## 2 Results
### Multivariate Statistical Analysis  
In order to further explore the difference of volatile compounds between female and male buds, multivariate statistical analysis such as principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) were carried out. The PCA was performed on the female and male buds and the result was shown in Figure 3A. The contribution rates of principal component 1 (PC1) and principal component 2 (PC2) were 88.20% and 9.54%, respectively, and the total contribution rate was 97.74%. R\({}^{2}\)X and Q\({}^{2}\) were 0.977 and 0.917, respectively, where R\({}^{2}\)X represented the fitting degree and Q\({}^{2}\) represented the prediction degree of the model. Additionally, it was indicated that the model was stable and reliable from these data. The classification results of the female and male buds were ideal, the female buds were distributed on the right side of the Y axis, and the male buds were distributed on the left side of the Y axis, which suggested that the volatile compounds of different sexes were significantly different. The Loadings analysis was shown in Figure 3B. The volatile compounds with the largest contribution rate to distinguishing the female and male buds were screened with the coordinate axis 0.2 or \(-\)0.2 as the limit value. It was found that the five compounds had the largest contribution rate, i.e., isoamyl alcohol (1), 4-oxoisophorone (25), \(\beta\)-ocimene (21), (\(Z\))-3-methylbutyratealdehyde oxime (3), and 1-nitropane (9). As an unsupervised analysis method, PCA cannot ignore within-group and eliminate irrelevant random errors. Therefore, supervised OPLS-DA was used to further determine the difference of volatile compounds between female and male buds. However, overfitting was easy to occur while expanding the differences between groups, so it was necessary to arrange experiments with the help of external model validation methods to prove the validity of the model. Different random Q\({}^{2}\) values were obtained by changing the arrangement order of the categorical variable y randomly multiple times (\(n=200\)). In this experiment, the greater the slope of the regression line, the smaller the difference between R\({}^{2}\) and Q\({}^{2}\), indicating that there were more data with which to explain the model; the predictive ability of the model was better. The test result was shown in Figure 4A. Among them, the R\({}^{2}\) and Q\({}^{2}\) values generated by any random arrangement on the left end were smaller than those on the right, the slope of the regression line was large, and the lower regression line intersected the negative half-axis of the Y-axis, indicating that the model was effective, stable, and predictable, and could continue to screen the difference compounds. From the OPLS-DA scatter plot of female and male buds (R\({}^{2}\)X: 0.977, R\({}^{2}\)Y: 0.998, and Q\({}^{2}\): 0.994) (Figure 4B), it could be seen that the two types of samples were clearly distinguished. According to the corresponding VIP value (Figure 4C), these variables whose VIP value was greater than 1 and whose confidence interval did not contain 0 were screened, and it was determined that there were nine differential volatile compounds between the female and male buds (Table 2). Among them were section A for isoamyl alcohol of higher levels in female buds and section B for (\(Z\))-3-methylbutyratealdehyde oxime, 1-nitropane, \(\beta\)-ocimene, 4-oxoisophorone, 2-methyl-1-undecene, 2,2,6-trimethyl-1,4-cyclohexanedione, \(p\)-xylene, and (\(E\))-3-methylbutyratealdehyde oxime of higher levels in male buds.