RÉSUMÉ
For the first time, several second-order calibration models based on artificial neural network-residual bilinearization (ANN-RBL), unfolded-partial least squares-RBL (U-PLS/RBL), multidimensional-partial least squares-RBL (N-PLS/RBL), multivariate curve resolution-alternating least squares (MCR-ALS), and parallel factor analysis 2 (PARAFAC2) were used to exploiting second-order advantage to identify which technique offers the best predictions for the simultaneous quantification of norepinephrine (NE), paracetamol (AC), and uric acid (UA) in the presence of pteroylglutamic acid (FA) as an uncalibrated interference at an electrochemically oxidized glassy carbon electrode (OGCE). Three-way differential pulse voltammetric (DPV) arrays were obtained by recording the DPV signals at different pulse heights. The recorded three-way arrays were both non-bilinear and non-trilinear therefore, the observed shifts in the recorded DPV data were corrected using correlation optimised warping (COW) algorithm. All the algorithms achieved the second-order advantage and were in principle able to overcome the problem of the presence of unexpected interference. Comparison of the performance of the applied second-order chemometric algorithms confirmed the more superiority of U-PLS/RBL to resolve complex systems. The results of applying U-PLS/RBL for the simultaneous quantification of the studied analytes in human serum samples were also encouraging.