摘要
目的:建立黑豆药材的指纹图谱和5种异黄酮类成分的含量测定方法,为更好地控制该药材的质量提供参考。方法:采用高效液相色谱法(HPLC)建立指纹图谱并检测5种异黄酮类成分的含量。色谱柱为Phenomenex C18,流动相为乙腈-0.12%甲酸水溶液(梯度洗脱),流速为1 mL/min,检测波长为260 nm,柱温为30℃,进样量为10μL。以大豆苷为参照,绘制12批黑豆药材样品的HPLC指纹图谱,采用《中药特征指纹图谱相似度评价系统》(2012A版)进行相似度评价,确定共有峰;分别采用SPSS 20.0软件和SIMCA 13.0软件进行聚类分析和主成分分析。结果:12批黑豆药材样品共有19个共有峰,相似度均大于0.94;指认了5个成分,分别为大豆苷、黄豆黄苷、染料木苷、大豆苷元、染料木素。聚类分析结果显示,12批黑豆药材样品可聚为两类,即S1~S3聚为一类,S4~S12聚为一类。主成分分析结果显示,2个主成分因子的方差贡献率分别为53.261%、40.715%,累积方差贡献率为93.976%。上述5种成分的质量浓度线性范围分别为5.97~191.00μg/mL(r=0.9999)、1.05~33.46μg/mL(r=0.9999)、8.93~285.61μg/mL(r=0.9995)、0.82~26.33μg/mL(r=0.9999)、0.93~29.64μg/mL(r=0.9997);定量限分别为0.8811、0.6116、0.0786、0.2433、0.5116μg/mL,检测限分别为0.2643、0.2447、0.0214、0.1248、0.1067μg/mL;精密度、稳定性、重复性、耐用性试验的RSD均小于5%;加样回收率为95.15%~96.56%(RSD=0.51%,n=6)、98.52%~103.45%(RSD=1.88%,n=6)、95.37%~97.91%(RSD=0.95%,n=6)、99.75%~102.00%(RSD=0.78%,n=6)、100.26%~103.65%(RSD=1.21%,n=6)。12批黑豆药材中上述5种成分的含量分别为0.1783~0.2659、0.0217~0.0962、0.2885~0.5972、0.0141~0.0588、0.0129~0.0829 mg/g。结论:所建指纹图谱和5种异黄酮类成分的含量测定方法均可用于黑豆药材的质量控制;不同产地黑豆药材中异黄酮类成分相似,但含量有所差异。
OBJECTIVE:To establish the fingerprint of Sojae Semen Nigrum and content determination method of 5 kinds of isoflavones,so as to provide reference for controlling its quality better.METHODS:HPLC method was adopted to establish the fingerprint and detect the contents of 5 kinds of isoflavones.The determination was performed on Phenomenex C18 column with mobile phase consisted of acetonitrile-0.12%formic acid solution(gradient elution)at the flow rate of 1 mL/min.The detection wavelength was set at 260 nm;the column temperature was 30℃and sample size was 10μL.Using daidzin as reference,HPLC fingerprints of 12 batches of samples were determined.The similarity of 12 batches of samples was evaluated by TCM Chromatographic Fingerprint Similarity Evaluation System(2012A)to confirm common peak.Cluster analysis and principal component analysis were performed by using SPSS 20.0 software and SIMCA 13.0 software.RESULTS:There were 19 common peaks in HPLC fingerprints of 12 batches of samples,the similarity of which was higher than 0.94.Totally 5 components were identified,such as daidzin,glycitin,genistin,daidzein,genistein.Cluster analysis showed that 12 batches of Sojae Semen Nigrum were clustered into 2 categories,i.e.S1-S3 clustered into one category,and S4-S12 clustered into the other category.By principal component analysis,the contribution rates of two principle components were 53.261%and 40.715%;accumulative contribution rate was 93.976%.The linear range of above 5 components were 5.97-191.00μg/mL(r=0.9999),1.05-33.46μg/mL(r=0.9999),8.93-285.61μg/mL(r=0.9995),0.82-26.33μg/mL(r=0.9999),0.93-29.64μg/mL(r=0.9997),respectively.The limits of quantitation were 0.8811,0.6116,0.0786,0.2433,0.5116μg/mL,respectively.The limits of detection were 0.2643,0.2447,0.0214,0.1248,0.1067μg/mL,respectively.RSDs of precision,stability,reproducibility and durability tests were all lower than 5%.Recoveries were 95.15%-96.56%(RSD=0.51%,n=6),98.52%-103.45%(RSD=1.88%,n=6),95.37%-97.91%(RSD=0.95%,n=6),99.75%-102.00%(RSD=0.78%,n=6),100.26%-103.65%(RSD=1.21%,n=6).Among 12 batches of Sojae Semen Nigrum,the contents of above 5 components were 0.1783-0.2659,0.0217-0.0962,0.2885-0.5972,0.0141-0.0588,0.0129-0.0829 mg/g.CONCLUSIONS:Established HPLC fingerprint and content determination method of 5 kinds of isoflavones can be used for quality control of Sojea Semen Nigrum.The Isoflavone components are similar,but the contents are different among Sojae Semen Nigrum from different producing areas.
作者
郭千祥
梁幼玲
史旭华
白俊其
黄娟
黄志海
丘小惠
GUO Qianxiang;LIANG Youling;SHI Xuhua;BAI Junqi;HUANG Juan;HUANG Zhihai;QIU Xiaohui(Guangdong Province Hospital of TCM&The Second College of Clinical Medicine,Guangzhou University of TCM,Guangzhou 510120,China;Guangdong Provincial Key Laboratory of Clinical Research of TCM Syndrome,Guangzhou 510006,China)
出处
《中国药房》
CAS
北大核心
2020年第4期428-434,共7页
China Pharmacy
基金
国家自然科学基金资助项目(No.81373967)
广东省中医药局科研项目(No.20184013)
广东省中医院中医药科学技术研究专项——中药精准煮散饮片标准化研究及应用启动项目
关键词
黑豆
异黄酮类
高效液相色谱法
指纹图谱
聚类分析
主成分分析
含量测定
Sojae Semen Nigrum
Isoflavones
HPLC
Fingerprint
Cluster analysis
Principal component analysis
Content determination
