## 3 Results
### PCA Based on VOCs Identified by GC-TOF/MS and GC\(\times\)GC-TOF/MS  
The VOCs identified using G and GG were analyzed by PCA (Figure 3). Figure 3A shows the PCA of the VOCs identified by G. The first principal component (PC1) accounted for 31.3% variance, and the second principal component (PC2) accounted for 17.0% variance. The cumulative contribution of PC1 and PC2 was 48.3%. The three coffee states were distributed in different spatial locations. The coffee beans were all located on the negative side of the \(y\)-axis, the coffee powder was located in the first quadrant, and the coffee brew was located on the negative side of the \(x\)-axis and was distributed in the second quadrant. The different brands of coffee were relatively separated. The distance between PA and CO was closer, and PA was relatively far away from them in the coffee beans. For the coffee powder, all three brands were gathered together. On the contrary, the three brands of coffee were relatively separated, especially ST. The above results indicated that different coffee samples could be clearly distinguished according to the state rather than the brand. The VOCs of the coffee samples in this experiment mainly depended on the treatment rather than the brand. On the other hand, the different brands of coffee could only be distinguished in the brewing stage.  
Figure 2: Venn diagrams of VOCs identified in three brands of coffee (CO, PA, ST) under three treatments. (**A–C**) Coffee bean, powder, and brew, respectively; G represents VOCs identified by GC–TOF/MS; GG represents VOCs identified by GC\(\times\)GC–TOF/MS. The bottom of the column bar chart shows the number of VOCs identified in each coffee brand using G or GG. The stacked bar chart shows the number of VOCs shared among different brands that were identified using G or GG.  
Figure 3B shows the PCA of the VOCs identified using GG. PC1 accounted for 32.8% variance, the second principal component (PC2) accounted for 23.0% variance, and the cumulative rate of the first two PCs was 55.8%. The different coffee states were still distributed in different spatial locations, but they did not have as clear boundaries as in the previous studies.  
Figure 3: Product space based on PCA of VOCs identified from three brands of coffee (CO, PA, ST) under three treatments (bean, powder, and brew) using GC–TOF/MS (**A**) and GC\(\times\)GC–TOF/MS (**B**). The black color represents bean, the red color represents brew, and the green color represents powder. Different shapes represent the brands: the circle for CO, the triangle for PA, and the star for ST.  
the PCA of G. The coffee beans were also located on the negative side of the \(y\)-axis, the coffee powder was located in the second quadrant, and the brewed coffee samples were mainly distributed along the negative \(y\)-axis; only ST was distributed in the first quadrant. Compared with the results of G, ST was more obviously separated from the other brands, and the PA and CO brands of coffee were more aggregated in the coffee brews. In the coffee powder, the coffee samples of different brands showed a better separation effect. In the coffee beans, the coffee brand ST separated from the group, which was the same for PA in the results of G. The results of GG showed that the separation of the different brands of coffee was more obvious, especially in the two states of powder and brew. In other words, GG was more conducive to the distinction between some brands in coffee powder and coffee brews.