## 2 Results and Discussion
### Large-Scale Profiling of Alfafla Volatile Metabolites  
The optimized SPME-GC-MS method was used for nontargeted volatile metabolic profiling of alfalfa glandular trichomes from stems, trichome-free stems, and leaves. The analyses were performed using four replicates and the optimized SPME-GC-MS method. Data processing was performed using Automated Mass spectral Deconvolution and Identification System (AMDIS) software [30]. A total of 87 volatile metabolites were detected in glandular trichomes, 59 in stems, and 99 in leaves (Supplemental Table S1) after background subtraction to remove contaminants present in ambient air and associated with the NaOH/EDTA buffer solution containing CaCl\({}_{2}\). Thirty-seven volatile metabolites were consistently detected in all samples while another 15 volatile metabolites were uniquely detected in glandular trichomes, 9 only in stems, and 33 specifically in leaves. Confident identification of 36 glandular trichomes, 31 stems, and 41 leaves volatiles was accomplished through mass spectral matching of experimental data with the NIST08/Wiley7 mass spectral library, retention indices (RIs) from the literature, mass spectra of authentic compounds, and RIs of authentic compounds consistent with Metabolomics Standards Initiative [31]. Additional metabolites were only tentatively identified due to the absence of authentic  
Figure 2: Effect of temperature and extraction time on SPME extraction performance. (**A**). The total number of volatile metabolites detected at different extraction temperatures for 20 min (white column) and different extraction times at 60 \({}^{\circ}\)C using a DVB/CAR/PDMS SPME fiber (gray column). (**B**). The total peak area of metabolites detected at different extraction temperatures for 20 min (white column), and different extraction times at 60 \({}^{\circ}\)C using a DVB/CAR/PDMS SPME fiber (gray column). Vertical bars with the same letter are not significant; however, those indicated with a different letter are significant according to the [PERSON]’s multiple range test (\(p<0.05\)). These results illustrate that the broadest cross-section of volatile and semi-volatile metabolites was detected using an extraction temperature of 60 \({}^{\circ}\)C and an extraction time of 20 min. standards or difficulty in measuring RIs of very early eluting compounds. Tentatively identified early-eluting metabolites without RIs included: sulfur dioxide, acetaldehyde, methanethiol, ethyl alcohol, 2-propenal, and 2-propanone (Supplemental Table S1). MET-IDEA software was used for quantitative data extraction using the most abundant fragment ion from the multiple model ions reported by AMDIS [32; 33]. The relative concentration (\(\upmu\)g g\({}^{-1}\) fr. wt) of each metabolite was calculated by dividing the extracted value of each metabolite by the extracted value of 3-pentanol (internal standard) and fresh weight of the sample. Reproducibility of the analytical process was good, as demonstrated by the small relative standard deviations (RSD) observed for four replicates of the internal standard equal to 7.1% for glandular trichomes, 10.3% for stems and 4.4% for leaves. The total relative concentrations of volatile metabolites in glandular trichomes, stems, and leaves were 0.66, 0.87, and 10.72 mg g\({}^{-1}\) fr. wt, respectively. The total relative concentration in leaves was 16.2 and 12.4 times higher than that in glandular trichomes and stems, indicating that alfalfa leaves are quantitatively the main source of volatile metabolites. The relative proportion of the primary classes of volatile metabolites in glandular trichomes, stems, and leaves were significantly different from other plant tissues as shown in Figure 3. Ketones (29.1%), C6 metabolites (24.9%), and aldehydes (14.7%) were quantitatively the most abundant metabolites in glandular trichomes. The major metabolites in stems were C6 metabolites (47.3%), terpenoids (15.5%), and aldehydes (13.0%).