Predicting the final metabolic profile based on the succession-related microbiota during spontaneous fermentation of the starter for Chinese liquor making.

Microbial inoculation is an effective way to improve the quality of fermented foods via affecting the microbiota structure. However, it is unclear how the inoculation regulates the microbiota structure, and it is still difficult to directionally control the microbiota function via the inoculation. In this work, using the spontaneous fermentation of the starter (Daqu) for Chinese liquor fermentation as a case, we inoculated different microbiota groups at different time points in Daqu fermentation, and analyzed the effect of the inoculation on the final metabolic profile of Daqu. The inoculated microbiota and inoculated time points both significantly affected the final metabolites via regulating the microbial succession (P < 0.001), and multiple inoculations can promote deterministic assembly. Twenty-seven genera were identified to be related to microbial succession, and drove the variation of 121 metabolites. We then constructed an elastic network model to predict the profile of these 121 metabolites based on the abundances of 27 succession-related genera in Daqu fermentation. Procrustes analysis showed that the model could accurately predict the metabolic abundances (average Spearman correlation coefficients >0.3). This work revealed the effect of inoculation on the microbiota succession and the metabolic profile. The established predicted model of metabolic profile would be beneficial for directionally improving the food quality.IMPORTANCEThis work revealed the importance of microbial succession to microbiota structure and metabolites. Multi-inoculations would promote deterministic assembly. It would facilitate the regulation of microbiota structure and metabolic profile. In addition, we established a model to predict final metabolites based on microbial genera related to microbial succession. This model was beneficial for optimizing the inoculation of the microbiota. This work would be helpful for controlling the spontaneous food fermentation and directionally improving the food quality.

Unraveling multifunction of low-temperature Daqu in simultaneous saccharification and fermentation of Chinese light aroma type liquor.

Chinese liquor is produced by a representative simultaneous saccharification and fermentation process. Daqu, as a starter of Chinese liquor fermentation, affects both saccharification and fermentation. However, it is still unclear how Daqu contributed to the simultaneous saccharification and fermentation process. Here, using Chinese light aroma type liquor as a case, we identified low-temperature Daqu-originated enzymes and microorganisms that contributed to the simultaneous saccharification and fermentation using metaproteomic analysis combined with amplicon sequencing analysis. α-Amylase and glucoamylase accounted for 95 % of total saccharifying enzymes and were identified as key saccharifying enzymes. Lichtheimia was the key producer of these two enzymes (> 90 %) in low-temperature Daqu. Daqu contributed 90 % α-amylase and 99 % glucoamylase to the initial liquor fermentation. These two enzymes decreased by 35 % and 49 % until day 15 in liquor fermentation. In addition, Daqu contributed key microbial genera (91 % Saccharomyces, 6.5 % Companilactobacillus) and key enzymes (37 % alcohol dehydrogenase, 40 % lactic acid dehydrogenase, 56 % aldehyde dehydrogenase) related with formations of ethanol, lactic acid and flavour compounds to the initial liquor fermentation. The average relative abundances of these fermentation-related key microorganisms and enzymes increased by 2.78 times and 1.29 times till day 15 in liquor fermentation, respectively. It indicated that Daqu provided saccharifying enzymes for starch hydrolysis, and provided both enzymes and microorganisms associated with formations of ethanol, lactic acid and flavour compounds for liquor fermentation. This work illustrated the multifunction of low-temperature Daqu in the simultaneous saccharification and fermentation of Chinese light aroma type liquor. It would facilitate improving liquor fermentation by producing high-quality Daqu.

Modelling and predicting population of core fungi through processing parameters in spontaneous starter (Daqu) fermentation.

Traditional fermented foods are usually produced by spontaneous fermentation with multiple microorganisms. Environmental factors play important roles in microbial succession. However, it is still unclear how the processing parameters regulate the microbiota during fermentation. Here, we reveal the effects of processing parameters on the core microbiota in spontaneous fermentation of Chinese liquor starter. Rhizopus, Pichia, Wickerhamomyces, Saccharomycopsis, Aspergillus and Saccharomyces were identified as core microbiota using amplicon sequencing and metaproteomics analysis. Fermentation moisture gradually decreased from 34.8% to 14.2%, and fermentation temperature varied between 17.0 °C and 35.3 °C during the fermentation. Mantel test showed that fermentation moisture (P < 0.001) and fermentation temperature (P < 0.05) significantly affected the core microbiota. Moreover, structural equation modelling analysis indicated that fermentation moisture (P < 0.001) and fermentation temperature (P < 0.001) were respectively influenced by the processing parameters, room humidity and room temperature. The succession of Rhizopus, Pichia, Wickerhamomyces, Saccharomycopsis and Aspergillus were significantly affected by room humidity (P < 0.05), and the succession of Saccharomyces was significantly affected by room temperature (P < 0.001). Further, models were constructed to predict the population of core microbiota by room humidity and room temperature, using Gaussian process regression and linear regression (P < 0.05). This work would be beneficial for regulating microorganisms via controlling processing parameters in spontaneous food fermentations.