Intelligent Baijiu Blending Model Based on Meta-Goal Programming

This paper proposes a new Baijiu blending optimization method, combining Meta-Goal Programming (MGP) and Whale Optimization Algorithm (WOA). The proposed method takes into account the complexity of Baijiu's composition and the significant influence of trace components on its flavor profile. To address this, a mathematical modeling framework that surpasses traditional linear and goal programming methods is introduced, which often neglects the nuances of liquor flavor in the pursuit of cost reduction. The study shows that the combination of MGP and WOA improves the accuracy of Baijiu blending by systematically balancing compound concentrations to achieve desired flavor profiles. The experimental results demonstrate the advantages of this approach over traditional methods, partic-ularly in reducing deviations in compound concentrations and better meeting the qualitative goals set by the blenders. This study contributes to the scientific and precise method for Baijiu blending and the intelligent automation of Baijiu production. It lays a theoretical foundation for automated blending.