Interactions of microbiota during solid-state cereal vinegar fermentation

Abstract

Background

Solid-state vinegar fermentation, predominantly practiced in China, is characterized by high microbial diversity, multi-substrate co-decomposition, abundant metabolite production, pronounced environmental heterogeneity, and complex interaction networks that collectively shape vinegar quality. However, this process remains largely experience-driven, with limited digitalization and an incomplete understanding of microbial interaction mechanisms. Key challenges include unclear interaction mechanisms, insufficient application of modeling tools such as genome-scale metabolic models, and limited capacity for dynamic process control. Addressing these gaps is essential for improving fermentation efficiency, stabilizing flavor quality, and advancing vinegar modernization.

Scope and approach

This review is centered on microbial interactions and outlines the metabolic division of labor of key microorganisms in solid-state fermentation and their roles in flavor formation. It examines microbial ecological relationships and interaction mechanisms, and analyzes how environmental heterogeneity regulates microbial interactions. The review further introduces synthetic microbial communities as tools for mechanism validation and functional reconstruction. Finally, based on clarified mechanisms, it discusses how key microbial, metabolic, and environmental information can be translated into model inputs to construct digital twin systems for directional control.

Key findings and conclusions

Environmental heterogeneity plays a role in shaping microbial interaction patterns, thereby influencing metabolic division of labor, fermentation efficiency, and flavor formation. Microbial interactions, rather than individual species, are drivers of community stability, functional metabolite production, and flavor complexity. The integrated application of synthetic microbial communities, metabolic flux models, and digital twin technologies constitutes a predictive framework for dissecting microbial interactions, optimizing key consortia, and achieving targeted regulation of cereal vinegar fermentation.