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Open AccessArticle

A Hybrid Lab-on-a-Chip Injector System for Autonomous Carbofuran Screening

1
Department of Food Analysis and Nutrition, Faculty of Food and Biochemical Technology, University of Chemistry and Technology Prague, Technická 5, 6–Dejvice, 166 28 Prague, Czech Republic
2
Optical Devices Laboratory, Department of Physics, Chemistry and Biology—IFM, Linköping University, S-58183 Linköping, Sweden
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(24), 5579; https://doi.org/10.3390/s19245579
Received: 13 November 2019 / Revised: 10 December 2019 / Accepted: 13 December 2019 / Published: 17 December 2019
(This article belongs to the Special Issue Lab-on-a-Chip and Microfluidic Sensors)
Securing food safety standards is crucial to protect the population from health-threatening food contaminants. In the case of pesticide residues, reference procedures typically find less than 1% of tested samples being contaminated, thus indicating the necessity for new tools able to support smart and affordable prescreening. Here, we introduce a hybrid paper–lab-on-a-chip platform, which integrates on-demand injectors to perform multiple step protocols in a single disposable device. Simultaneous detection of enzymatic color response in sample and reference cells, using a regular smartphone, enabled semiquantitative detection of carbofuran, a neurotoxic and EU-banned carbamate pesticide, in a wide concentration range. The resulting evaluation procedure is generic and allows the rejection of spurious measurements based on their dynamic responses, and was effectively applied for the binary detection of carbofuran in apple extracts. View Full-Text
Keywords: lab-on-a-chip; 3D-printed devices; microfluidics; food safety; pesticides; acetylcholinesterase; screening method lab-on-a-chip; 3D-printed devices; microfluidics; food safety; pesticides; acetylcholinesterase; screening method
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Tsagkaris, A.S.; Pulkrabova, J.; Hajslova, J.; Filippini, D. A Hybrid Lab-on-a-Chip Injector System for Autonomous Carbofuran Screening. Sensors 2019, 19, 5579.

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