## 3 Results
### Characterization of the Polar Metabolites Based on LC-MS  
The metabolite profiles of polar compounds were investigated using a global untargeted metabolomic platform with LC-MS. A total of 10,864 and 6677 compounds were extracted in the positive and negative ionization modes, respectively. Distinct cluster distribution between all detected samples and QC samples was shown in the PCA score plots, indicating that the analytical process was reproducible. Except for the samples of days 16 and 27, which displayed in the negative (Figure 2A) or the positive mode (Figure 2B), other samples were assigned to separate groups according to the fermentation time, implying that dynamic change in the metabolites was taken place during fermentation. The NLS fermentation is divided into three stages, including early stage (0 to 6 days), middle stage (6 to 12 days), and late stage (12 to 28 days), according to the previous reports [11]. Therefore, we postulated that the similarity of metabolites between 16 and 27 days gave the overlapping of two groups in PCA. When a supervised OPLS-DA was performed, all groups displayed significant separation. The OPLS-DA model was validated using k-fold cross-validation (k = 7). The explained variation R2X (0.507) and R2Y (0.976) was obtained, and the predictive capability Q2 was 0.958 in the negative ionization mode (Figure 2C). In the positive ionization mode, the obtained R2X, R2Y, and Q2 were 0.473, 0.986, and 0.949, respectively, which indicated that the model had good explanatory and predictive ability (Figure 2D). Meanwhile, the permutation test (\(n=200\)) was performed to evaluate whether the discriminant model was overfitting. The \(p\) values of Q2 and R2Y were all lower than 0.005, implying that the models were statistically valid without overfitting (Figure 2E,F). Figure 2: Principle component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) of the compounds based on the LC-MS detection. **(A)** The PCA model in negative ion mode. **(B)** The PCA model in positive ion mode. **(C)** The OPLS-DA score plot of the different fermentation samples in negative ion mode. **(D)** The OPLS-DA score plot of the different fermentation samples in positive ion mode. **(E)** The 200\(\times\) permutation tests of the different fermentation samples in negative ion mode. **(F)** The 200\(\times\) permutation tests of the different fermentation samples in positive ion mode. D0, D6, D8, D16, and D27 represent the fermentation samples collected on 0, 6, 8, 16, and 27 days, respectively. A total of 395 compounds were annotated and classified into nine categories, including amino acids (147, 37.22%), carbohydrates (66, 16.71%), lipids (60, 15.19%), xenobiotics (45, 11.39%), cofactors and vitamins (35, 8.86%), nucleotides (27, 6.84%), energy (6, 1.52%), peptides (1, 0.25%), and unclassified compounds (8, 2.03%) according to the KEGG and metabolon database (Table S2). Subsequently, hierarchical cluster analysis (HCA) was applied to elucidate the relationship between the compounds and concentrations (Figure S3). The color from sky blue to pink indicated that the relative intensity increased in the heatmap. Based on HCA, all compounds were clearly classified into four clusters. Cluster I contained 50 compounds whose concentrations were significantly increased after 16 and 27 days of fermentation. Cluster I was composed of 19 amino acids, polyols  
A total 83 compounds were annotated by GC-MS and they comprised organic acids (24, 28.92%), amino acids (21, 25.30%), sugars (9, 10.84%), polyols (8, 9.64%), phosphoric acids (7, 8.43%), fatty acids (5, 6.02%), amines (2, 2.41%) and unclassified compounds (7, 8.43%) according to the KEGG and metabolome database (Table S3). All these compounds were classified into two clusters by HCA (Figure S4). A total of 53 compounds, mainly including 19 amino acids, 16 organic acids (heptanoic acid, aminobutyric acid, lactic acid, succinic acid, hydroxybutanoic acid, etc.