# Analysis of Volatile Components of Jasmine and Jasmine Tea during Scenting Process
## 2 Results
### Analysis of the Difference in Volatile Components of Jasmine and Jasmine Tea  
#### 2.4.1 Analysis of the Difference in Volatile Components of Jasmine  
PCA and HCA Analysis  
Seven effective principal components were extracted by PCA (principal component analysis), which reached 97.5883% of the effective information on volatile components. In the fitting curve, the fit parameters R\({}^{2}\) and Q\({}^{2}\) were 0.956 and 0.887, respectively, which indicated that the model fitted well. The PCA score chart is shown in Figure 3A. The model could well distinguish jasmine under different treatments, and the model classification was completely consistent with the experimental classification. In the HCA (hierarchical cluster analysis) chart (Figure 3B), the samples could be divided into three categories which were completely consistent with the PCA chart and experimental classification. Figure 3: Comparison and analysis of volatile components in jasmine during the different scenting processes. **(A)** PCA score chart. **(B)** HCA chart. For potential differential volatile components, the following indicators were used to screen the differential volatile components at different stages of the scenting process: 1 variable importance in projection (VIP) value was greater than 1; 2 for each comparison, statistical significance was characterized by a fold change of 1.5 (ratio > 1.5 or < 0.67) and \(p\) < 0.05; in addition, the statistical S-plots of the OPLS-DA(discriminant analysis of orthogonal partial least squares) model and the corresponding jack-knifed confidence interval loading chart were used as an auxiliary tool for the identification of differential volatile components. Analysis of the Volatile Components between TP and NP  
To explore the different volatile components of jasmine during different scenting processes, all jasmine samples under TP and NP were analyzed by OPLS-DA. As shown in Figure 4A, the statistical parameters were R\({}^{2}\)X(cum) = 0.983, R\({}^{2}\)Y(cum) = 0.945, and Q\({}^{2}\)(cum) = 0.908 in OPLS-DA analysis of NP and TP. In OPLS-DA analysis, the closer each value was to 1, the higher the predictability and applicability of the model. Figure 4C shows that both [PERSON]'s T\({}^{2}\) and D Mod X had no outliers within the 95% confidence range, and in 200 permutation tests, the intercept value of the model was significantly lower than that of the original model distance (R\({}^{2}\) = 0.271, Q\({}^{2}\) = \(-\)0.699). The results showed that the model was not overfitting. According to the VIP prediction (Figure 4B,D), there were 7 kinds of differential volatile component with a VIP > 1.0 and FC > 1.5 or < 0.67 (_p_ < 0.05), including benzyl acetate, benzyl butyrate, linalool, \(\alpha\)-farnesene, n-hexyl benzoate, (E)-3-hexen-1-acetate, and methyl benzoate. Analysis of the Volatile Components between JF and TP  
The statistical parameters of the OPLS-DA model of JF and TP are shown in Figure 5A: R\({}^{2}\)X(cum) = 0.891, R\({}^{2}\)Y(cum) = 0.949, and Q\({}^{2}\)(cum) = 0.927. Figure 5C of the permutation  
Figure 4: Comparative analysis of volatile components of TP and NP. (**A**) OPLS-DA score plot. (**B**) VIP plot. (**C**) permutation tests plot. (**D**) S-plot. The color in B and D means that differential volatile component is marker in green color; those significant differential volatile components with a VIP > 1 and FC > 1.5 or <0.67 (_p_ < 0.05) marker in red color. test shows that [PERSON]'s T\({}^{2}\) and D Mod X had no outliers within the 95% confidence range. In 200 permutation tests, the intercept value of the model was significantly lower than that of the original model (R\({}^{2}\) = 0.225, Q\({}^{2}\) = \(-\)0.528). This result showed that this model did not appear to be overfitting. According to the VIP prediction (Figure 5B) and S-plot (Figure 5D), there were 5 kinds of differential volatile component, including ethyl benzoate, cis-3-hexanol, linalool, benzyl acetate, and benzyl butyrate.