Synergistic Optimization of In Vitro Digestibility and Sensory Quality of Moderately Milled Rice Based on the...

To address over-processing and limited process-control precision in rice manufacturing, this study developed an artificial intelligence (AI)-assisted optimization framework for rice milling. A data-driven predictive model was constructed to characterize the nonlinear relationships between milling conditions and the in vitro starch-digestibility and sensory attributes of rice. Across the nine physicochemical, in vitro starch-digestibility, and sensory indicators, RiceMambaOpt achieved a mean coefficient of determination (R²) of 0.975. Explainability analyses were used to examine process-quality relationships and characterize nonlinear trade-offs among appearance, texture, and starch-digestibility attributes across rice cultivars with different genetic backgrounds. A target-oriented inverse optimization procedure was then developed, which estimates feasible process parameters subject to process-feasibility constraints, tailored to differentiated orientations such as low rapidly digestible starch (RDS) content or high palatability. On the independent 100-sample test set, the inverse predictions achieved a milling-time MAE of 0.960 s with an R² of 0.986 and a milling-speed MAE of 19.522 r/min with an R² of 0.851. In a prospective experimental validation, 10 of the 12 prespecified target quality profiles (83.3%) were attained across 36 independently milled samples, with an RDS mean absolute error of 1.8 percentage points and a joint normalized root-mean-square error of 6.7%. For the Qiuguang cultivar, the model identified a representative processing condition of 38.5 s and 1020 r/min, corresponding to a predicted in vitro RDS content of 21.6% and a palatability score of 26.5. The results demonstrate the feasibility of combining predictive modeling with process-feasibility-constrained inverse optimization and provide a computational approach for investigating moderate rice-milling conditions.