Comprehensive Evaluation of Chestnut Quality Based on Principal Component and Cluster Analysis

To develop an appropriate method for evaluating the quality of chestnut resources. The 21 quality indicators of 25 chestnut varieties were detected and analyzed. The key indicators of affecting the quality of chestnut were selected through principal component analysis (PCA) coupled with correlation analysis and descriptive statistical analysis. The weights of these key indicators were calculated based on the entropy weight method to construct the gray correlation evaluation model. Our findings revealed notable differences (P