## 3 Results and Discussion
### Characterization of Non-Volatile Metabolites of FAA Tea at Four Harvest Times
#### 3.3.2 Multivariate Statistical Analysis of Non-Volatile Metabolites  
To systematically explore the metabolic differences among FAA tea samples harvested at different periods, multivariate statistical analyses were performed, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) [34; 35]. All analyses were conducted using the R package Ropls, with data preprocessed through mean-centering and unit variance scaling.  
The PCA multivariate statistical analysis of metabolites was performed to compare the metabolite peaks among the four sample groups and the QC group (Figure 2a,b). The first principal component (PC1) explained 31.8% and 30.7% of the variance in positive and negative modes, respectively, while the second principal component (PC2) accounted for 15.2% and 13.3% of the variance. The PCA results revealed significant segregation of metabolites during different harvest periods (PC1), with greater PC1 values indicating larger metabolomic differences among sample groups. The PC2 results indicated changes in FAA tea metabolism during its developmental stages.  
To enhance class separation and validate multiclass discrimination, PLS-DA was employed. In the positive mode, the first principal component (PC1) captured 30.2% of the variance, and in the negative mode, it explained 29%. Meanwhile, the second principal component (PC2) represented 15.4% and 13.9% of the variance in the positive and negative modes, respectively (Figure S1a,b). The model demonstrated high explanatory power, with R2Y = 0.99 and Q2 = 0.917 in positive ion mode and R2Y = 0.992, Q2 = 0.896 in negative ion mode. Permutation tests (200 iterations) confirmed that the model could fit and predict the data quite well (Figure S1c,d).  
Figure 2: (**a,b**) Metabolome principal component analysis (PCA) of 12 FAA samples and quality control samples (QC) in positive (+) and negative (\(-\)) ion modes; (**c,d**) results of PLS-DA arrangement tests in positive (+) and negative (\(-\)) ion modes; (**e,f**) the heat map of cluster analysis in positive ion (+) and negative ion (\(-\)) modes, respectively.  
For pairwise comparisons, OPLS-DA models were constructed to identify key discriminative metabolites [36]. The Q2(cum) of the OPLS-DA model was 0.99(+) and 0.992(-), respectively, which indicated that the model a good predictive performance. Then, 7-fold cross-validation and 200 response alignment tests were performed, and the results showed that the model was not overfitted (Figure 2c,d). Hierarchical cluster analysis indicated that the FAA-4 group and FAA-5 group had the closest distance and were clustered together, while the FAA-6 group had the farthest relationship with the other three groups. The qualitative difference in metabolites between different harvest periods of FAA tea was illustrated (Figure 2e,f).  
The integration of PCA, PLS-DA, and OPLS-DA provided complementary insights. PCA revealed that the harvest time was the dominant source of variation (PC1), and PLS-DA confirmed multiclass separability. OPLS-DA-driven pairwise comparisons pinpointed FAA-6 as the most metabolically distinct group, which was consistent with its unique differentially accumulated metabolite (DAM) profile. Moreover, the minimal differences between FAA-4 and FAA-5 were in line with their clustering in HCA, suggesting a transitional metabolic state during these periods.