## 4 Conclusions  
During low-salt Yulu fermentation with _B. subtilis_, there were 133 volatile compounds, among which hydrocarbons, aldehydes, esters, and alcohols were the compounds with the highest total concentrations. Most aldehydes, alcohols, ketones, furans, and hydrocarbons reached their maximum concentrations in the BS15 group, while most esters, aromatic compounds, acids, nitrogen compounds, and sulfur compounds peaked in the BS45 group. _Bacillus_ remained prominent in Yulu with a relative abundance of 93.63% at 45 d. Higher characteristic volatile flavor compounds and lower microbial contamination were found in Yulu fermented with _B. subtilis_ than that in the absence of a microbial starter. The correlation network map suggested that the increase in _Bacillus_ played a key role in decreasing microbial contamination which was mainly due to the significant decrease in _Pseudomonas_, _Achromobacter_, _Stentrorophomonas_, _Cyanobium_, _Rhodococcus_, _Brucella_, _Tetragenococcus_, and _Chloroplast_ after _B. subtilis_ addition. The improved volatile flavor in Yulu mainly resulted from the higher abundance of _Bacillus_. The salt-tolerant _B. subtilis_ can perform as a potential starter to improve the volatile flavor and inhibit the spoilage microorganisms in low-salt Yulu in the future.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/fermentation9060515/s1](https://www.mdpi.com/article/10.3390/fermentation9060515/s1)]([https://www.mdpi.com/article/10.3390/fermentation9060515/s1](https://www.mdpi.com/article/10.3390/fermentation9060515/s1)), Table S1: Change of volatile compounds (mg/kg) during the fermentation of low-salt Yulu with _B. subtilis_.  
Figure 4: Correlation network map constructed using [PERSON]’s correlation coefficient between the microbial genera and characteristic volatile flavor compounds or \(\alpha\) diversity indexes in the BS45 and C45 groups. The red and blue lines indicated the significantly positive and negative correlation (\(\,\mathrm{r}\,\mathrm{r}\,\mathrm{l}\)\(>\) 0.6 and \(p\)\(<\) 0.05), respectively.  
**Author Contributions:** Conceptualization, C.L.; methodology, C.L.; software, C.L.; validation, S.C. and Y.Z.; formal analysis, C.L. and Y.Z.; investigation, C.L.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, L.L. and Y.W.; visualization, C.L. and Y.Z.; supervision, L.L.; project administration, S.C.; funding acquisition, C.L., S.C. and Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (32272348), the Earmarked fund for CARS (CARS-47), the Hainan Provincial Natural Science Foundation of China (322 QN431), the Youth Talent Support Programme of Guangdong Provincial Association for Science and Technology (SKXRC202210), and the Central Public-interest Scientific Institution Basal Research Fund, CAFS (2020 TD69 and 2020 TD73).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.