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Issue 14, 2017
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Preparation and application of magnetic molecularly imprinted polymers for the selective extraction of coumarins from food and plant samples

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Abstract

In this study, magnetic molecularly imprinted polymers (MMIPs) were synthesized and characterized. Coumarin and 7-hydroxycoumarin were tested as templates for the preparation of MMIPs using the surface molecular imprinting technique on a magnetite (Fe3O4 nanoparticles) surface. Morphology, adsorption capacity, adsorption kinetics, and selectivity of the prepared MMIPs were investigated. From the abovementioned studies, it was found out that MMIP-7-hydroxycoumarin was capable to bind 80 μg of 7-hydroxycoumarin per g of polymer, showed relatively fast adsorption kinetics (30 min) and good selectivity towards target analogues. MIP-coumarin exhibited worse sorption, selectivity, and morphological properties. MMIP-7-hydroxycoumarin was applied for the selective extraction of coumarins from complex samples (food and plant). Coumarins were determined by HPLC with diode array detection. Precision, accuracy, and linear working range were determined under optimal experimental conditions. The developed methodology provided a limit of detection and quantification of 1.04 μg g−1 and 3.44 μg g−1 for coumarin and 1.76 μg g−1 and 5.92 μg g−1 for 7-hydroxycoumarin and 7-methoxycoumarin, respectively. The intra-day and inter-day repeatability of the method, in terms of the relative standard deviation, was less than 6.0% and 9.1%, respectively. The recovery values ranged from 71.4 to 90.3%.

Graphical abstract: Preparation and application of magnetic molecularly imprinted polymers for the selective extraction of coumarins from food and plant samples

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Publication details

The article was received on 24 Feb 2017, accepted on 08 Mar 2017 and first published on 09 Mar 2017


Article type: Paper
DOI: 10.1039/C7AY00502D
Citation: Anal. Methods, 2017,9, 2168-2176
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    Preparation and application of magnetic molecularly imprinted polymers for the selective extraction of coumarins from food and plant samples

    A. Machyňáková and K. Hroboňová, Anal. Methods, 2017, 9, 2168
    DOI: 10.1039/C7AY00502D

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