## 3 Results and Discussion
### PCA and OPLS-DA Statistical Analyses of the Volatile Compounds  
PCA is the principle of data dimension reduction, which mainly studies the material, its corresponding variables, and the potential relationship with each cultivar [38]. PCA was performed on the 90 volatile components detected in the samples of white tea, and PCA was performed separately. The results (Figure 5) showed that four groups (SH-S, FD-S, SH-A, FD-A) can be clearly divided into four regions. Meanwhile, the samples in the two seasons are far apart, indicating that there is a big difference in the aroma composition of white teas in spring and summer. It can be seen from Figure 5 that the variance contribution rates of PC1 and PC2 are 71.3% and 13.2%, respectively, and the cumulative contribution rate reaches 84.5%. Notably, PC1 can better represent the information reflected by the original data. Figure 3: The concentration of volatile compounds of different categories from white tea samples. SH-S: Sanhua1951, spring; FD-S: Fudingdabai, spring; SH-A: Sanhua1951, autumn; FD-A: Fudingdabai, autumn. Different lowercase letters in the same line indicate a significant difference among white teas (\(p<0.05\)). In order to further explore the different volatile components of white teas in 'Sanhua1951' and 'Fudingdabai', a supervised pattern recognition method, orthogonal projections to latent structures-discriminant analysis (OPLS-DA), was introduced. The OPLS-DA score plot (R\({}^{2}\)X = 0.806, R\({}^{2}\)Y = 0.998, Q\({}^{2}\) = 0.993) demonstrated that SH-S and SH-A were clearly distinguished in Figure 6, which was consistent with the PCA results. Subsequently, the accuracy of the model was tested with a 200-permutation test (R\({}^{2}\) = 0.873, Q\({}^{2}\) = \(-\)0.020), and there was no overfitting of the model (Figure 7). The variables important in projection (VIP) were further utilized to focus on the volatile fraction explanation of key aroma compounds in white tea samples from two seasons using VIP > 1 as a criterion, where the larger VIP indicated that the key aroma compounds contributed more to the differentiation of white tea samples. As shown in Table S3, there were a total of 60 compounds with VIP > 1, which were the best discriminators between SH-S and SH-A, including the characteristic components of different tea cultivars. To investigate the impact of harvest seasons on white teas, significantly differential volatile components were selected according to the principle of \(p\) < 0.05, VIP > 1, and fold change \(\geq\)2 (up-regulated)  
Figure 4: Percentage of volatile components of different categories from white tea samples. SH-S: Sanhua1951, spring; FD-S: Fudingdabai, spring; SH-A: Sanhua1951, autumn; FD-A: Fudingdabai, autumn. Figure 5: Principal component analysis (PCA) score plot for volatile compounds of the four white teas. SH-S: Sanhua1951, spring; FD-S: Fudingdabai, spring; SH-A: Sanhua1951, autumn; FD-A: Fudingdabai, autumn. or \(\leq\) 0.5 (down-regulated) in each cultivar (Table S4). For 'Sanhua1951', there were 52 differential volatile compounds in SH-S vs. SH-A (34 upregulated and 18 downregulated) (Figure 8). As shown in Figure 2A, there were 18 volatile compounds detected only in SH-S and 25 volatile compounds detected only in SH-A. In addition to the above-mentioned compounds, the comparison of other significantly differential volatile compounds was performed (Figure 9), including (\(Z\))-linaloid oxide, (\(E\))-linaloid oxide, styrene, phenylethyl alcohol, (\(Z\))-citral, 3,7-dimethyl-2,6-octadienal, 2-methylnaphthalene, a-cubebene, 2-butyl-2-octenal, (\(Z\))-jasmone, (\(E\))-geranylacetone, \(\delta\)-cadinene, and (\(E\))-nerolidol. As shown in Figure 10, OPLS-DA (R\({}^{2}\)X = 0.830, R\({}^{2}\)Y = 0.994, Q\({}^{2}\) = 0.987) was performed to analyze the significantly different volatile compounds between FD-S and FD-A. The permutation test (n = 200) revealed intercepts of R\({}^{2}\) at 0.503 and Q\({}^{2}\) at \(-0.663\), and there was no overfitting for the model (Figure 11).