## 3 Results and Discussion
### Analysis of Key Volatile Flavor Compounds  
Aroma is a multifaceted expression resulting from the interplay of different flavor elements. It is vital to pinpoint which VFCs contribute to the recognized flavor characteristics. The final contribution of a VFC to the overall aroma is not solely dependent on its concentration but also its odor threshold. In this study, the relative odor activity value (ROAV) method was employed to investigate the aroma contributions of different compounds, with the specific calculation method referring to [PERSON] et al. [18]. An ROAV greater than 1 indicates that the compound is a primary contributor to the sample flavor, and a higher ROAV signifies an increasing contribution of the compound to the overall flavor profile. Table 1 lists 25 VFCs (ROAV > 1) identified as key contributors to distinct aroma profiles. The results demonstrated that in the control group, the compounds with higher contributions were primarily aldehydes, including 2-methyl-propanal, 3-methyl-butanal, 2-methyl-butanal, hexanal, and nonanal. In contrast, the fermented groups exhibited a more diverse set of compounds with significant contributions, notably 1-octen-3-ol, 2-methyl-1-butanol, benzaldehyde, ethyl acetate, \(\beta\)-damascenone, 3-methylthioprepanal, and 2-pentanone. These substances give soybean protein hydrolysate fruit, caramel, malt, wine, mushroom, cheese, almond, honey, grass, fat, and meat flavors, which are important compounds of overall aroma. Figure 4: Orthogonal partial least squares analyses (OPLS-DAs) of the volatile compounds in soybean hydrolysate samples based on the GC-MS and GC-IMS data. The three OPLS-DA models are as follows: (**A**,**B**) SH-0 and SH-1; (**C**,**D**) SH-0 and SH-2; (**E**,**F**) SH-1 and SH-2. (**A**,**C**,**E**) represent the score plots, while (**B**,**D**,**F**) illustrate the variable importance in projection values for the respective models. A1, A2, and A3; B1, B2, and B3; and C1, C2, and C3 denote the three replicates of the SH-0, SH-1, and SH-2 groups, respectively. The Var ID numbers are consistent with the compound numbers in Tables S1 and S2. To further assess whether the key flavor compounds reflected the flavor characteristics of the fermented soybean protein hydrolysates, 25 key VFCs and sensory results were analyzed using partial least squares regression (PLSR) analysis, and the data were preprocessed using unit variance (UV) scaling. As depicted in Figure 5, the model was successfully established with R\({}^{2}\)X = 0.992, R\({}^{2}\)Y = 0.952, and Q\({}^{2}\) = 0.846. The model's validity was further confirmed based on 200 permutation tests. Consequently, this model effectively elucidates the correlation between the key VFCs and sensory results. Figure 5B indicates that the control group was predominantly positively correlated with beauty and fatty flavor, with strong correlations attributed to compounds, such as hexanal, nonanal, and 3-methyl-butanol. In contrast, the treatment groups displayed positive correlations with aromatic, caramel-like, sour, overall aroma, and the majority of key VFCs. Notably, SH-2 exhibited a stronger correlation than SH-1. Figure 5C shows that a total of 16 compounds (VIP > 1, \(p\) < 0.05) were identified as key differential compounds. Based on the key VFCs, Figure 5D predicted the overall aroma of different groups, showing that both fermentation methods significantly enhanced the overall aroma. In summary, fermentation decreased the fatty and bean-flavors of the soy protein hydrolysates, elevating its overall flavor profile, with sequential inoculation proving to be more effective than simultaneous inoculation.