## 2 Materials and Methods
### Data Processing  
Multiple regression analysis of the acceptance evaluation results of oolong tea samples and logistic analysis of the purchase intention evaluation results were performed using SAS 3.0 software (for details, refer to Tables S1, S2 and S4). For data analysis, R 4.1.3 and R studio were used for Student t-test, cluster analysis, Kruskal-Wallis test, Spearman's correlation analysis, PCA analysis, PCoA analysis, Adonis test and plotting Figures 1-3. Gephi 0.9.2 was used to draw Figure 4. The significant differences between different groups were analyzed using the specific methods shown in the related figures and tables, unless stated otherwise. Figure 1: Sensory evaluation score plot for samples of six different tea cultivars. (**A**) Histogram of scores from expert sensory evaluation for the six different tea samples (HD, MZ, TGY, FD, WNZ, and YS). (**B**) Boxplot of expert sensory evaluation scores of oolong tea samples made from suitable cultivars (HD, MZ, TGY) and unsuitable cultivars (FD, WNZ, YS). (**C**) Histogram of consumer acceptance evaluation scores of six tea cultivars. (**D**) Boxplot of consumer acceptance evaluation scores of oolong tea made from suitable cultivars (HD, MZ, TGY) and unsuitable cultivars (FD, WNZ, YS). Oolong tea samples made from suitable cultivars (HD, MZ, TGY); Notoolong: oolong tea samples made from unsuitable cultivars (HD, MZ, TGY). “*” indicates significant difference at \(p\) < 0.01 in Student t-test. The black dots: the scores from every expert and consumer. Figure 2: Stable response values of electronic nose sensors for samples of six different tea cultivars. (**A**) Heat map of electronic nose response values for samples of six different tea cultivars. (_p_: significant results based on Kruskal–Wallis test; _”***_ indicates \(p\) < 0.05; _”***_” indicates \(p\) < 0.01, and _”NA” indicates \(p\) > 0.05. expression: the size of the response values of different samples to the same sensor. (**B**) The PCA analysis score plot of electronic nose response values for six different tea cultivars. (grouping method: ggplot2:stat_ellipse, confidence interval: 0.95). Oologo- oolong tea samples made from suitable cultivars (HD, MZ, TGY); Notoolong: oolong tea samples made from unsuitable cultivars (HD, MZ, TGY). Figure 3: Aroma characteristics of samples from six different tea cultivars detected by HS-SPME-GC-MS. (**A**) HS-SPME-GC-MS bar graph of total aroma in the samples of six different tea cultivars. (**B**) Aroma classification stacking map of samples from six different tea cultivars measured by HS-SPME-GC-MS. (**C**) PCoA analysis score plot of HS-SPME-GC-MS results for samples from six different tea cultivars. (Grouping method: ggplot2:stat_ellipse, confidence interval: 0.95). (**D**) Heat map of HS-SPME-GC-MS results of six different tea cultivars. p_value: the left annotation heatmap color represents the size of the p-value data based on the Kruskal—Wallis test. _p_: significant results based on Kruskal-Wallis test; _\"\"_ means _p_\(<\) 0.05; _\"\"_ means _p_\(<\) 0.01; \"NA” means _p_\(>\) 0.05. SC: classification of substances according to different metabolic pathways; classification: clustering results between sample groups using the Spearman method; expression: relative content of the same substance in different samples. Oolong: oolong tea samples made from suitable cultivars (HD, MZ, TGY); Notoolong: oolong tea samples made from unsuitable cultivars (HD, MZ, TGY). Detailed information is also presented in Table 59. Since this study focused on suitability differences between the six oolong tea cultivars, PCoA analysis was performed to further examine the aroma contents of six oolong tea cultivars measured by HS-SPME-GC-MS. In the PCoA analysis score chart (Figure 3C), the principal coordinate components PCoA1 and PCoA2 explained more than 75% of the inter-group variation in this model, and the inter-sample distribution of aroma content was significant in the six tea samples (R\({}^{2}\) = 0.97109, _p_\(<\) 0.01) (Table 58).