## 3 Results and Discussion
### Multivariate Statistical Analysis
#### 3.4.1 Analysis of Differential Volatile Components in Black Tea from Different Seasons  
First, an unsupervised PCA model was established using the relative contents of each volatile component. As shown in Figure 4A, the cumulative contribution rate of the first two principal components was 0.875. On the PCA score plot, the black tea samples from different seasons were clearly separated, indicating significant differences among samples from different seasons. Moreover, the three replicates within the same sample were relatively concentrated, suggesting good repeatability. Consistent results were obtained from HCA (Figure 4B). To screen out the differential volatile components of Rucheng Baimao black tea in different seasons, a supervised PLS-DA model was further established. As shown in Figure 4C, for the PLS-DA model, R\({}^{2}\)X = 0.922 and R\({}^{2}\)Y = 0.993, indicating a good predictive effect of the model. Through 200 permutation tests, it was concluded that the model did not overfit (Q\({}^{2}\) = \(-\)0.115), and it could be used to screen differential volatile components (Figure 4D). Variable Importance in the Projection (VIP value) can be used to measure the contribution of a single variable to the PLS-DA model. Variables with VIP > 1 are generally considered to play an important role in the model discrimination. According to the principle of VIP > 1 and \(p\) < 0.05, the differential volatile components of Rucheng Baimao black tea in different seasons were screened. A total of 34 volatile components were selected, and the results are shown in Table 2. These volatile components can be used as potential differential volatile components to distinguish Rucheng Baimao black tea in spring, summer, and autumn. Figure 3: Flavor index of Rucheng Baimao black tea in different seasons. *, **, *** represent significant differences; 'ns’ indicates no significant difference. (**A**) [PERSON]’s flavor index of black tea in different seasons; (**B**) wickremasinghe–yamanishi ratio of black tea in different seasons. To better visualize the distribution of the abovementioned differential volatile components among Rucheng Baimao black tea in different seasons, an HCA heatmap was drawn using the relative contents of the differential volatile components. As shown in Figure 5, the distribution of the 34 differential volatile components in black tea from spring, summer, and autumn showed a certain regularity and could be roughly divided into three groups. The first group included methyl salicate, 2,6-di-tert-butyl-4-methylphenol, methyl geraoate, 5-methyl-2-hexanol, 3,4,5-trimethyloxazole, (\(Z\))-3-hexenoate, nonanal, geraniol, (\(E\))-geranic acid, (\(Z\))-6-methyl-2-undecene, and (\(E\))-citral, which accumulated the most in spring black tea. The second group consisted of caffeine, 2-hexenal, 2,6,10-Trimethyldodecane, linalool, phenylacetaldehyde, benzaldehyde, 2,6,11-trimethyldodecane, 2-heptanol, quinuclidine, 4,6,8-trimethyl-1-nonene, heptadecane, phenethyl alcohol, benzyl alcohol, tetradecane, and \(\beta\)-ionone, which accumulated the most in summer black tea. The third group contained (\(E\))-linal oxide (furanoid), _trans_-nerolidol, (\(E\))-linalool oxide (pyranoid), benzyl nitrile, 2,6-dimethyl-3,7-octadien-2,6-diol, jasmine lactone, hotrienol, and \(\alpha\)-farnesene, which accumulated the most in autumn black tea. Figure 4: Multivariate statistical analysis of Rucheng Baimao black tea in different seasons. (**A**) Principal component analysis; (**B**) hierarchical cluster analysis; (**C**) partial least squares discriminant analysis; (**D**) permutation test. Figure 5: Heat map of differential volatile components in Rucheng Baimao black tea in different seasons.