## 3 Results and Discussion
### PCA and OPLS-DA Analysis of Volatile Compounds  
SIMCA 14.1 was employed to carry out multidimensional statistical analysis. The results of unsupervised PCA and supervised OPLS-DA analysis are shown in Figures 1 and 2. Principal component analysis (PCA) is an unsupervised method of data analysis that combines the original set of compounds into a new set of comprehensive variables known as principal components (PCs). The goal of PCA is to reduce the dimension of the data by selecting several PCs that reflect as much information as possible about the original variables. Figure 1a displays the score scatter plot of the first two principal components (PC1 and PC2), showing the differences and similarities between IBW and FBW samples. Figure 1b is the loadings plot, illustrating the correlation between volatile compounds and their relative importance in contributing to the differences or similarities observed in the scatter plot. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Volatile Compounds** & **Odor Description** & **RI** & \multicolumn{2}{c}{**Relative Abundance (\%)**} \\ \cline{4-6}  & & & **IBW** & **FBW** \\ \hline Esters & ethyl acetate & pineapple, overripe & \textless{}800 & 11.19 \(\pm\) 1.77 & 20.18 \(\pm\) 1.96 \\  & methyl formate & & \textless{}800 & 2.18 \(\pm\) 0.72 & 1.55 \(\pm\) 0.65 \\  & 3-methyl-1-butanol & banana & 974 & 0.79 \(\pm\) 0.36 & nd \\  & acetate & & & & \\ ethyl hexanoate & apple peel, fruit & 1073 & 1.46 \(\pm\) 0.94 & 0.53 \(\pm\) 0.48 \\  & ethyl octanoate & fruit, fat & 1255 & 4.49 \(\pm\) 0.61 & 1.57 \(\pm\) 0.72 \\  & diethyl succinate & wine, fruit & 1272 & 1.56 \(\pm\) 0.37 & 3.73 \(\pm\) 0.83 \\  & ethyl benzoate & chamomile, flower, celery, fruit & 1292 & nd & 0.87 \(\pm\) 0.44 \\  & ethyl nonanoate & fruit, flower & 1367 & 2.08 \(\pm\) 0.36 & 0.78 \(\pm\) 0.37 \\  & ethyl decanoate & & 1375 & 0.24 \(\pm\) 0.24 & 0.04 \(\pm\) 0.09 \\  & ethyl phenylacetate & fruit, sweet & 1387 & 0.12 \(\pm\) 0.20 & 0.29 \(\pm\) 0.26 \\  & phenylylethyl acetate & fruit, sweet, rose & 1402 & nd & 0.20 \(\pm\) 0.19 \\  & ethyl decanoate & grape & 1473 & 5.94 \(\pm\) 1.43 & 2.14 \(\pm\) 0.65 \\  & ethyl laurate & leaf & 1637 & 1.89 \(\pm\) 0.30 & 1.63 \(\pm\) 0.67 \\ Terpenes & 1-terpine-d-ol & wood, turpentine, nutmeg & 1283 & nd & 0.49 \(\pm\) 0.21 \\  & 8-caryophyllene & wood, spice & 1547 & 25.89 \(\pm\) 2.59 & 6.93 \(\pm\) 1.38 \\ others & 3,3,4,4-tetrafluoro-1,5- & & 812 & 1.89 \(\pm\) 0.77 & 1.89 \(\pm\) 0.71 \\  & hexadiene & & & & \\  & 2-methoxy-1,3- & & 844 & 0.05 \(\pm\) 0.13 & 0.87 \(\pm\) 0.27 \\  & dioxolane & & & & \\  & 2,4,5-trimethyl-1,3- & & 850 & 1.04 \(\pm\) 0.31 & 1.31 \(\pm\) 0.48 \\  & dioxolane & & & & \\  & 3,3-dimethyl-1,2- & & 857 & 1.62 \(\pm\) 0.74 & 12.03 \(\pm\) 2.37 \\  & epoxybutane & & & & \\  & cis-1,2- & & 1234 & 0.06 \(\pm\) 0.09 & 0.30 \(\pm\) 0.31 \\  & dimethylcyclopentane & & & & \\  & hexadecane & & 1600 & 0.73 \(\pm\) 0.34 & nd \\ \hline \hline \end{tabular}  
* nd, not detected.  
\end{table} 
Table 2: _Cont._As shown in Figure 1, the first two PCs (PC1 and PC2) account for 73.5% of the total contribution of the original dataset. The plot reveals that the 12 wine samples are grouped into two clusters, marked with an ellipse. The IBW samples form one cluster on the left side (represented by blue diamonds), while the FBW samples form another cluster on the right side (represented by red circles). The clear distinction between the two groups indicates that the model provides good discrimination. Orthogonal partial least-squares discriminant analysis (OPLS-DA) is a supervised statistical method distinct from principal component analysis (PCA). Unlike PCA, OPLS-DA uses partial least-squares regression to model the relationship between the concentration of compounds and sample categories, and to discriminate samples into multiple categories. In the OPLS-DA score plot, there are two principal components: the predictive principal component and the orthogonal principal component.