Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid-liquid microextraction using -sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response surface methodology.

A bat inspired algorithm with the aid of artificial neural networks (ANN-BA) has been used for the first time in chemistry and food sciences to optimize solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a green, fast and low cost technique for determination of Cu ions in water and food samples using -sulfonatocalix (4) arene as a complexing reagent. For this purpose, the influence of four important factors four factors which was influenced on the extraction efficiency such as salt addition, solution pH and disperser and extraction solvent volumes were investigated. Central composite design (CCD) as a comparative technique was employed for optimization of ST-DLLME efficiency.