## 3 Results and Discussion
### Screening of Key Differential VFCs among the Four Types of WRTs  
To explore the differences in the distribution of VFCs among the four types of WRTs, unsupervised PCA and supervised PLS-DA were applied based on the quantitative ion peak areas of the 171 identified VFCs. As shown in Figure 2A, the WRTs were spontaneously clustered according to their cultivars in the score scatter plot of the PCA model (R\({}^{2}\)X = 0.889, Q\({}^{2}\) = 0.578). More specifically, the scatters of JF and RG were distributed with slight overlaps, whereas they were clearly distinguished from the DHP and SX samples, which presented a similar trend to the former two types of samples. The above distribution trend might indicate that the chemical basis of the aroma between JF and RG was similar, as well as that between DHP and SX. Furthermore, the four cultivars of WRTs samples were well distinguished by the PLS-DA model (Figure 2B; R\({}^{2}\)Y = 0.946, Q\({}^{2}\) = 0.837) using the Pareto scaling model, thereby indicating that some significantly different VFCs existed among DHP, RG, SX, and JF. Moreover, the validation model for the 200 repeated calculations (Figure 2C) showed no overfitting phenomenon in the obtained PLS-DA model (R\({}^{2}\) = 0.321, Q\({}^{2}\) = \(-\)991). A total of 40 components were further identified as the key differential VFCs among the four types of WRTs, following the selection principle of variable importance in the projection (VIP) values higher than 1.0 in the PLS-DA model [24; 25] and _p_-values lower than 0.05, as determined by the Kruskal-Wallis non-parametric test [26]. Thereafter, detailed and visually apparent differences among different groups of WRTs were presented by HCA after normalizing the peak area data. As shown in Figure 3, the concentration distribution of key differential VFCs was visually presented by the heat map, and their distribution trends were generally divided into six classes, including 4, 3, 6, 8, 17 and 2 compounds in classes I-VI. _trans_-Linalool oxide (furanoid), limonene, _p_-cymene, and _m_-xylene in class I presented higher concentrations in most of the JF and DHP samples, while their concentrations were moderate in the RG samples and extremely low in the SX samples. Hexyl butyrate, geraniol, and linalool in class II exhibited high concentrations in the JF samples, whereas they were scarce in the other WRTs. In class III, _cis_-3-hexenyl hexanoate, hexyl hexanoate, (_E_)-2-hexenyl hexanoate, phenethyl butyrate, _cis_-3-hexenyl benzoate, and hexyl benzoate were far more abundant in the JF and RG samples than in the DHP and SX samples, all of which are esters and emit fresh and fruity scents. Figure 2: (**A**) Principal component analysis (PCA) model of volatile fragrant compounds (VFCs) in Wuyi rock teas (WRTs) with four different cultivars; (**B**) Partial least squares discriminant analysis (PLS–DA) model of VFCs in WRTs with four different cultivars; (**C**) Validation model of the PLS–DA model. In addition to the VFCs in class III, the compounds in class IV, composed of phenylethyl alcohol, (\(E\))-\(\beta\)-famesene, (\(E\))-nerolidol, phenethyl acetate, benzyl nitrile, phenethyl 2-methylbutyrate, (2-nitroethyl)-benzene, and indole, showed significantly higher concentrations in the RG samples and the lowest concentration levels in the SX samples. However, these compounds were not abundant in the JF samples. Among them, (\(E\))-nerolidol and indole are considered as key aroma compounds, which are mainly generated in response to continuous wounding stress during the manufacturing of oolong tea [27; 28].