MODELING AND OPTIMIZATION OF SACCHARIFICATION AND FERMENTATION OF BROKEN RICE

Rice wine is a traditional alcoholic beverage derived from fermented glutinous rice or broken rice. The method is separated into two steps: first, the rice is cooked and liquefied/saccharified by molds and enzymes, followed by fermentation. The study examined how Aspergillus oryzae (0.1 - 0.2%) and α-amylase (0.01 - 0.04%) affect starch liquefaction and saccharification, as well as how Saccharomyces bayanus concentration (0.02 - 0.05%) and total soluble solids content (22 - 26%) impact rice wine fermentation. To improve process prediction and optimization, an artificial neural network integrated with a genetic algorithm (ANN-GA) was applied to model the nonlinear relationships between process variables and fermentation performance. The optimization approach utilizing a machine learning-based model demonstrated better prediction ability. Compared with conventional regression approaches, the ANN-GA model provided improved predictive accuracy and enabled the identification of optimal processing conditions for both saccharification and fermentation stages. The optimum content of Aspergillus oryzae and α-amylase was 0.181% and 0.036%, respectively, resulting in high starch saccharification efficiency with a total soluble solids content of 27.2^oBrix. The volume of sugar solution achieved was 34.01 mL (from 50 g rice, yield 68.02%). In addition, using the optimal content of Saccharomyces bayanus of 0.043% and fermenting in an environment with high soluble solids content of 24.88^oBrix produced wine with high ethanol and ester content, 12.19% by volume and 0.93 g/L, respectively. The methanol content of the fermented product under these optimal conditions was lower (49.8 mg/L). These findings demonstrate that the integration of machine-learning-based optimization can effectively enhance fermentation efficiency while maintaining product safety. Overall, the optimized saccharification and fermentation parameters provide a viable approach for producing rice wine with higher quality and safety assurances for this traditional product.