Deep neural network-based prediction of sauce-flavor Baijiu fermentation process and regulation of flavor compound synthesis.

Abstract

Sauce-flavor Baijiu is a Chinese distilled spirit with a highly nonlinear, multivariable, and coupled pit fermentation process, complicating real-time parameter acquisition and regulation. In this study, a deep neural network (DNN) based multi-task framework was developed using industrial-scale microbial community profiles and physicochemical parameters, enabling prediction of fermentation states and in silico optimization of controllable parameters to regulate target flavor compounds. Firstly, microbial communities, physicochemical parameters, and volatile flavor compounds were analyzed across seven production rounds. The results suggested that dominant microorganisms included Acetilactobacillus, Kroppenstedtia, Pichia, Monascus, and Kazachstania. Across rounds, acidity increased while pH decreased. Total esters and alcohols increased from rounds 1-3, then declined toward round 7, with ethyl acetate and phenethyl alcohol being key discriminants. Using this seven-round fermentation-parameter dataset, a DNN framework was developed with two functions: (i) fermentation parameter prediction and (ii) targeted flavor regulation via an optimization module coupled to the trained model. Baseline comparisons and ablation experiments demonstrated that the model achieved good predictive performance (Pearson's r = 0.801; R2 = 0.690; CCC = 0.783). Evaluation using data from a subsequent production year showed encouraging predictive agreement for key parameters. As a concept validation, the DNN-guided ethyl acetate regulation strategy was evaluated in a simulated round-6 pit fermentation by inoculating with Companilactobacillus pabuli, Fructilactobacillus fructivorans, and Pichia kudriavzevii. This intervention increased ethyl acetate and ethanol levels, supporting the feasibility of the model-guided intervention direction. Overall, this work supports intelligent monitoring and flavor-oriented control in sauce-flavor Baijiu production, providing a foundation for automated manufacturing.