## 3 Results and Discussion
### Characterization of Non-Volatile Metabolites of FAA Tea at Four Harvest Times
#### 3.3.3 Characterization of Differential Non-Volatile Metabolites in FAA Tea  
Metabolite accumulation is a complex and important process in plants, influenced by genetic and environmental factors [33]. In this study, UPLC-MS/MS analysis detected a large number of metabolite profiles belonging to different classes, offering valuable insights for discovering new compounds related to the flavor and quality control of FAA tea. DAMs were identified through a two-step strategy: first, pairwise OPLS-DA comparisons were carried out, where for each harvest time pair (such as FAA-3 vs. FAA-4), metabolites with VIP > 1 and \(p\) < 0.05 (_t_-test) were selected; second, cross-group integration was performed, with metabolites that were consistently altered across multiple comparisons being prioritized as candidate markers. Differentially accumulated metabolites were identified using a _t_-test with screening criteria of VIP >1 and \(p\) < 0.05 [37]. The volcano plots (Figure 3c-h) explained the up-and-down-regulation information of various metabolites among FAA tea samples at different harvest periods. The metabolite changes of FAA-3 vs. FAA-4, FAA-3 vs. FAA-5, FAA-3 vs. FAA-6, FAA-4 vs. FAA-5, and FAA-5 vs. FAA-6 were 146 (76 up/70 down), 144 (54 up/90 down), 216 (89 up/127 down), 92 (23 up/69 down), 214 (88 up/126 down), and 207 (98 up/109 down). These results highlight significant differences in metabolic profiles across developmental stages of FAA tea. To differentiate FAA tea samples, an upset plot (Figure 3a) identified 13 unique metabolites in FAA-6 and FAA-3, 11 unique differential metabolites in the comparison group of FAA-6 and FAA-5, and 12 unique differential metabolites in the comparison group of FAA-6 and FAA-4. At the super classification level, differential metabolites were grouped into 11 categories, including lipids and lipid-like molecules (29.07%), organic acids and derivatives (18.66%), benzenoids (12.97%), phenylpropanoids and polyketides (11.20%), organoheterocyclic compounds (10.41%), and organic oxygen compounds (8.64%) (Figure 3b). Metabolite abundances varied significantly across harvest periods, demonstrating a strong influence of the developmental stage [38]. Figure 3: (**a**) Upset plot of differentially accumulated metabolites (DAMs) in four FAA tea samples; (**b**) classification loops of identified differential metabolites at the superclass level; (**c**–**h**) volcano plot of FAA tea samples. A total of 154 differential metabolites (Table S1 and Figure S2) were screened out through the comparison of four groups of samples, with five significant marker metabolites identified: a compound tentatively identified as kynure acid, 5-methoxyindoleacetate, 4-quinolinescarboxylic acid, gamma-aminobutyric acid (GABA), and fructose-1P. As illustrated in Figure S3, which shows the characterization of the chemical structures of key compounds in FAA tea harvested at four different times, the structural features of these metabolites can provide insights into their potential functions. Kynure acid is an antagonist of excitatory amino acid receptors that also reduces quinolinic-acid-mediated neurotoxicity, obtained by metabolizing L-tryptophan [39]. The kynure acid in FAA-6 was the highest, which was 2.5 times that in FAA-3, the lowest. Fructose-1P, contributing to the sweetness of FAA tea, also showed distinct variations across the harvest periods. These findings highlight the dynamic metabolic changes in FAA tea across harvest times and invite further analysis of their biological implications. The dynamic metabolic profile revealed by our study, particularly the tentative identification of kynure acid as a crucial differential metabolite, differs from previous reports on FAA, which primarily focused on flavonoids (e.g., quercetin, kaempfer) and volatile oils (e.g., eucalypt) as key components [40]. This discrepancy may be attributed to several factors, including variations in growing conditions, extraction methods (HS-GC-IMS, HS-SPME/GC\(\times\)GC-TOFMS, and UPLC-MS/MS), or metabolic regulation [41; 42].