Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference

Abstract

In this work, we are going to develop an efficient electroanalytical methodology based on generation of second-order differential pulse voltammetric (DPV) data at different pulse heights to exploit second-order advantage for simultaneous determination of levodopa (LDP), carbidopa (CDP), methyldopa (MDP), benserazide (BA), tolcapone (TOL) and entacapone (ENT) in the presence of dopamine (DPA) as uncalibrated interference. The recorded data were baseline- and potential shift-corrected by asymmetric least square spline regression (AsLSSR) and correlation optimized warping (COW) algorithms, respectively. After data pre-processing, multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis 2 (PARAFAC2) were used to develop three-way calibration models and then, the abilities of the developed models to predict analytes’ concentrations in the absence and presence of DPA were examined in validation and test sets, respectively. MCR-ALS acted better than PARAFAC2 to predict analytes’ concentrations in the absence and presence of DPA as uncalibrated interference. Therefore, MCR-ALS was chosen to predict antiparkinson agents’ concentrations in spiked human serum samples as real cases. Fortunately, acceptable results were obtained which were comparable to those obtained by high performance liquid chromatography with UV detection (HPLC-UV) as reference method.