## 3 Results and Analysis
In the region outlined by the green box, the concentrations of trans-2-pentenal, methyl 2-methylbutyrate, hexanal, (E)-2-hexenal, 1-penten-3-ol, 1-penten-3-one, and acetone in the samples from the ALTw region were significantly higher than those in other regions. Figure 2: _Cont_. Analysis of Differences in Volatile Compounds of Koumiss from Different Regions Based on PLS-DA Technology  
PLS-DA is a supervised discriminant analysis statistical technique. It establishes a relationship model between volatile compounds and sample categories to predict sample categories [19]. Therefore, in this study, a PLS-DA model was adopted to identify specific marker compounds for the volatile substance differences of koumiss samples from four regions based on the detected volatile substance content data matrix. As shown in Figure 4a, the four groups of samples were successfully distinguished in the quadrant plot. Specifically, the samples from the YL region were in the fourth quadrant, those from the TC region were in the first quadrant, samples from the ALtw region were in the second quadrant, and samples from the ALte region were in the third quadrant. Additionally, as shown in Figure 4b, 200 permutation tests were performed,  
Figure 3: The Wayne diagram of the number of aroma substances determined by GC-MS and GC-IMS. Figure 2: GC-IMS analysis of koumiss from different regions. (**a**) Top view of the 3D spectrum of the volatile components of koumiss from different regions. (**b**) 2D comparison chart of the volatile components of koumiss from different regions. (**c**) The fingerprint spectra of the volatile constituents in koumiss from distinct regions. The delineated color zones represent the variances in volatile substances among different regions. and the model predictive index (Q2) was 0.69 for the prediction of different origins of koumiss, indicating a prediction accuracy of 69%. A Q2 value exceeding 0.5 indicates an acceptable fit of the model. The intersection of the Q2 regression line and the vertical axis is less than zero, indicating the absence of overfitting, thus validating the model's effectiveness [20]. The variable importance in projection (VIP) method was employed to select the most statistically significant variables. It is generally recognized that volatile substances with VIP values >1.0 play a crucial role in distinguishing geographical origins [21]. As shown in Figure 4c, 18 substances were selected based on their VIP values, including 2-heptanone, ethyl butyrate, dimethyl sulfide, 2-methylbutanal, ethyl acetate, isobutyraldehyde, methyl ethyl ketone, isobutyl acetate, isopentyl acetate, acetone, ethyl propionate, nonanal, ethyl caproate, isobutanol, propyl acetate, amyl alcohol, ethyl 2-methylpropanoate, and n-propanol. Figure 4: PLS-DA analysis of koumiss from different regions. (**a**) Score plot of PLS-DA; (**b**) Cross-validation results based on 200 permutation tests; (**c**) Ranking of volatile substances based on VIP scores. The red part is the substance with VIP value greater than 1. The green area is for substances with VIP finger less than 1. To ensure the accuracy of identifying the selected key volatile compounds' traceability for koumiss from different regions, a standard was adopted that included volatile compounds with VIP values greater than 1 and shared by GC-IMS fingerprinting discrimination of volatile compounds. Based on this standard, the following eight volatile compounds were selected as the final markers for distinguishing koumiss from different regions: dimethyl sulfide, ethyl butyrate, 2-methylbutanal, ethyl propionate, ethyl 2-methylpropanoate, isobutyl acetate, propan-1-ol, and methyl ethyl ketone.