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Article

Sensor Based on Molecularly Imprinted Polymer Membranes and Smartphone for Detection of Fusarium Contamination in Cereals

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Institute of Molecular Biology and Genetics, 150 Zabolotnogo str., 03143 Kyiv, Ukraine
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School of Chemistry, College of Science and Engineering, University of Leicester, Leicester LE1 7RH, UK
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Department of Chemistry, Taras Shevchenko National University of Kyiv, 64/13 Volodymyrska Street, 01601 Kyiv, Ukraine
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Biology Department, National University “Kyiv-Mohyla Academy”, 2 Skovorody str., 04070 Kyiv, Ukraine
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Institute of Macromolecular Chemistry, 48 Kharkivske Shosse, 02160 Kyiv, Ukraine
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(15), 4304; https://doi.org/10.3390/s20154304
Received: 18 June 2020 / Revised: 24 July 2020 / Accepted: 30 July 2020 / Published: 1 August 2020
The combination of the generic mobile technology and inherent stability, versatility and cost-effectiveness of the synthetic receptors allows producing optical sensors for potentially any analyte of interest, and, therefore, to qualify as a platform technology for a fast routine analysis of a large number of contaminated samples. To support this statement, we present here a novel miniature sensor based on a combination of molecularly imprinted polymer (MIP) membranes and a smartphone, which could be used for the point-of-care detection of an important food contaminant, oestrogen-like toxin zearalenone associated with Fusarium contamination of cereals. The detection is based on registration of natural fluorescence of zearalenone using a digital smartphone camera after it binds to the sensor recognition element. The recorded image is further processed using a mobile application. It shows here a first example of the zearalenone-specific MIP membranes synthesised in situ using “dummy template”-based approach with cyclododecyl 2, 4-dihydroxybenzoate as the template and 1-allylpiperazine as a functional monomer. The novel smartphone sensor system based on optimized MIP membranes provides zearalenone detection in cereal samples within the range of 1–10 µg mL−1 demonstrating a detection limit of 1 µg mL−1 in a direct sensing mode. In order to reach the level of sensitivity required for practical application, a competitive sensing mode is also developed. It is based on application of a highly-fluorescent structural analogue of zearalenone (2-[(pyrene-l-carbonyl) amino]ethyl 2,4-dihydroxybenzoate) which is capable to compete with the target mycotoxin for the binding to zearalenone-selective sites in the membrane’s structure. The competitive mode increases 100 times the sensor’s sensitivity and allows detecting zearalenone at 10 ng mL−1. The linear dynamic range in this case comprised 10–100 ng mL−1. The sensor system is tested and found effective for zearalenone detection in maize, wheat and rye flour samples both spiked and naturally contaminated. The developed MIP membrane-based smartphone sensor system is an example of a novel, inexpensive tool for food quality analysis, which is portable and can be used for the “field” measurements and easily translated into the practice. View Full-Text
Keywords: biosensors; molecularly imprinted polymer membranes; smartphone-based sensors; fluorescence; mycotoxins; zearalenone biosensors; molecularly imprinted polymer membranes; smartphone-based sensors; fluorescence; mycotoxins; zearalenone
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MDPI and ACS Style

Sergeyeva, T.; Yarynka, D.; Dubey, L.; Dubey, I.; Piletska, E.; Linnik, R.; Antonyuk, M.; Ternovska, T.; Brovko, O.; Piletsky, S.; El’skaya, A. Sensor Based on Molecularly Imprinted Polymer Membranes and Smartphone for Detection of Fusarium Contamination in Cereals. Sensors 2020, 20, 4304. https://doi.org/10.3390/s20154304

AMA Style

Sergeyeva T, Yarynka D, Dubey L, Dubey I, Piletska E, Linnik R, Antonyuk M, Ternovska T, Brovko O, Piletsky S, El’skaya A. Sensor Based on Molecularly Imprinted Polymer Membranes and Smartphone for Detection of Fusarium Contamination in Cereals. Sensors. 2020; 20(15):4304. https://doi.org/10.3390/s20154304

Chicago/Turabian Style

Sergeyeva, Tetyana, Daria Yarynka, Larysa Dubey, Igor Dubey, Elena Piletska, Rostyslav Linnik, Maksym Antonyuk, Tamara Ternovska, Oleksandr Brovko, Sergey Piletsky, and Anna El’skaya. 2020. "Sensor Based on Molecularly Imprinted Polymer Membranes and Smartphone for Detection of Fusarium Contamination in Cereals" Sensors 20, no. 15: 4304. https://doi.org/10.3390/s20154304

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