Research on PSO‐SVM base wine grade recognition based on Max‐Relevance and Min‐Redundancy feature selection

Summary In response to the challenges inherent in Baijiu classification, characterized by ambiguity and limited methodologies, this study introduces a novel framework for comprehensive Baijiu brewing process management. By integrating mathematical models and machine learning algorithms, our aim is to standardize and enhance the accuracy of the Baijiu brewing process. Through a meticulous analysis of 13 input features using the MRMR algorithm, we identified key variables that significantly influence base wine grades. Subsequently, we compared the efficacy of support vector machine (SVM) algorithms under three kernel functions for base wine grading classification. Additionally, we developed an enhanced particle swarm‐support vector machine (IPSO‐SVM) algorithm, which incorporates nonlinearly decreasing inertia weights to enhance global and local search capabilities. Evaluation metrics, including accuracy and F1‐SCORE, were employed to assess the model's performance. Our experimental results demonstrate that IPSO‐SVM significantly improves convergence speed and accuracy, achieving a classification accuracy of 98.31% on the test set with an F1‐SCORE of 98.18%. Furthermore, experimental tests conducted at a prominent strong‐flavored wine enterprise in China validated the model's effectiveness, revealing no significant difference in grading quality between model and manual Baijiu classification, with a mean error below 1%. This study presents a pioneering approach to digitize and standardize Baijiu classification processes, laying theoretical foundations for the development of intelligent Baijiu classification systems.