超高效液相色谱-串联质谱法快速测定凉皮汤料中5种罂粟壳生物碱
Rapid determination of five poppy shell alkaloids in Liangpi soup seasoning by ultra performance liquid chromatography-tandem mass spectrometry
王书舟 1李俊玲 1张宇 1王冬梅1
作者信息
- 1. 安阳市疾病预防控制中心,河南安阳 455000
- 折叠
摘要
目的 建立超高效液相色谱-串联质谱法快速测定凉皮汤料中5种罂粟壳生物碱的方法.方法 精确称量样品后加入乙腈涡旋混匀,加入无水醋酸钠和无水硫酸镁辅助提取后离心,上清液经乙二胺-N-丙基硅烷粉末净化,HILIC色谱柱分离,多反应监测模式下测定,外标法定量.结果 罂粟碱、那可丁、蒂巴因在0.05～2.0 ng/mL,吗啡、可待因在0.25～10.0 ng/mL范围内线性关系良好,相关系数均大于0.999 0,检出限为0.03 μg/kg(罂粟碱、那可丁、蒂巴因)和0.15μg/kg(吗啡、可待因),加标回收率为81.3％～98.6％,相对标准偏差为2.1％～4.9％.结论 该方法前处理简单高效,经济科学,灵敏度高,准确度好,适用于批量凉皮汤料中罂粟壳生物碱的快速准确测定.
Abstract
Objective To establish a method for the rapid determination of five poppy shell alkaloids in Liangpi soup seasoning using ultra-high-performance liquid chromatography-tandem mass spectrometry.Methods The accurately weighed sample was mixed with acetonitrile,and after vortex mixing,anhydrous sodium acetate and anhydrous magnesium sulfate were added for extraction and centrifugation.The supernatant was purified by ethylenediamine-N-propylsilane powder and separated by HILIC column,and then determined by multiple reaction monitoring mode and external standard method.Results The linear relationship was good in the range of 0.05-2.0 ng/mL for papaverine,noscapine,and thebaine and in the range of 0.25-10.0 ng/mL for morphine and codeine,The correlation coefficients were all greater than 0.999 0.The detection limits were 0.03 μg/kg for papaverine,noscapine,and thebaine,and 0.15 μg/kg for morphine and codeine.The spiked recovery rates were between 81.3％and 98.6％,and the relative standard deviations were between 2.1％and 4.9％.Conclusions With simple and efficient pretreatment,this method is economic,scientific,sensitive and accurate,and suitable for the rapid and accurate determination of alkaloids from poppy shells in batch Liangpi soup seasoning.
关键词
超高效液相色谱-串联质谱法/罂粟壳/生物碱/凉皮汤料Key words
Ultra-high-performance liquid chromatography-tandem mass spectrometry/Poppy shell/Alkaloid/Liangpi soup seasoning引用本文复制引用
基金项目
安阳市科技发展计划科技攻关项目(2021C01NY017)
出版年
2024
NETL
NSTL
万方数据