Evaluation of nonlinear modeling based on artificial neural networks for the spectrophotometric determination of Pd(II) with CPA-mK

A new method is proposed for the spectrophotometric determination of Pd(II) , based on the reaction of Pd(II) with 2-(4-chloro-2-phosphonophenylazo)-7- (3-carboxyphenylazo)-1,8-dihydroxynaphthalene-3,6-disulfonic acid(CPA-mK) i n sulfuric acid without heating. Beer's law is obeyed for 1.0-4.0 mu g of P d (II) in 10 mL of solution. The calibration curve from 1.0 to 42.0 mu g in 10 mt of solution is modeled successfully by artificial neural networks (A NNs). The maximum relative error between experimental values and the values predicted by ANNs is 1.5%. In comparison with some mathematical functions, ANNs show better ability for curve fitting, thus greatly extending the app licable range of the calibration curve of this new system. The method has b een applied to determine Pd (II) in ore and catalyst samples with a relativ e error of less than 4% and with a recovery range between 94% and 103%.