Deciphering Spatiotemporal Dynamics of Fermented Grains in the Jiangxiangxing Baijiu Production Process: Insights From a Transformer‐Based Deep Learning Model

The solid‐state fermentation of Jiangxiangxing Baijiu exhibits marked spatiotemporal heterogeneity in microbial communities and physicochemical parameters. We characterized the microbial succession and physicochemical dynamics of Zaopei (fermented grains) across Da‐hui rounds (Rounds 3–5) and applied LimiX, a Transformer‐based deep learning framework, for fermentation state monitoring. Spatial stratification explained 99% of microbial community variance (PERMANOVA, P < 0.001), with inner layers harboring greater bacterial diversity than surface layers. Staphylococcus and Weissella correlated negatively with starch and reducing sugar, while Oceanobacillus and Acetobacter strongly discriminated among rounds. LimiX achieved an AUC of 1.000 for round classification and outperformed GLM and Random Forest (RF) in discriminating fermentation layers. External validation yielded 100% accuracy for round differentiation and 92.86% for temporal stage prediction. SHAP analysis identified Klebsiella and Thermoascus as the primary drivers of spatial and temporal stratification, respectively. These results demonstrate the capability of deep learning to decode complex fermentation data and provide a theoretical and technical basis for the intelligent digitalization of Baijiu production.

Practical Applications

A Transformer‐based deep learning model was developed to integrate amplicon sequencing data and physicochemical indices for decoding the spatiotemporal dynamics of fermented grains in Jiangxiangxing Baijiu fermentation. This framework supports intelligent process monitoring and quality control, with potential applicability to other complex food fermentation systems.