Comparison of single best artificial neural network and neural network ensemble in modeling of palladium microextraction

A simple, efficient, and fast method based on in-syringe dispersive liquid-liquid microextraction (IS-DLLME) for preconcentration of trace amounts of palladium from aqueous samples was developed. After complexation with 5-(4-dimethylaminobenzylidene)rhodanine, Pd was extracted into benzyl alcohol before its measurement with UV-Vis spectrophotometer, equipped with cubic millimeter cells. Thereafter, a comparative study between single best artificial neural network (SB-NN) and neural network ensemble (NNE) was performed to find the best mathematical model for palladium extraction process to simulate IS-DLLME. Two NNE models were built, one without pruning (NNE-WP) the ensemble members and another with pruning using genetic algorithm (NNE-GA). The predictive and generalization ability of SB-NN, NNE-WP, and NNE-GA was compared based on 20 runs. The average % error for SB-NN, NNE-WP, and NNE-GA models was 0.234, 0.146, and 0.115 and the correlation coefficient was 0.902, 0.948, and 0.973, respectively; indicating superiority of NNE approaches specially NNE-GA in capturing the non-linear behavior of the system.