Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China.
Shanghai Changning Center for Disease Control and Prevention, Shanghai 200051, China.
Food Chem. 2020 Aug 1;320:126576. doi: 10.1016/j.foodchem.2020.126576. Epub 2020 Mar 13.
A rapid and high-throughput method using both GC-MS/MS and UPLC-Q-Orbitrap systems was applied for pesticide multi-residues analysis in food samples. Strategies based on QuEChERs extraction, intelligent data mining tools with in-house/online database, and in-silico fragment prediction system were introduced to screen and identify target/untargeted features. Full-scan combined with data-independent-acquisition modes was evaluated in real sample in an attempt to improve and facilitate the pesticide screening process, of which the results showed that FS-vDIA provided equal detection rate (100%) and far less false positive results than FS-AIF did. The proposed methodology was evaluated in analysis of pesticide multi-residues in several proficiency test samples provided by EURL, and exhibited a high detection rate (>90%) of various pesticide residues with satisfactory recoveries (70-130%) without reporting false positive results. The method was also applied in China's national surveys from 2016 to 2019, and results showed its high performance in pesticide analysis in different food matrices.
本文采用 GC-MS/MS 和 UPLC-Q-Orbitrap 系统相结合的快速高通量方法,用于食品样品中的农药多残留分析。本研究引入了基于 QuEChERs 提取的策略、具有内部/在线数据库的智能数据挖掘工具以及基于计算的碎片预测系统,以筛选和鉴定目标/非目标特征。在实际样品中评估了全扫描结合数据非依赖性采集模式,旨在改进和简化农药筛选过程,结果表明 FS-vDIA 提供了与 FS-AIF 相同的检测率(100%),且假阳性结果要少得多。该方法还用于分析 EURL 提供的几种能力验证样品中的农药多残留,表现出对各种农药残留的高检测率(>90%),回收率令人满意(70-130%),且无假阳性结果报告。该方法还应用于 2016 年至 2019 年中国的国家调查,结果表明其在不同食品基质中的农药分析中具有优异的性能。