## 2 Materials and Methods
### Bioinformatics and Statistical Analysis  
The sequence data were processed by Quantitative Insights Into Microbial Ecology (QIIME v1.9.1, [[http://qime.org/install/index.html](http://qime.org/install/index.html)]([http://qime.org/install/index.html](http://qime.org/install/index.html)), accessed on 18 February 2024). The OTUs were clustered from the high-quality sequences with a cut-off value of 97% using UPARSE (v7.0.1090, [[http://drive5.com/uparse/](http://drive5.com/uparse/)]([http://drive5.com/uparse/](http://drive5.com/uparse/)), accessed on 18 February 2024). After removing chimera, OTU representative sequences were taxonomically classified using RDP Classifier (v2.13, [[https://sourceforge.net/projects/rdp-classifier/](https://sourceforge.net/projects/rdp-classifier/)]([https://sourceforge.net/projects/rdp-classifier/](https://sourceforge.net/projects/rdp-classifier/)), accessed on 18 February 2024) against the Silva Database (Silva v138, [[http://www.arb-silva.de/](http://www.arb-silva.de/)]([http://www.arb-silva.de/](http://www.arb-silva.de/)), accessed on 18 February 2024) for bacteria and against the Unite Database (Unite v8.0, [[http://unite.ut.ee/index.php](http://unite.ut.ee/index.php)]([http://unite.ut.ee/index.php](http://unite.ut.ee/index.php))) for fungi with a minimum confidence threshold of 0.7. USEARCH (v11, [[http://www.drive5.com/usearch](http://www.drive5.com/usearch)]([http://www.drive5.com/usearch](http://www.drive5.com/usearch)), accessed on 18 February 2024) was used to obtain the OTU abundance statistics table of each sample by comparing all high-quality sequences back to OTUs.  
Alpha and beta diversity were evaluated using Mothur (v1.30.2, [[https://mothur.org/wiki/calculators/](https://mothur.org/wiki/calculators/)]([https://mothur.org/wiki/calculators/](https://mothur.org/wiki/calculators/)), accessed on 18 February 2024) [22] and QIIME (v1.9.1) at the OTU level, respectively, and principal coordinate analysis (PCoA) was used to visualize the beta diversity based on Bray-Curtis dissimilarity. The LDA effect size (LEfSe) method (LDA \(>\) 3.5, \(p\) \(<\) 0.05) [23] was performed to estimate the significant variations in microbial genus from different samples, and Wilcoxon rank-sum test was used to investigate the differential genera of two grades of NXDQ. Pathway analysis and key enzyme analysis were performed and the functional composition of microbial community was predicted by PictorSU2 according to MetaCyc ([[https://metacyc.org/](https://metacyc.org/)]([https://metacyc.org/](https://metacyc.org/)), accessed on 18 February 2024) and KEGG databases ([[https://www.kegg.jp/](https://www.kegg.jp/)]([https://www.kegg.jp/](https://www.kegg.jp/)), accessed on 18 February 2024) [24].  
Microbial counts, physicochemical properties and volatile compounds among different grades of NXDQ samples were compared using SPSS (version 16.0) by one-way ANOVA and \(p\) \(<\) 0.05 was considered significant. Redundancy analysis (RDA) by R software (version 3.3.1) with vegan packages was used to reveal the correlation between dominant microbial genera and physicochemical properties based on [PERSON]'s rank [25], and Cytoscape (v3.7.1) was performed to visualize the correlation network between dominant volatile compounds and microbial genera based on [PERSON]'s rank. The correlation indices \(|\)\(p\)\(|\)\(>\) 0.6 and \(p\)\(<\) 0.05 were defined as significant relationships. Heatmaps of the key enzymes were drawn by R software (version 3.3.1) with heatmap packages.