贵州省野生桑果实品质指标的主成分和聚类分析

Principal component and cluster analysis of fruit quality indexes of wild mulberry in Guizhou province

【目的】评价贵州省15份野生桑种质资源果实品质,筛选综合品质优良的资源,为果桑新品种选育提供参考。【方法】以15份贵州野生桑资源为试材,测定单果质量、硬度、色泽等7个感官和pH值、出汁率、可溶性固形物含量等8个营养品质指标,并采用主成分分析和聚类分析对其品质指标进行综合评价。【结果】不同野生桑资源果实品质之间存在显著差异,提取的5个主成分累积贡献率达87.998%,可以较好地反映桑椹品质的综合信息。聚类分析将15份野生果桑资源分为了两大类,分类结果与主成分综合得分结果基本一致。【结论】果2、果11、果24、果33等可作为贵州优良果桑种质资源加以利用。

【Objective】Mulberry(Morus alba Linn.) fruits has become popular due to its rich nutrition and high medicinal value in recent years. Wild mulberry resources in Guizhou are widely distributed,and have many ecological types. Some of them have good fruit-bearing properties, good taste and strong disease resistance. In this study, the fruit quality of 15 wild mulberry germplasm resources was evaluated comprehensively to provide reference for the selection of high-quality varieties.【Methods】A total of 15 wild mulberry resources were collected from the mulberry germplasm collection of Guizhou Sericulture Research Institute. Seven indexes of sensory and p H value, juice yield and soluble solids were determined. The fruit quality of different resources was analyzed in the single factor analysis, principal component analysis, cluster analysis and comprehensive grading were conducted using the technology of spss 23 and excel software.【Results】There were significant differences in fruit quality traits among 15 germplasm resources, indicating great potential for selection. In principal component analysis, 5 principal components were extracted, the cumulative contribution rate was 87.998%, which reflected most of the quality characteristics of mulberry fruits. The contribution rate of the first principal component was 34.706%, including soluble solid, total acid, total sugar and longitudinal diameter. The second principal component was highly correlated with Color L*and Color b*, which could explain20.009% of the character information. The third principal component explained 12.768% of the trait information highly related to juice yield. The fourth principal component was single fruit weight, the contribution rate was 11.169%.The fifth principal component was mainly Color a*, and the contribution rate was 9.347%. According to the principal component analysis, the comprehensive scores of each resource were obtained, and the order was as follows: Guo2 > Guo11 > Guo24 > Guo 33 > Guo1 > Guo31 >Guo5 > Guo19 > Guo6 > Guo16 > Guo12> Guo3 > Guo23 > Guo34 > Guo4. The results of data cluster analysis showed that in the condition of European distance at 10, 15 mulberry germplasm resources could be clustered into 2 categories, Guo12, Guo24, Guo33, Guo23, Guo1, Guo11, Guo19, Guo31,Guo2 and Guo5 were classified as I;Guo34, Guo4, Guo3, Guo6 and Guo16 were classified as class II.The results of classification were highly consistent with the principal component scores.【Conclusion】It is reliable to use principal component analysis and data clustering to evaluate the quality of mulberry fruits. The comprehensive quality evaluation model established in this experiment can be used to evaluate the fruit quality of wild mulberry germplasm resources in Guizhou. Guo2, Guo11, Guo24, and Guo33 can be used as excellent fruit mulberry germplasm resources in Guizhou.