Screening of new tomato promising varieties in Huzhou area based on multivariate statistical analysis

QIAN Wenhao; CHEN Liping; ZHANG Feixue; LU Hongying

doi:10.16861/j.cnki.zggc.2025.0058

Abstract：To clarify the differences among 22 new tomato varieties recommended by the Agricultural Extension Center of Zhejiang province for trial planting,nine indicators were measured,combining cluster analysis,correlation analysis,principal component analysis and stepwise regression analysis.The results showed significant differences among the 22 varieties across various indicators,with variation index ranging from 5.91％to 189.30％,the proportion of deformed fruits exhibited the highest degree of dispersion.Correlation analysis revealed that the soluble solids content and the number of fruits per cluster showed highly significant positive correlation,while both the number of fruits per cluster and the soluble solids content showed highly significant negative correlation with the single fruit mass.Three principal components were extracted from nine indicators,the variance contribution rates are 42.444％,21.552％,and 14.025％,respectively.The loading coefficient matrix was used to identify indicators covered by each principal component,and a comprehensive eval-uation function was established to rank the varieties based on composite scores.The small-fruited tomato variety Hangza 504 and the medium-to-large-fruited variety S21289-1 ranked first.Stepwise regression analysis identified seven indica-tors that significantly influenced tomato quality evaluation.Through cluster analysis,the small-fruited tomato varieties and the medium-to-large-fruited tomato varieties were categorized into three distinct groups based on shared characteris-tics respectivily:Exhibition-type tomato,productivity-enhancing tomato and culinary-purpose tomato.According to the comprehensive evaluation,the small-fruited varieties Hangza 504,Hangza 515,and Ouxiu Huangying,the medi-um-to-large-fruited varieties S21289-1,Zhewei No.2 and Jiahong 100 were demonstrated optimal performance.