Deciphering the role of traditional flipping crafts in medium-temperature Daqu fermentation: Microbial succession and metabolic phenotypes

Abstract

Medium-temperature Daqu (MTD) serves as the saccharification and fermentation starter for Nongxiangxing Baijiu. Flipping Daqu (FD) during fermentation is a key craft in traditional MTD preparation. However, the mechanism underlying this flipping craft remains unclear. To address this, we systematically compared FD with non-flipping Daqu (NFD) to elucidate microbial succession dynamics, metabolic phenotypes, and environmental drivers. Our results demonstrated divergent microbial community succession patterns between FD and NFD during the stable fermentation phase (days 9-25). FD exhibited significantly higher enzyme activities and volatile ketone content, along with lower core temperatures compared to NFD. Metabolite production in FD was influenced by both bacteria and fungi, whereas fungi predominantly controlled metabolite production in NFD. Co-occurrence network analysis revealed that the microbial community in FD was simpler yet more stable compared to that in NFD. Microbial succession in MTD was primarily driven by interspecies interactions and environmental factors. Furthermore, deterministic processes and stochastic processes jointly governed microbial assembly both FD and NFD, with temperature, moisture, and acidity as the key driving factors. These findings highlight the pivotal role of the flipping crafts in enhancing microbial functionality and metabolic diversity, offering a theoretical basis for optimizing MTD production and advancing intelligent fermentation systems.

Keywords: Driving factors; Medium-temperature Daqu; Metabolic phenotypes; Microecology; Traditional flipping crafts.

Graphical abstract

Fig. 1

Dynamics of physicochemical characteristics during MTD fermentation. (a) Changes in fermentation parameters, including temperature, moisture, total acidity, pH, starch content, and reducing sugar content. (b) Changes in enzymatic indicators, including saccharifying ability, liquefying ability, acid protease ability, and esterifying activity. ∗, ∗∗, and ∗∗∗ represent p < 0.05, p < 0.01, and p < 0.001, respectively.

Fig. 2

Diversity and structure of the microbial community during MTD fermentation. Alpha diversity indices for bacterial (a) and fungal (b) communities at the species level. Dynamic changes in the bacterial (c) and fungal (d) communities at the species level, with species comprising less than 1 % relative abundance categorized as 'others'. PCoA analysis of bacterial (e) and fungal (f) communities based on the Bray-Curtis dissimilarity matrix at the species level. ∗, ∗∗ and ∗∗∗ represent p < 0.05, p < 0.01 and p < 0.001, respectively.

Fig. 3

Dynamics of metabolites during MTD fermentation. (a) Bubble charts depicting the temporal changes in the 4 sugars, 2 alcohols, and 9 organic acids. (b) Concentration changes in total amino acid content. (c) Bubble charts showing the temporal changes in the 17 free amino acids. (d) Concentration changes in 9 types of volatile compounds. (e) Bubble charts illustrating the temporal changes in the 71 volatile compounds. The asterisks above the bars within one group represent significant differences were detected between FD and NFD by paired t-test. ∗, p < 0.05.

Fig. 4

Identifying the biomarkers and metabolic markers during MTD fermentation. Cladograms and LDA scores of bacteria (a) and fungi (c). Histograms of relative abundance differences for microorganisms between the two production processes: (b) bacteria and (d) fungi. Orthogonal partial least squares discriminant analysis (OPLS-DA) plots, cross-validation results, and heatmaps depicting differential compounds (e), including heatmaps of differential compounds during fermentation, OPLS-DA score plots, and OPLS-DA validation models with 200 permutation tests.

Fig. 5

Relationship between metabolite types and biomarkers (Mantel test, left) and correlations between biomarkers and differential compounds (heatmaps, right) in FD (a) and NFD (b). ∗, ∗∗ and ∗∗∗ represent p < 0.05, p < 0.01 and p < 0.001, respectively.

Fig. 6

Co-occurrence network analysis of microbial interactions at the species level in two craft-fermented Daqu. Bacterial-bacterial (a, d), fungal-fungal (b, e), and bacterial-fungal (c, f) communities during FD and NFD fermentation, respectively. ASVs with relative abundance ≥0.01 % and present in at least 20 % of the total samples were used to construct the microbial co-occurrence networks based on Spearman's correlation (|r| > 0.6, p < 0.05).

Fig. 7

Correlation analysis between endogenous environmental factors and dominant microbial communities, and their roles in driving community assembly pattern changes during Daqu fermentation. Mantel tests: dominant microbial communities and endogenous environmental factors in FD (a) and NFD (d). Redundancy analysis (RDA): dominant bacterial (b, e) and fungal (c, f) species associated with endogenous environmental factors in FD and NFD. Random forest models: endogenous environmental factors drivers of bacterial (g, i) and fungal (h, j) community assembly patterns in FD and NFD, respectively. ∗, ∗∗ and ∗∗∗ represent p < 0.05, p < 0.01 and p < 0.001, respectively.