Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid, uric acid, dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage

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 analytesʹ 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.