title: Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid, uric acid, dopamine and nitrite: application of non-bilinear voltammetric data for exploiting first-order advantage.
abstract: For the first time, several multivariate calibration (MVC) models including partial least squares-1 (PLS-1), continuum power regression (CPR), multiple linear regression-successive projections algorithm (MLR-SPA), robust continuum regression (RCR), partial robust M-regression (PRM), polynomial-PLS (PLY-PLS), spline-PLS (SPL-PLS), radial basis function-PLS (RBF-PLS), least squares-support vector machines (LS-SVM), wavelet transform-artificial neural network (WT-ANN), discrete wavelet transform-ANN (DWT-ANN), and back propagation-ANN (BP-ANN) have been constructed on the basis of non-bilinear first order square wave voltammetric (SWV) data for the simultaneous determination of ascorbic acid (AA), uric acid (UA), dopamine (DP) and nitrite (NT) at a glassy carbon electrode (GCE) to identify which technique offers the best predictions. The compositions of the calibration mixtures were selected according to a simplex lattice design (SLD) and validated with an external set of analytes' mixtures. An asymmetric least squares splines regression (AsLSSR) algorithm was applied for correcting the baselines. A correlation optimized warping (COW) algorithm was used to data alignment and lack of bilinearity was tackled by potential shift correction. The effects of several pre-processing techniques such as genetic algorithm (GA), orthogonal signal correction (OSC), mean centering (MC), robust median centering (RMC), wavelet denoising (WD), and Savitsky-Golay smoothing (SGS) on the predictive ability of the mentioned MVC models were examined. The best preprocessing technique was found for each model. According to the results obtained, the RBF-PLS was recommended to simultaneously assay the concentrations of AA, UA, DP and NT in human serum samples.
authors.affiliation: Faculty of Chemistry, Razi University, Kermanshah 671496734, Iran. Electronic address: mbgholivand@yahoo.com.
authors.forename: Mohammad-Bagher
authors.initials: MB
authors.lastname: Gholivand
authors.affiliation: Faculty of Chemistry, Razi University, Kermanshah 671496734, Iran; Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Universidad Nacional del Litoral, Ciudad Universitaria, CC 242, S3000ZAA Santa Fe, Argentina.
authors.forename: Ali R
authors.initials: AR
authors.lastname: Jalalvand
authors.affiliation: Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Universidad Nacional del Litoral, Ciudad Universitaria, CC 242, S3000ZAA Santa Fe, Argentina.
authors.forename: Hector C
authors.initials: HC
authors.lastname: Goicoechea
authors.affiliation: Quality and Technology group, Department of Food Science, Faculty of Life Sciences, University of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg, Denmark.
authors.forename: Thomas
authors.initials: T
authors.lastname: Skov
mesh_terms: D000465:Algorithms; D001205:Ascorbic Acid / Q000097:blood*; D002138:Calibration; D004298:Dopamine / Q000097:blood*; D055664:Electrochemical Techniques / Q000379:methods*; D006801:Humans; D006863:Hydrogen-Ion Concentration; D008956:Models, Chemical; D009573:Nitrites / Q000097:blood*; D060388:Support Vector Machine
chemical_list: D009573:Nitrites; D001205:Ascorbic Acid; D004298:Dopamine
keywords: AA; AsLSSR; Ascorbic acid; BP-ANN; COW; CPR; DP; DWT-ANN; Dopamine; GA; GCE; LOO-CV; LS-SVM; LVs; Linear and non-linear multivariate calibration models; MC; MLR; MVC; NT; Nitrite; OSC; PDC; PLS-1; PLY-PLS; PRESS; PRM; Q(2); RBF-PLS; RCR; REP; RMC; RMSECV; RMSEP; SGS; SLD; SPA; SPL-PLS; SWV; Savitsky–Golay smoothing; Simultaneous determination; UA; Uric acid; WD; WT-ANN; ascorbic acid; asymmetric least squares splines regression; back propagation-artificial neural network; continuum power regression; correlation optimized warping; discrete wavelet transform-artificial neural network; dopamine; genetic algorithm; glassy carbon electrode; latent variables; least squares-support vector machines; leave one out cross-validation; mean centering; multiple linear regression; multivariate calibration; nitrite; orthogonal signal correction; partial least squares-1; partial robust M-regression; percentage of data contamination; polynomial-partial least squares; prediction residual error sum of squares; rPCA; radial basis function-partial least squares; relative error of prediction; robust continuum regression; robust median centering; robust principal component analysis, MLP, multilayer perceptron; root mean square errors of prediction; root mean squared errors of cross-validation; simplex lattice design; spline-partial least squares; square wave voltammetry; successive projections algorithm; the square correlation coefficient of cross-validation; uric acid; wavelet denoising; wavelet transform-artificial neural network