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

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Abstract

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.

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Science, University of Sistan and Baluchestan, 98135-674, Zahedan, Iran

    Effat Dehghanian, Massoud Kaykhaii & Maryam Mehrpur

Authors
  1. Effat Dehghanian
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  2. Massoud Kaykhaii
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  3. Maryam Mehrpur
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Correspondence to Effat Dehghanian.

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Dehghanian, E., Kaykhaii, M. & Mehrpur, M. Comparison of single best artificial neural network and neural network ensemble in modeling of palladium microextraction. Monatsh Chem 146, 1217–1227 (2015). https://doi.org/10.1007/s00706-014-1396-1

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  • DOI: https://doi.org/10.1007/s00706-014-1396-1

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