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. 2025 Nov 27;32:103313. doi: 10.1016/j.fochx.2025.103313

Unveiling the impact of soft wheat proportions on microbial communities and quality attributes of medium-temperature Daqu

Yong Yang c,1, Mengyao Niu a,1, Xiangrong Fan b, Xiang Chen c, Xiangyang Ge c, Beizhong Han a,b, Xiaoxue Chen a,b,⁎
PMCID: PMC12704255  PMID: 41404046

Abstract

Daqu serves as a pivotal fermentation starter in Baijiu and vinegar production, driving complex microbial interactions that critically shape its physicochemical properties and sensory qualities. This study systematically explored the impact of varying soft wheat proportions on the microbial community dynamics, physicochemical parameters, metabolic activities, sensory profiles, and resultant quality attributes of medium-temperature Daqu. The findings demonstrated that increased soft wheat content significantly enhanced sensory attributes but led to a reduction in the diversity and abundance of volatile flavor compounds. Microbial profiling revealed distinct bacterial and fungal community structures, with key taxa exhibiting strong correlations with specific physicochemical and sensory parameters. These results underscore the critical role of raw material composition in modulating the microbial ecosystem and functional attributes of Daqu, offering actionable insights for optimizing starter production and enhancing Baijiu quality.

Keywords: Daqu, Soft wheat, Hard wheat, Microbial community, Volatile compounds

Highlights

  • •

    Higher soft wheat ratio improves Daqu sensory quality but reduces flavor diversity.

  • •

    Wheat composition shapes microbial communities, critical for Daqu characteristics.

  • •

    Provides practical guidance for optimizing Daqu to enhance Baijiu quality.

1. Introduction

Baijiu is a traditional Chinese alcoholic beverage with deep cultural significance. Among its various types, strong-flavor Baijiu stands out due to its characteristic fragrant aroma, smooth mouthfeel, and long-lasting aftertaste. This variety dominates the Baijiu industry, accounting for over 70 % of the total production and market share (Zhu et al., 2022). The quality and distinctive flavor profile of strong-flavor Baijiu are critically influenced by its saccharification and fermentation starter, medium-temperature Daqu (MTD). MTD plays a pivotal role in shaping the biochemical and sensory attributes of the final product (Kang, Huang, et al., 2024; Zheng et al., 2011). MTD is primarily produced using wheat as the main raw material, which is moistened and compacted into a solid-state substrate. This substrate undergoes a spontaneous fermentation process mediated by complex and diverse microbial communities at moderate temperatures (50–60 °C) (Hou et al., 2023; Kang, Xue, et al., 2022).

Raw materials play a pivotal role in the fermentation process and significantly influence the quality of the final product (Pang et al., 2020a). In MTD, the composition of raw materials—particularly the ratio of soft wheat to hard wheat, −has been shown to impact the microbial community structure, physicochemical properties, and overall fermentation performance (Yu, 2019). Wheat is broadly classified as soft or hard based on grain hardness (Sharma et al., 2022), with distinct processing characteristics arising from differences in the microstructure of starch and protein (Katyal et al., 2018; Katyal et al., 2020; Kumar et al., 2016; Sharma et al., 2022). In flour-based foods, hard wheat is primarily used for bread production due to its superior stability during extended fermentation, whereas soft wheat is preferred for biscuits and cakes because of its finer texture and lower protein content (Kumar et al., 2016; Rinaldi et al., 2015). Although there has been research on wheat varieties and their geographical origins concerning Daqu properties, limited attention has been given to the specific impact of soft and hard wheat ratios on Daqu quality. Traditionally, soft wheat has been the preferred choice for Daqu production in Baijiu distilleries. However, the effects of varying ratios of soft and hard wheat on the microbial, physicochemical, and functional properties of Daqu remain underexplored. Addressing this knowledge gap could provide valuable insights into optimizing raw materials of Daqu and enhancing the fermentation processes that drive the sensory and quality attributes of Baijiu.

In this study, we investigated the impact of altering the ratio of soft wheat to hard wheat in the raw materials used for medium-temperature Daqu (MTD) fermentation. Our objective was to evaluate how these variations influence the physicochemical properties, sensory attributes, volatile flavor profiles, and microbial community structure of Daqu. Through gaining deeper insights into these factors, this study aims to elucidate the key determinants of Daqu quality and provide practical guidance for optimizing its production to achieve high-quality Baijiu.

2. Materials and methods

2.1. Experimental design and sampling

Medium temperature Daqu were collected in March 2022 from prominent Baijiu distilleries. Based on the ratio of soft wheat to hard wheat, the Daqu samples were classified into five groups: RM0 (100 % hard wheat and 0 % soft wheat), RML (10–30 % soft wheat with the remainder being hard wheat), RMM (40–60 % soft wheat), RMH (70–90 % soft wheat), and RM100 (100 % soft wheat and 0 % hard wheat). For each group, three independent biological replicates were prepared. Each replicate was a composite sample created by pooling sub-samples from the top, middle, and bottom of the storage room. All the samples were stored at −80 °C for further analysis.

2.2. Physicochemical and sensory analysis

Moisture content of Daqu samples was determined using a drying method, where 5.0 g of each sample was dried at 105 °C for 3 h (Pang et al., 2020b). For pH and acidity analysis, 1 g of each sample was homogenized with 10 mL of distilled water. The pH of the homogenized solution was measured using an S210 digital pH meter (Metler-Toledo, Switzerland). Titratable acidity was measured by titration with 0.1 mol/L NaOH to a phenolphthalein endpoint pH of 8.2 (Pang et al., 2018). Total starch content was quantified following the method outlined by (Huang et al., 2021). Fermentation capacity was assessed by measuring the weight change of the fermentation system over a 72 h period, which corresponded to the amount of carbon dioxide produced (unit: g/ (1 g-72 h); symbol: U). Saccharification power (U/g) was determined according to (Hu et al., 2021). Sensory evaluation was conducted by a trained panel of ten members experienced in sensory descriptive analysis. Four batches of Daqu samples were assessed after 40 days of incubation, focusing on three sensory attributes—aroma, cross section and sensory score—that aligned with the study objectives.

During the saccharification process, environmental parameters at the sampling site， including ambient temperature, core temperature, and relative humidity were recorded at 5 min intervals using an electronic temperature sensor (iButton, Maxim, USA), (Zhang et al., 2022).

2.3. Volatile compounds analysis

Each Daqu sample (4 g) was subjected to ultrasonic extraction in 20 mL of deionized water for 30 min. Following extraction, 8 mL of the suspension was transferred to 15 mL vials, to which 3 g of NaCl, and 10 μL of internal standard (4-methyl-2-amyl alcohol, 125 mg/L) were added. Flavor profiling was performed with an GC–MS system from Agilent Technologies Inc., equipped with an HP-FFAP column (50 m × 200 μm × 0.33 μm). Gas chromatography (GC) conditions included a helium carrier gas flow rate of 0.8 mL/min. The temperature program began with an isothermal hold at 50 °C for 2 min, followed by a ramp of 2 °C/min to 85 °C, held for 1 min, then a ramp of 5 °C/min to 230 °C, and a final isothermal hold at 230 °C for 2 min. Mass spectrometry (MS) was performed under electron ionization at an ionization energy of 70 eV. Data were acquired in full-scan mode over a mass range of m/z 20–350. The temperatures of the transfer line, ion source, and quadrupole were maintained at 250 °C, 230 °C, and 150 °C, respectively. Compound identification is based on matching against the National Institute of Standards and Technology 17th Edition (NIST 17) mass spectrometry database. And retention index (RI, RI tolerance ±20) calculations derived from a series of standard alkanes (C8-C39) and comparison with relevant literature (Kang et al., 2022).

Semi-quantification was based on the relative peak area ratio of each compound to the internal standard (IS, 4-methyl-2-amyl alcohol). The concentration of each volatile compound was calculated using the following formula.

Concentration=PeakAreacompoundPeakAreaIS××V_solventW_sample

Quality control included analysis of procedural blanks (ultrapure water processed identically to samples), triplicate injections of each sample, and monitoring of relative standard deviations (RSD). The RSD for key volatile compounds was maintained below 10 %, ensuring quantitative reliability. For data normalization, the concentration values of each compound were standardized using the z-score method. Sample dissimilarity was assessed using Bray–Curtis distance, and PLS-DA was performed to visualize clustering and identify discriminant volatile compounds. Data analysis and figure generation were conducted using OriginPro 2023 (OriginLab, USA) and R (version 4.3.2).

2.4. DNA extraction and Illumina MiSeq sequencing analysis

Total metagenomic DNA was extracted using the Fast DNA® SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA) following the manufacturer's instructions. The integrity of Genomic DNA was assessed via agarose gel electrophoresis, and DNA concentration and purity were determined using a Qubit 3.0 spectrophotometer.

Synthetic mock community sequencing with known gradient copy numbers was performed for quality assurance of each metagenomic DNA sample. The V3–V4 hypervariable region of the 16S rRNA gene was amplified using primers 341F (5’-CCTACGGGNGGCWGCAG-3′) and 805R (5’-GACTACHVGGGTATCTAATCC-3′). The PCR reaction mixture (10 μL) comprised of 1 μL 10× Taq buffer, 0.8 μL 2.5 mM dNTPs, 0.2 μL each of 10 μM forward and reverse primers, 0.2 μL Taq DNA polymerase, 3 μL template DNA, 1 μL spike-in DNA, and 3.6 μL ddH2O. Amplification conditions included an initial denaturation at 94 °C for 2 min, followed by 25 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 30 s, extension at 72 °C for 1 min, and a final extension at 72 °C for 10 min. PCR products were visualized on a 2 % agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). Quantification of purified amplicons was performed using QuantiFluor™-ST (Promega, USA). For fungal community analysis, the ITS2 region was amplified with primers ITS3 (5’-GCATCGATGAAGAACGCAGC-3′) and ITS4 (5’-TCCTCCGCTTATTGATATGC-3′). The PCR reaction volume (20 μL) contained 2 μL 10× Taq buffer, 1.6 μL 2.5 mM dNTPs, 0.4 μL each of 10 μM forward and reverse primers, 0.4 μL Taq DNA polymerase, 5 μL template DNA, 1 μL spike-in DNA, and 9.2 μL ddH2O. Amplification conditions mirrored those used for bacterial amplification. Purified amplicon sequences were then submitted to Genesky Biotechnologies Inc. (Shanghai, China) for sequencing on an Illumina NovaSeq 6000 platform (Illumina, San Diego, USA). The data presented in the study were deposited in the NCBI Sequence Read Archive (SRA) repository (PRJNA1311319).

2.5. Bioinformatics and statistical analysis

Raw sequencing reads were processed using QIIME2. Adapter and primer sequences were trimmed using the Cutadapt plugin, followed by quality control and identification of amplicon sequence variants (ASVs) using the DADA2 plugin. Bacterial and fungal ASV representative sequences were classified using pre-trained naive Bayes classifiers based on the RDP (version 11.5) and UNITE (version 8.2) databases, respectively. Quantification of ASVs involved identifying peak insertion sequences and determining read counts. Standard curves were generated for each sample using the read counts and peak copy numbers, enabling the calculation of the absolute copy number of each ASV. Functional prediction of bacterial and fungal communities was conducted using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) with reference to the MetaCyc database (Douglas et al., 2020).

3. Results and discussion

3.1. Changes of physicochemical characteristics of different Daqu during fermentation

Temperature changes in the different Daqu samples, characterized by varying initial ratios of soft wheat to hard wheat (RM0 ∼ RM100), were monitored over time (Fig. 1A &B). All samples exhibited a similar trend: an initial temperature increase, followed by a peak, and a gradual decline. While this overarching pattern was consistent across samples, substantial differences were observed in peak temperatures and the rates of temperature increase and decrease, which reflected variations in the characteristics of each sample. Additionally, a periodic decline in temperature was observed, coinciding with scheduled operational procedures for venting and turning of the Daqu blocks to facilitate cooling (Hou et al., 2024). The gradual increase in Daqu temperature would be attributed to microbial growth and the associated heat production during fermentation (Xiao et al., 2017). During fermentation, the moisture content of the samples changed in response to operational activities such as ventilation (Fig. 1C). Fig. 1(D) displayed the physicochemical properties and sensory qualities of the matured Daqu. Among the samples, RMM exhibited higher acidity, saccharification power, and fermentation activity. RM0 showed higher starch and water content, while RMH and RM100 received high sensory scores, including favorable flavor and cross-sectional characteristics, aligning with the experiences reported by workers from Baijiu distilleries. Interestingly, Hoque et al. suggested that the starch content in hard wheat was slightly lower than in soft wheat (Hoque & Islam, 2024). This difference may be attributed to the branching structure of amylopectin, which makes its molecules looser and, therefore, easier for microorganisms to break down and utilize (Luo et al., 2022).

Fig. 1.

Fig. 1

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Changes of physicochemical characteristics of Daqu with different ratio of soft wheat and hard wheat during fermentation. (A): Sample temperature, (B): environmental temperature, (C): moisture content, and (D): other physicochemical indexes and sensory characteristics of Daqu.

3.2. Flavor profiles of different Daqu with varying initial ratios of soft wheat to hard wheat

To further investigate the metabolic profiles of different Daqu samples with varying initial ratios of soft wheat to hard wheat, the volatile compound profiles of matured Daqu were analyzed using HS-SPME-GC–MS. A total of 103 volatile compounds were identified, including 19 aldehydes, 24 esters, 16 alcohols, 13 aromatics, 10 pyrazines, 4 furans, 6 alkanes, 5 acids, 2 sesquiterpenes, 2 sulfur compounds, and 2 others (Fig. 2A). Among these, esters were the most abundant class, followed by aldehydes, alcohols, and acids. Additionally, trace amounts of sulfides were detected in all samples (Fig. 2B). The composition of volatile compounds varied among the samples. Notably, RM0 exhibited higher levels of ethyl linoleate, acetal, acetaldehyde, phenylethanol, ethyl acetate, isovalerate, and acetic acid. RM0 exhibited significantly elevated concentrations of ethyl acetate, caproic acid, and ethyl caproate - key characteristic flavor components of Nong-flavor Baijiu (Wang, Tang, & Qiu, 2020) - compared to other samples. It also demonstrated a richer profile of volatile flavor compounds (including esters, alcohols, and acids). Aromatic compounds were particularly abundant in RML, while pyrazines were notably rich in RMM. Interestingly, a higher proportion of soft wheat in the initial mix was associated with fewer types of flavor compounds in the mature Daqu. This aligns with previous research indicating that the fermentation stability of soft wheat is weaker than that of hard wheat (Rinaldi et al., 2015), potentially which highlights the influence of hard wheat on Daqu flavor development during fermentation.

Fig. 2.

Fig. 2

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Volatile compounds profile in different matured Daqu samples. (A): Heatmap of volatile compounds, (B): abundance of different categories of volatile compounds and (C): Partial least squares discriminant analysis (PLS-DA) of volatile compounds.

Partial least squares discriminant analysis (PLS-DA) was conducted to further analyze the volatile flavor profiles, eliminating substances with lower variable importance in projection (VIP) values. As shown in Fig. 2C, the analysis explained 29.2 % of the total variance, with R2X at 5.7 % and R2Y at 23.5 %. The results revealed distinct differences in the flavor profiles of Daqu samples with varying proportions of soft wheat. Key differential flavor compounds (VIP > 1) included 3-octanol, caprylic acid, 2-nonanone, nonanal, 1-octen-3-ol, 2-methyl-1-butanol, ethyl acetate, hexyl hexanoate, ethyl nonanoate, and hexalactone.

3.3. Composition of bacterial communities of different Daqu

A total of 108 OTUs (bacteria) and 123 OTUs (fungi) were identified at a 100 % similarity threshold. Among the bacteria genera, Kroppenstedtia was the most prevalent accross all Daqu samples, followed by Virgibacillus. These genera play crucial roles in in Daqu fermentation (Mu et al., 2023). Kroppenstedtia was the key bacteria influencing the color formation of Daqu (Dong et al., 2024). The bacterial composition of mature Daqu varied with different proportions of soft wheat. Specifically, Bacillus was the most abundant in RMM, Staphylococcus dominated in RM0, and Scopulibacillus was more abundant in RMH. The LEfSe analysis identified nine bacterial genera with significant differences in abundance among the Daqu groups: Chloroplast, Scopulibacillus, Saccharopolyspora, Pseudonocardiaceae, Brachybacterium, Mycobacterium, Corynebacterium, Streptomyces, and Sphingomonas.

To investigate the effect of raw materials on microbial communities and their subsequent physicochemical properties, redundancy analysis (RDA) was performed (Fig. 3C). RDA was performed to investigate the relationship between bacterial communities and physicochemical factors. The model was statistically significant (permutation test, p = 0.02), with the first two axes explaining 57.1 % and 18.97 % of the total variance, respectively. Under consistent production conditions, the soft-to-hard wheat ratio in raw materials modulates the physical structure and matrix properties of Daqu, leading to divergent microenvironmental temperature and moisture profiles. These microgradients drive shifts in microbial succession and metabolic activity, which subsequently alter key physicochemical indices—including fermentation activity, saccharification power, acidity, and starch content. Ultimately, these cascading effects shape the sensory attributes and flavor characteristics of the final product. Starch content were the most influential factors driving changes in microbial abundance across the groups. Along the RDA1 axis, major bacterial genera exhibited distinct correlations with starch. Pseudonocardiaceae, Chloroplast, and Scopulibacillus displayed negative correlations with starch in their relative abundances, possibly because they have strong saccharification abilities, thereby reducing residual starch. While Corynebacterium, Mycobacterium, and Sphingomonas showed positive correlations, where acid accumulation suppressed enzymatic activity, causing starch accumulation. Chloroplasts, Streptomyces, and Brachybacterium were negatively correlated with sensory scores and aroma, suggesting that they may affect the quality of Daqu, whereas Corynebacterium, Pseudonocardiaceae, and Scopulibacillus demonstrated positive correlations. Notably, Scopulibacillus has been identified as a key microbial genus during the later stages of high-temperature Daqu production and is likely to play a crucial role in the development of Baijiu flavor (Jiang et al., 2021).

Fig. 3.

Fig. 3

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(A): Composition of bacterial communities of Daqu with different ratio of soft wheat and hard wheat. (B): Cladogram showing the bacterial taxa with significant differences identified by LEfSe analysis (LDA score > 3, p<0.05). (C): RDA of the dominant bacterial genera and physicochemical factors (p < 0.05).

3.4. Composition of fungal communities of different Daqu

The composition of fungal communities in mature Daqu with varying ratio of soft wheat and hard wheat was also analyzed (see Fig. 4). The abundance of Talaromyces, Aspergillus, and Thermomucor was lower in RMM, while Thermomyces, Thermoascus, and Rhizopus were more abundant. In addition, RM0 exhibited a higher abundance of Aspergillus, while both RM100 and RMH were rich in Penicillium. These findings suggest that an imbalanced proportion of soft wheat or hard wheat can disrupt the equilibrium of filamentous fungi in mature Daqu. This imbalance may be attributed to factors such as the high content of hard wheat, which leads to a compact Daqu structure and impedes water evaporation (Jun, 2022). These conditions promote mold growth, consistent with the results shown in Fig. 1D. In addition, studies have shown that soft wheat can harbor a greater abundance of indigenous microorganisms (Yu, 2019), and raw materials are a well-established source of Daqu microbiota (Zhang et al., 2021). Therefore, an excessively high proportion of soft wheat may also lead to an increased abundance of molds in Daqu.

Fig. 4.

Fig. 4

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(A): Composition of fungal communities of Daqu with different ratio of soft wheat and hard wheat. (B): The LEfSe analysis showed fungal taxa with significant differences of different Daqu smaples (LDA score > 3, p<0.05). (C): RDA for the dominant fungal genera and physiochemical factors (p < 0.05).

LEfSe analysis identified 12 fungal genera exhibiting significant differences in abundance across Daqu groups with varying wheat compositions (LDA score > 3.0, p < 0.05). Rhizomucor was significantly enriched in RMM, while Monascus, Wickerhamomyces, and Meyerozyma were enriched in RML. Rasamsonia and Exobasidium were abundant in RM100, whereas Aspergillus, Thermomucor, Scopulariopsis, Microascus, Lichtheimia, and Sterigmatomyces were enriched in RM0.

To investigate linkages between fungal communities and physicochemical parameters, RDA was performed. The first two principal components accounted for 37.43 % and 29.61 % of the fungal compositional variation, respectively. A significant permutation test (p = 0.037) validated the microbiota-trait relationships. Variations in wheat hardness proportions contributed to divergent Daqu microenvironments under standardized processes, thereby altering physicochemical properties and ultimately shaping distinct sensory scores and aroma profiles. Among these traits, starch content, fermentation activity, and saccharification power exhibited the strongest responses. Lichtheimia, Aspergillus, and Microascus were negatively correlated with starch, while Exobasidium and Rasamsonia showed positive correlations with it. Additionally, Aspergillus, Rasamsonia, Meyerozyma, and Rhizomucor were positively correlated with saccharification and fermentation abilities but negatively correlated with starch. Aspergillus and Rhizomucor key filamentous fungi in Daqu, play critical roles in the starch hydrolysis (Wang et al., 2020). Conversely, Sterigmatomyces, Scopulariopsis, and Thermomucor were positively correlated with starch and moisture, but negatively correlated with saccharification ability and fermentation ability.

3.5. Metabolic functions of different Daqu

To better understand the role of microbial communities of Daqu, PICRUSt2 was used to predict the abundance of enzymes-encoding genes related to bacterial and fungal metabolism (Fig. 5). The results revealed distinct metabolic activities across different Daqu samples. Bacterial metabolism was particularly active in RM0 and RMH, with gene functions related to carbohydrate metabolism, amino acid metabolism, and energy production significantly enriched. This indicates that bacteria in these samples play a pivotal role in breaking down complex substrates and facilitating the saccharification process. Key metabolic pathways identified include glycolysis, the TCA cycle, and various amino acid biosynthesis pathways. In contrast, fungal metabolism was more active in RMM and RML. The genes associated with fungal activities were significantly enriched in pathways related to lipid metabolism, secondary metabolite biosynthesis, and xenobiotic biodegradation. These findings indicate that fungi in RMM and RML contribute to the transformation of substrates into flavor compounds and other secondary metabolites, which are critical for the final sensory characteristics of Daqu.

Fig. 5.

Fig. 5

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Heatmap depicts the abundance of enzymes encoding genes related to bacterial (A) and fungal (B) metabolism based on PICRUSt2 analysis.

Interestingly, neither bacterial nor fungal metabolism was particularly active in RM100. Genes associated with both bacterial and fungal metabolic process were less represented, indicating a lower overall microbial activity in this sample. This reduced activity could be attributed to factors such as limited substrate availability, suboptimal environmental conditions, or microbial interactions that inhibit metabolic processes (Wang, 2022).

3.6. Correlation network between microbial genera and physichemical factors

The Spearman correlation network between dominant microorganisms and driving factors is shown in Fig. 6. The network provides a detailed view of the intricate relationships between microbial communities and the physicochemical and sensory properties of Daqu. This analysis provides valuable insights that enable producers to optimize the fermentation process to enhance desirable traits and mitigate undesirable ones. Among bacterial genera, Bacillus exhibited a significant negative correlation with starch, highlighting its critical role in starch degradation and saccharification through enzymatic activity. (He et al., 2019). Penicillium showed positive correlations with aroma, sensory scores, and cross-sectional quality, suggesting its involvement in producing aromatic compounds and enzymes that enhance texture and flavor. Penicillium has been shown to be closely related to carbohydrate metabolism (Yi et al., 2019). Scopulibacillus, a dominant bacterium in high-temperature Daqu (Shi et al., 2022), was also positively correlated with cross-sectional structure, sensory scores, and aroma, indicating its role in producing compounds that elevate sensory attributes. The abundance of Weissella was positively correlated with saccharification ability and acidity, implying a potential role in the processes of carbohydrate conversion and acid production. As supported by previous studies, Weissella could efficiently convert complex carbohydrates into fermentable sugars while producing acidic compounds (Huang et al., 2021; Jin et al., 2017). These activities contribute to an efficient fermentation process and the development of a balanced flavor profile, with optimal acidity levels that enhance the overall taste of Daqu.

Fig. 6.

Fig. 6

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A correlation network between dominant microbial genera and physiochemical factors. Blue triangles present bacteria; green circle denote fungal genera, and orange cubes indicate physicochemical factors.

Rhizopus was positively correlated with fermentation ability, reinforcing its essential function in breaking down complex substrates into fermentable sugars. Conversely, among fungal genera, Thermoascus was negatively correlated with starch, signifying its role in starch degradation and the saccharification process. The negative correlation suggests that an increase in Thermoascus abundance corresponds to a decrease in starch content, benefiting the saccharification process by maintaining a continuous supply of fermentable sugarsT (McClendon et al., 2012). Rhizopus is positively correlated with fermentation ability. Playing a crucial role e in the process through its enzyme production capabilities, which break down complex substrates into simpler fermentable sugars (Jiang et al., 2021). Rhizopus enhanced the fermentation efficiency and activity, contributing to the production of high-quality Daqu. Conversely, Rhizomucor showed negative correlations with cross-section, aroma, and sensory score indicating that its presence may negatively impact these sensory attributes. This effect might result from the production of undesirable compounds or the inhibition of favorable ones (Jiang et al., 2021), ultimately reducing the overall quality of Daqu.

4. Conclusion

This study presents a comprehensive evaluation of medium-temperature Daqu produced with varying proportions of soft and hard wheat, revealing the significant impact of wheat composition on its physicochemical properties, sensory qualities, volatile flavor profiles, and microbial communities. Higher proportions of soft wheat were found to enhance sensory attributes but result in a reduction in the diversity of volatile flavor compounds. The observed variations in microbial communities corresponding to different wheat compositions highlight the pivotal role of microbial activity in determining Daqu characteristics. By establishing clear connections between wheat composition, microbial dynamics, and Daqu quality, this research provides valuable insights for optimizing Daqu production to enhance Baijiu quality. The findings offer practical guidance for producers to adjust wheat compositions in Daqu formulations to achieve targeted flavor profiles and desired quality attributes in Baijiu. Different enterprises can select suitable raw materials for production based on their specific production needs and priorities. Future studies should delve deeper into the mechanisms of microbial interactions and metabolic pathways to further advance our understanding of Daqu fermentation and its critical role in Baijiu production.

CRediT authorship contribution statement

Yong Yang: Formal analysis, Data curation. Mengyao Niu: Writing – original draft, Formal analysis, Data curation. Xiangrong Fan: Writing – original draft, Formal analysis, Data curation. Xiang Chen: Validation, Funding acquisition. Xiangyang Ge: Validation, Funding acquisition. Beizhong Han: Validation. Xiaoxue Chen: Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was financially supported by National Natural Science Foundation of China (No. 32502152).

Data availability

Data will be made available on request.

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