Effects of optimized wheat conditioning on extraction efficiency and milling performance under industrial conditions

This study aimed to optimize key operational parameters in a commercial flour milling process using U.S. soft white wheat to improve flour yield, moisture, and ash index. A factorial design was employed to evaluate the effects of wheat humidification, conditioning time, and cumulative break release as independent variables (wheat moisture content: 13.5%, 14.5%, 15.5%; tempering time: 6 h, 8 h, 10 h; cumulative break release of four breaks: 73.50%, 73.60%, 73.70%). Linear regression analysis was applied to model and predict process responses. The models showed high predictive accuracy, with coefficients of determination of 99.30% for flour yield, 97.07% for flour moisture, and 97.20% for ash index. Optimization results indicated that flour moisture content and yield could be maximized, while tempering time and ash index were minimized, maintaining cumulative break release within the operational range. These findings highlight the effectiveness of statistical modeling for improving flour quality and process efficiency. The developed regression models provide a practical and precise alternative to traditional trial-and-error adjustments, enabling millers to determine optimal conditions and achieve consistent product performance in industrial-scale milling operations.