Optimization Techniques for a Voltammetric Signal to Predict Green Tea Quality Parameters Using MIP Electrode

In this treatise, an integration of feature transformation, optimization, and prediction algorithm has been proposed for voltammetric signal to improve the prediction accuracy of an electrochemical system. A three electrode voltammetric system comprises a Ag/AgCl reference electrode, a platinum counter electrode, and a synthesized working electrode (WE). Two molecularly imprinted polymer (MIP) electrodes have been used for gallic acid (GAL) and epicatechin (EC) detection in green tea samples using differential pulse voltammetry (DPV). Discrete cosine transform (DCT) data have been optimized by genetic algorithm (GA), bat algorithm (BA), and whale optimization algorithm (WOA). A significant improvement was obtained in the model prediction accuracy using 83 and 129 features by GA optimized dataset. Reduced datasets were then used for prediction models using the partial least square regression (PLSR) and principal component regression (PCR). The root means square error of calibration (RMSEC) obtained from PLSR and PCR is 0.253 and 0.094 and 0.239 and 0.088 for GAL and EC, respectively. Prediction accuracies obtained for GAL and EC through PLSR and PCR are 92.24% and 96.24% and 97.95% and 97%, respectively.