Analysis and comprehensive evaluation of fruit quality of Morus macroura in Yunnan province
[Objective]The paper aimed to screen the germplasm resources of Morus macroura with high comprehensive quality and meet the demand of diversified development for food.[Method]23 Morus macroura and 3 other mulberry varieties were collected and preserved in the provincial mulberry germplasm resource nursery garden of Yunnan province to determine 18 indicators such as fruit nutrition,active ingredi-ents and antioxidant activity,and these indicators were comprehensively evaluated by correlation analysis,principal component analysis and cluster analysis.[Result]The 26 M.macroura germplasm resources had a single fruit mass of 1.15-8.38 g,hardness of 1.17-2.08 kg/m2,moisture content of 63.23％-79.00％,juice yield of 45.28％-77.97％,pH of 2.22-4.05,soluble solids of 17.07％-35.50％,a-mino acid content of 6.29-27.93 mg/g,Vc content of 0.02-1.72 mg/g,total sugar content of 114.60-352.93 mg/g,reducing sugar content of 25.30-205.24 mg/g,total flavonoids content of 10.16-73.42 mg/g,total alkaloid content of 0.04-2.46 mg/g,total phenolic content of 0.61-2.68 mg/g,total anthocyanin content of 0.01-1.02 mg/g,total GABA content of 88.43-938.27 pg/g,total antioxidant capacity,ABTS scavenging capacity,and DPPH free radical scavenging capacity were 11.63-93.66,11.32-7.53,and 9.61-65.09 mg/g water-soluble Vc equivalent,respectively.After principal component analysis,the 18 traits were combined into 6 principal component fac-tors,with a cumulative contribution of 83.49％,which could better reflect the basic information of fruit quality.[Conclusion]The PCA results show that Yunsang 2,Hongfenjiaren,Guo 3,Hongxiangcheng,Heijinshen,Chaziyanhong,Hongyanzhiji and Zijinshen with the best fruit quali-ty are suitable for deep development in Yunnan province as an excellent fruit mulberry resource;Single fruit weight,Vc,anthocyanins,SSC,a-mino acids,and total sugar are the core indicators for evaluating the quality of M.macroura fruits.
Morus macrouraFruit qualityPrincipal component analysisCluster analysisComprehensive evaluationYunnan province
NETL
NSTL
万方数据
