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. 2026 Feb 3;10:100558. doi: 10.1016/j.crmicr.2026.100558

Integrated metagenomic and metabolomic analysis reveals regional style differences in Maotai-flavour Baijiu

Xin Shi a,1, Chenming Fan b,1, Ming Hui a,c,⁎, Qing Tian a,c, Fangli Zhang d, Chunmei Pan e
PMCID: PMC12907668  PMID: 41704740

Highlights

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    MT is dominated by Saccharomyces and Lactobacillus.

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    MT has high ester, JS features elevated aldehydes, reflecting regional sensory.

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    Saccharomyces and Pichia correlate with esters.

Keywords: Maotai-flavour Baijiu, Production region, Metabolomic, Metagenomic, Volatile compounds

Abstract

This research focused on Maotai-flavour Baijiu from three representative production regions in the Chishui River Basin, namely Maotai Town (MT), Jinsha (JS), and Renhuai (RH). By integrating metabolomics and macrogenomics techniques, the study analyzed the differences in volatile flavor compounds and microbial community structures in the fourth-round fermented grains and base liquor. Additionally, it explored the associative mechanism between microorganisms and flavor metabolism. The findings indicate that the microbial community compositions of fermented grains vary significantly across different production areas. The production area of Maotai Town mainly consists of Saccharomyces and Lactobacillus, with the highest content of ester substances. The fungal community in Jinsha is extremely stochastic, abundant in Mucoromycota, and has elevated contents of aldehydes and phenols. The distribution of microorganisms and flavor substances in the Renhuai production area lies between the two. The sensory evaluation of the base liquor indicates that the Jinsha production area features prominent floral and fruity aromas, the Maotai Town exhibits significant sour and sauce aromas, and the Renhuai production area has a well-balanced flavor. The correlation analysis shows that yeasts such as Saccharomyces and Pichia are positively correlated with esters such as ethyl acetate, while bacteria such as Limosilactobacillus are closely associated with short-chain fatty acid metabolism. This research reveals the microbiological basis for the differences in the Maotai style among different production regions and provides a theoretical foundation for regional characteristic production and process optimization.

Graphical abstract

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1. Introduction

Maotai-flavor Baijiu, a typical representative of traditional Chinese solid-state fermented distilled spirits, is celebrated for its distinctive style. It is characterized by a “prominent sauce aroma, elegance and delicacy, a mellow taste, and a long-lasting aftertaste” (Li et al., 2025; Yang et al., 2020). The traditional “12987” brewing technique of Maotai-flavour Baijiu has established a distinctive brewing system in the Chishui River Basin (Huang et al., 2023). This basin lies in the transitional area between the Yunnan-Guizhou Plateau and the Sichuan Basin. Predominantly consisting of mountains and hills with a notable altitude variance, it has given rise to a diverse microclimate environment (Wang et al., 2021; Wu et al., 2022). This environment offers a natural foundation for the regional style variations of Maotai-flavour Baijiu (Hao et al., 2021). The purple sand shale soil abounds in minerals, and the high-quality water source provides ideal conditions for brewing (Di et al., 2024; Zhou et al., 2024a).

Maotai Town, Jinsha County, and Renhuai City are the three main production areas of Maotai-flavour Baijiu in the Chishui River Basin. Maotai Town, the birthplace of sauce-flavored liquor, crafts the mellow sauce-flavored liquor, represented by Maotai liquor, using the traditional “12987” process (Liu et al., 2025). The Jinsha production region, owing to its cooler climate, yields a soft soy sauce style. The Renhuai production region (encompassing Maotai Town and its surrounding areas) produces a comprehensive range of Maotai-flavour Baijiu, spanning from high-end to mass-market products, with a style that falls between the two (Tang et al., 2025). Although the brewing process and raw material standards of the three regions are similar, they are located in different microclimate zones of the Chishui River Basin, and different geographical environment, climatic conditions and microecosystems lead to obvious regional differences in microbial communities and flavor metabolism characteristics of the fermented grains (Dai et al., 2024; Wang et al., 2021). Maotai Town (MT) is characterized by a warmer and more humid environment, with an annual average temperature of around 17.5 °C and relative humidity often exceeding 80% (Wang et al., 2021). In contrast, the Jinsha (JS) production area is situated at a higher altitude, resulting in a cooler and drier climate with an average temperature approximately 1-2°C lower than that of MT (Zhou et al., 2024). This cooler temperature is a plausible environmental driver for the enrichment of psychrotolerant fungi and halophilic bacteria observed in our study, as these genera are known to thrive in such conditions. Furthermore, the soil composition across the basin varies, with JS area soils being reported to have a higher salinity, which naturally selects for halotolerant microbial communities (Liu et al., 2023).

Currently, research on Maotai-flavour Baijiu primarily focuses on the analysis of flavor substances or the exploration of microbial diversity in the Maotai Town production area (Leng et al., 2024; Zhou et al., 2024). In contrast, relatively few studies have been conducted on the volatile flavor characteristics of fermented grains in different production regions and their correlations with microbial communities. Specifically, the question of whether the differences in style between the “soft and refined sauce aroma” of the Jinsha production region and the “rich and mellow sauce aroma” of Maotai Town stem from variations in the microorganisms and metabolic activities of the fermented grains remains unanswered (Ding et al., 2024; Yang et al., 2025). Additionally, as an extension of Maotai Town, the Renhuai production region also requires a thorough analysis of how the geographical location influences the micro-ecology and flavor compounds of its fermented grains.

This study takes the fourth round of Maotai-flavour Baijiu fermented grains and the fourth round of base liquor from three different production areas of the same distillery in 2024 (the core production area of Maotai Town, Jinsha production area and Renhuai production area) as the research objects (the fourth round is in the middle of brewing, and the accumulation of microorganisms and flavor substances is relatively balanced (Chen et al., 2024; Ge et al., 2024; Xu et al., 2023). The headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) technique, in combination with metagenomics, was utilized to analyze the composition of volatile flavor compounds and the disparities in microbial community structure of fermented grains from different production regions. Multivariate statistical analysis was employed to uncover the correlations. This research can provide a theoretical foundation for elucidating the microbiological mechanisms underlying the style differences in Maotai-flavor Baijiu production areas. Simultaneously, it can offer scientific guidance for the characteristic production and process optimization in these regions.

2. Materials and methods

2.1. Sample source and collection

The samples for this study were collected from three standardized production plants of a certain Maotai-flavour liquor manufacturing enterprise in Guizhou Province, located in the core production area (MT) of Maotai Town, the Jinsha production area (JS), and the Renhuai production area (RH), as shown in Fig. 1a. All factory areas strictly follow the traditional “12987” Maotai-flavour liquor production process and use the same raw materials and water sources. In all three sampled factories, the main raw material for liquor production was local “Hongyingzi” sorghum. To ensure consistency, the company sources these sorghum centrally and distributes them to all production plants. The general composition of this batch of sorghum provided by the supplier is as follows: Starch, 64%; Protein, 9%; Fat, 2%; Tannins, 1%; Total dietary fiber, 16%.

Fig. 1.

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Comparison of the microbial community structure (NST) of fermented grains in different production areas. (a) Distribution map of the three production areas. (b) Comparison of fungal NST analysis. (c) Comparison of bacterial NST analysis.

The samples are the fermented grains from the fourth round of the 2024 production cycle, collected at three key process nodes: the initial stage of stacking fermentation, the end of stacking fermentation, the end of pitting fermentation, and the base liquor for the round. Three biological replicates were collected at each time point in each production area, for a total of 36 samples. Sampling was performed using the five-point sampling method, which was mixed evenly and immediately placed in sterile sampling bags. The samples were stored at −80 °C until use.

2.2. Metagenomic sequencing

Total genomic DNA was extracted from fermented grain samples using the Mag-Bind® DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer's instructions. The concentration and purity of extracted DNA were determined with TBS-380 and NanoDrop2000, respectively. DNA extract quality was checked on 1% agarose gel. The extracted DNA samples were sequenced using the Illumina Novaseq X Plus platform. The comparison and processing of the original data, including the assembly of the original sequence and species composition, were done by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

2.3. Detection of volatile substances in the fermented grains

The analytical instrument used in this experiment is the Thermo Orbitrap Exploris GC Gas Chromatography-Mass Spectrometry (Thermo, Germany). The extraction head was SPME Arrow Fiber (120 μm, Thermo, DVB/Carbon WR/PDMS). 1 g of sample was placed in a 20 mL headspace bottle containing 2.5 µL of internal standard (20 µg/mL of naphthalene-d8) and 4 mL of saturated sodium chloride solution, and the headspace bottle was immediately sealed. The samples were injected into the GC-MS system in shunt mode for analysis with a shunt ratio of 10:1. The samples were separated using a VF-WAXms capillary column (25 m × 0.25 mm × 0.2 µm, Agilent CP9204) and then introduced into the mass spectrometry for detection. The inlet temperature was 240 °C, the carrier gas was high-purity helium, the carrier gas flow rate was 1.0 mL/min, and the spacer purge flow rate was 3 mL/min. Heating procedure: initial temperature 40 °C, balance 0 min, then at 8 °C/min to 120 °C, followed by 20 °C/min to 230 °C, and maintained for 4.5 min. The total running time is 20 min. Ion source temperature 250 °C, ion source type: EI, electron energy 70 eV. The identification of volatile compounds was performed by comparing their retention indices (determined using a standard series of C10-C29 saturated alkanes) and mass spectra stored in the data system library.

2.4. Detection of volatile substances in the base liquor

10 ml base liquor was sucked into a colorimetric tube, and 100 μL (2-Methyl-2-butanol 0.2215 g/L, n-amyl acetate 0.2108 g/L, 2-ethylbutyric acid 0.2080 g/L) chromatographically pure mixed internal standard was accurately added by pipetting guns, and then shaken well.

The column was DB-Wax (60 m × 0.25 mm × 0.25 μm). Detector: hydrogen flame ionization detector; The column temperature was maintained at 50 °C for 5 min, and then increased to 140 °C at 5 °C/ min. Injection port temperature: 200 °C; Detector temperature: 200 °C; Carrier gas (high purity nitrogen) flow rate: 3 ml/min; Injection volume: 1.0 μL; The split ratio was 20:1.

2.5. Sensory evaluation

A sensory evaluation panel consisting of 8 experienced tasters was formed, with the following flavor characteristic descriptors: Burnt incense, Daqu aroma, Ester fragrance, Floral and fruity fragrance, Grain fragrance, Sour fragrance, and Soy sauce aroma. The sensory intensity levels were recorded on a scale of 1 to 5 (Aliya et al., 2024). The score for each sample was the average of the three evaluations made by each taster. A sensory radar chart was then drawn based on the results (Gao et al., 2025).

2.6. Statistical analysis and visualization

One-way analysis of variance(ANOVA) was used to test the differences. Principal component analysis (PCA) was used to analyze the similarity between the samples and species. The Wilcoxon rank-sum test was used to analyse the differences in microbial and functional composition between the two groups. LEfSe used linear discriminant analysis to estimate universities with significant species-pair difference effects. Pearson correlation analysis was used to establish the correlation between microorganisms and volatile components, and the results were visualized using Cytoscape 3.9.1. The data were analysed and visualized on the Majorbio Cloud Platform's online platform (www.majorbio.com).

3. Results and discussion

3.1. NST analysis of the microbial community structure of fermented grains in different production areas

The Normalized Stochasticity Ratio (NST) was used to quantify differences in the construction mechanisms of fungal and bacterial communities across the three production areas (Caselle et al., 2022), as shown in Fig. 1b and 1c. For the fungal community, the randomness of the JS group was the strongest, which was significantly higher than that of the MT and RH groups (p < 0.001). Additionally, the randomness of the RH group was significantly higher than that of the MT group (p < 0.001). The construction mechanism of the bacterial community varied. The JS group demonstrated the strongest deterministic dominance, which was significantly lower than that of the MT and RH groups (p < 0.001). Fungi were more prone to random processes, whereas bacteria were more strongly driven by deterministic processes. The assembly mechanism of the microbial community in different production regions had specific impacts, with the two microorganisms in the JS production region showing the most pronounced effects. The distinct community assembly mechanisms have profound functional implications for flavor metabolism. The highly stochastic fungal assembly in JS likely fosters greater functional diversity and metabolic novelty, allowing for the emergence of diverse aroma-producing pathways that collectively contribute to its complex “floral and fruity” bouquet. In contrast, the deterministic bacterial assembly in MT, particularly the strong selection for acid-tolerant specialists like Limosilactobacillus, establishes a predictable and efficient metabolic pipeline. Limosilactobacillus reliably drives high-rate short-chain fatty acid (SCFA) production, directly shaping the prominent “sour fragrance” and, through acid-mediated proteolysis, ensuring a steady supply of amino acid precursors essential for the formation of “soy sauce aroma” compounds during distillation. Thus, the stochastic versus deterministic assembly not only describes ecological processes but also predicts metabolic strategies: diversification leading to a blended aroma profile (JS) versus specialization leading to targeted, amplified production of key flavor families (MT).

3.2. Distribution of fungal community structure in fermented grains of different production areas

The phylum level structure characteristics of the fungal communities in the three production areas are shown in Fig. 2a, where Ascomycota was the absolute dominant fungus in all samples (He et al., 2024). The abundance of Ascomycota was greatest in the Maotai production area, whereas that of Mucoromycota was lower compared to other production areas. The abundance of Mucoromycota in the Jinsha production area (JS) was significantly higher than that in Maotai and Renhuai. This indicates that the relatively mild climatic conditions in the Jinsha production area may preserve more cryophilic fungi. The fungal community structure in the Renhuai region (RH) is similar to that in the Maotai region (MT). Nevertheless, the abundance of Basidiomycota is slightly higher at the end of pitting fermentation.

Fig. 2.

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Microbial community composition in the different production areas. (a) and (b) were the relative abundance of bacterial and fungal at the phylum level, respectively. (c) and (d) were the relative abundance of bacterial and fungal at the genu level, respectively.

The distribution of fungal genus levels in the three production areas showed obvious stage and regional differences, as shown in Fig. 2c. In the Jinsha production area (JS), Zygosaccharomyces and Aspergillus were the dominant genera in the early stage of fermentation. As fermentation progressed, Aspergillus and Paecilomyces gradually became the dominant genera. The Maotai production area (MT) exhibits typical S. cerevisiae enrichment characteristics, with Saccharomyces and Pichia being the dominant genera at the end of stacking and pitting. The Renhuai production area (RH) demonstrates a unique fungal composition, with Kazachstania enriched at the end of the pit. This genus may be adapted to the specialized pit environment, and the high abundance of Monascus during the accumulation stage may influence the color and flavor of the liquor (Wang et al., 2025). It is worthy of note that Aspergillus levels were remarkably higher during the early accumulation stage of Maotai production than in other regions. Functional analysis of the metagenomic data revealed that the Aspergillus-dominated community in this stage exhibited enriched genes encoding starch- and cellulose-degrading enzymes and elevated pathway activities for amino acid metabolism, which aligns with the high-temperature, high-humidity, and aerobic microenvironment created by the traditional “high-temperature Daqu” process (Hou et al., 2024). This enzymatic profile suggests Aspergillus plays a key role in providing precursors for the Maillard reaction and Strecker degradation, which are critical for generating the characteristic “roasted” and “nutty” notes of the “Maotai fragrance” . Conversely, its lower abundance in Jinsha may contribute to a softer, less pronounced roasted aroma profile in that region's “soft and elegant sauce fragrance” (Duan et al., 2022).

3.3. Distribution of bacterial community structure in fermented grains of different production areas

The phylum level structure characteristics of the bacterial communities in the three production areas are shown in Fig. 2b. Among all the samples, Bacillota was dominant phylum, and its abundance generally increased towards the end of pitting fermentation. The anaerobic environment facilitated its growth. At the initial stage of piling, the Bacillota in the Jinsha region (JS) had the lowest abundance. Conversely, Actinomycetota and Pseudomonadota had relatively higher abundance, which might be associated with the differences in temperature and humidity control during stacked fermentation. At the end of pitting fermentation, the Bacillota from the Maotai region (MT) had an abundance of approximately one, while the growth of other bacterial phyla was strongly inhibited. The dynamics of the bacterial community in the Renhuai production area (RH) were highly similar to those in the Maotai production area.

The distribution of bacterial genus levels in the three production areas demonstracted a succession pattern, as shown in Fig. 2d. All production areas were dominated by Acetilactobacillus at the end of pitting fermentation. Its acid and ethanol resistance were highly compatible with the cellar environment (Pang et al., 2021). The abundance of Lentibacillus and Oceanobacillus in the Jinsha production area (JS) were significantly higher than those in other production areas (Liu et al., 2025). These halophilic bacteria might be associated with the local special ecological setting. In the initial fermentation of the accumulation in the Maotai production area (MT), Staphylococcus and Kroppenstedtia were dominant, which could be related to the unique high-temperature accumulation microenvironment in Maotai Town. The bacterial composition in the Renhuai production area (RH) resembled that of Maotai, yet more Lactobacillus were retained. For instance, halophilic bacteria in the Jinsha production area may participate in the synthesis of distinctive flavor compounds. Conversely, thermotolerant bacteria in the Maotai production area may contribute to the synthesis of specific aroma precursors (Yan et al., 2025). The absolute dominance of Acetilactobacillus during the pitting fermentation stage indicates its crucial role in the brewing of Maotai-flavour Baijiu. It influences the overall flavor formation by regulating lactic acid metabolism.

3.4. LEfSe analysis of significantly differentially bacterial and fungal genera in fermented grains

LEfSe is a tool for discovering high-dimensional biomarkers, which were revealed in this study for different production regions (Gao et al., 2023). Fungal markers, as shown in Fig. 3a, Zygosaccharomyces, Wickerhamiella, Wickerhamomyces, Ascoidea, Agaricus for JS region, such as fungal markers; Pichia, Hyphopichia, Daedalea, Anncaliia, Enterocytozoon as MT region of fungi, such as markers; RH region has a significant difference of fungi have Paecilomyces, Monascus, Candolleomyces, Fibularhizoctonia, Naumovozyma, etc. Bacterial markers, as shown in Fig. 3b, include Sphingobium, Fructilactobacillus, Thermomonas, Herbaspirillum, Bifidobacterium, and other bacterial markers for the JS region; Lactobacillus, Limosilactobacillus, Acetobacter, Lentilactobacillus, and Ruania as MT production areas, such as bacterial markers; The bacterial markers in the RH production area were Lactococcus only. The enrichment of these bacterial markers is not coincidental but reflects a metabolically synergistic consortium that underpins the characteristic Maotai flavour profile. Specifically, Lactobacillus and Limosilactobacillus, as primary lactic acid producers, directly contribute to the accumulation of organic acids (e.g., lactic acid, acetic acid) in the fermented grains, which corresponds to the prominent “sour fragrance” noted in the sensory evaluation of MT base liquor. Concurrently, the acidic environment they create promotes proteolysis, releasing abundant free amino acids. These amino acids serve as critical precursors for the Maillard reaction and Strecker degradation during the subsequent distillation process, generating key “soy sauce aroma” compounds such as phenylacetaldehyde and 4-ethylguaiacol. Acetobacter further oxidizes ethanol to acetic acid. Acetic acid not only adds to the sour fragrance but also acts as a major substrate for esterification, reacting with ethanol to form substantial amounts of ethyl acetate, imparting distinct fruity and ester notes that integrate into the complex matrix of the sauce aroma. Thus, the functional complementarity and metabolic interactions among these three bacterial genera collectively form the microbiological foundation for the “prominent sour fragrance and rich, mellow sauce aroma” style of the Maotai production area.

Fig. 3.

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Identification of significantly differential fungal (a) and bacterial (b) genu in fermented grains by LEfSe analysis.

3.5. Analysis of volatile flavor metabolites in fermented grains

As shown in Fig. 4a, the PCA analysis of all samples from the three production areas revealed that the samples from the MT production area exhibited a significant degree of dispersion, while those from the JS and RH production area had a relatively small degree of dispersion. This indicates that there were significant difference in metabolites at different fermentation stages in the MT production area. More than 500 volatile substances were detected in all the fermented grains samples. These include acids, alcohols, aldehydes, hydrocarbons, organicnitrogen compounds, organoheterocyclic compounds, esters, ethers, haloalkanes, ketones, terpenoids, phenol esters, phenol ethers, phenols, and organosulfur compounds. The proportion of various compounds is shown in Fig. 4b, and the proportion of various compounds in each group of samples is shown in Fig. 4c. The contents of volatile compounds with relatively high concentrations (including acids, alcohols, aldehydes, esters, ketones, phenols, organoheterocyclic compounds) were calculated and presented in Fig. 4d. There are notable differences in the volatile substances of fermented grains among the three production areas. Esters are the primary aroma components and accumulate continuously during the fermentation process. The content is the highest at the end of the pitting fermentation in the Maotai production area (2218.9 μg/kg), significantly higher than that of Jinsha (1521.17 μg/kg) and Renhuai (1587.8 μg/kg). This high ester characteristic is consistent with the “rich and mellow sauce aroma” style of Maotai (Qin et al., 2024). The content of aldehydes (94.5 μg/kg) and phenols (67.58 μg/kg) in the early stage of stacking fermentation in the Jinsha production area was the highest among the three major production areas. At the same time, organic heterocyclic compounds (87.59 μg/kg) were also significantly enriched. These substances might serve as the flavor foundation for the “mellow and elegant sauce aroma”. The distribution of metabolites in the Renhuai production area exhibited transitional features, with the substance contents varying between the Jinsha production area and the Maotai production area (Liu et al., 2023).

Fig. 4.

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Comparison of volatile compounds in the different production areas. (a) PCA analysis of all samples.(b)The proportion of volatile substances. (c)The proportion of each volatile species in each group. (d)The content of the major volatile substances.

Volatile substances with a content greater than 1 μg/kg in each sample were identified, as presented in Table S1. Overall, there were significant differences in the content of volatile substance among production regions and fermentation stages. For instance, the content of phenylethyl alcohol (with a rose fragrance) in the Jinsha production area is generally high. In contrast, in the Maotai and Renhuai production areas, the contents of ethyl phenylacetate (with a cocoa fragrance) and diethyl succinate (with a fruit fragrance) increase significantly at the end of pitting fermentation. Specifically, the content of phenylethyl alcohol (rose fragrance) in the Jinsha production area is generally high. At the end of the pitting fermentation process in the Maotai and Renhuai areas, the contents of ethyl phenylacetate (cocoa fragrance) and diethyl succinate (fruit fragrance) increase significantly. Among the acid substances, hexanoic acid (with a cheese flavor) has a higher content during the initial accumulation stage in the Jinsha area. At the same time, its content significantly decreases in the Maotai and Renhuai areas. In the Maotai area, linoleic acid ethyl ester and hexadecanoic acid ethyl ester have higher contents at the end of the pitting fermentation process and play an important role in contributing to the fat and wax aroma flavors of Maotai liquor (Qin et al., 2023). Phenolic substances such as 4-ethylguaiacol are significantly more abundant in the Jinsha area than in other areas. Overall, the flavor compounds in the Jinsha area are more abundant during the initial stage of accumulation. In contrast, in the Maotai and Renhuai areas, esters accumulate more significantly during the pit fermentation stage. These different joinly contribute to the distinct styles of various liquor producing regions.

3.6. Analysis of volatile compounds in fermented grains among different producing areas

The Maotai production area is the core area for Maotai-flavour Baijiu and serves as a reference standard for other production areas (Li et al., 2025). This research carried out metabolite difference analyses among the Renhuai production area, the Jishan production area, and the Maotai production area. The metabolites differences between MT vs RH, as well as between MT vs JS, were statistically analyzed, as shown in Fig. 5a and 5b. Compared to the Maotai production area, there were 37 metabolites with increased expression levels in Renhuai, while 95 had decreased expression levels. In comparison with the Maotai production area, Jinsha had 61 metabolites with increased expression levels and 121 with decreased expression levels. For the MT vs RH and MT vs JS groups, OPLS-DA analysis was separately performed, and S-plot diagrams were drawn. This enabled us to comprehend the relative alterations in metabolite content between the sample of two groups. The results are shown in Fig. 5c and 5d. The red dots indicate that the VIP values of these metabolites are greater than or equal to 1, whereas the green dots denote those with VIP values less than 1. The metabolites with VIP > 1 and T-test p < 0.005 in the OPLS-DA were selected as the differentially expressed metabolites. To guarantee the reliability of the results, a 200-times permutation test analysis was performed on the OPLS-DA model. The intercept of the Q2 regression line being less than 0 indicates that the model was not overfitted, as shown in Fig. 5e and 5f.

Fig. 5.

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Comparison of volatile substances in fermented grains between the two production areas. (a) and (b) are MT vs RH, MT vs JS difference volcano plots, respectively. (c) and (d) are MT vs RH, MT vs JS S-plot diagrams, respectively. (e) and (f) are MT vs RH, MT vs JS tests of permutation, respectively. (g) and (h) are MT vs RH, MT vs JS VIP value, respectively.

Fig. 5g depicts the VIP values of metabolites in the MT vs RH groups. Among them, the VIP values of trans-2-methyl-2-butenedioic acid dimethyl ester, pentanoic acid, 4-oxo-, butyl ester, and 3,4-dimethoxytoluene are close to 3.0, indicating that these metabolites contribute significantly to the differences between the groups. Fig. 5h shows the VIP values of metabolites in the MT vs JS two groups. The VIP values of 15 metabolites exceed 2.0, indicating that they contribute significantly to the differences between the groups. In particular, the VIP values of 1,2-dichloro-4-methylbenzene, hexanoic acid hexyl ester, and 4-ethyl-1,2-dimethoxybenzene are higher than 3.0, indicating a highly significant contribution to the differences.

3.7. Comparison of volatile substances in the fourth round of base liquor in each production area

The main volatile substances in the fourth-round base liquor from the three production areas are shown in Table S2. The base liquor from the Maotai production area contains a relatively high level of ester substances. For instance, the contents of ethyl acetate and ethyl lactate are significantly higher than those in the base liquor from Jinsha and Renhuai (p < 0.05), endowing Maotai liquor with more intense fruity and sweet aroma characteristics. The base liquor from the Jinsha region has a relatively high content of ethyl caproate and certain acid substances, which contribute to the cheese and fat aromas in its flavor profile. The base liquor from the Renhuai production area has a relatively high ethyl acetal content, endowing it with an apple-like aroma. Additionally, the content of isopentanol in the Maotai and Renhuai production areas was similar to and higher than that in Jinsha. Jinsha has a very low content of furfural, while Maotai and Renhuai have a high content. Their caramel aroma adds a roasting aroma to the liquor. Overall, there are significant differences in the distribution of esters, acids, and alcohols in the base liquor of the three production areas. These differences directly influence the aroma style of the finished liquor. The ester aroma advantage of Maotai, the sour aroma characteristics of Jinsha, and the aldehyde features of Renhuai jointly reflect the uniqueness of the production areas.

3.8. Sensory evaluation of base liquors of different production areas

Based on 29 main flavor substances, a principal component analysis was conducted on the base liquor from each production area in the fourth round. The analysis results are shown in Fig. 6a. The individual contribution rates of the first and second principal components were 60.2% and 19.9% respectively. The cumulative contribution rate of these two principal components exceeded 80%, indicating a statistically significant result. From the load plot, it is clear that the base liquors from the three production areas can be distinguished.

Fig. 6.

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Analysis of main flavor substances of base liquor from various production areas (a) Sensory radar chart of base liquor from each production area (b) Score chart for correlation analysis between main flavor substances and sensory characteristics of base liquor from each production area (c) Load plot (d).

A radar chart was plotted according to the evaluation results of the seven sensory characteristics of the base liquor by the sensory evaluation team, as shown in Fig. 6b. The sensory flavor characteristics of base liquor from different production areas exhibit variations, yet there are also certain similarities. The floral and fruity fragrance, as well as burnt incense, are more prominent in Jinsha (Mou et al., 2025). In contrast, the Sour fragrance and Soy sauce aroma are more prominent in Maotai, and the sensory characteristics of Renhuai are essentially in the middle of those of Jinsha and Maotai (Qiao et al., 2023).

To further explore the relationship between sensory characteristics and key flavor substances, Partial least squares regression (PLSR) was used to create correlation models between sensory characteristics and key flavor substances. Among them, the content of key differential flavor substances is X, the sensory score value is Y, Fig. 6c is the sample distribution score plot, and Fig. 6d is the PLSR scatter plot of sensory characteristics and flavor substances. As shown in the figure, sensory characteristics and flavor substances are predominantly distributed between the two ellipses, indicating a strong correlation between them. Burnt incense, Grain fragrance, Sour fragrance, Daqu aroma, Soy sauce aroma, and Ester fragrance are distributed above the load map (Wei et al., 2022). Floral and fruity fragrance is distributed below the load graph, and there are significant differences in the sensory flavors of the base liquor in the three regions. The JS production area is located in the second quadrant and exhibits a strong correlation with Floral and fruity fragrances, Burnt incense, and grainy fragrances. At the same time, it was strongly correlated with ethyl butyrate, isovaleric acid, phenylethyl alcohol, pentanol, and acetaldehyde. Specifically, the strong association of the JS samples with “Floral and fruity fragrance” is primarily driven by its significantly higher concentrations of ethyl butyrate (imparting a core fruity character) and phenylethyl alcohol (contributing a floral note), as confirmed by both quantitative analysis (Table S2) and the PLSR model (Fig. 6d). Acetaldehyde also contributes to the fresh, fruity top note. In comparison, ethyl caproate, though present in JS base liquor (Table S2), exhibited a weaker association with "Floral and fruity fragrance" in the PLSR model (Fig. 6d), suggesting a more modest contribution to this aroma attribute relative to ethyl butyrate and phenylethyl alcohol. The MT production area is located in the first quadrant, which shows a good correlation with Sour fragrance, Daqu aroma, and Soy sauce aroma, and a strong correlation with acetic acid, furfural, etc. The RH region is located in the fourth quadrant, which is strongly correlated with trimethylpyrazine, hexanol, and ethyl acetate, and the presentation of different sensory characteristics is closely related to flavor substances.

3.9. Correlation analysis of microorganisms and volatile substances in fermented grains with the flavor of base liquor

RDA analysis demonstrated the potential associations between dominant fungi and dominant bacteria and the volatile substances in fermented grains, as shown in Fig. 7a and 7b. The results showed that the RDA1 and RDA2 axes explained 66.7% of the variation in fungi and 78.3% of the variation in bacteria. Benzeneacetaldehyde, phenethylacetate, and ethyl phenylacetate were highly correlated with the distribution of microbial community structure, and ethyl phenylacetate was negatively correlated with n-hexadecanoic acid.

Fig. 7.

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RDA analysis of fungal (a), bacterial (b), and volatile substances in fermented grains from different production areas. (c) Mantel test analysis of volatile substances and microbial communities in fermented grains. (d) and (e) show the network relationship between fungal and bacterial, and the volatile substances in the base liquor, respectively.

A Mantel test was employed to analyze the relationship between volatile substances in fermented grains and the microbial community of fermented grains, aiming to identify the factors driving microbial changes, as decipted in Fig. 7c. Volatile substances exerted a certain effect on the alterations in the community structure. The fungal community was negatively correlated with benzeneacetaldehyde and n-hexadecanoic acid. ethyl phenylacetate, acetic acid, and diethyl succinate were positively correlated (p < 0.01). The bacterial community was positively correlated with phenethyl acetate and Ethyl phenylacetate (p < 0.01, p < 0.05).

Fig. 7d shows the network relationship between fungal in the fermented grains and the flavor substances of the fourth round of base liquor. Saccharomyces was significantly positively correlated with ethyl acetate (p < 0.01), and negatively correlated with acetaldehyde and hacid (p < 0.01), indicating that it had an inhibitory effect on them. The strong positive correlation between Pichia and ethyl acetate, furfural, and valeric acid (p < 0.001) highlighted its contribution to the production of esters and furfural (Ni et al., 2022). Kazachstania was positively correlated with hexanol and trimethylpyrazine (p < 0.05), and negatively correlated with isovaleric acid (p < 0.01). These results indicated that this strain could promote the production of hexanol and trimethylpyrazine and inhibit the production of isovaleric acid (Hu et al., 2025; Xu et al., 2025). In addition, filamentous fungi such as Aspergillus and Penicillium were generally negatively correlated with Octanoic acid (p < 0.01), indicating that they can inhibit the accumulation of octanoic acid (Chen et al., 2021). Paecilomyces was strongly positively correlated with ethyl acetal (p < 0.001), revealing the dominant role of Paecilomyces in the synthesis of acetal (fruit fragrance). Overall, fungi significantly influence the composition of flavor substances in base liquor through synergistic or antagonistic effects. In particular, yeast genera such as Saccharomyces, Pichia, and Kazachstania play a crucial role in shaping key flavor characteristics, including fruity, herbal, and roasted aromas (Deng et al., 2020; Zhang et al., 2020). Aspergillus was more involved in inhibiting fatty acid metabolism (Chen et al., 2025).

Fig. 7e depicts the network relationship between bacteria in the fermented grains and the volatile substances in the fourth round of base liquor. There was a significant positive correlation between Acetobacter and ethyl propionate (p < 0.05). Limosilactobacillus showed a strong positive correlation with ethyl butyrate, isobutyric acid, and octanoic acid (p < 0.01), indicating its key role in the production of short-chain fatty acids (Zhou et al., 2023). Limosilactobacillus was negatively correlated with methanol and trimethylpyrazine (p < 0.05), indicating inhibition of their accumulation. Oceanobacillus was significantly positively correlated with isovaleric acid (p < 0.001), and Streptococcus was negatively correlated with phenylethyl alcohol (p < 0.01), further indicating that specific bacterial groups were selective in regulating flavor substances. In addition, the positive correlation between Anoxybacillus and hexanoic acid (p < 0.05) and the negative correlation between Anoxybacillus and acetic acid (p < 0.05) reflected its differential effect on the fatty acid metabolism pathway. The bacterial community in the fermented grains regulates the composition of flavor substances in the base liquor through synergistic or antagonistic effects. Particularly, genera such as Limosilactobacillus, Acetobacter, and Oceanobacillus play a core role in flavor formation. Its metabolic activities are directly related to the typical aroma characteristics of the base liquor, such as fruity, fatty, and roasted aromas.

4. Conclusion

This research utilized multi-omics techniques to uncover the microbial community structure, flavor substance characteristics, and their inherent correlations in Maotai-flavour Baijiu from the three major production areas of Maotai Town, Jinsha, and Renhuai in the Chishui River Basin. It was discovered that the microbial community composition in different production areas differed significantly. Among them, the Maotai Town production area was predominantly composed of yeast and lactic acid bacteria, with the highest content of Ester substances. The Jinsha producing area was rich in psychrophilic fungi and Aldehydes and Phenols, while the Renhuai production area displayed transitional characteristics. These differences directly shape the unique styles of baijiu in each producing area. Further analysis revealed that yeasts, such as Saccharomyces, are closely related to ester synthesis, while bacteria, such as Limosilactobacillus, significantly impact the metabolism of short-chain fatty acids. Importantly, these functional roles are embedded within distinct community assembly processes: the stochastic fungal assembly in Jinsha facilitates metabolic diversity underlying floral/fruity notes, whereas the deterministic bacterial assembly in Maotai Town enriches specialized functional guilds that drive efficient, targeted production of sour and sauce aroma precursors. This provides a deeper ecological and mechanistic understanding of how microbial communities drive regional flavor formation. In the future, the metabolic pathways of key functional microorganisms can be further explored to achieve targeted flavor regulation.

Credit authorship contribution statement

Shi Xin: Conceptualization, Investigation, Methodology, Data curation, Writing – original draft. Fan Chenming: Writing – review & editing, Software, Visualization. Zhang Fangli: Formal analysis. Pan Chunmei: Supervision, Conceptualization. Tian Qing: Data curation. Hui Ming: Resources, Supervision.

Data availability

Data will be made available on request.

Declaration of competing interest

All authors have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Acknowledgments

This work was supported by the Science and Technology Plan Project of Guizhou Province (Guizhou Unified Research [2024] 210), Renhuai City Science and Technology Plan Project (Renhuai Science Plan [2024] 010), Renhuai City Science and Technology Plan Project (Renhuai Science Plan [2025] 16), and Demonstration Application of Key Technologies for Ecological Brewing of Henan Baijiu, China (Grant number 231111112000).

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.crmicr.2026.100558.

Appendix. Supplementary materials

mmc1.xls (63.5KB, xls)
mmc2.xls (25.5KB, xls)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.xls (63.5KB, xls)
mmc2.xls (25.5KB, xls)

Data Availability Statement

Data will be made available on request.

Articles from Current Research in Microbial Sciences are provided here courtesy of Elsevier

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