## 3 Results and Discussion
### Regional Differences in Volatile Compounds of SDT  
The aromatic profiles of tea leaves exhibit significant regional variation, playing a pivotal role in determining the flavor quality of SDT [12]. To characterize the volatile organic compounds (VOCs) in SDT, this study employed headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). This analytical approach identified 463 distinct volatile compounds, which were systematically classified into 12 chemical categories: 78 alkenes, 66 alcohols, 67 esters, 70 alkanes, 47 ketones, 42 aldehydes, 22 nitrogen-containing compounds, 21 heterocyclic oxygen compounds, 18 carboxylic acids, 10 phenols, 18 aromatic compounds, and 4 miscellaneous compounds (Figure 3). Quantitative analysis revealed substantial variation in volatile composition across different regions. The PS group exhibited the highest diversity of volatile compounds (133 compounds) and the greatest total mass concentration (14.13 mg/g). In contrast, the DG and BS groups demonstrated the lowest compound diversity, each containing 97 volatile compounds. Notably, linalool emerged as the predominant volatile compound across all 13 regional samples, with its relative mass concentration ranging from 0.67 mg/g (XN group) to 26.69 mg/g (DG group). Figure 3: Classification of volatile compounds in SDT. Although numerous volatile compounds have been identified in tea, only a limited subset significantly contributes to its sensory profile [34]. These influential volatile compounds, which substantially impact the aromatic characteristics of tea, are scientifically defined as \"key aroma substances\" [35]. Through comprehensive analysis incorporating aroma threshold values and sensory characterization, 118 aroma-active compounds were identified. Subsequent calculation of their relative odor activity values (ROAV) enabled the selection of 36 key aroma-active compounds with ROAV \(\geq\) 1.00 (Table 2). Cluster analysis of these 36 key aroma-active compounds (Figure 4A) demonstrated distinct regional differentiation, classifying the 13 studied regions into two primary groups. Group C1 comprised PS, BD, DBS, DG, BS, and BDao regions, characterized by predominant tender and floral aroma profiles. Group C2 included XN, BKN, MA, MN, SH, GFZ, and WLS regions, exhibiting distinctive clean-sweet and honey-like aromatic characteristics. This classification pattern exhibited remarkable consistency with the cluster analysis results obtained from electronic nose detection data (Figure 4B). Figure 4: Regional difference analysis of SDT volatile compounds: cluster heat map of 36 aroma active substances (**A**); electronic nose 10 sensor clustering heat map (**B**); volcano chart (**C**); VIP value diagram (**D**); key differential substances (**E**). Orthogonal partial least squares discriminant analysis (OPLS-DA) provides a robust statistical approach for the systematic classification of distinct sample groups. To elucidate the critical volatile compounds contributing to regional differentiation in SDT aroma profiles, OPLS-DA was employed to analyze the volatile organic compounds (VOCs) between C1 and C2 groups. The model demonstrated satisfactory goodness of fit, with parameter values of R2X = 0.726, R2Y = 0.996, and Q2 = 0.993. According to established validation criteria in multivariate analysis, both R2 (coefficient of determination) and Q2 (cross-validated predictive ability) values exceeding the threshold of 0.5 indicate acceptable model performance in both explanatory and predictive capacities. Through this analysis, seven key differential aroma-active compounds were identified based on stringent selection criteria (FC \(>\) 1.5 or \(<\) 0.8, VIP \(>\) 1, and \(p\)-value \(<\) 0.05) (Figure 4C-E). Comparative analysis revealed significant upregulation of five compounds in the C2 group relative to C1: isophorone (woody), 2-pentyl furan (caramel), 1-octanol (fatty), D-limonene (fruity), and benzaldehyde (fruity).