## 3 Results and Discussion
### The Variation Rule of Volatile Compounds in Daqu Production
#### 3.2.3 PLS-DA  
In order to probe deeper into the effects of volatile compounds in daqu, the supervised multivariate statistical method PLS-DA can establish a useful statistical model of the data, which can be adopted as an analysis method to distinguish the differences between samples. This experiment uses the PLS-DA model to screen out specific volatile compounds on the basis of the volatile compounds detected in strong-flavor daqu in dissimilar production processes. The most representative variables were identified through variable importance for the projection (VIP) value. For strong-flavor daqu, as exhibited in Figure 4a, it explains 84.5% of the total variance, R\({}^{2}\)= 99.9% and Q\({}^{2}\) = 99.7%. Apart from D in the second quadrant and C in the third quadrant, the rest of the daqu samples are clustered in the fourth quadrant. This result is generally consistent with the PCA results. The samples in the mid-production period have the most noticeable difference compared to the samples in the early and late periods of production. Apart from that, as illustrated by the permutation plot results, the model does not overfit the data, the established PLS-DA model is valid, and the initial model is better than the predicted random arrangement model (Figure 4b). Aside from that, the permutation plot was applied to evaluate whether the PLS-DA model had a satisfactory predictive value. To put it in another way, it was used as the criteria for judging whether the model was successful. The permutation plot was compared on the basis of the fit of the data model for randomly sorted categorical variables (Y). This figure proves that the established PLS-DA model is valid, and there is no over-fitting of data because all the Q\({}^{2}\)-value blue boxes on the left are lower than the original point (standard) on the right or the blue Q\({}^{2}\) point. The color regression line intersects the vertical axis (on  
Figure 3: Dendrograms of strong-flavor daqu. A, B, C, D, E, and F represent samples from different periods, respectively. the left) at or below zero, as exhibited in Figure 4b. As a result, the performance of the initial model PLS-DA is significantly better than the predicted random array model. The volatile compounds with high VIP values (VIP \(>\) 1.0) were selected in strong-flavor daqu, as exhibited in the red box in Figure 4c, namely, methacrolein, ethyl palmitate, acetic acid, methyl hexadecanoate, 9,12-octadecadienoic acid ethyl ester, 2-butenal, ethanol, ethyl oleate, ethyl acetate, phenethyl alcohol, 2-ethyl-furan, acetaldehyde, trimethyl-pyrazine, ethyl (9E)-9-octadecenoate, (R,R)-2,3-butanediol, ethyl caporate, 4-hydroxy-2-butanone, 2,3-butanediol, crotonaldehyde, 2-methyl-pyrazine, 2-methyl-2,4-hexadiene, 2-furanmethanol, 2,2,4,6,6-pentamethyl-heptane, and isobutyric acid. In order to control the entire maturation process, the present study further analyzed the substances present in the six maturation stages and summarized their concentration changes. Six key compounds were selected as indicators of the production process, namely, ethyl acetate, ethanol, phenethyl alcohol, (R,R)-2,3-butanediol, ethyl caporate, and 2,3-butanediol. Figure 4: Schemes follow the same formatting. PLS-DA analysis based on volatile compounds. (**a**) Scores, (**b**) permutation test, and (**c**) VIP plot constructed by adopting the concentrations of strong-flavor daqu’s volatile compounds. The consecutive numbers of the horizontal coordinates in (**c**) correspond to the different compounds, as shown in Table S2, the red boxed line indicates substances with VIP \(>\) 1. A, B, C, D, E, and F represent samples from different periods, respectively. When the six indicator compounds are used to determine the different maturity stages of daqu and when ethyl acetate is detected in the range of 1000-1400 ng/g and ethyl caproate and 2,3-butanediol are both below 200 ng/g, the barley is still in the early stages of maturity and still needs a long time to be put into use.