Simultaneous determination of copper, lead and cadmium by cathodic adsorptive stripping voltammetry using artificial neural network

Abstract In this work, simultaneous determination of two groups of elements consisting of Pb(II)–Cd(II) and Cu(II)–Pb(II)–Cd(II) using adsorptive cathodic stripping voltammetry are described. The method is based on accumulation of these metal ions on mercury electrode using xylenol orange as a suitable complexing agent. The potential was scanned to the negative direction and the differential pulse stripping voltammograms were recorded. The instrumental and chemical factors were optimized using artificial neural network. The optimized conditions were obtained in pH of 5.5, xylenol orange concentration of 4.0 μM, accumulation potential of −0.50 V, accumulation time of 30 s, scan rate of 10 mV/s and pulse height of 70 mV. The relationship between the peak current versus concentration was linear over the range of 5.0–150.0 ng ml−1 for cadmium and 5.0–150.0 ng ml−1 for lead. The limits of detection were 0.98 and 1.18 ng ml−1 for lead and cadmium ions, respectively. In simultaneous determination of Cu(II), Pb(II) and Cd(II) there are inter-metallic interactions, which result a non-linear relationship between the peak current and the ionic concentration for each of the element. Therefore, an artificial neural network was used as the multivariate calibration method. The ANN was constructed with three neurons as the output layer for the simultaneous determination of the three elements. The constructed model was able to predict the concentration of the elements in the ranges of 1.0–50.0, 5.0–200.0 and 10.0–200.0 ng ml−1, for Cu(II), Pb(II) and Cd(II), respectively.

[1]  C. V. D. Berg.  Determination of copper, cadmium and lead in seawater by cathodic stripping voltammetry of complexes with 8-hydroxyquinoline , 1986 .

[2]  A. Ensafi,et al.  Differential pulse adsorption stripping voltammetric determination of copper(II) with 2-mercaptobenzimidazol at a hanging mercury-drop electrode. , 2001, Analytical sciences : the international journal of the Japan Society for Analytical Chemistry.

[3]  Joseph Wang,et al.  Highly sensitive and selective measurements of lead by stripping voltammetry/potentiometry following adsorptive accumulation of the lead-o-cresolphthalexon complex , 1993 .

[4]  T. Khayamian,et al.  Differential pulse cathodic stripping adsorption voltammetric determination of trace amounts of lead using factorial design for optimization. , 2003, Talanta.

[5]  B. Goldstein,et al.  Soft plastic bread packaging: lead content and reuse by families. , 1991, American Journal of Public Health.

[6]  R. Hajian,et al.  Application of adsorptive stripping voltammetry to the determination of bismuth and copper in the presence of morin , 2003 .

[7]  E. Achterberg,et al.  Stripping voltammetry for the determination of trace metal speciation and in-situ measurements of trace metal distributions in marine waters , 1999 .

[8]  J. Zupan,et al.  Neural networks: A new method for solving chemical problems or just a passing phase? , 1991 .

[9]  E. Underwood.  Trace Elements in Human and Animal Nutrition , 1956, Agronomy Journal.

[10]  T. Khayamian,et al.  Simultaneous voltammetric determination of molybdenum and copper by adsorption cathodic differential pulse stripping method using a principal component artificial neural network. , 2002, Talanta.

[11]  Petr Zuman,et al.  Role of Mercury Electrodes in Contemporary Analytical Chemistry , 2000 .

[12]  O. A. Farghaly.  Direct and simultaneous voltammetric analysis of heavy metals in tap water samples at Assiut city: an approach to improve the analysis time for nickel and cobalt determination at mercury film electrode , 2003 .

[13]  M. García-Vargas,et al.  Experimental designs in the development of a new method for the sensitive determination of cadmium in seawater by adsorptive cathodic stripping voltammetry , 2003 .

[14]  D J Livingstone,et al.  Novel method for the display of multivariate data using neural networks. , 1991, Journal of molecular graphics.

[15]  Paul J. Gemperline,et al.  Nonlinear multivariate calibration using principal components regression and artificial neural networks , 1991 .

[16]  K. Ogura,et al.  Electroanalytical and spectroscopic studies on metal complexes with sulphonphthalein derivatives—I: Determination of stability constant and composition of Cu(II)—Xylenol orange complex , 1980 .

[17]  Zhengquan Zhang,et al.  Simultaneous determination of copper, nickel, lead, cobalt and cadmium by adsorptive voltammetry , 1993 .

[18]  Conrad Bessant,et al.  Simultaneous Determination of Ethanol, Fructose, and Glucose at an Unmodified Platinum Electrode Using Artificial Neural Networks , 1999 .

[19]  M. Freund,et al.  Artificial neural network processing of stripping analysis responses for identifying and quantifying heavy metals in the presence of intermetallic compound formation. , 1997, Analytical chemistry.

[20]  R. Brereton.  Multilevel multifactor designs for multivariate calibration , 1997 .

[21]  T. Khayamian,et al.  Extending the dynamic range of the determination of copper by adsorption differential pulse stripping method using a principal component artificial neural network , 2000 .

[22]  Julius Ju Lurje,et al.  Handbook of analytical chemistry , 1975 .

[23]  Ayumi Yamaguchi,et al.  Direct determination of trace concentrations of lead in fresh water samples by adsorptive cathodic stripping voltammetry of a lead-Calcein Blue complex , 1995 .

[24]  K. Yokoi,et al.  Determination of Cadmium by Adsorptive Stripping Voltammetry of a Cadmium-Calcein Blue Complex , 1995 .

[25]  José Joaquín Hernández-Brito,et al.  Rapid determination of copper, lead and cadmium in unpurged seawater by adsorptive stripping voltammetry , 1996 .

[26]  Ruisheng Zhang,et al.  Prediction of Programmed-temperature Retention Values of Naphthas by Wavelet Neural Networks , 2001, Comput. Chem..

[27]  A. Ensafi,et al.  Determination of Copper by an Adsorption Differential Pulse Stripping Method with Naphthol Derivative , 2000 .