Microbial architecture and metabolic profiles in Xiaoqu: insights into traditional glutinous rice wine fermentation

Xiaoqu is a traditionally propagated multi-species starter used in Chinese sweet glutinous rice wine fermentation and represents a low-temperature solid-state fermentation system distinct from Japanese pure-culture Aspergillus oryzae koji and Western malt-based saccharification platforms. Unlike defined inoculation systems, Xiaoqu functions as a process-shaped microbial architecture assembled through repeated ecological selection under artisanal production conditions. This review synthesizes current knowledge regarding the microbial ecology, guild organization, fermentation trajectories, and metabolite modulation associated with Xiaoqu-mediated glutinous rice wine production. Xiaoqu communities are typically structured around amylolytic molds, fermentative yeasts, non-Saccharomyces yeasts, and lactic acid bacteria (LAB), which collectively regulate saccharification, acidification, ethanol restraint, and flavor formation through coupled ecological and metabolic interactions. We propose a process-phytochemical selection framework, presented as a literature-derived conceptual hypothesis, to explain how botanical inputs, matrix properties, and processing conditions jointly shape microbial succession, interaction topology, and metabolic trajectories. The review further develops an architecture-to-trajectory perspective in which sugar-acid-ethanol coupling emerges as a systems-level phenotype governed by guild interactions, cross-feeding, feedback regulation, and environmental constraints rather than by individual taxa alone. Functional redundancy, hybrid starter systems, temporal trajectory analysis, and safety constraints associated with toxigenic risks are also discussed as key factors influencing fermentation robustness, reproducibility, and sensory stability. Finally, we highlight the need for architecture-preserving control strategies integrating strain-level multi-omics, synthetic-community reconstruction, and trajectory-oriented process analysis to support mechanistic understanding and controllable design of Xiaoqu-mediated fermentations.