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综合二维离子色谱法(2D-IC)与柱后光化学荧光检测系统联用,用于测定食品样品中的新烟碱类农药(吡虫啉和噻虫胺)。

Comprehensive two-dimensional ion chromatography (2D-IC) coupled to a post-column photochemical fluorescence detection system for determination of neonicotinoids (imidacloprid and clothianidin) in food samples.

作者信息

Muhammad Nadeem, Wang Fenglian, Subhani Qamar, Zhao Qiming, Qadir Muhammad Abdul, Cui Hairong, Zhu Yan

机构信息

Department of Environmental Engineering, Wuchang University of Technology Wuhan 430223 China.

Department of Chemistry, Xixi Campus, Zhejiang University Hangzhou 310028 China

出版信息

RSC Adv. 2018 Mar 2;8(17):9277-9286. doi: 10.1039/c7ra12555k. eCollection 2018 Feb 28.

Abstract

There are increasing concerns about the dietary risks of neonicotinoids (NNIs); therefore their sensitive and accurate determination in dietary products is indispensable. However, the complex composition of agricultural food matrixes makes their extraction and quantitative determination a challenging task. Realizing this need, we herein report a simple, cost-effective, selective and sensitive fluorescence analytical workflow for analyses of two non-fluorescent neonicotinoids imidacloprid (IMI) and clothianidin (CLT) in six complex food samples (honey, ginger, durian, apple, tomato, cucumber) by online clean-up of sample extracts using two-dimensional ion chromatography (2D-IC) and a subsequent online post column UV induced fluorescence detection system. This online clean-up setup has proven advantageous to improve the limit of detection, potentially diminish matrix effects, and reduce analysis time and labor. The developed method showed excellent analytical figures of merit including linearity, selectivity, repeatability, recovery, and resolution for analysis of IMI and CLT in food samples.

摘要

人们对新烟碱类农药(NNIs)的膳食风险越来越关注;因此,在膳食产品中对其进行灵敏且准确的测定必不可少。然而,农业食品基质的复杂成分使其提取和定量测定成为一项具有挑战性的任务。意识到这一需求,我们在此报告一种简单、经济高效、选择性强且灵敏的荧光分析工作流程,通过二维离子色谱(2D-IC)在线净化样品提取物,并结合随后的在线柱后紫外诱导荧光检测系统,用于分析六种复杂食品样品(蜂蜜、生姜、榴莲、苹果、番茄、黄瓜)中的两种非荧光新烟碱类农药吡虫啉(IMI)和噻虫胺(CLT)。这种在线净化装置已证明有利于提高检测限、潜在地减少基质效应,并减少分析时间和人力。所开发的方法在分析食品样品中的IMI和CLT时,展现出包括线性、选择性、重复性、回收率和分离度在内的优异分析性能指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33a9/9078649/e7729ef34921/c7ra12555k-f1.jpg

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