Titanium Oxide Nanocubes Embedded Molecularly Imprinted Polymer-Based Electrode for Selective Detection of Caffeine in Green Tea

The present study reports the development of a low cost electrode by means of molecularly imprinted polymer (MIP) technique for detection and quantification of caffeine in green tea. The sensing material has been synthesized using the co-polymer of acrylonitrile (AN) and ethylene glycol dimethacrylate (EGDMA) followed by impregnation of TiO
₂
nanocubes into it. The electrode demonstrated a wide linear range from
$5~\mu \text{M}$
to
$120~\mu \text{M}$
with limit of detection (LOD) being
$0.6~\mu \text{M}$
. The analytical characteristics revealed acceptable selectivity, repeatability, stability and reproducibility of the electrode. Real time application of developed sensor has been ascertained by measuring the caffeine content in different variants of green tea. The partial least square regression (PLSR) and principal component regression (PCR) models have been explored to estimate the predictive ability of the electrode in terms of their caffeine content by correlating the response of the obtained differential pulse voltammetry (DPV) datasets of green tea samples with HPLC data. In case of PLSR technique, an average prediction accuracy of 92.94% is obtained with root mean square error of calibration (RMSEC) value being as low as 0.07. Additionally for PCR, an average prediction accuracy of 93.75% is acquired with RMSEC value being 0.07.