\ F4 & 2.148 & 10.228 & 100.00 \\ \hline \end{tabular}
\end{table}