Identification and reconstruction of the core microbiota in natural fermentation systems: a case study of jiuyao

Abstract

As a saccharifying and fermenting starter, jiuyao is indispensable for huangjiu brewing by providing abundant microorganisms and hydrolytic enzymes. However, the current production of jiuyao still relies on the preceding year’s jiuyao inoculation and natural fermentation. Due to the unpredictable and unstable assembly of core microbial community, the quality of jiuyao fluctuates across different batches, thus the quality of huangjiu suffers subsequently. In this study, we took jiuyao as a case study to identifying and reconstructing the core microbiota. Five species, Pediococcus pentosaceus, Saccharomycopsis fibuligera, Saccharomyces cerevisiae, Mucor indicus, and Rhizopus microsporus, were comprehensively identified as the core microbiota. A synthetic microbiota was constructed through inoculating the core microbiota to artificial jiuyao, and further supplemented by the spontaneous inoculation of geography-dependent microorganisms (Weissella cibaria and Rhizopus arrhizus) from original factory environment during open fermentation. The artificial jiuyao ultimately exhibited comparable microbial community and physicochemical indexes with traditional jiuyao. Specifically, their gelatinized-starch-hydrolyzing glucoamylase activities were 343.28 ± 32.27 and 340.59 ± 39.80 U/g respectively. Furthermore, huangjiu brewed with artificial and traditional jiuyao showed similar physicochemical and flavor profiles, with the ester content of the former being 5.32% higher and the content of higher alcohols being 9.64% lower compared to the latter. These results suggested that the rational synthetic core microbiota could substitute preceding year’s jiuyao and facilitate production to be controllable and tractable. Combined with a specific production environment, it could effectively reproduce the function of jiuyao and the terroir flavor of huangjiu, providing a scientific guidance for similar fermentation control and optimization.

Funding

National Natural Science Foundation of China,(32072205)

Shaoxing Bureau of Science and Technology,(2022B43001)