Molecular Imprinted Polymer Based Electrode for Sensing Catechin (+C) in Green Tea
| Autor: | Runu Banerjee Roy, Trisita Nandy Chatterjee, Debangana Das, Bipan Tudu, Panchanan Pramanik, Rajib Bandyopadhyay, Santanu Sabhapondit, Pradip Tamuly |
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| Rok vydání: | 2018 |
| Předmět: |
Detection limit
Materials science Ethylene glycol dimethacrylate 010401 analytical chemistry Molecularly imprinted polymer Analytical chemistry Catechin 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences chemistry.chemical_compound chemistry Linear range Electrode Differential pulse voltammetry Electrical and Electronic Engineering Cyclic voltammetry 0210 nano-technology Instrumentation |
| Zdroj: | IEEE Sensors Journal. 18:2236-2244 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2018.2791661 |
| Popis: | Green tea is believed to be a healthy beverage due to a number of therapeutic benefits. Catechin, one of its constituents, is an important antioxidant and possesses free radical scavenging abilities. This paper demonstrates a low cost solution related to the sensing of catechin (+C) using the principle of molecular imprinted polymer technique. Here the electrode was synthesized using the co-polymer of acrylonitrile and ethylene glycol dimethacrylate and was subsequently imprinted with catechin. The material was extensively characterized using Fourier transform infra-red spectroscope and field emission scanning electron microscope, respectively. Cyclic voltammetry and differential pulse voltammetry using the three electrode system were employed for determining the electrochemical characteristics of the proposed electrode. It exhibited a linear range from 5 to 100 $\mu \text{M}$ with the limit of detection of 37 nm (S/N = 3). On studying the analytical characteristics, the electrode was found to be repeatable, reproducible, and offered a good selectivity. Our sensing device was subjected to green tea samples in order to study their catechin content. A partial least square regression model was developed for correlating the response with that of the high performance liquid chromatography data and it resulted in a prediction accuracy of about 92%. |
| Databáze: | OpenAIRE |
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