Application of Non-bilinear Voltammetric Data for the First-Order Excellence in Simultaneous Determination of Ascorbic Acid, Uric Acid, Acetaminophen and Noradrenalin in Interfering Media

For the first time, an analytical methodology based ondifferential pulse voltammetry (DPV) at a glassy carbon electrode (GCE) assisted by two multivariate calibration (MVC) models including back propagation-artificial neural network (BP-ANN), non-linear class, and partial least squares-1 (PLS-1), classical class, thatthey have been constructed on the basis of non-bilinear first order differential pulse voltammetry (DPV) data,was developed and validated for the simultaneous determination of Ascorbic acid, Uric acid, Acetaminophen, and Noradrenalinto identify which approach offers the best predictions.The baselines of the DPV signals were corrected by asymmetric least square spline regression (AsLSSR) algorithm. Before applying the PLS-1,lack of bi-linearity was tackled by potential shift correction using correlation optimised warping (COW) algorithm. The multivariate calibration (MVC) model was developed as a quaternary calibration modelin a blank human serum sample (drug-free) provided by a healthy volunteer to regard the presence of a strong matrix effect which may be caused by the possible interferents present in the serum, and it was validated and tested with two independent sets of analytes mixtures in the blank and actual human serum samples, respectively.According to the obtained results, the PLS-1 was recommended for simultaneous determination of AA, UA, AC, and NA in both blank and actual human serum samples .