Artificial Neural Networks for Simultaneous Spectrophotometric Determination of Palladium, Cobalt and Copper in Their Mixtures


Artificial neural network (ANN) was used for simultaneous spectrophotometric determination of trace quantities of palladium and cobalt in their binary mixtures as well as palladium, cobalt and copper in their ternary mixtures. The simultaneous determination method was developed based on formation of their complex with 1-(2-pridylazo)-2-naphtol (PAN) in sodium dodecyl sulfate (SDS) micellar media. Neural networks were trained by applying the back-propagation rule with Levenberg-Marquardt training algorithm. Affecting chemical and network parameters were optimized for obtaining the maximum sensitivity using a one at a time optimization procedure. The results showed a good agreement between measured and predicted values. Reproducibility and accuracy of the methods were checked for various known amounts of these metal ions in their mixtures. The effects of various ions on the selectivity of the methods were studied. The methods showed satisfactory results on the simultaneous determination of these metals in various aqueous matrices and several synthetic alloys.

Keywords: Simultaneous determination, Artificial neural network, Palladium, Cobalt, Copper

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