CuO Nanoparticles Decorated MIP-Based Electrode for Sensitive Determination of Gallic Acid in Green Tea

Abstract

The present work elucidates a method for the economic development of a reproducible electrode for sensitive determination and prediction of the amount of gallic acid (GA) in green tea (GT) by molecularly imprinted polymer (MIP) technology. The electrode material has been synthesized by co-polymerization of itaconic acid and ethylene glycol dimethacrylate (EGDMA) followed by implantation of copper oxide nanoparticles (CuO NPs). The electrode demonstrated two wide linear ranges, i.e., 1 μM - 100 μM and 100 μM to 900 μM with a low detection limit of 12.6 nM. The practical applicability of the electrode has been validated by quantifying the amount of GA in GT samples. Principal component analysis (PCA) has been performed and class separability index (SI) of 31.27 has been obtained. To explore the predictive ability of the electrode, partial least square regression (PLSR) and principal component regression (PCR) models have been developed. This has been done by correlating the differential pulse voltammetry (DPV) signals from the electrode and the corresponding high performance liquid chromatography (HPLC) data. While with PLSR, average prediction accuracy of 88.97 % is obtained with root mean square error of calibration (RMSEC) as low as 1.35, PCR results in an average prediction accuracy of 87.24 % with RMSEC being 1.38.