Explainable Artificial Intelligence for Evaluation of Liquor

Comparative analysis of quality and safety monitoring technology has always been the most sensitive topic in the food field. Quality and safety monitoring of liquor is the prerequisite for maintaining market stability and guaranteeing quality. Therefore, it is very important to judge the quality of liquor correctly. In this study, the XGBoost model was used to evaluate the liquor quality of 4899 liquor samples from the Vinho Verde white wine sample dataset in northern Portugal. The influence of 11 liquor characteristics such as volatile Acidity, alcohol, density, and free sulfur dioxide on liquor quality was explained by SHAP, PDP, and other interpretation techniques. The results showed that the XGBoost model had better evaluation performance than the general model. Among the 11 liquor characteristics studied in this paper, liquor quality was affected not only by alcohol content but also by volatile acid, density, and residual sugar. This study can not only provide an objective evaluation method for liquor quality but also make people a further intuitive understanding of the evaluation results, so as to produce better liquor quality.