## 3 Results and Discussion
### Multivariate Statistical Analysis of Volatile Aroma Components  
PCA is an unsupervised discriminant method, which can maximize the components of data variation, reduce data from a high dimension to a low dimension, and ensure orthogonality between each dimension. The reliability of the PCA model is strengthened when the PC cumulative ratio approaches 1. Following the detection of 156 aroma components, PCA dimensionality reduction and HCA were conducted (Figure 3a,b). The PCA results indicated clear differentiation among the four sample groups, with a PC1 variance  
Figure 2: Proportion of volatile components in four types of tea samples (**a**) Histogram of the total concentration of volatile compounds of four samples (ns: \(p\) > 0.05, *\(p\) < 0.05, **: \(p\) < 0.01, ***: \(p\) < 0.001) (**b**). Volatile aroma species of four groups samples (**c**). Four groups of samples of different types of aroma content line chart (**d**).  
contribution rate of 42.77%, a PC2 variance contribution rate of 31.34%, and a cumulative variance contribution rate of 74.11%, demonstrating a certain degree of reliability for the PCA model. HCA further supported the distinct clustering of the four sample groups, signifying significant differences in the aroma profiles of Liupao tea resulting from different fermentation methods. Specifically, the Cl group was clearly distinguishable from the CMI, DI, and DMI groups, indicating differences in aroma composition between traditional cold fermentation and other fermentation methods. Additionally, the DMI and CMI/DI groups were clearly distinguishable, highlighting significant differences in aroma profiles resulting from different fermentation methods used to process Liupao tea samples.  
Figure 3: Scatter plot of PCA model scores of volatile compounds of tea samples. (**a**) Hierarchical cluster analysis (HCA) plot. (**b**) OPLS-DA model score of volatile compounds of tea samples. (**c**) Permutation  
test for validation of OPLS-DA model. (**d**) Differential compounds VIP value plot (Cl vs. CMI). (**e**) Differential compounds' VIP value plot Cl vs. DI). (**f**) Differential compounds' VIP value plot (CMI vs. DMI). (**g**) Differential compounds' VIP value plot (DI vs. DMI). (**h**) Screening criterion for differential aroma compounds with VIP \(>\) 1, \(p\) \(<\) 0.05 and top 20 VIP ranking.  
In order to further explore the differential aroma components of the four groups of samples, OPLS-DA analysis was performed on the 156 aroma components (Figure 3c). By using the 156 aroma components as the dependent variable and the different fermentation methods as the independent variables, effective differentiation of the four samples of Liupao tea with different fermentation methods could be achieved through OPLS-DA analysis. The screening conditions for differential aroma components were based on VIP \(>\) 1 and \(p\) \(<\) 0.05, as indicated by the OPLS-DA model. The model prediction index (Q2) in this analysis was 0.993, the independent variable fitting index (Rx2) was 0.941, and the dependent variable fitting index (Ry2) was 0.997. With R2 and Q2 exceeding 0.5, the model fitting result was deemed acceptable [36]. After 200 substitution tests, as shown in Figure 3d, the intersection point of the Q2 regression line with the vertical axis was less than 0, suggesting that the model validation was effective and that overfitting was not present. Therefore, the results are considered reliable for the differential analysis of the fermentation mode of the aroma in the four different groups of Liupao tea samples.