4.5 Article

Biomimetic ELISA detection of malachite green based on magnetic molecularly imprinted polymers

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.jchromb.2016.09.015

关键词

Magnetic molecularly imprinted polymer; ELISA; Malachite green; Bionic antibody; Fish

资金

  1. Science and Technology Planning Project of Fujian Province, China [2014Y0045, 2016Y0064]
  2. Innovative Research Team of Jimei University, China [2010A007]
  3. Natural Science Foundation of Fujian Province of China [2015J01615]
  4. Science and Technology Planning Project of Xiamen, China [3502Z20143018]
  5. National Undergraduate Training Programs for Innovation and Entrepreneurship [201510390051, 201510390026]

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A direct competitive enzyme-linked immunosorbent assay (ELISA) method was used for the detection of malachite green (MG) with a high sensitivity and selectivity using magnetic molecularly imprinted polymers (MMIPs) as a bionic antibody. MMIPs were prepared through emulsion polymerization using Fe3O4 nanoparticles as magnetic nuclei, MG as a template, methacrylic acid (MAA) as a functional monomer, ethylene glycol dimethacrylate (EGDMA) as a crosslinking agent and span-80/tween-80 as mixed emulsifiers. The MMIPs were characterized by scanning electron micrographs (SEM), thermal-gravimetric analyzer (TGA), Fourier transform infrared spectrometer (FT-IR) and vibrating sample magnetometer (VSM), respectively. A high magnetic saturation value of 54.1 emu g(-1) was obtained, resulting in rapid magnetic separation of MMIPs with an external magnet. The IC50 of the established ELISA method was 20.1 mu g L-1 and the detection limit (based on IC85) was 0.1 mu g L-1. The MMIPs exhibited high selective binding capacity for MG with cross-reactivities less than 3.9% for MG structural analogues. The MG spiking recoveries were 85.0%-106% with the relative standard deviations less than 4.7%. The results showed that the biomimetic ELISA method by using MMIPs as bionic antibody could be used to detect MG rapidly in fish samples with a high sensitivity and accuracy. (C) 2016 Elsevier B.V. All rights reserved.

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