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使用超高效液相色谱-串联质谱法检测食用和药用草药中的七种毒素。

Detection of seven toxins in edible and medicinal herbs using ultra-high performance liquid chromatography-tandem mass spectrometry.

作者信息

Zhao Xiangsheng, Liu Dan, Yang Xinquan, Zhang Lei, Yang Meihua

机构信息

Key Laboratory of Resources Conservation and Development of Southern Medicine of Hainan Province & Hainan Branch of the Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Haikou 570311, China.

School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, China.

出版信息

Food Chem X. 2021 Dec 11;13:100186. doi: 10.1016/j.fochx.2021.100186. eCollection 2022 Mar 30.

Abstract

mycotoxins are ubiquitous mycotoxins that contaminate food and animal feed. Here, an UPLC-MS/MS was developed and used for the detection of seven mycotoxins in 19 different edible and medicinal herbs. Extensive optimization resulted in a simple and convenient sample preparation procedure with satisfactory extraction and a lower matrix effect. LOQs ranged from 0.01 to 2.0 ng/mL. Recoveries varied between 71.44% and 112.65%, with RSD less than 12%. The method was successfully applied for use in the mycotoxin analysis of 260 samples. A high percentage (28.46%) of samples were contaminated by 1-5 mycotoxins. Alternariol mono methylether was the predominant mycotoxin with high percentage of positive samples (37.5%), followed by alternariol (22.5%), alternariol (17.5%), tentoxin (10.83%), altertoxin Ⅰ (7.5%), and altenusin (4.17%). Collectively, the natural incidence data obtained from this study will help with better, validated risk assessments and efforts towards more comprehensive, future regulation.

摘要

霉菌毒素是污染食品和动物饲料的常见霉菌毒素。在此,开发了一种超高效液相色谱-串联质谱法(UPLC-MS/MS)并用于检测19种不同食用和药用草药中的7种霉菌毒素。经过广泛优化,得到了一种简单便捷的样品制备程序,具有令人满意的提取效果和较低的基质效应。定量限范围为0.01至2.0 ng/mL。回收率在71.44%至112.65%之间,相对标准偏差小于12%。该方法成功应用于260个样品的霉菌毒素分析。高比例(28.46%)的样品被1至5种霉菌毒素污染。单甲基交替链孢酚是主要的霉菌毒素,阳性样品比例较高(37.5%),其次是交替链孢酚(22.5%)、交链孢酚(17.5%)、细交链孢菌酮酸(10.83%)、互隔交链孢酚单甲醚(7.5%)和阿特非毒素(4.17%)。总体而言,本研究获得的自然发生率数据将有助于进行更好的、经过验证的风险评估,并为未来更全面的监管努力提供帮助。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2a4/9039941/cdcc2a1f540f/gr1.jpg

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