Optimization of Solvent Terminated Dispersive Liquid-Liquid Microextraction of Copper Ions in Water and Food Samples Using Artificial Neural Networks Coupled Bees Algorithm

A multivariate method based on solvent terminated dispersive liquid-liquid microextraction was developed for the determination of Cu2+ ions in aqueous samples. In the proposed approach, di-2-ethylhexylphosphoric acid, xylene and acetone were used as chelating agent, dispersive and extraction solvents, respectively. The effects of various factors on the extraction efficiency such as extraction and dispersive solvent volumes, salt addition and pH were studied using central composite design (CCD) and artificial neural networks coupled bees algorithm (ANN-BA). Upon comparison of these techniques, ANN-BA model was considered to be better optimization method due to its higher percentage relative recovery (about 5%) as compared to the CCD approach. The linear range and the limits of detection (S/N = 3) and quantitation (S/N = 10) were 0.22-140, 0.08 and 0.22 A mu g L-1, respectively. Under the optimal conditions, the recoveries for real samples spiked with 0.1 and 0.3 mg L-1 were in the range of 85-98%.