摘要
目的:建立同时测定连钱草中咖啡酸、咖啡酰羟基乙酸、迷迭香酸和丹酚酸A 4个酚酸类成分含量的高效液相色谱法。方法:采用CAPCELL PAK C_(18)色谱柱(250 mm×4.6 mm,5μm),以乙腈(A)-0.1%甲酸水溶液(B)为流动相,梯度洗脱,流速1.0 m L·min^(-1),检测波长330 nm,柱温30℃。结果:咖啡酸、咖啡酰羟基乙酸、迷迭香酸和丹酚酸A进样量分别在0.034~0.85μg(r=0.999 8)、0.014~0.35μg(r=0.999 8)、0.524 5~13.112 5μg(r=0.999 8)和0.008 4~0.21μg(r=0.999 9)范围内与峰面积呈现良好的线性关系;平均回收率(n=3)分别为98.2%~99.6%(RSD<0.8)、97.6%~99.4%(RSD<1.3%)、100.2%~100.5%(RSD<1.0%)和98.1%~99.2%(RSD<1.1%);8批样品中咖啡酸、咖啡酰羟基乙酸、迷迭香酸和丹酚酸A含量范围分别为0.238~1.121、0.036~0.180、1.461~11.011和0.042~0.124 mg·g^(-1)。结论:该方法可用于连钱草的质量控制。
Objective:To establish an HPLC method for the simultaneous determination of four phenolic acids in Herba Glechomae including caffeic acid,caffeoylglycolic acid,rosmarinic acid and salvianolic acid A.Methods:The CAPCELL PAK C18 column(250 mm×4.6 mm,5 μm)was used for separation.And the mobile phase consisted of acetonitrile(A)-0.1% formic acid(B)with gradient elution at a flow rate of 1.0 mL·min^-1.The detection wavelength was 330 nm and the column temperature was at 30 ℃.Results:The linear range of caffeic acid,caffeoylglycolic acid,rosmarinic acid and salvianolic acid A were 0.034-0.85 μg(r=0.999 8),0.014-0.35 μg(r=0.999 8),0.524 5-13.112 5 μg(r=0.999 8)and 0.008 4-0.21 μg(r=0.999 9),respectively.The average recoveries(n=3)were 98.2%-99.6%(RSD0.8%),97.6%-99.4%(RSD1.3%),100.2%-100.5%(RSD1.0%)and 98.1%-99.2%(RSD1.1%),respectively.The contents of caffeic acid,caffeoylglycolic acid,rosmarinic acid and salvianolic acid A in eight batches of samples were 0.238-1.121,0.036-0.180,1.461-11.011 and 0.042-0.124 mg·g^-1,respectively.Conclusion:The method can be used to control the quality of the Herba Glechomae.
作者
邓渝
陈友生
王茜
曾顺
吴西
陈露
周国平
DENG Yu;CHEN You-sheng;WANG Xi;ZENG Shun;WU Xi;CHEN Lu;ZHOU Guo-ping(Nanchang University, Nanchang 330006, China;Jiangxi Institute for Drug Control, Jiangxi Provincial Engineering Research Center for Drug and Medical Device Quality, Nanchang 330029, China;The 94th Hospital of PLA, Nanchang 330002, China)
出处
《药物分析杂志》
CAS
CSCD
北大核心
2018年第4期643-647,共5页
Chinese Journal of Pharmaceutical Analysis
基金
南昌大学研究生创新专项(cx2016293)
江西省重点研发计划项目(20171BBG70104)