## 3 Results and Discussion
### The Variation Rule of Volatile Compounds in Daqu Production  
#### 3.2.1 PCA Analysis  
In order to identify the distribution correlation of the sample, principal component analysis was conducted in accordance with the data of volatile compounds in daqu detected by HS-SPME-GC-MS. The principal component analysis score and load diagram are depicted in Figure 2. The score graph of the data obtained in the positive-negative mode indicates the trend of separation between groups. PC1 and PC2 are in Figure 2a. PC1 accounted for 32.7% of the total variance, while PC2 accounted for 21.9% of the total variance. The cumulative contribution rate of the first two PCs accounted for 54.6%, which demonstrates that they are both sufficient to explain the total variance in the dataset. PC1 and PC2 are positively correlated with the C and D daqu samples and are negatively correlated with the A, B, E, and F daqu samples, as illustrated in Figure 2a. As demonstrated by the experimental results, with the prolongation of the production duration, the variability between samples heightens. In the middle stage of production (8-12 days), the distance between C and the other data points in the PAC diagram is the greatest, not only indicating that the content of volatile components in the strong-aroma daqu changed most noticeably, but also indicating that the substance concentration changed considerably during the maturation process, which is extremely important for the maturation of daqu. Combined with Figure 2b, it was further resolved that the special state of the C-stage daqu was correlated with dozens of volatile compounds, such as acetic acid, ethyl acetate, 2,3-butanediol, isoamyl acetate, ethyl palmitate, and isobutyl alcohol. In the early stages of maturation, i.e., the A and B periods, which are in the same quadrant of the PCA diagram, there is an overall consistency, but there are still some gaps between them because of the large variation in compound species at this time. In contrast, D and E are both on the axes, again with some similarity, while F is more independently positioned, indicating that matured macrographs are easily distinguished from those in process, which is related to substances such as acetaldehyde, crotonaldehyde, 2-acetyl pyrrole, and 2,5-dimethyl-pyrazine. PCA analysis further proved the evidence of compound types and content changes in daqu samples at each maturity stage and associated them with specific compounds.  
This was mutually verified with the changes in the quantity and concentration of the above compounds. The above results show that the number of compounds in stage C was the largest, and the change was the largest compared with the adjacent stage. The number of substances in stages D and E is similar, while stage F is separated from stage D and E due to the appearance of some compounds, which indicates that the determination of the mature state of daqu is scientific.  
Figure 2: PCA analysis based on volatile compounds. (**a**) Scores and (**b**) loading plot of PCA for classification of samples of strong-flavor daqu with different production durations. The specific compounds represented by the numbers are shown in Table S1. A, B, C, D, E, and F represent samples from different periods, respectively.