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Article

High-Throughput Sequencing Reveals the Microbial Community Succession Process During the Fermentation of Traditional Daizhou Huangjiu

by
Linhua Cui
1,
Jiaying Zhu
1,
Ting Zhang
1,
Qi Li
1,
Yunlong Li
1,* and
Guoqiang Gao
2
1
Shanxi Institute for Functional Food, Shanxi Agricultural University, Taiyuan 030000, China
2
Shanxi Daixian Guixi Wine Co., Ltd., Xinzhou 034000, China
*
Author to whom correspondence should be addressed.
Fermentation 2025, 11(3), 138; https://doi.org/10.3390/fermentation11030138
Submission received: 22 January 2025 / Revised: 4 March 2025 / Accepted: 6 March 2025 / Published: 12 March 2025
(This article belongs to the Section Probiotic Strains and Fermentation)
Browse Figures
Versions Notes

Abstract

This study analyzed the succession patterns of a microbial community structure and key genera during the fermentation process of Daizhou Huangjiu (DZHJ). The results indicated that bacterial diversity decreased while fungal diversity increased in the traditional DZHJ fermentation process. Bacillota and Proteobacteria were the dominant bacterial phyla, whereas Ascomycota, Basidiomycota, and Mortierellomycota were the dominant fungal phyla. Weissella, Enterococcus, and Paucibacter were identified as the predominant bacterial genera, with Paucibacter being reported for the first time in Huangjiu research, marking it as a unique signature of DZHJ. Saccharomyces, Aspergillus, and Candida were the predominant fungal genera. Through Beta diversity analysis and LEfSe differential discriminant analysis, Enterococcus, Weissella, and Saccharomyces were confirmed as key differential genera. Additionally, Weissella and Saccharomyces showed significant negative correlations with the majority of bacteria and fungi, respectively. This study elucidates the brewing mechanism of DZHJ, providing a theoretical basis for its quality improvement.

1. Introduction

Huangjiu, also known as rice wine, is a popular fermented beverage in China, celebrated as one of the “world’s three ancient wines”. With an alcohol content of less than 20% and high nutritional value, Huangjiu is called “liquid cake” [1]. Traditional Huangjiu is mainly classified as southern Huangjiu and northern Huangjiu according to its producing region. Southern Huangjiu is mainly made of glutinous rice, represented by Shaoxing Huangjiu. In northern regions, the raw materials for Huangjiu production include glutinous rice, broomcorn millet, millet, wheat, maize, sorghum, and others. Daizhou (Daixian County, Shanxi Province), located at 112°43′–113°21′ E, 38°49′–39°21′ N, has become the “golden producing area” of broomcorn millet with its complex topography and diverse climate. Daizhou Huangjiu (DZHJ), one of the representatives of Huangjiu in northern China, is a Huangjiu brewed by traditional technology with Daizhou broomcorn millet as the main raw material, which is acknowledged by the statement “southern Huangjiu is in Shaoxing, northern Huangjiu is in Daizhou”. It has multiple effects such as warming the stomach and strengthening the spleen, soothing the muscles and promoting blood circulation, and eliminating fatigue [2].
The fermentation process is a key link to determine the quality of Huangjiu. It is well known that Huangjiu is fermented by mixed strains in an open environment [3]. During the early stage of Huangjiu fermentation, the lees are rich in a variety of microorganisms and exhibit vigorous metabolic activity. As the fermentation proceeds, the alcohol concentration in the fermentation broth increases, the oxygen content decreases, and a large number of microbial metabolites accumulate, making the microbial structure also change significantly. This fermentation process makes the microbial community complex and special and finally forms the special flavor of Huangjiu [4]. High-throughput sequencing technology, known for its rapid sequencing speed and high data accuracy, has been widely applied in the detection and research of microbial system diversity. Using high-throughput sequencing, Wang et al. [5] found that Rhizopus, Pantoea, Weissella, Pediococcus, and Saccharomyces are the ascendent genera in the fermentation of Hebei Huangjiu. Yin et al. [6] used high-throughput sequencing technology to study the microbial dynamics of Beizong Tartary buckwheat Huangjiu and found that bacterial diversity changed with the progress of fermentation, and Oxalobacteraceae, Ralstonia, cinetobacter, cyanobacteria, and Lactococcus were the most abundant bacterial genera. Zhao et al. [7] studied Xiecun Huangjiu with high-throughput sequencing technology and concluded that Pseudomonas, Ochrobactrum, Moesziomyces, and Aspergillus were the dominant microbial genera. Song et al. [8] studied Nanyang Hong Qu Huangjiu with high-throughput sequencing technology and showed that Weissella and Lactococcus were the main dominant lactic acid bacteria in the early stage of fermentation and evolved into a lactic acid bacteria community dominated by Lactobacillus as the fermentation progressed. Studies indicate that microbial communities vary across different stages of Huangjiu fermentation; however, the microbial community structure and dynamics during the fermentation process of Shanxi DZHJ Huangjiu have not yet been investigated.
This study aims to fill this research gap by employing high-throughput sequencing technology to investigate the composition and succession of the microbial community during the traditional fermentation of DZHJ. Our objective is to understand the microbial dynamics and to provide a theoretical foundation for the screening of functional microorganisms and the enhancement of DZHJ quality.

2. Materials and Methods

2.1. Materials and Reagents

All samples were provided by Shanxi Daixian Guixi Wine Co., Ltd. (Xinzhou, China). DZHJ was fermented with broomcorn millet as the raw material and Jiuqu as the starter. The fermentation process was carried out in accordance with the traditional fermentation method, and the fermentation time was 4 months (as displayed in Figure 1). During the PRF (pre-fermentation) phase spanning days 1–6, triplicate samples were collected daily. In the subsequent POF (post-fermentation) stage, sampling was conducted triweekly with three parallel replicates acquired at each sampling interval. A total of 36 samples were stored at −80 °C until DNA extraction. The alcohol content of the samples is presented in Table 1.

2.2. DNA Extraction

We used the TGuide S96 Magnetic Soil/Stool DNA Kit (Tiangen Biotech (Beijing) Co., Ltd., Beijing, China) according to the instructions to extract DNA. The quality and quantity of the extracted DNA were examined using electrophoresis on a 1.8% agarose gel, and DNA concentration and purity were determined with a NanoDrop 2000 UV-Vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA).

2.3. Amplicon Sequencing

The information on amplification primers is shown in Table 2. The PCR reaction conditions were based on the method of Zhang et al. [9]. The PCR was performed in a total reaction volume of 10 μL: DNA template 5–50 ng, forward primer (10 μM) 0.3 μL, reverse primer (10 μM) 0.3 μL, KOD FX Neo Buffer 5 μL, dNTP (2 mM each) 2 μL, KOD FX Neo 0.2 μL, and ddH2O up to 10 μL. Initial denaturation at 95 °C for 5 min was followed by 25 cycles of denaturation at 95 °C for 30 s, annealing at 50 °C for 30 s, extension at 72 °C for 40 s, and a final step at 72 °C for 7 min. The total PCR amplicons were purified with Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN, USA) and quantified using the Qubit dsDNA HS Assay Kit and Qubit 4.0 Fluorometer (Invitrogen, Thermo Fisher Scientific, Hillsboro, ON, USA). After purification and quantification [10], PCR-amplified products were sequenced using Illumina novaseq6000 at Beijing Baimaike Biotechnology Co., Ltd., Beijing, China.

2.4. Bioinformatic Analysis

Trimmomatic (version 0.33) was used to filter the original data. Cutadap (version 1.9.1) identified and removed the primer sequences. Raw data were filtered using Trimmomatic (version 0.33). Cutadap (version 1.9.1) recognizes and removes primer sequences. The PE readings obtained in the former steps were combined together by USEARCH (version 10), and then using UCHIME (version 8.1), we deleted chimeras. Through USEARCH (v10.0), the similarity of 97% or more sequence clustering to the same operating classification unit (OTU) showed a select abundance OTU of less than 0.005%.

2.5. Statistical Analysis

The naive Bayes classifier in QIIME2 was used for classification labeling, and the SILVA database (version 132) was used with 70% confidence. Using QIIME2 (2019.4) software and the R language to determine the diversity of its classification, the Alpha and Beta diversity and LEfSe (LDA Effect Size) differences were analyzed. For Beta diversity, PERMANOVA was used to assess significant differences between groups based on Bray–Curtis distances. The Spearman method was used for correlation analysis, and PICRUSt software (version 2.3.0)was used to predict bacterial function based on the KEGG and COG functional databases. The diversity indices were calculated as follows:
Shannon index (H′):
H ′ = − ∑ i = 1 S p i ln ⁡ p i
Simpson index (D):
D = ∑ i = 1 S p i 2
where p i is the proportion of the i-th species in the community (individuals of species i/total individuals) and S is the total number of species (species richness).

3. Results and Discussion

3.1. High-Throughput Sequencing Results

PRF samples were recorded as PR1, PR2, PR3, PR4, PR5, and PR6; the POF samples were recorded as PO1, PO2, PO3, PO4, PO5, and PO6. After gene sequencing and quality control analysis of the collected DZHJ samples, 1,612,440 bacterial and 1,477,560 fungal amplification fragments were obtained. All high-quality sequences were classified by OTUs based on 97% similarity, and the number of OTUs identified was 2190 bacteria and 8147 fungi (Table 3). At the bacterial level, the PO5 stage had the highest number of OTUs and the PR3 stage had the lowest number of OTUs. At the fungal taxonomic level, the PO6 stage exhibited the highest number of operational taxonomic units (OTUs), whereas the PR3 stage showed the lowest OTU quantity. As shown in Figure 2A,B, for microbial petal diagrams of samples with different fermentation stages, petals can intuitively show shared and unique OTUs between different samples. The number of common OTUs at the bacterial level was 19, accounting for 0.87% of the total OTUs, and the number of common OTUs at the fungal level was 11, accounting for 0.14% of the total OTUs. In addition, a Veen analysis of OTUs shared between successive stages revealed that bacterial and fungal OTUs were shared between adjacent stages (Figures S1 and S2). This suggests a gradual succession of microbial communities during fermentation. Consequently, the microbial community structure of samples at different fermentation stages was significantly different. The feature numbers curve (Figure 2C,D) and Shannon index curve (Figure 2E,F) were used to analyze the microbial sequencing amount at each fermentation stage. Along with the increase in the depth of the sequencing samples not changing, the amount of microorganism sequencing in the sequencing depth of the sample can reveal samples of most microorganisms in a sequence, which can satisfy the requirement of the following bioinformatics analysis.

3.2. Alpha Diversity Analysis

Alpha diversity reflects the species richness and species diversity of a single sample. The Chao1 and Ace index measure species richness; that is, the number of species. The larger the index is, the higher the number of species is [11]. The Shannon and Simpson index were used to measure species diversity. The larger the index is, the higher the species diversity is [12]. As shown in Table 3, species abundance and diversity changed greatly at the bacterial level. The Shannon and Simpson index increased first and then decreased, reaching the maximum at PR5, where the species diversity reached the highest. With continuous fermentation, the species diversity gradually decreased. During the fermentation process, the bacterial species diversity of the PRF samples was above that of the samples of POF. For fungi, the ACE, Chao1, and Shannon index increased rapidly and gradually stabilized at the PO1 and PO2 stages, and the Simpson index also increased, indicating that the number and richness of fungi in the POF stage were much larger than those in the PRF stage, and the number of fungi did not decrease in the final stage of fermentation.
During the fermentation of DZHJ, the bacterial diversity gradually decreased, while the fungal diversity gradually increased, which was related to the tolerance of microorganisms in Jiuqu. As the fermentation progressed, the alcohol content increased, and some of the growth of bacteria was restrained, while the alcohol-resistant fungi further propagated, resulting in an increase in fungal diversity and a decrease in bacterial diversity, which is also found in the results of Yu et al. [13].

3.3. Microbial Community Analysis at the Phylum Level

An in-depth study of the succession process of DZHJ colonies provides a theoretical basis for understanding the formation mechanism of rice wine quality and determining the key methods to improve the quality of Huangjiu. On the level of the phylum were identified 37 bacteria phyla, and Figure 3A shows the top 10 bacterial phyla at the abundance level. The bacterial communities were dominated by Bacillota and Proteobacteria, accounting for 93.17% and 5.42% of the total community, respectively. Bacillota remained the dominant phylum throughout the fermentation process, while Proteobacteria decreased in abundance, which has been found in many studies [5,14,15]. These two phyla contain many strains that are beneficial to Huangjiu fermentation [16]. The increase in Bacillota abundance was the key factor driving the change in the active microbial community in fermented grains [17]. The decrease in Proteobacteria in the POF period was due to the fact that some of its bacteria were aerobic, which were not suitable for the environment of hypoxia, acid production, and alcohol production during the fermentation of Huangjiu, and thus the abundance decreased [18].
As shown in Figure 3B, the main dominant fungal phyla in the fermentation process of DZHJ were Ascomycota, Basidiomycota, and Mortierellomycota. Jia et al. [18] analyzed the microbial structure in the natural fermentation of Huangjiu lees based on the phylum level and also found that the three phyla were the dominant fungal phyla. Ascomycota was dominant in the fermentation process; its abundance was highest in the relatively stable stage of PRF, and in the stage of POF, it declined gradually. Basidiomycota first decreased and then increased. The relative content of Mortierellomycota in the PRF stage was almost 0, and the relative content increased in the POF stage.

3.4. Microbial Community Analysis at the Genus Level

At the genus level, samples of fermented DZHJ were identified in 759 bacteria genera, including 9 core bacteria having a relative abundance of 0.1% or higher, accounting for 97.48% of the bacteria. The 9 bacterial genera were Weissella (76.85%), Enterococcus (14.33%), Paucibacter (4.22%), Pediococcus (0.73%), Klebsiella (0.60%), Lactococcus (0.23%), Staphylococcus (0.19%), Lactobacillus (0.18%), and Limosilactobacillus (0.12%), as shown in Figure 4A. Weissella and Enterococcus are present in many fermented foods and have been identified as an advantage in Shaoxing Huangjiu Jiuyao [19]. Consistent with Zhao et al. [20], Weissella is the dominant genus throughout the fermentation process, with its abundance decreasing first and then increasing. This is because Weissella members are mainly facultative anaerobic bacteria, have strong acid resistance, and grow well in low-oxygen and low-pH environments [21]. The trends of Enterococcus, Paucibacter, Pediococcus, Staphylococcus, and Lactobacillus were opposite. Weissella members are lactic-acid-producing bacteria, which are widely distributed in fermented foods. They are important microorganisms in the brewing process and are crucial in the synthesis of aromatic esters in Huangjiu and are conducive to the accumulation of flavor in the brewing process of Huangjiu [22]. Enterococcus members can accelerate the fermentation process, proteolysis, and lipid formation so that the food has a good flavor [23]. Enterococcus members made full use of the nutrients in the fermentation broth at the beginning of fermentation so that their number remained stable after a rapid increase. With the increase in the POF alcohol concentration, a part of the flora disappeared, and the number gradually decreased, which was also found in the study of Gao et al. [24]. Paucibacter is the third dominant genus in DZHJ. It has been found in freshwater [25], baijiu [26], wine [27], fruit wine [28], and solid fermented vinegar [29], and studies have shown that Paucibacter is associated with flavor substances such as total phenols, D-erythronolactone, and pyrazines [28,29]; therefore, Paucibacter could be potential flavor bacterium producers of DZHJ. In the study of natural fermented passion fruit wine, Ye et al. [28] found that nearly half of the bacteria were members of Paucibacter, indicating that the bacteria were mainly derived from the environment. Paucibacter in DZHJ may come from the environment or Jiuqu. However, Paucibacter has not been found in current Huangjiu [30,31,32], so it is the characteristic genus of DZHJ, which can be used as the identification genus of DZHJ. During DZHJ fermentation, the abundance of this genus first increased and then decreased, reaching a peak (7.16%) on day 5. Previous studies have demonstrated that Pediococcus exhibits a remarkably strong acid-producing ability, which plays a pivotal role in defining the flavor profile of Huangjiu [33]. Lactobacillus members are the core bacteria of flavor synthesis in Huangjiu fermentation [4].
Lactobacillus members are lactic-acid-producing bacteria, which can promote the conversion of sugars to lactic acid, and ethyl lactate, which is formed after further esterification of lactic acid, is one of the main aroma substances in wine [34]. At the same time, Lactobacillus can produce a variety of antibacterial substances in the brewing process, such as bacteriocins, to inhibit the growth of pathogens and spoilage microorganisms. As shown in Figure 4B, the abundance of Klebsiella and Lactococcus on the first day of fermentation was 2.88% and 0.40%, respectively, and the abundance gradually decreased to 0.15% and 0.17% as the fermentation progressed.
Fungi are essential for maintaining the normal fermentation of food [9]. At the genus level, a total of 32 key fungal genera (abundance ≥ 0.1%) were detected, of which 8 fungal genera had an abundance ≥ 1%. They are Saccharomyces, Aspergillus, Candida, Mortierella, Pichia, Thermoascus, Hygrocybe, Clavispora, etc. Saccharomyces and Aspergillus also have advantages among the Shaoxing Huangjiu fungi genera [13].
Saccharomyces is dominant in the PRF stage, followed by Aspergillus and Candida (Figure 4C,D). The abundance of Aspergillus and Candida decreased gradually with fermentation, which was in line with the results of Xu et al. [35]. After the first day, Saccharomyces abundance increased, indicating the beginning of alcoholic fermentation. Saccharomyces plays a crucial role in alcoholic fermentation. It has an efficient sugar conversion ability, which can produce a variety of flavor esters and contribute to ethanol and organic acids [36,37]. Aspergillus can secrete amylase and protease in the fermentation environment, thereby promoting the hydrolysis of residual starch and protein after fermentation [38] and contributing greatly to the formation of fermentation flavor [39,40]. Candida is also found in many microorganisms of Huangjiu, but it is not the dominant fungal genus.
In the POF stage, Saccharomyces, Aspergillus, and Mortierella were present as major populations. The relative content of Saccharomyces decreased significantly compared with that in the PRF stage because the alcohol concentration in the fermentation environment in the POF stage was higher, and the fermentation function, fermentation activity, and survival rate of Saccharomyces cells were inhibited. The trend of Aspergillus and Mortierella was opposite to that of Saccharomyces, which was consistent with the study of PRF. Mortierella also has an advantage among the Huangjiu lees fermentation bacteria genera [18]. At the same time, the relative content of Pichia, Hygrocybe, and Clavispora increased, indicating that these fungi were not sensitive to alcohol. Pichia is important in the brewing process because it has a strong fermentation capacity and can produce higher alcohols, lipids, and acids [41]. In the POF stage, there are still a lot of unknown fungi, and rich microbial resources in the Huangjiu fermentation process need further research.
In summary, microbial diversity is rich during DZHJ fermentation, and the fermentation process has a significant effect on the relative abundance of microorganisms.

3.5. Beta Diversity Analysis

To determine the different DZHJ fermentation stages involving bacteria and fungi microbial communities, a principal component analysis (PCA) was used. According to Figure 5A, the variance contribution rates of PC1 and PC2 were 96.67% and 2.58%, respectively. The two principal components explained 99.25% of the variables, indicating that the two principal components could represent the similarity of bacterial community structures. The samples distributed in the same quadrant are similar, and the farther away, the lower the similarity. PR2, PR3, PR4, PR5, and PR6 were distributed in the second and third quadrants, indicating that the bacterial community structure in the PRF stage was similar. PO2, PO3, PO4, PO5, and PO6 were in the first and fourth quadrants, indicating that the bacterial communities in the POF stage were similar. The PO1 stage is also in the second quadrant, which is similar to the community structure in the PRF stage because the increase in alcohol concentration has little effect on the microbial community in a short time, which is also reflected in the previous study. According to Figure 5B, the variance contribution rates of PC1 and PC2 were 99.58% and 0.21%, respectively, and the two principal components explained 99.79% of the variables, indicating that the two principal components could represent the similarity of fungal community structures. The PRF stage was concentrated in the second and third quadrants, and PO1 was also similar to the PRF stage in the third quadrant, which was consistent with the bacterial community structure. According to the PCA loading diagram (Figure S3), we can observe the community dynamics of microorganisms.
In order to analyze the differences of microbial communities in different fermentation periods, 12 Huangjiu samples were grouped on the bacterial and fungal communities according to the above results, namely bacteria: group A (PR2, PR3, PR4, PR5, PR6, and PO1) and group B (PR1, PO2, PO3, PO4, PO5, and PO6) and fungi: group C (PR1, PR2, PR3, PR4, PR5, PR6, and PO1) and group D (PO2, PO3, PO4, PO5, and PO6) for further analysis.

3.6. Significant Differences Between Groups of Analysis

To further examine the variations in bacterial and fungal community structures across different Huangjiu samples, based on the results of the previous part, 12 Huangjiu samples were grouped on the basis of bacterial and fungal communities, and an LEfse analysis was performed with an LDA threshold score of 3.0. As shown in Figure 6A,B, there are 8 different genera in the bacteria and 47 different genera in the fungal communities (p < 0.05). In the bacterial community, the genera with significant differences in group A were Enterococcus and Paucibacter, and in group B was Weissella. In the fungal community, Saccharomyces and Thermoascus were significantly different in group C, and Mortierella, Aspergillus, Hygrocybe, Clavispora, and Malassezia were significantly different in group D. This is associated with the fermentation environment in the process of Huangjiu fermentation as fermentation conditions affect the growth of microorganisms.

3.7. Correlation Analysis at Genus Level

Microbial interactions are one of the important factors affecting the structure of microbial communities. In order to determine the relationship between different genera in the microbial community structure, the Pearson correlation coefficient and p value were used to construct a correlation network diagram between dominant bacteria and fungi [42]. As shown in Figure 7A, Weissella and Enterococcus, Paucibacter, Pediococcus, Staphylococcus, and Klebsiella were negatively correlated. Klebsiella was negatively correlated with Lactobacillus and positively correlated with Pediococcus, Staphylococcus, and Lactococcus.
For fungi (Figure 7B), Saccharomyces was negatively correlated with Aspergillus, Wickhamomyces, Mortierella, and Clavispora, which corresponds with Yu et al.’s [13] results. There was a positive correlation between Aspergillus and Mortenella. Wickerhamomyces and Clavispora were positively correlated. Microbes in the fermentation process experienced mutual coordination and restrained each other to improve DZHJ, which makes a significant contribution to the flavor [43].

3.8. Bacteria Gene Function Prediction

3.8.1. KEGG Metabolic Pathway Analysis

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics database that includes information on genomes, chemical substances, and systemic functions. According to the KEGG database’s primary annotation level (Figure 8), all genes are classified into six major categories, with metabolism being the most abundant, accounting for 76.92% of all annotated genes in the DZHJ microorganisms. This is followed by genetic information processing and environmental information processing, which account for 8.77% and 7.76% of annotated genes, respectively. A total of 45 secondary pathways were annotated, the first 15 of which are shown in Figure 8B. The most prevalent pathways in the DZHJ microorganisms are global and overview maps (40.71%), carbohydrate metabolism (10.75%), amino acid metabolism (5.17%), and membrane transport (5.45%). Other significant functions in the DZHJ microbiota include nucleotide metabolism (4.38%), energy metabolism (4.14%), the metabolism of cofactors and vitamins (3.64%), translation (3.83%), replication and repair (3.24%), lipid metabolism (2.21%), and signal transduction (2.26%). These metabolic pathways indicate that DZHJ possesses a robust genetic repertoire capable of utilizing the carbohydrates, amino acids, and lipids present in the brewing raw materials.

3.8.2. COG Functional Annotation Analysis

The protein sequences of the DZHJ samples were compared with the eggNOG database to obtain the corresponding COGs (clusters of orthologous groups of proteins). As shown in Figure 9, the most abundant functional genes in the Huangjiu fermentation microorganisms are carbohydrate transport and metabolism (9.35%), amino acid transport and metabolism (8.18%), translation, ribosomal structure and biogenesis (7.78%), transcription (6.95%), and replication, recombination, and repair (6.71%). Other functional genes with relatively high proportions include inorganic ion transport and metabolism (5.44%), cell wall/membrane/envelope biogenesis (5.35%), energy production and conversion (4.56%), nucleotide transport and metabolism (4.02%), coenzyme transport and metabolism (3.70%), signal transduction mechanisms (3.56%), post-translational modification, protein turnover, chaperones (3.12%), and lipid transport and metabolism (3.02%). The growth, proliferation, and genetic stability of microorganisms are inseparable from DNA replication, recombination, repair, and transcription, hence the abundance of related functional genes. Additionally, the main components of the brewing raw materials are carbohydrates and proteins, and the high proportion of genes involved in carbohydrate metabolism, amino acid metabolism, and energy metabolism in the Huangjiu fermentation microorganisms ensures the normal progress of the fermentation process to a certain extent [44]. Besides the annotated functional genes, there is also a significant proportion of unknown functional genes (9.34%) in the DZHJ microorganisms, indicating the richness of the microbial resources and suggesting that it is a treasure trove of functional genes awaiting further exploration.
In summary, carbohydrate and amino acid metabolism are highly representative in the DZHJ microbial community, playing pivotal roles in Huangjiu fermentation. Carbohydrate metabolism drives the conversion of complex sugars into fermentable monosaccharides, which are subsequently utilized by yeasts (e.g., Saccharomyces) to produce ethanol, a key component of Huangjiu. Concurrently, lactic acid bacteria (e.g., Lactobacillus and Weissella) metabolize carbohydrates to produce organic acids, such as lactic acid and acetic acid, which not only lower the pH of the fermentation environment but also contribute to the tangy flavor and microbial stability of Huangjiu. Amino acid metabolism, on the other hand, is crucial for the synthesis of flavor compounds. Through pathways such as the Ehrlich pathway, amino acids like leucine, valine, and phenylalanine are converted into higher alcohols (e.g., isoamyl alcohol and phenylethanol) and esters (e.g., ethyl acetate), which impart fruity, floral, and aromatic notes to Huangjiu. These metabolic activities collectively influence the alcohol content, flavor complexity, and acidity, directly or indirectly determining the sensory quality and taste of Huangjiu. Future research can further explore the specific mechanisms of these metabolic pathways in Huangjiu fermentation and how to optimize the microbial community structure to improve the quality and stability of DZHJ.

4. Conclusions

This research adopts high-throughput sequencing technologies to determine the composition of the microbial community structure and succession in the process of fermentation in traditional DZHJ. The OTUs results showed that in traditional DZHJ, a large number of microbial fermentation processes exist, and the depth of the sequencing proved the reliability of the sequencing results. An Alpha diversity analysis showed that abundant microbial flora existed in the traditional DZHJ fermentation process. With continuous fermentation, the microbial community was also in succession, and the bacterial diversity gradually decreased, while the fungal diversity gradually increased. Throughout the fermentation process, Bacillota, Proteobacteria, Ascomycota, Basidiomycota, and Mortierellomycota were the main dominant phyla. Weissella, Enterococcus, Paucibacter, Pediococcus, Klebsiella, Lactococcus, Staphylococcus, Lactobacillus, and Limosilactobacillus were the dominant bacterial genera. Among them, Paucibacter is the characteristic genus of DZHJ, which can be used as the origin identification genus of DZHJ. This is great progress in the research on DZHJ. Saccharomyces, Aspergillus, Candida, Mortierella, Pichia, Hygrocybe, Thermoascus, and Clavispora were the dominant fungal genera. A Beta diversity analysis showed that the microbial communities of PRF and POF were significantly different, which were divided into bacteria: group A (PR2, PR3, PR4, PR5, PR6, and PO1) and group B (PR1, PO2, PO3, PO4, PO5, and PO6) and fungi: group C (PR1, PR2, PR3, PR4, PR5, PR6, and PO1) and group D (PO2, PO3, PO4, PO5, and PO6). Further, an LEfse analysis showed that the differential microbial genera were Enterococcus, Paucibacter (A), Weissella (B), Saccharomyces, Thermoascus (C), Mortierella, Aspergillus, Hygrocybe, Clavispora, and Malassezia (D). The results of a Pearson correlation analysis on microbial communities at the genus level showed that Weissella and Lactobacillus were negatively correlated with Paucibacter, Pediococcus, Klebsiella, and Staphylococcus, and Saccharomyces was negatively correlated with Aspergillus, Wickerhamomyces, Mortierella, and Clavispora. This study reveals the succession patterns of the microbial community during the DZHJ fermentation process, providing theoretical support for enhancing the quality and stability of DZHJ.
This study did not measure the nutritional characteristics of the samples (such as the sugar content, aromatic compounds, etc.), which limits the in-depth analysis of the relationship between microbial communities and the fermentation process. Future research could incorporate these data to more comprehensively reveal the dynamic changes during fermentation.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/fermentation11030138/s1. Table S1. Quantitative data on bacterial phyla in samples. Table S2. Quantitative data on bacterial genera in the sample. Table S3. Quantitative data on fungal phyla in samples. Table S4. Quantitative data on fungal genera in the sample. Figure S1. Bacterial operational taxonomic units (OTUs). Venn diagrams across different fermentation stages. Figure S2. Fungal operational taxonomic units (OTUs). Venn diagrams across different fermentation stages. Figure S3. Bacterial and fungal PCA loading plot.

Author Contributions

Conceptualization, Y.L.; methodology, L.C. and G.G.; software, T.Z.; validation, Y.L.; formal analysis, J.Z.; investigation, J.Z.; resources, Y.L. and G.G.; data curation, L.C.; writing—original draft preparation, L.C.; writing—review and editing, Q.L.; visualization, L.C. and T.Z.; supervision, Q.L.; project administration, Q.L.; funding acquisition, Q.L. and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Fundamental Research Program of Shanxi Province, grant number 202303021212102; the earmarked fund for the Modern Agro-Industry Technology Research System of Shanxi Province, grant number 2024CYJSTX03-04; and the Science and Technology Innovation and Promotion Project of Shanxi Agricultural University, grant number CXGC2023093.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Most of the data generated or analyzed during this study were incorporated into this manuscript; further inquiries can be directed to the corresponding author.

Conflicts of Interest

Author Guoqiang Gao was employed by Shanxi Daixian Guixi Wine Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Zheng, C.Q.; Chen, C.; Jiang, Y.J.; Zhang, L.; Xie, G.F. Studies on Yellow Rice Wine Technology Using Mixed Fermentation of Saccharomyces cerevisiae and Hansenula anomala. J. Chin. Inst. Food Sci. Technol. 2024, 24, 146–159. [Google Scholar] [CrossRef]
  2. Liu, M.W. Determination of 9 Nutrient Elements and 5 Pollution Elements in Millet Huangjiu by ICP-MS. Liquor-Mak. Sci. Technol. 2023, 126–129. [Google Scholar] [CrossRef]
  3. Xie, G.; Zheng, H.; Qiu, Z.; Lin, Z.; Peng, Q.; Dula Bealu, G.; Elsheery, N.I.; Lu, Y.; Shen, C.; Fu, J.; et al. Study on relationship between bacterial diversity and quality of Huangjiu (Chinese Rice Wine) fermentation. Food Sci. Nutr. 2021, 9, 3885–3892. [Google Scholar] [CrossRef]
  4. Peng, Q.; Zheng, H.; Meng, K.; Yu, H.; Xie, G.; Zhang, Y.; Yang, X.; Chen, J.; Xu, Z.; Lin, Z.; et al. Quantitative study on core bacteria producing flavor substances in Huangjiu (Chinese yellow rice wine). LWT 2022, 168, 113900. [Google Scholar] [CrossRef]
  5. Wang, K.; Wu, H.J.; Wang, J.X.; Ren, Q. Microbiota Composition during Fermentation of Broomcorn Millet Huangjiu and Their Effects on Flavor Quality. Foods 2023, 12, 2680. [Google Scholar] [CrossRef] [PubMed]
  6. Yin, S.; Huang, M.Q.; Wang, J.X.; Liu, B.; Ren, Q. Microbial Community Dynamics and the Correlation between Specific Bacterial Strains and Higher Alcohols Production in Tartary Buckwheat Huangjiu Fermentation. Foods 2023, 12, 2664. [Google Scholar] [CrossRef]
  7. Zhao, Y.; Li, M.; Zhan, P.; Wang, P.; He, W.Y.; Tian, H.L. A quality comparison for Xiecun Huangjiu with different aging stages based on chemical profile, aroma composition and microbial succession. Food Chem. X 2024, 21, 101132. [Google Scholar] [CrossRef]
  8. Song, J.Y.; Cen, D.Y.; Liang, L.Y.; Bai, Y.Z.; Liu, M.C.; Wang, C.Y. Correlation analysis between volatile flavor compounds and microbial community of Nanyang red-millet Huangjiu during fermentation process. China Brew. 2023, 42, 135–139. [Google Scholar] [CrossRef]
  9. Zhang, H.; Wang, L.; Tan, Y.; Wang, H.; Yang, F.; Chen, L.; Hao, F.; Lv, X.; Du, H.; Xu, Y. Effect of Pichia on shaping the fermentation microbial community of sauce-flavor Baijiu. Int. J. Food Microbiol. 2020, 336, 108898. [Google Scholar] [CrossRef]
  10. Gu, X.D.; Liu, Y.L.; Xi, X.L.; Chen, Z.Z.; Ma, Y.L.; Liu, X.; Wang, Y.; Li, S. Microbial Diversity in Six Kinds of Huangjiu Koji Based on High-Throughput Sequencing Techniques. Sci. Technol. Food Ind. 2022, 43, 148–157. [Google Scholar] [CrossRef]
  11. Zhang, J.X.; Li, T.; Zou, G.; Wei, Y.J.; Qu, L.B. Advancements and Future Directions in Yellow Rice Wine Production Research. Fermentation 2024, 10, 40. [Google Scholar] [CrossRef]
  12. Chen, L.; Dai, Y.J. Analysis of microbial community diversity in Danyang Huangjiu Jiuqu based on Illumina MiSeq high-throughput sequencing technique. China Brew. 2024, 43, 141–145. [Google Scholar]
  13. Yu, H.; Li, Q.; Guo, W.; Chen, C.; Ai, L.; Tian, H. Dynamic analysis of volatile metabolites and microbial community and their correlations during the fermentation process of traditional Huangjiu (Chinese rice wine) produced around Winter Solstice. Food Chem. X 2023, 18, 100620. [Google Scholar] [CrossRef] [PubMed]
  14. Yan, Y.; Chen, H.; Sun, L.; Zhang, W.; Lu, X.; Li, Z.; Xu, J.; Ren, Q. The changes of microbial diversity and flavor compounds during the fermentation of millet Huangjiu, a traditional Chinese beverage. PLoS ONE 2022, 17, e262353. [Google Scholar] [CrossRef]
  15. Luo, Y.; Zhang, C.H.; Liao, H.; Luo, Y.C.; Huang, X.L.; Wang, Z.Y.; Xia, X.L. Integrative metagenomics, volatilomics and chemometrics for deciphering the microbial structure and core metabolic network during Chinese rice wine (Huangjiu) fermentation in different regions. Food Microbiol. 2024, 122, 104569. [Google Scholar] [CrossRef]
  16. Xie, G.F.; Wang, L.; Gao, Q.K.; Yu, W.J.; Hong, X.T.; Zhao, L.Y.; Zou, H.J. Microbial community structure in fermentation process of Shaoxing rice wine by Illumina-based metagenomic sequencing. J. Sci. Food. Agric. 2013, 93, 3121–3125. [Google Scholar] [CrossRef]
  17. Zhang, J.P.; Yu, J.J.; Jia, F.C.; Ma, W.R.; Jin, Y.L.; Zhu, Z.J.; Guo, Q.; Zhang, H.P.; Zhang, Y.H.; Xue, J. Macrogenomics-based analysis of the relationship between microbial diversity and flavour formation during the fermentation of Tibetan highland barley wine. Food Ferment. Ind. 2024, 1–15. [Google Scholar] [CrossRef]
  18. Jia, M.; Yang, X.; Zhou, G.Y.; Guo, Y.Q.; Zhu, L.; Zheng, Y. High-throughput Sequencing Analysis of Microbial Community Diversity in Xiangzao of Made from Huangjiu (Yellow Rice Wine) Vinasse during Spontaneous Fermentation. Food Sci. 2023, 44, 190–196. [Google Scholar] [CrossRef]
  19. Peng, Q.; Zheng, H.; Li, S.; Meng, K.; Yu, H.; Zhang, Y.; Yang, X.; Li, L.; Xu, Z.; Xie, G.; et al. Analysis on driving factors of microbial community succession in Jiuyao of Shaoxing Huangjiu (Chinese yellow rice wine). Food Res. Int. 2023, 172, 113144. [Google Scholar] [CrossRef]
  20. Zhao, C.; Su, W.; Mu, Y.C.; Jiang, L.; Mu, Y. Correlations between microbiota with physicochemical properties and volatile flavor components in black glutinous rice wine fermentation. Food Res. Int. 2020, 138, 109800. [Google Scholar] [CrossRef]
  21. Ren, Q.; Sun, L.P.; Sun, Z.B.; Liu, Q.S.; Lu, X.; Li, Z.P.; Xu, J.L. Bacterial succession and the dynamics of flavor compounds in the Huangjiu fermented from corn. Arch. Microbiol. 2020, 202, 299–308. [Google Scholar] [CrossRef] [PubMed]
  22. Yang, Z.; Li, W.; Yuan, Y.; Liang, Z.; Yan, Y.; Chen, Y.; Ni, L.; Lv, X. Metagenomic Insights into the Regulatory Effects of Microbial Community on the Formation of Biogenic Amines and Volatile Flavor Components during the Brewing of Hongqu Rice Wine. Foods 2023, 12, 3075. [Google Scholar] [CrossRef]
  23. Zhang, E.H.; Feng, Z.T.; He, P.; Liu, P.P.; Jian, Y.; Xu, Y.T. Microbial Community Structure and Diversity of Highland Barley Koji in Tibet. J. Chin. Inst. Food Sci. Technol. 2024, 24, 381–392. [Google Scholar] [CrossRef]
  24. Gao, R.J.; Peng, P.; Yu, L.; Wan, B.; Liang, X.T.; Liu, P.L. Metagenomic analysis reveals the correlations between microbial communities and flavor compounds during the brewing of traditional Fangxian huangjiu. Food Biosci. 2024, 58, 103816. [Google Scholar] [CrossRef]
  25. You, D.J.; Chen, X.G.; Xiang, H.Y.; Ouyang, L.; Yang, B. Isolation, Identification and Characterization of a Microcystin-degrading Bacterium Paucibacter sp. Strain CH. Environ. Sci. 2014, 35, 313–318. [Google Scholar] [CrossRef]
  26. Sun, L.P. Culturing the Baijiu Microorganism from Fermentation Vinasse and Pit Mud with Culturomics and Amplicon-Based Metagenomic Approaches. Master’s Thesis, Beijing Technology and Business University, Beijing, China, 2020. [Google Scholar] [CrossRef]
  27. Barata, A.; Malfeito-Ferreira, M.; Loureiro, V. The microbial ecology of wine grape berries. Int. J. Food Microbiol. 2012, 153, 243–259. [Google Scholar] [CrossRef]
  28. Ye, X.F.; Zhang, X.Y.; Hao, L.F.; Lin, Q.; Bao, Y.Y. Effects of Different Fermentation Methods on the Quality and Microbial Diversity of Passion Fruit Wine. Fermentation 2023, 9, 439. [Google Scholar] [CrossRef]
  29. Zhang, X.L.; Zhang, X.R.; Yan, Y.H.; Liu, Y.; Zhao, X.Y.; Xu, H. Relationship between flavor compounds and changes of microbial community in the solid fermented vinegar. Biosci. Biotechnol. Biochem. 2022, 86, 1581–1589. [Google Scholar] [CrossRef]
  30. Xu, J.Z.; Zhang, Y.Y.; Zhang, W.G. Correlation between changes in flavor compounds and microbial community ecological succession in the liquid fermentation of rice wine. World J. Microbiol. Biotechnol. 2023, 40, 17. [Google Scholar] [CrossRef]
  31. Wang, X.L.; Cai, G.L.; Wu, D.H.; Lu, J. Correlation between the bacterial community succession and purine compound changes during Huangjiu fermentation. Food Microbiol. 2024, 121, 104522. [Google Scholar] [CrossRef]
  32. Yang, Y.; Xia, Y.; Yan, X.; Li, S.; Ni, L.; Zhang, H.; Ni, B.; Ai, L. Insights into whereby raw wheat Qu contributes to the flavor quality of Huangjiu during brewing. LWT 2023, 178, 114619. [Google Scholar] [CrossRef]
  33. Yang, Y.; Xia, Y.; Hu, W.; Tao, L.; Liu, H.; Xie, C.; Bai, W.; Ai, L. Soaking induced discrepancies in oenological properties, flavor profiles, microbial community and sensory characteristic of Huangjiu (Chinese rice wine). LWT 2020, 139, 110575. [Google Scholar] [CrossRef]
  34. Ma, N. Diversity Analysis of Baijiu-Making Microorganisms. Liquor-Mak. Sci. Technol. 2022, 3, 105–110. [Google Scholar] [CrossRef]
  35. Liu, S.P.; Chen, Q.L.; Zou, H.J.; Yu, Y.J.; Zhou, Z.L.; Mao, J.; Zhang, S. A metagenomic analysis of the relationship between microorganisms and flavor development in Shaoxing mechanized huangjiu fermentation mashes. Int. J. Food Microbiol. 2019, 303, 9–18. [Google Scholar] [CrossRef] [PubMed]
  36. Wu, Q.; Chen, L.Q.; Xu, Y. Yeast community associated with the solid state fermentation of traditional Chinese Maotai-flavor liquor. Int. J. Food Microbiol. 2013, 166, 323–330. [Google Scholar] [CrossRef]
  37. Wu, Q.; Xu, Y.; Chen, L.Q. Diversity of yeast species during fermentative process contributing to Chinese Maotai-flavour liquor making. Lett. Appl. Microbiol. 2012, 55, 301–307. [Google Scholar] [CrossRef]
  38. Yang, Y.J.; Hu, W.Y.; Xia, Y.J.; Mu, Z.Y.; Tao, L.R.; Song, X. Flavor Formation in Chinese Rice Wine (Huangjiu): Impacts of the Flavor-Active Microorganisms, Raw Materials, and Fermentation Technology. Front. Microbiol. 2020, 11, 580247. [Google Scholar] [CrossRef]
  39. Chen, B.; Wu, Q.; Xu, Y. Filamentous fungal diversity and community structure associated with the solid state fermentation of Chinese Maotai-flavor liquor. Int. J. Food Microbiol. 2014, 179, 80–84. [Google Scholar] [CrossRef]
  40. Diganta, N.; Nitesh, B.; Ashis, B.; Takashi, O.; Hideto, T. Community structure and metabolic potentials of the traditional rice beer starter ‘emao’. Sci. Rep. 2021, 11, 14628. [Google Scholar] [CrossRef]
  41. Huang, Y.G.; Chen, Y.Q.; Wu, G.Q.; Yang, G.H.; Zhang, X.K.; Huang, P.; Jiang, Y.; Xu, X.L. Analysis of Yeast Strains in Fermented Grains during Stacking Fermentation of Jiang-flavor Liquor. Liquor-Mak. Sci. Technol. 2013, 6, 8–13. [Google Scholar] [CrossRef]
  42. Yan, Y.; Sun, L.; Xing, X.; Wu, H.; Lu, X.; Zhang, W.; Xu, J.; Ren, Q. Microbial succession and exploration of higher alcohols-producing core bacteria in northern Huangjiu fermentation. Amb Express 2022, 12, 79. [Google Scholar] [CrossRef] [PubMed]
  43. Liang, Z.C.; Su, H.; Lin, X.Z.; He, Z.G.; Li, W.X.; Deng, D.L. Microbial communities and amino acids during the fermentation of Wuyi Hong Qu Huangjiu. LWT 2020, 130, 109743. [Google Scholar] [CrossRef]
  44. Zheng, D.W. Study on Microbial Community and Functional Genes Diversity in Starters of Huangjiu Based on Metagenomic Approach. Master’s Thesis, Shanghai Institute of Technology, Shanghai, China, 2022. [Google Scholar]
Fermentation 11 00138 g001
Figure 1. Flow chart of traditional DZHJ brewing.
Figure 1. Flow chart of traditional DZHJ brewing.
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Figure 2. (A) Bacterial OTUs petal diagram. (B) Fungal OTUs petal diagram. (C) Bacterial dilution curve. (D) Fungal dilution curve. (E) Bacterial Shannon index curve. (F) Fungal Shannon index curve.
Figure 2. (A) Bacterial OTUs petal diagram. (B) Fungal OTUs petal diagram. (C) Bacterial dilution curve. (D) Fungal dilution curve. (E) Bacterial Shannon index curve. (F) Fungal Shannon index curve.
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Figure 3. Bacterial (A) and fungal (B) community structure succession at the phylum level.
Figure 3. Bacterial (A) and fungal (B) community structure succession at the phylum level.
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Fermentation 11 00138 g004aFermentation 11 00138 g004b
Figure 4. Bacterial community structure succession (A) and heat map (B) at the genus level. Fungal community structure succession (C) and heat map (D) at the genus level.
Figure 4. Bacterial community structure succession (A) and heat map (B) at the genus level. Fungal community structure succession (C) and heat map (D) at the genus level.
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Figure 5. (A) Bacteria PCA score plot. (B) Fungi PCA score plot.
Figure 5. (A) Bacteria PCA score plot. (B) Fungi PCA score plot.
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Figure 6. (A) LEfse analysis of bacterial community. (B) LEfse analysis of fungal community.
Figure 6. (A) LEfse analysis of bacterial community. (B) LEfse analysis of fungal community.
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Figure 7. (A) Dominant bacteria-associated network diagram. (B) Dominant fungi-associated network diagram.
Figure 7. (A) Dominant bacteria-associated network diagram. (B) Dominant fungi-associated network diagram.
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Figure 8. Statistical analysis of KEGG functional taxonomic abundance. (A) KEGG level-1; (B) KEGG level-2.
Figure 8. Statistical analysis of KEGG functional taxonomic abundance. (A) KEGG level-1; (B) KEGG level-2.
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Figure 9. Statistical analysis of COG functional taxonomic abundance. (A) COG level-1; (B) COG level-2.
Figure 9. Statistical analysis of COG functional taxonomic abundance. (A) COG level-1; (B) COG level-2.
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Table 1. Alcohol content of fermentation samples at different stages.
Table 1. Alcohol content of fermentation samples at different stages.
SampleAlcohol Content (% vol)SampleAlcohol Content (% vol)
PR13.00 ± 0.10PO123.30 ± 0.20
PR27.80 ± 0.10PO223.10 ± 0.20
PR39.80 ± 0.20PO322.40 ± 0.10
PR410.80 ± 0.10PO422.80 ± 0.10
PR510.80 ± 0.10PO523.05 ± 0.05
PR611.00 ± 0.10PO622.60 ± 0.10
Table 2. Amplification of primer information.
Table 2. Amplification of primer information.
TypeAmplification RegionPrimer NamePrimer Sequence
bacteriabacteria 16SV3 + V4338F5′-ACTCCTACGGGAGGCAGCA-3′
806R5′-GGACTACHVGGGTWTCTAAT-3′
fungifungi ITS1ITS1F5′-CTTGGTCATTTAGAGGAAGTAA-3′
ITS25′-GCTGCGTTCTTCATCGATGC-3′
Table 3. OUTs and Alpha diversity index.
Table 3. OUTs and Alpha diversity index.
Sample
Number		OTUsACE IndexChao1 IndexSimpson IndexShannon Index
BacteriaFungiBacteriaFungiBacteriaFungiBacteriaFungiBacteriaFungi
PR11002271022271072270.44950.54941.77142.3647
PR21211241211251211250.53960.37221.90071.3745
PR3898090819980.60.57390.23991.93000.9415
PR42061762061762061760.54610.50581.89702.0268
PR51041371051381071420.59230.39392.00161.4793
PR61381071411071421070.56860.43161.91281.5614
PO12286732286742286740.56300.42381.97572.0964
PO22421850242188524218770.39760.90301.53396.2342
PO31971694197169519916970.39630.97491.45207.7386
PO43051649305165130616500.33730.96251.42937.1064
PO57341818734182073518210.37220.95921.70187.1900
PO63702007370203837020290.38830.97031.61857.3695
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Cui, L.; Zhu, J.; Zhang, T.; Li, Q.; Li, Y.; Gao, G. High-Throughput Sequencing Reveals the Microbial Community Succession Process During the Fermentation of Traditional Daizhou Huangjiu. Fermentation 2025, 11, 138. https://doi.org/10.3390/fermentation11030138

AMA Style

Cui L, Zhu J, Zhang T, Li Q, Li Y, Gao G. High-Throughput Sequencing Reveals the Microbial Community Succession Process During the Fermentation of Traditional Daizhou Huangjiu. Fermentation. 2025; 11(3):138. https://doi.org/10.3390/fermentation11030138

Chicago/Turabian Style

Cui, Linhua, Jiaying Zhu, Ting Zhang, Qi Li, Yunlong Li, and Guoqiang Gao. 2025. "High-Throughput Sequencing Reveals the Microbial Community Succession Process During the Fermentation of Traditional Daizhou Huangjiu" Fermentation 11, no. 3: 138. https://doi.org/10.3390/fermentation11030138

APA Style

Cui, L., Zhu, J., Zhang, T., Li, Q., Li, Y., & Gao, G. (2025). High-Throughput Sequencing Reveals the Microbial Community Succession Process During the Fermentation of Traditional Daizhou Huangjiu. Fermentation, 11(3), 138. https://doi.org/10.3390/fermentation11030138

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