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利用可视化微阵列筛选法同时检测蜂蜜中的四种硝基呋喃代谢物。

Simultaneous detection of four nitrofuran metabolites in honey by using a visualized microarray screen assay.

机构信息

State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.

State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China; School of Chemistry and Biological Science, Nanjing University Jingling College, Nanjing 210089, China.

出版信息

Food Chem. 2017 Apr 15;221:1813-1821. doi: 10.1016/j.foodchem.2016.10.099. Epub 2016 Oct 24.

DOI:10.1016/j.foodchem.2016.10.099
PMID:27979167
Abstract

A visualized microarray sensing technique has been developed and applied to the screening of honey samples for residues of banned nitrofuran antibiotics. Using a multiplexed approach, metabolites of four main nitrofuran antibiotics can be detected simultaneously. Individual antigens were spotted onto 96-well plates. An indirective competitive assay format, with visualized signal response, was employed. An extraction method, based on derivatization with 2-nitrobenzaldehyde and partition into ethyl acetate, was used for screening. The limits of detection were 0.10, 0.04, 0.04, and 0.10ngg for 3-amino-5-morpholino-2-oxazolidone (AMOZ), 3-amino-2-oxazolidinone (AOZ), semicarbazide (SEM), and 1-aminohydantoin (AHD), respectively. The recovery rate ranged from 78% to 93% for the four targets. In addition, this method was easy to operate with low detection cost and fast speed. This microarray method possesses the potential to be a fit-for-purpose screening technique in the arena of food safety monitoring.

摘要

一种可视化的微阵列传感技术已经被开发出来,并应用于蜂蜜样品中禁用硝基呋喃类抗生素残留的筛选。该技术采用多重分析方法,可以同时检测四种主要硝基呋喃类抗生素的代谢物。将单个抗原点样到 96 孔板上。采用可视化信号响应的间接竞争测定法作为检测模式。基于 2-硝基苯甲醛衍生化和乙酸乙酯分配的提取方法用于筛选。对于 3-氨基-5-吗啉基-2-恶唑烷酮(AMOZ)、3-氨基-2-恶唑烷酮(AOZ)、氨基脲(SEM)和 1-氨基乙内酰脲(AHD),检测限分别为 0.10、0.04、0.04 和 0.10ng/g。四种目标的回收率在 78%至 93%之间。此外,该方法操作简单,检测成本低,速度快。这种微阵列方法有可能成为食品安全监测领域的一种合适的筛选技术。

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