1 Introduction
Baijiu is widely recognized as one of the world’s most famous distilled spirits [
1]. Baijiu is produced through a series of complex processes such as cooking, saccharification, fermentation, distillation, aging, and blending [
2]. The special and complex process gives baijiu a unique and rich flavor [
3–
5]. Aroma and taste are important indicators of baijiu quality and consumer choice [
6,
7]. There are more studies on the volatile compounds of baijiu [
8–
11], but there is a lack of studies on the non-volatile compounds [
12].
The blending of baijiu is one of the most important processes in the final shaping, and the Dazongjiu, Dajiu, Daijiu, and Tiaoweijiu, which were all base baijiu, the flavors obtained from brewing vary greatly, but all were important for the final commercial baijiu [
13]. Among these, Dazongjiu forms the basic skeleton of baijiu, determining its fundamental aroma profile and style, and is added in the largest quantity; Dajiu serves to compensate for the shortcomings of Dazongjiu and plays a supporting role; Daijiu is generally used to enrich the aromatic characteristics; while Tiaoweijiu, though added in the smallest quantity, determines the overall style of the baijiu. Typically, the first three base baijiu form the “foundation” of baijiu, after which the final flavoring process is carried out using Tiaoweijiu. After an in-depth investigation by researchers, it was found that the volatile compounds of baijiu included esters, alcohols, acids, nitrogen compounds, sulfur compounds, aldehydes, ketones, and other compounds [
14–
16]. Although baijiu contains only small amounts of non-volatile compounds, these played a crucial role in shaping its flavor profile. These compounds primarily include: organic acids, which impart acidity; polyols and sugars, which impart sweetness; amino acids, phenolic acids, and higher alcohols, which impart bitterness; amino acids, which impart umami; and salts, which impart a salty taste [
17,
18]. The interaction between volatile and non-volatile compounds collectively gives baijiu its unique and rich flavor profile [
1]. The most abundant non-volatile compound in baijiu was lactic acid, which has been studied by researchers [
19–
21], but few other substances have been studied. Therefore, a comprehensive understanding of non-volatile compounds can significantly improve our knowledge of baijiu quality.
In recent years, non-targeted metabolomics has been widely used in food science to characterize all metabolite information in biological samples and facilitate the discovery of new metabolites and metabolic pathways [
22–
24]. There have been some applications in alcoholic beverages. For example, Zhang et al. investigated the network of microbial metabolites during the fermentation of baijiu [
25]. Zhang et al. conducted a comprehensive analysis of the compounds in waxy wheat baijiu and investigated the dynamic changes in these compounds across samples from different vintages [
26]. Luo et al. used a non-targeted metabolomics approach to determine the components and causes of formation in sauce-flavored Daqu [
27]. Compared to traditional gas chromatography-mass spectrometry (GC–MS) and high performance liquid chromatography (HPLC), ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC–MS/MS) has the advantage of analyzing difficult volatile compounds. The UPLC–MS/MS technique has many advantages in the analysis of difficult volatile compounds, such as high selectivity, high sensitivity, and high efficiency [
28,
29]. The combination of multivariate statistical analysis and UPLC–MS/MS-based non-targeted metabolomics analysis can be effective in detecting multiple compounds, exploring the differences between samples, and extracting valuable metabolic information [
30].
Building on prior research that identified key aroma compounds and the functional roles of different base baijiu during blending [
31]. Differences in non-volatile metabolites among various blending base baijiu have not yet been systematically compared. Therefore, this study focused on non-volatile compounds, specifically examining Dazongjiu—the most heavily added component, Tiaoweijiu—which enhances the overall style, and the final blended product and aimed to (a) to elucidate the non-volatile compound composition of baijiu based on ultra performance liquid chromatography combined with electrospray ionization-triple quadrupole-linear ion trap-MS/MS (UPLC–ESI–Q TRAP–MS/MS), (b) to identify key differential metabolites and their patterns of change by multivariate statistical analysis, and (c) to combine KEGG database annotations to analyze differential metabolic pathways and combining random forest to elucidate the potential roles of metabolites in the flavor and quality of baijiu.
2 Materials and Methods
2.1 Materials
Three strong flavor baijiu samples were provided by a distillery in China. Dazongjiu (DZJ, 70.7% ethanol by volume, produced in 2012), and Tiaoweijiu (TWJ, 52% ethanol by volume, produced in 1991) were base baijiu samples used to blend commercial baijiu (CPJ, 42% ethanol by volume, produced in 2021). The samples were stored at 4 °C until analysis. The chromatographic purity of acetonitrile, formic acid, methanol, and ethanol was > 99.999%, and was purchased from Aladdin (Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China). [2H5]-phenoxy acetic acid (99.0%), 2-chlorophenylalanine (99.0%), and lidocaine (99.0%) were purchased from Yuanye (Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China).
2.2 Sample preparation and extraction
The samples were taken from a 4 °C refrigerator, thawed, and mixed by vortexing for 30 seconds. Appropriate amount of sample was taken and put into a 50 mL centrifuge tube, frozen in a −80 °C refrigerator, and freeze dried in a freeze-dryer (SCIENTZ-10 N/D, Ningbo Xinzhi). Add 70% methanol with mixed internal standards ([2H5]-phenoxy acetic acid, 2-chlorophenylalanine, and lidocaine, all at a concentration of 1 mg/L) according to the ratio of 30 times concentration, vortex for 15 min, and sonicate for 10 min in an ice water bath (KQ5200E). The samples were centrifuged (5424R, Eppendorf) at 12000 r/min, 4 °C for 3 minutes. The supernatant was pipetted and filtered through a microporous filter membrane (0.22 μm pore size) and stored in the injection vial for LC–MS/MS detection.
2.3 Detection of UPLC combined with electrospray ionization–triple quadrupole-linear ion trap–MS/MS (ESI–Q TRAP–MS/MS)
The sample extracts underwent meticulous analysis utilizing a UPLC–ESI–Q TRAP–MS/MS system, integrating a UPLC module (SHIMADZU Nexera X2) and an MS detector (Applied Biosystems 6500 Q TRAP). Specifically, the UPLC analytical parameters were carefully optimized as follows: an Agilent SB-C18 column (1.8 µm, 2.1 mm × 100 mm) was used. The mobile phase consisted of two solvents, A (pure water with 0.1% formic acid) and B (acetonitrile with 0.1% formic acid). The separation process adopted a precise gradient elution program, initiated with a composition of 95% solvent A and 5% solvent B, which gradually shifted to 5% A and 95% B over 9 minutes, maintaining this ratio for 1.0 minute. Subsequently, the composition reverted to 95% A and 5% B within 1.1 minutes and remained stable for an additional 2.9 minutes. The flow rate was maintained at 0.35 mL/min, while the column oven temperature was set at 40 °C. In addition, the injection volume was precisely controlled at 2.0 μL.
The operational specifications for ESI were set as follows: a source temperature maintained at 500 °C, an ion spray voltage (IS) adjusted to 5500 V in positive ion mode and −4500 V in negative ion mode, and the ion source gases, GSI and GSII, along with the curtain gas (CUR), were regulated at 50, 60, and 25 psi, respectively. In addition, high collision-activated dissociation was used to enhance the analysis. For precise instrument tuning and mass calibration, 10.0 μmol/L and 100 μmol/L solutions of polypropylene glycol were utilized in the triple quadrupole (QQQ) and linear ion trap (LIT) modes. In the QQQ mode, scans were acquired utilizing the multiple reaction monitoring (MRM) technique, facilitated by a medium of nitrogen as the collision gas. Further refinement led to the optimal setting of the declustering potential (DP) and collision energy (CE) for each individual MRM transition, ensuring the utmost precision and accuracy in the analysis.
2.4 Qualitative analysis and screening of metabolites
Raw data were processed using Progenesis QI (Waters Corporation, USA) for peak identification, extraction, alignment, and integration. Compound characteristic peaks were compared against the HMDB, Metlin, and the Metware database of Wuhan Metware Biotechnology Co., Ltd. based on retention time (RT), secondary mass spectrometry data covering all fragment ions of the compound, parent ion molecular weight, characteristic fragment ions, and the compound’s DP and CE parameters. Additionally, rigorous data curation was performed to exclude isotopic signals, duplicate signals contaminated by K+, Na+, NH4+ ions, and repetitive signals originating from fragment ions of other higher molecular weight substances, ensuring the accuracy and purity of the analysis.
The response intensity of mass spectrometry peaks was normalized using the sum normalization method, where the calculation formula was: each value/(the sum of each column/a coefficient derived from the maximum sum among all columns). Specifically, each chromatographic peak is divided by a coefficient to obtain a relative abundance value, where the coefficient is the quotient of the total signal intensity of all peaks in a given sample divided by the maximum total sum of all samples. Furthermore, logarithmic (lg) transformation was applied to the processed data. The resulting data were presented as means with relative expression, and metabolites with a relative standard deviation (RSD) greater than 30% in quality control (QC) samples (prepared by mixing portions of each analytical sample) were excluded, resulting in the final dataset for subsequent analyses.
2.5 Weighted gene co-expression network analysis (WGCNA)
Co-expression network analysis based on the R package WGCNA [
32] (min Module Size for 50, and merge Cut Height for 0.25). Upon calculating the Topological Overlap Measure (TOM) through the adjacency matrix, the dissimilarity TOM served as the basis for constructing a plot dendrogram. Subsequently, the DynamicTreeCut algorithm was employed to delineate distinct modules, each assigned a unique color for easy identification. This process effectively grouped highly correlated metabolites into cohesive modules, facilitating the analysis and interpretation of their intricate relationships.
2.6 Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation and enrichment analysis
The identified metabolites underwent meticulous annotation utilizing the authoritative KEGG database. Subsequently, these annotated metabolites were systematically mapped onto the KEGG pathway database. The mapped pathways, particularly those of significantly regulated metabolites, were then subjected to Metabolite Set Enrichment Analysis for insightful evaluation. The statistical significance of these enrichments was rigorously determined through the application of P-values derived from the hypergeometric test, ensuring the reliability and validity of the obtained results.
2.7 Sensory analysis
Analyses were repeatedly performed by 12 trained panelists (4 males and 8 females, aged 22–27 years) from the Laboratory of Baijiu Flavor Chemistry, School of Food and Health at Beijing Technology and Business University. Approval for this study was granted by the University Scientific Research Ethics Committee of Beijing Technology and Business University (#54). All participants provided the study with written consent and were assigned a unique code to ensure anonymity. Prior to formal testing, our sensory evaluators undergo at least one month of training, including exercises on aroma intensity using standard samples and distinguishing the distinct sensory attributes of baijiu. Daily olfactory sessions last no less than 30 minutes. Through quantitative descriptive analysis, the most frequently occurring words (≥ 8) in each sample were selected as flavor descriptors for each baijiu sample. Twelve highly skilled panelists then conducted sensory evaluations of each sample’s aroma profile, scoring them on a scale of 0 to 5, where 0 = none, 1 = weak, 2 = moderate, 3 = little strong, 4 = strong, and 5 = very strong. Ultimately, 6 sensory attributes were identified: sourness (a basic taste produced by dilute aqueous solutions of acidic substances), bitter (a basic taste produced by dilute aqueous solutions of substances such as quinine), sweet (a basic taste produced by dilute aqueous solutions of natural or artificial substances such as sucrose), astringency (a complex sensory experience produced by substances like tannins, accompanied by a sensation of contraction, tightening, or puckering of the oral mucosa or skin surface), salty (a basic taste produced by dilute aqueous solutions of substances such as sodium chloride), and umami (a basic taste produced by dilute aqueous solutions of specific types of amino acids or nucleotides).
2.8 Data analysis
The bar, scatter charts, and donut chart were drawn using Origin Pro 9.1 (OriginLab Corporation, Northampton, MA, USA). OPLS-DA analysis was performed in R, and a permutation test (200 permutations) was performed to avoid overfitting. WGCNA, corrplot heatmap, differential metabolite analysis, and random forest were performed using Metware Cloud, a free online platform for data analysis.
3 Results and Discussions
3.1 The non-volatile metabolites of baijiu
According to the multidimensional databases, a total of 861 metabolites were identified in the three baijiu (Table S1), including 13 major groups, of which 111 amino acids and derivatives, 73 phenolic acids, 33 nucleotides and derivatives, 78 flavonoids, 6 quinones, 22 lignans and coumarins, 116 alkaloids, 49 terpenoids, 69 organic acids, 149 lipids, 42 saccharides, 12 vitamins, and 101 others. 797, 827, and 809 metabolites were detected in DZJ, TWJ, and CPJ, respectively, as shown in Figure S1. Among the top 10 metabolites with high expression were 4-nitrophenol, kaurenoic acid, kaurane-16,17-diol, 8,15R-epoxypimaran-16-ol, 3,4,8-trihydroxy-2-(2-hydroxypropan-2-yl)-7-methoxy-6-(3-methylbut-2-en-1-yl)-2h,3h-furo[3,2-b]xanthen-5-one, stearic acid, nicotine, succinic acid, isophorone, and 6,9,10-trihydroxyoctadec-7-enoic acid. The total relative expression of metabolites in TWJ was higher than that in DZJ and CPJ. This was related to the characteristics of TWJ itself, and as the finishing touch to baijiu, which has a high concentration of particular and prominent styles to enhance the overall quality of baijiu.
Lipids were the most numerous non-volatile substances in baijiu (149), and included 6 subclasses, free fatty acids, glycol esters, sphingolipids, phosphatidylcholines (PC), lysophosphatidylcholines (LPC), and lysophosphatidylethanolamine (LPE). Sorghum is one of the main substances involved in the production of ethanol and flavor enhancement during the fermentation of baijiu, and lipids were also the main non-volatile metabolites in sorghum [
33]. The lowest number of lipids was detected in CPJ followed by DZJ. 2-Aminododecane-1,4-diol, stearic acid, 2-aminohexadecane-1,16,16-triol, 2-aminotetradecane-1,4-diol, and palmitaldehyde, all of which had higher relative expression than the other lipids. In addition, 1,18-octadecanediol was detected only in TWJ.
There were 116 species of alkaloids, which were categorized into 10 subclasses: pyridine alkaloids, plumerane, alkaloids, benzylphenylethylamine alkaloids, quinoline alkaloids, isoquinoline alkaloids, phenolamine, piperidine alkaloids, pyrrole alkaloids, and tropan alkaloids. Alkaloids are a class of nitrogen-containing alkaline organic compounds that are one of the important active ingredients in herbal medicine. It has many biological functions, such as anti-cancer, anti-atherosclerotic, and anti-inflammatory [
34]. Fagomine was present only in DZJ, isokuraramine, dihydrocaffeoylputrescine, 1-methoxyindole-3-carbaldehyde, and guvacoline were detected only in TWJ. Nicotine, tetradecyldiethanolamine, N-(4-oxopentyl)-acetamide, 3-chloroaniline, and 2-amino-4,5-dihydro-1H-imidazole-4-acetic acid were highly expressed in baijiu. Herein, 111 amino acids and derivatives were identified.
L-norleucine,
D-allo-isoleucine,
L-isoleucine,
L-leucine, and
L-phenylalanine were the metabolites that were highly expressed. TWJ had the highest number of amino acids and derivatives with 106, whereas
L-leucyl-
L-leucine was detected only in DZJ, and 5-oxo-
L-proline and dencichin were detected only in CPJ.
The following subclasses were included in flavonoids, which were anthocyanidins, chalcones, flavanols, flavanones, flavanonols, flavones, flavonols, isoflavones, and other flavonoids. CPJ has the widest variety of flavonoids. 2′,4′-Dihydroxy-4-methoxydihydrochalcone, galangin-7-glucoside, eriodictyol-7-O-rutinoside, and 3,4,8-trihydroxy-2-(2-hydroxypropan-2-yl)-7-methoxy-6-(3-methylbut-2-en-1-yl)-2h,3h-furo[3,2-b]xanthen-5-one metabolites showed high relative expression. Phenolic acids were the most abundant metabolites in baijiu brewing and fermentation raw materials [
35], and 73 were detected in the three baijiu types. The studies underscore the pivotal role of phenolic acids, exemplified by the production of aroma compounds such as 4-methylguaiacol and 4-ethylguaiacol from ferulic acid in wheat, as well as syringic acid derived from tannins in sorghum, in contributing to the intricate flavor profile and distinctive taste of baijiu [
36]. Organic acids (69) were the most abundant identified in TWJ, with 2-n-propyl-3-pentenoic acid, succinic acid, and 4-pentenoic acid having high relative expression. In addition, 2-aminoethanesulfonic acid was the metabolite with the greatest expression detected only in CPJ.
Compared with the above metabolite species, terpenoids (49), saccharides (42), nucleotides and derivatives (33), lignans and coumarins (22), vitamins (12), and quinones (6) were less diverse in baijiu. Among them, terpenoids included 5 subclasses, diterpenoids, monoterpenoids, sesquiterpenoids, terpenes, and triterpenes. Kaurane-16,17-diol, confertifoline, and kaurenoic acid were the metabolites with the highest relative expression in the three baijiu. Saccharides are the main substances that give baijiu its sweet flavor. D-maltose, D-lactose, isomaltulose, and galactinol, which were highly expressed, were only highly expressed in DZJ, while D-glucose, D-galactose, L-glucose, and inositol were highly expressed metabolites in both TWJ and CPJ. This further suggests that DZJ was somewhat different from TWJ and CPJ. Angelicin, bakuchicin, and psoralen in lignans and coumarins, and nicotinamide and nicotinic acid in vitamins had high expression in the three baijiu. Nucleotides and derivatives, and quinones were the metabolites identified in all three baijiu. Others include alcohol compounds (7), aldehyde compounds (19), chromone (2), ketone compounds (18), lactones (13), stilbene (2), and others (40).
3.2 Association of non-volatile metabolites in different baijiu via WGCNA
The co-expression network was constructed using WGCNA to associate metabolites with baijiu samples for preliminary correlation exploration. Module clustering (Fig. 1A) and correlation coefficients (Fig. 1B) categorized the metabolites into 6 co-expression modules, including blue (142), turquoise (235), green (104), yellow (111), brown (116), and red (67). DZJ was most highly correlated with red (r = 0.63, P = 0.07), and highly negatively correlated with green (r = −0.66, P = 0.05). TWJ was highly correlated with blue (r = 0.61, P = 0.08) and showed a strong negative correlation with brown (r = −0.65, P = 0.06). CPJ, on the other hand, has a strong correlation with yellow (r = 0.6, P = 0.09) and a highly negative correlation with turquoise (r = −0.61, P = 0.08). From Figure 1C, it can be seen that DZJ mainly has a strong correlation with amino acids and derivatives; in addition, TWJ and CPJ also have some correlation with amino acids and derivatives. TWJ mainly had a positive correlation with lipids and a strong negative correlation with alkaloids, while the CPJ had a strong correlation with flavonoids and a strong negative correlation with lipids, which was opposite to the result of TWJ.
3.3 Identification of differential metabolites
Variable Importance of the Projection (VIP) analysis was performed by the OPLSR.Anal function in R software, in addition to the fold change (FC) values of the metabolites. Differentially accumulated metabolites (DAMs) were screened for VIP based on VIP ≥ 1, | log
2(FC) ≥ 1|, and
P ≤ 0.05 to provide a preliminary understanding of the overall metabolic differences between the baijiu sample [
37]. Differentially accumulated metabolites (DAMs) were performed, and the expression of DZJ and TWJ differential metabolites was calculated for CPJ to gain a preliminary understanding of the overall metabolic differences between baijiu.
A total of 497 metabolites (DZJ vs. CPJ) and 549 metabolites (TWJ vs. CPJ) had VIP ≥ 1, which can be used as markers to discriminate between base baijiu and commercial baijiu. The top 20 differential metabolites with higher VIP values for each group were shown in Figure 2A and 2B, such as 5-oxo-L-proline, malvidin-3-O-rutinoside, and γ-linolenic acid in DZJ vs. CPJ and 6-methyl flavone, riboflavin, and dehydroabietic acid in TWJ vs. CPJ.
As shown in Figure 2C and 2D, the horizontal coordinates were metabolite categories, one color represents one type of substance, and the vertical coordinates represent the significance of differences, and −lg was done for the P-value. A total of 94 differential metabolites were screened based on DAMs. From the 829 differentiated substances identified/annotated in DJZ vs. CPJ, further screening was performed to obtain 21 up-regulated and 33 down-regulated significant differentiators, which were 7 species belonging to amino acids and derivatives, 8 species of phenolic acids, 17 species of flavonoids, 3 species of lignans and derivatives, 2 species of alkaloids, 3 species of terpenoids, 4 species of organic acids, 7 species of lipids, and 3 species of others. The up-regulation of differentially expressed metabolites was mainly dominated by the alkaloids and lipids pathways, while the down-regulation was dominated by the phenolic acids and flavonoids pathways. Among the 857 differentially expressed metabolites in TWJ vs. CPJ, 51 up-regulated and 34 down-regulated significant differentially expressed metabolites were further screened out, of which were 15 belonged to amino acids and derivatives, 10 to phenolic acids, 19 to flavonoids, 4 to lignans and coumarins, 5 to alkaloids, 3 to terpenoids, 8 to organic acids, 10 to lipids, 1 to saccharides, 2 to vitamins and 8 to others. The up-regulation of differentially expressed metabolites was mainly in the pathways of lipids, amino acids and derivatives, and alkaloids, while the down-regulation was mainly in the pathways of flavonoids and phenolic acids.
In addition, to evaluate the difference in the relative expression of non-volatile metabolites in different groups, the relative expression of all differential metabolites was scaled by unit variance scaling. The 94 differential metabolites were categorized into 10 subclasses according to the K-means method (Fig. 2E). Subclass 1 contained 7 metabolites with lower expression in TWJ than in DZJ and CPJ. Subclass 9 had 7 metabolites that were consistent with the classification trend of Subclass 1. In contrast, 4 metabolites in subclass 2 and 12 metabolites in subclass 3 had higher expression in DZJ and TWJ than in CPJ. In subclass 4, 27 metabolites were significantly higher in CPJ than in DZJ and TWJ. Flavonoids had the highest number of compounds (13), such as malvidin-3-O-rutinoside, peonidin-3-O-rutinoside, and apigenin-7-O-(6′′-malonyl)glucoside. However, in subclasses 5 (4), 6 (7), 7 (11), and 10 (10), all metabolites were significantly more expressed in TWJ than in DZJ and CPJ. In addition, 5 metabolites in subclass 8 had higher expression in TWJ and CPJ than in DZJ. Among them, flavonoids were the most abundant (2), such as eriodictyol-7-O-(6′′-O-p-coumaroyl)glucoside.
3.4 KEGG annotation and enrichment analysis of differential metabolites
As a fermented beverage, it was important to understand the metabolic pathways of baijiu to regulate the direction of baijiu fermentation and control the production. The metabolic pathways related to the different metabolites were annotated using the KEGG [
38]. KEGG is a public database of metabolic pathways, which can be used for gene function studies and key gene screening of differential gene sets.
A total of 21 differential metabolites were enriched, as shown in Table 1, including 9 and 21 differential metabolites of 2 comparisons (DZJ vs. CPJ and TWJ vs. CPJ) and localized to different pathways. Finally, 31 (DZJ vs. CPJ) (Fig. 3A) and 78 (TWJ vs. CPJ) (Fig. 3B) metabolic pathways were found to be involved. In addition, metabolic pathways (7, 15, respectively), biosynthesis of secondary metabolites (3, 6, respectively), linoleic acid metabolism (2, 4, respectively), and microbial metabolism in diverse environments (1, 4, respectively) were annotated and enriched for a greater number of differential metabolites than the other metabolites, and the results suggest that they were the major metabolic pathways.
KEGG pathway enrichment analysis was performed using the characteristics of the differential metabolites. In KEGG enrichment analysis, the rich factor is the ratio of the number of differential metabolites in the corresponding pathway to the total number of metabolites annotated by the pathway, and the rich factor value corresponds to the degree of enrichment. The larger the rich factor, the more reliable the significance of the differential metabolite enrichment in the pathway. The top 20 pathways detected by KEGG enrichment analysis were shown in Figure 3 (Figure 3C lists all the pathways with less than 20). In DZJ vs. CPJ, diterpenoid biosynthesis showed the most significant enrichment (P < 0.04), with xylene degradation (P < 0.08) (Fig. 3C) as the next most significantly enriched pathway. Metabolic pathways (7) showed the highest amount of enriched metabolites, followed by biosynthesis of secondary metabolites (3) and biosynthesis of unsaturated fatty acids (3). The most significantly enriched pathway in TWJ vs. CPJ was diterpenoid biosynthesis (P < 0.09) (Fig. 3D). Metabolic pathways (15) were found to have the highest number of enriched differential metabolites, followed by biosynthesis of secondary metabolites (6), and microbial metabolism in diverse environments (4), and linoleic acid metabolism (4). In summary, based on P-values and the number of enriched metabolites, the 7 most important pathways were selected as the most important pathways, including diterpenoid biosynthesis, xylene degradation, metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of unsaturated fatty acids, linoleic acid metabolism, and microbial metabolism in diverse environments. The 7 pathways identified by KEGG pathway enrichment analysis may be responsible for the different chemical composition of DZJ, TWJ, and CPJ. Further by KEGG preliminary speculation, corresponding to a total of 18 specific metabolic pathways, and all metabolites annotated by these pathways were 19, as shown in Figure 4, including kaurenoic acid, 3-methylbenzaldehyde, 5-oxo-L-proline, L-ornithine, 2-aminoethanesulfonic acid, 5-aminovaleric acid, quinic acid, linoleic acid, lactic acid, γ-linolenic acid, N-acetyl-D-galactosamine, riboflavin, 2,5-dihydroxybenzaldehyde, p-coumaraldehyde, orotic acid, syringic acid, 9,10,13-trihydroxy-11-octadecenoic acid, 9,10-dihydroxy-12,13-epoxyoctadecanoic acid and 1-O-sinapoyl-β-D-glucose. They can be used as metabolic pathway regulatory markers.
3.5 Random forest of differential metabolites
Considering that there were complex metabolites in the baijiu samples, it would be too one-sided to analyze the markers with metabolic pathways only by KEGG, so we also used the random forest method of random forest to further screen the 94 differential metabolites. Random forest, have the advantage of discovering potential markers in a large amount of data with high prediction accuracy [
39]. Demonstrates outstanding predictive capability in food samples [
40,
41]. Analysis was conducted using varSelRF 0.7.8 in R version 4.2.0, with parameter settings based on vars.drop.frac = 0.2. The top 30 significant differential markers for each of DZJ vs. CPJ, and TWJ vs. CPJ were shown in Figure S2. The serial numbers were the same as in Table S1. “Mean Decrease Accuracy”, and “Mean Decrease Gini” are two important measures of contribution in the random forest model. Receiver operating characteristic curve (ROC) can effectively evaluate the performance of model classification and is widely used in machine learning [
42].
In order to evaluate the significance of the markers screened by random forest for the prediction model, the ROC graph was plotted. The abscissa represents 1 − specificity, which is also known as the false positive rate. The false positive rate is calculated as false positives/(false positives + true negatives). The ordinate represents sensitivity, which is also referred to as the true positive rate. The true positive rate is calculated as true positives/(true positives + false negatives). The area between the ROC curve and the abscissa is known as the Area Under Curve (AUC), which serves as a quantitative evaluation metric for the ROC curve. The closer the AUC value is to 1, the better the predictive performance of the model. The text marked in red on the graph indicates the AUC value and its 95% confidence interval corresponding to this curve. As shown in Figure S2C, the AUC value of the best model obtained using cycloalliin (110) and myrtucommulone B (839) was 1, indicating that these two substances could correctly classify all true positive instances as positives and all true negative instances as negatives, without any misclassifications. This demonstrates that these two metabolites were effective markers for distinguishing DZJ vs. CPJ. Similarly, the ROC curve for TWJ vs. CPJ, plotted using Leu-Met (73) and linoleoyl ethanolamine (704), as shown in Figure S2D, could also achieve effective distinction. It should be noted here that the best model represented only an extreme case, yet it further underscores the significance of the screened substances and the rational validity of the model’s screening. In addition, the Mean Decrease Accuracy and Mean Decrease Gini for the aforementioned four substances were also relatively high.
In summary, cycloalliin, myrtucommulone B, Leu-Met, linoleoyl ethanolamine, kaurenoic acid, 3-methylbenzaldehyde, 5-oxo-L-proline, L-ornithine, 2-aminoethanesulfonic acid, 5-aminovaleric acid, quinic acid, linoleic acid, lactic acid, γ-linolenic acid, N-acetyl-D-galactosamine, riboflavin, 2,5-dihydroxybenzaldehyde, p-coumaraldehyde, orotic acid, syringic acid, 9,10,13-trihydroxy-11-octadecenoic acid, 9,10-dihydroxy-12,13-epoxyoctadecanoic acid, and 1-O-sinapoyl-β-D-glucose were of great significance in distinguishing blending samples and could be used as differential markers for sample classification.
3.6 Sensory analysis
Based on sensory evaluation, profile diagrams were created for the baijiu samples, as shown in Figure 5A. CPJ exhibited relatively high levels of sweet, bitter, and astringency. DZJ maintained fundamental sensory attributes without any particularly prominent characteristics. In contrast, TWJ featured pronounced sweet and noticeable bitter.
Exploring the correlation between non-volatile compounds and flavor using Pearson’s correlation coefficient, the relationship between compounds was visualized as a corrplot heatmap by calculating their correlation coefficients, as shown in Figure 5B. First, analyzing compounds with positive correlations: 5-oxo-L-proline, 2-aminoethanesulfonic acid, and 1-O-sinapoyl-β-D-glucose all showed strong positive correlations with all six sensory attributes, with the most significant effects on sourness and umami (P < 0.001). Lactic acid exhibited positive correlations with all sensory attributes except sweetness, where its effect was relatively weak. Kaurenoic acid showed significant effects on sourness (P < 0.01) followed by umami (P < 0.05). Cycloalliin, myrtucommulone B, linoleoyl ethanolamine, 3-methylbenzaldehyde, linoleic acid, γ-linolenic acid, 9,10,13-trihydroxy-11-octadecenoic acid, and 9,10-dihydroxy-12,13-epoxyoctadecanoic acid exhibited strong negative correlations with six sensory attributes, particularly sourness (P < 0.001) and umami (P < 0.01).
4 Conclusions
In this study, UPLC–ESI–Q TRAP–MS/MS non-targeted metabolomics was used to comprehensively analyze the non-volatile compounds in baijiu samples from different blending processes, and further explore the relationship between baijiu samples. A total of 861 non-volatile compounds were detected in DZJ, TWJ, and CPJ. The species and total relative expression of non-volatile compounds were higher in TWJ than in DZJ and CPJ. Exploration of the correlation of non-volatile compounds in different baijiu based on WGCNA. Based on differential metabolite analysis, a total of 21 significantly up-regulated metabolites and 33 significantly down-regulated differential substances were found in DZJ vs. CPJ; TWJ vs. CPJ had 51 significantly up-regulated metabolites and 34 significantly down-regulated metabolites, and the differentially expressed metabolites were mainly up-regulated in lipid, amino acid and its derivatives, and alkaloid pathways, and down-regulated in flavonoid and phenolic acid pathways. KEGG pathway enrichment analysis identified 7 major metabolic pathways. Further combined with random forest, a total of 23 effective markers were screened to distinguish the samples. The impact of markers on flavor was further investigated through sensory experiments and correlation analysis. In this study, baijiu samples from different blending processes were analyzed from the perspective of non-volatile compounds, and the differences with the final baijiu products were investigated, which will provide a reference value for future research on baijiu flavor and quality. Future work should validate the identified non-volatile markers using a substantially larger and more diverse sample set, encompassing multiple baijiu origins. Furthermore, the analytical methodology can be extended to other fermented food matrices to discover markers.
The Author(s) 2026. This article is published by Higher Education Press.
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