A chemometric strategy based on peak parameters to resolve overlapped electrochemical signals

Abstract A new chemometric methodology based on the use of peak parameters as direct input data into different multivariate calibration methods is proposed. Different regression techniques such as multilinear regression (MLR), partial least square regression (PLS), principal component regression (PCR) and artificial neural networks (ANN), were utilized in order to resolve hard overlapped electrochemical signals belonging to the well-known Tl + /Pb 2+ system, which was used as a benchmark. This strategy was studied as an alternative to traditional procedures that apply pre-treatment techniques (dimension reduction methods or feature selection processes) to the full voltammograms of the signals. Good predictive and effective models were obtained, being the RMS errors very similar in all cases, independent of the calibration method. However, ANN-based regression models performed slightly better. The average relative errors ranged from 5 to 10% for Tl + and from 4 to 12% for Pb 2+ . A study of the relevance of the voltammetric peak parameters was also carried out. This parameters-based strategy can involve a fast and efficient alternative to resolve multicomponent systems in those analytical techniques whose signals can be represented by peak parameters.

[1]  Biserka Raspor,et al.  Comparative quantitative analysis of overlapping voltammetric signals , 1994 .

[2]  F. Salinas,et al.  Resolution by polarographic techniques of atrazine-simazine and terbutryn-prometryn binary mixtures by using PLS calibration and artificial neural networks. , 2000 .

[3]  Application of wavelet transforms to determine peak shape parameters for interference detection in graphite-furnace atomic absorption spectrometry , 1998 .

[4]  Paul Geladi,et al.  Some recent trends in the calibration literature , 2002 .

[5]  J. Havel,et al.  Use of artificial neural networks for the evaluation of electrochemical signals of adenine and cytosine in mixtures interfered with hydrogen evolution , 2001 .

[6]  R. Henrion,et al.  Application of partial least-squares regression for signal resolution in differential pulse anodic stripping voltammetry of thallium and lead , 1990 .

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

[8]  Use of artificial neural networks in capillary zone electrophoresis , 1999 .

[9]  I. Habib,et al.  Multivariate analysis of Cd(II), In(III), Tl(I) and Pb(II) in mixtures using square wave anodic stripping voltammetry. , 1998, Talanta.

[10]  Johann Gasteiger,et al.  Neural Networks for Chemists: An Introduction , 1993 .

[11]  R. Tauler,et al.  Multivariate curve resolution of overlapping voltammetric peaks: quantitative analysis of binary and quaternary metal mixtures. , 2002, The Analyst.

[12]  C. Mello,et al.  Simultaneous determination of phenol isomers in binary mixtures by differential pulse voltammetry using carbon fibre electrode and neural network with pruning as a multivariate calibration tool , 2000 .

[13]  V. Cerdà,et al.  Resolution of highly overlapping differential pulse anodic stripping voltammetric signals using multicomponent analysis and neural networks , 1997 .

[14]  A. Yan,et al.  Flow injection analysis of fluoride: optimization of experimental conditions and non-linear calibration using artificial neural networks. , 2000, The Analyst.

[15]  M. Ghoneim,et al.  Assay of dipyridamole in human serum using cathodic adsorptive square-wave stripping voltammetry , 2002, Analytical and bioanalytical chemistry.

[16]  M. C. Ortiz,et al.  Modelling the background current with partial least squares regression and transference of the calibration models in the simultaneous determination of Tl and Pb by stripping voltammetry. , 1998, Talanta.

[17]  Richard G. Brereton,et al.  Introduction to multivariate calibration in analytical chemistry , 2000 .

[18]  Lei Yan,et al.  Differential Pulse Polarography for a First‐Order EC Process and Its Diagnostic Parameters , 2003 .

[20]  Clinio Locatelli,et al.  Peak Area, Peak Current: Critical Comparison. Application to Real Samples , 1998 .

[21]  Li Wang,et al.  Voltammetric determination of chlorpromazine hydrochloride and promethazine hydrochloride with the use of multivariate calibration , 2001 .

[22]  Gregory W. Kauffman,et al.  Prediction of Surface Tension, Viscosity, and Thermal Conductivity for Common Organic Solvents Using Quantitative Structure-Property Relationships , 2001, J. Chem. Inf. Comput. Sci..

[23]  Conrad Bessant,et al.  A chemometric analysis of dual pulse staircase voltammograms obtained in mixtures of ethanol, fructose and glucose , 2000 .

[24]  Zulfiqur Ali,et al.  Total luminescence spectroscopy with pattern recognition for classification of edible oils. , 2003, The Analyst.

[25]  Víctor Cerdà,et al.  Simultaneous flow injection analysis of cadmium and lead with differential pulse voltammetric detection , 1997 .

[26]  F Despagne,et al.  Neural networks in multivariate calibration. , 1998, The Analyst.

[27]  Javier Saurina,et al.  Cyclic voltammetric simultaneous determination of oxidizable amino acids using multivariate calibration methods , 2000 .

[28]  Alessandro Ulrici,et al.  Multicomponent analysis of electrochemical signals in the wavelet domain. , 2003, Talanta.

[29]  Johanna Smeyers-Verbeke,et al.  Handbook of Chemometrics and Qualimetrics: Part A , 1997 .

[30]  Miquel Esteban,et al.  Multivariate curve resolution with alternating least squares optimisation: a soft-modelling approach to metal complexation studies by voltammetric techniques , 2000 .

[31]  R. Brereton.  Chemometrics in analytical chemistry. A review , 1987 .

[32]  Luis A. Sarabia,et al.  Using continuum regression for quantitative analysis with overlapping signals obtained by differential pulse polarography , 1996 .

[33]  F. Ricci,et al.  Electroanalytical study of Prussian Blue modified glassy carbon paste electrodes , 2003 .

[34]  D. Kell,et al.  Improving the interpretation of multivariate and rule induction models by using a peak parameter representation , 1997 .

[35]  F. Pablos,et al.  Voltammetry of surface redox processes perturbed by a father-son reaction , 2000 .

[36]  J. M. Palacios-Santander,et al.  Use of Artificial Neural Networks, Aided by Methods to Reduce Dimensions, to Resolve Overlapped Electrochemical Signals. A Comparative Study Including other Statistical Methods , 2003 .

[37]  A. Bond.  Modern Polarographic Methods in Analytical Chemistry , 1980 .