摘要
为探讨野生滇龙胆(Gentiana rigescens Franch.ex Hemsl.)优质高产植株性状特征,以采自不同地区的877株滇龙胆为研究材料,利用主成分分析(PCA)、层次聚类(HCA)、隶属函数等方法对有效成分产量性状进行评价。结果显示,17个性状中,根部龙胆苦苷产量多样性指数最高,叶部马钱苷酸和当药苷产量的多样性指数较低;结合隶属函数对所有样品进行评分,发现优质高产种源共214株,占总样品数的24.40%,分布于云南、四川和贵州;变量投影重要性准则(VIP)分析表明,云南与四川的优质种源主要性状特征较为接近,均为当药苷、马钱苷酸及6'-O-β-D-葡萄糖基龙胆苦苷高产;贵州优质种源则为獐牙菜苦苷高产。基于3种机器学习算法建立不同等级种源的鉴别模型,结果发现随机森林(RF)算法建立的判别模型的预测精度和稳定性较高,能对不同等级种源进行有效划分。
We explored the phenotypic characteristics and established a classification strategy of high-quality germplasm resources of wild Gentiana rigescens Franch.ex Hemsl.In total,887 samples of G.rigescens collected from different regions were used as research materials.Principal component analysis(PCA),hierarchical clustering analysis(HCA),and membership function analysis were used to analyze and evaluate 17 active ingredient yield traits of the roots,stems,and leaves.Results showed that gentiopicroside yield in the roots had the highest Shannon-Wiener index value(I=1.64),while loganic acid and sweroside acid yields in the leaves had the lowest I values(I=0.73).Based on D value scoring and membership function analysis,we identified 214 high-quality and high-yield seed sources,accounting for 24.40%of the total sample size,distributed in Yunnan,Sichuan,and Guizhou.Variable importance in projection(VIP)analysis showed similar phenotypic characteristics among the high-quality germplasms in Yunnan and Sichuan,which were characterized by high sweroside,loganic acid,and 6'-O-β-D-glucopyranosylgentiopicroside yield.The high-quality germplasms in Guizhou were characterized by high swertiamarin yield.Among the three different machine learning algorithms,results showed that the discrimination model established using the Random Forest(RF)algorithm had the highest prediction accuracy and stability and could effectively identify different provenances.
作者
沈涛
王元忠
Shen Tao;Wang Yuan-Zhong(College of Chemistry,Biology,and Environment,Yuxi Normal University,Yuxi,Yunnan 653100,China;Institute of Medicinal Plants,Yunnan Academy of Agricultural Sciences,Kunming 650200,China)
出处
《植物科学学报》
CAS
CSCD
北大核心
2023年第4期479-489,共11页
Plant Science Journal
基金
国家自然科学基金项目(32060086)
云南省中青年学术和技术带头人后备人才项目(202205AC160088)。
关键词
滇龙胆
高产优质
隶属函数
机器学习
资源评价
Gentiana rigescens
High yield and high quality
Membership function
Machine learning
Resource evaluation