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基于HPLC法和多元统计分析的不同产地香附挥发油中4种成分含量的比较研究 被引量:15

Comparative Study on the Contents of 4 Components in the Volatile Oil of Cyperus rotundus from Different Origins Based on HPLC Method and Multivariate Statistical Analysis
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摘要 目的:建立同时测定香附挥发油中香附烯酮、圆柚酮、α-香附酮、马兜铃酮含量的方法,并比较不同产地香附样品中4种成分的含量差异,为该药材的种质筛选和开发利用提供参考。方法:以12个产地共46批香附为样品,提取挥发油后,采用高效液相色谱法(HPLC)测定挥发油中香附烯酮、圆柚酮、α-香附酮、马兜铃酮含量。色谱柱为Kromasil C18,流动相为甲醇-水(68∶32,V/V),流速为1.0 mL/min,柱温为30℃,检测波长为242 nm,进样量为20μL。以上述4个成分的含量为评价指标,通过雷达图分析、聚类热图分析和主成分分析等多元统计分析,比较不同产地香附样品的质量。结果:含量测定方法学考察结果均符合相关要求;不同产地香附挥发油中4种成分的总含量范围为136.9864~538.8321 mg/g,其中以云南产样品的总含量最高(平均值为476.0592 mg/g)。雷达图分析结果显示,广东、江西、广西、云南4个产地样品的整体轮廓较大、分布平衡度较好,其中以云南产样品的整体轮廓最大、分布平衡度最好;聚类热图分析结果显示,12个产地样品可聚成两大类,即产地为湖北、江西、云南、四川、广东、山东、河南、陕西的样品聚为第Ⅰ类,产地为广西、山西、安徽、海南的样品聚为第Ⅱ类,且第Ⅰ类产地样品的质量优于第Ⅱ类;主成分分析结果显示,前3个主成分的累计贡献率为96.1%,12个产地样品主要聚为两大类,与聚类热图分析结果一致。结论:本研究所建HPLC法可用于香附挥发油中香附烯酮、圆柚酮、α-香附酮和马兜铃酮含量的同时测定;12个产地样品中以云南产香附的质量更优。 OBJECTIVE:To establish the method for simultaneous determination of the contents of cyperotundone,nootkatone,α-cyperone and aristolone in the volatile oil of Cyperus rotundus,compare the content differences of 4 components in C.rotundus samples from different origins,and to provide reference for germplasm screening,development and utilization of the medicinal material.METHODS:The volatile oil was extracted from 46 batches of C.rotundus from 12 origins.The contents of cypermenone,nootkatone,α-cyperone and aristolone in volatile oil were determined by HPLC.The determination was performed on Kromasil C18 column with mobile phase consisted of methanol-water(68∶32,V/V)at the flow rate of 1.0 mL/min;the column temperature was 30℃;the detection wavelength was set at 242 nm;the sample size was 20μL.Using the contents of above 4 components as evaluation indexes,radar image analysis,cluster thermal map analysis and principal component analysis were performed for comparing the quality of C.rotundus from different origins.RESULTS:The results of content determination methodology investigation met relevant requirements;the total contents of 4 components in volatile oil from C.rotundus from different origins ranged from 136.9864 to 538.8321 mg/g,of which the total content of samples from Yunnan was the highest(the average value was 476.0592 mg/g).Radar image analysis results showed that the whole contour in the 4 origins of Guangdong,Jiangxi,Guangxi and Yunnan was large relatively and better balanced,among which the samples from Yunnan had the largest overall contour and the best balance.The cluster thermal map analysis results showed that the samples from 12 origins could be grouped into 2 categories,the first category was from Hubei,Jiangxi,Yunnan,Sichuan,Guangdong,Shandong,Henan and Shaanxi;the second category was from Guangxi,Shanxi,Anhui and Hainan;the quality of samples from the first category were better than that of samples from the second category.The principal component analysis results showed that the cumulative contribution rate of the first three principal components was 96.1%,and the samples from 12 origins were mainly clustered into two categories,which was consistent with the results of cluster thermal map analysis.CONCLUSIONS:Established HPLC method can be used for simultaneous determination of cypermenone,nootkatone,α-cyperone and aristolone in volatile oil of C.rotundus from different origins.Among the samples from 12 origins,the quality of medicinal material from Yunnan is better.
作者 许娜 牟玉侦 李文兵 傅超美 陈胡兰 王世宇 卢君蓉 XU Na;MOU Yuzhen;LI Wenbing;FU Chaomei;CHEN Hulan;WANG Shiyu;LU Junrong(College of Pharmacy,Chengdu University of TCM,Chengdu 611137,China;Institute of Qinghai-Tibetan Plateau,Southwest University for Nationalities,Chengdu 610225,China;West China School of Pharmacy,Sichuan University,Chengdu 610041,China)
出处 《中国药房》 CAS 北大核心 2020年第23期2833-2840,共8页 China Pharmacy
基金 国家自然科学基金青年科学基金资助项目(No.8180-3730) 四川省科技计划项目(No.18YYJC0960) 成都中医药大学“杏林学者”学科人才科研提升计划-“青年学者”项目(No.QNXZ2019032)。
关键词 香附 产地 挥发油 含量测定 高效液相色谱法 雷达图分析 聚类热图分析 主成分分析 Cyperus rotundus Origins Volatile oil Content determination HPLC Radar image analysis Cluster thermal map analysis Principal component analysis
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