Selective determination of closantel by artificial neural network-genetic algorithm optimized molecularly imprinted polypyrrole using UV-visible spectrophotometry.

Abstract

A molecularly imprinted polymer (MIP) for the selective solid-phase extraction (SPE) was prepared applying polymerization of pyrrole monomer in the presence of closantel (CLS) as a template molecule. The quantitative measurements were carried out using UV-Vis spectrophotometry. Several important parameters control the performance of polypyrrole sorbent. The influence of seven factors including loading time, polymerization time, amount of sorbent, stirring rate, desorption time, initiator concentration and monomer to template ratio were investigated. The optimization of parameters was performed using Plackett-Burman design (PBD), central composite design (CCD), artificial neural network (ANN) and genetic algorithm (GA). The Pareto plot showed that the effects of loading time, reaction time and amount of sorbent are most important to the process. These significant factors were investigated using CCD and the obtained data were used to train the ANN. The predicted model obtained from the trained ANN was introduced to GA as the fitness function to be optimized. The calibration curve demonstrated linearity over a concentration range of 0.010-10mM with a correlation coefficient (R2) of 0.9833 under optimal condition. The synthesized MIP sorbent showed a good selectivity and sensitivity toward CLS. The limit of detection (LOD) for CLS was obtained 1.0μM. The real sample analysis was performed to determine CLS in pharmaceutical and human serum samples.