Issue 16, 2015

A sensitive colorimetric aptasensor for chloramphenicol detection in fish and pork based on the amplification of a nano-peroxidase-polymer

Abstract

A novel colorimetric aptasensor was developed for sensitive and selective determination of chloramphenicol (CAP) based on gold nanoparticles (AuNPs) labeled with Power Vision (PV) and magnetic separation. PV, with a high enzyme-to-antibody ratio, is composed of a compact enzyme-linker antibody conjunction. In this assay, an aptamer of CAP was immobilized on Fe3O4@Au magnetic nanoparticles as a capture probe (AuMNPs–Apt) to concentrate target CAP. The complementary DNA (cDNA) and PV were both labeled on AuNPs to form a nano-peroxidase polymer as a signal tag (cDNA–AuNPs–PV). And the special tags could hybridize with the aptamer and cDNA to form AuMNP–Apt/cDNA–AuNP–PV conjugates. In the presence of CAP, the aptamer preferentially bound to CAP and caused the dissociation of some cDNA–AuNPs–PV on the conjugates with magnetic separation. PV, carried on signal tags, could greatly catalyze 3,3′,5,5′-tetramethylbenzidine (TMB) leading to color development, which could be quantified by Ultraviolet-visible (UV-vis) spectroscopy. A linear response to CAP concentration in the range of 0.05–200 ng mL−1 was obtained by this proposed method, with a low detection limit down to 0.02 ng mL−1. Besides, this assay was successfully employed to analyze CAP in fish and pork samples, whose results were consistent with those of the conventional enzyme-linked immunosorbent assay (ELISA) method.

Graphical abstract: A sensitive colorimetric aptasensor for chloramphenicol detection in fish and pork based on the amplification of a nano-peroxidase-polymer

Supplementary files

Article information

Article type
Paper
Submitted
28 May 2015
Accepted
30 Jun 2015
First published
02 Jul 2015

Anal. Methods, 2015,7, 6528-6536

Author version available

A sensitive colorimetric aptasensor for chloramphenicol detection in fish and pork based on the amplification of a nano-peroxidase-polymer

H. Gao, N. Gan, D. Pan, Y. Chen, T. Li, Y. Cao and T. Fu, Anal. Methods, 2015, 7, 6528 DOI: 10.1039/C5AY01379H

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