## 2 Results and Discussion
### Multivariate Analysis  
Multivariate statistical analyses including HCA and PCA allow for the classification and visualization of volatile SPME-GC-MS data. They also enable the determination of specific variables that contribute to the difference among the tissues based upon the mass spectrometry data [40]. For example, multivariate analyses of volatile metabolites emitted from ripe tomato fruits allowed distinct separation of structurally related metabolites derived from the same biochemical precursors, and treatments of wounded leaves from Nicotiana attenuate were separated based upon their volatile composition [41; 42]. HCA was used in this study to differentiate volatile profiles from alfalfa trichomes, stems, and leaves. HCA was performed using the average linkage method and the quantitative data for 133 volatile metabolites. The HCA dendrogram allowed distinct visualization of three different groups (Figure 4): cluster 1 (glandular trichome), cluster 2 (stem), and cluster 3 (leaf). In addition, the dendrogram identified specific metabolites contributing to the separation of glandular trichome, stem, and leaf volatile profiles. The level of precision of the analytical method was indicated by the correct classification of four biological replicates among samples and the precise separation of the individual samples. PCA was applied using the relative concentration of volatile metabolites in each sample to determine which volatile metabolites had the highest loadings and contributed the most to differences in volatile metabolic profiles among the samples (Figure 5). Figure 5A shows the PCA pattern for the different alfalfa tissues, confirming three clusters similar to those obtained by HCA. Volatile metabolic profiles in glandular trichome, stem, and leaf were clearly separated, accounting for 87.5% of the total variance by combining the first (PC 1) and second (PC 2) principal components. PC 1 accounts for 66.8% of the total variance and represents the difference in the volatile metabolic profiling of leaf from that of glandular trichome and stem. PC 2 accounts for 20.7% of the total variance and represents the difference in the volatile metabolic profile of glandular trichome and stem. Figure 5B and Supplemental Table S2 list the 21, 17, and 87 independent volatile metabolites contributing to the separation of glandular trichome, stem, and leaf, respectively. Figure 4: Dendrogram obtained from the hierarchical cluster analysis of plant tissue SPE-GC-MS data (i.e., glandular trichomes isolated from stems, trichome-free stems, and leaves of alfalfa plants) and 133 volatile metabolites using Expander package (ver. 6). The dendrogram provides distinct visualization of the differences in volatile metabolites emitted from the various plant tissues: cluster 1 (glandular trichome), cluster 2 (stem), and cluster 3 (leaf). The metabolites contributing to the separation of glandular trichome, stem, and leaf are also shown in the dendrogram. The major metabolites that influenced the volatile metabolic profile of glandular trichomes, in decreasing order of contribution, were 2-propanone (6; numbers correspond to that in Table S2), ethyl alcohol (4), unknown (108), 1-butanol (18), unknown (43), 2-methylpropanol (14), unknown (38), \(N\),\(N\)-dibutylformamide (107), unknown (49), butyl acetate (33), unknowns (133, 51, 106, 113), methoxycarbonyl isothiocyanate (35), and other unknown/unidentified metabolites (119, 63, 122, 93, 116, 129). The primary metabolites that influenced the volatile metabolic profile of stem, in decreasing order of contribution, were \(\alpha\)-terpinol (96), unknowns (103, 54, 70, 52, 16, 120), ocimenol (91), decanal (97), unknown (111), and (\(Z\))-2-dodecenol (115).