## 3 Results and Discussion
### Analysis of VFCs Combining GC-MS with GC-IMS  
Only 47 compounds were detected in the hydrolysate samples using GC-IMS alone, while 150 compounds could be detected by combining the two methods. The common VFCs in different soybean hydrolysate samples under different identification methods are shown in Venn diagrams (Figure S1). There were 22 to 26 volatile compounds detected simultaneously via GC-MS and GC-IMS. Most of the changes in VFCs detected using GC-IMS were consistent with the changing trends of the GC-MS data, including the increases in the contents of quite a few VFCs after the fermentation with the LAB and yeast. Especially, some flavor compounds detected only using GC-IMS were 1-butanol, pentan-2-ol, pentan-1-ol, (E)-2-methyl-2-butenal, (E, E)-2,4-hexadienal, hydroxyacetone, and 4-heptanone, which also made a key contribution to the overall flavors of samples. GC-IMS and GC-MS are known to operate based on different principles. The GC-IMS technique eliminates the need for enrichment, concentration, and other pretreatments before direct headspace injection, thus avoiding the difficult fragmented ion dissociation spectra ([PERSON], 2016; [PERSON], 2016). IMS technology is particularly sensitive to compounds with a high proton affinity or electronegativity (like many volatile aroma compounds). In comparison, GC-MS requires more complicated preprocessing steps and more efforts to resolve fragmented peaks, as MS distinguishes ions according to the mass-to-charge ratio of the ions ([PERSON], 2016; [PERSON], 2016). Quite often, GC-MS can identify the volatile compounds with relatively high contents, whilst GC-IMS can detect the volatile substances at low concentrations as this technique has a low detection limit. Therefore, the combined use of these two techniques allows for complementary analyses of VFCs, providing in-depth and comprehensive investigations on the characteristics and roles of various volatile compounds.  
To further analyze the impact of different fermentation methods on the flavor of soybean protein hydrolysates, three OPLS-DA models were developed based on sensory evaluation, GC-MS, and GC-IMS results. The OPLS-DA score plots (Figure 4A,C,E) illustrated a distinct separation between the SH-0 and SH-1 groups, between the SH-0 and SH-2 groups, and between the SH-1 and SH-2 groups. These results indicated that there were significant differences in the contents of VFCs associated with different fermentation methods. In the three models, the R\({}^{2}\)X, R\({}^{2}\)Y, and Q\({}^{2}\) were close to 1. Furthermore, a permutation test was conducted 200 times to validate that the model was not overfitted. This indicates that the model accurately represented the data distribution and demonstrated good explanatory and predictive capabilities. Moreover, the importance and explanatory power of each volatile compound was assessed by calculating the variable importance in projection (VIP) values from different models, aiming to screen key compounds that exert the most significant effect on aroma differences. A total of 20 variables with VIP \(>\) 1 and \(p\) \(<\) 0.05 were determined as differential VFCs (as shown in Figure 4B,D,F). Seven key differential compounds were consistently present in all three models, namely 3-methyl-1-butanol, ethanol, acetic acid, 2-methyl-1-propanol, 3-methyl-butanol, acetone, and 2-butanone. The differences in the contents of these VFCs might be the primary contributors to the aroma variations observed among the different soybean protein hydrolysate samples.  
Figure 4: _Cont._  
Figure 4: \(Cont.\)