Advanced and tailored applications of an efficient electrochemical approach assisted by AsLSSR–COW–rPLS and finding ways to cope with challenges arising from the nature of voltammetric data

Abstract

The use of chemometric data processing is becoming an important part of modern voltammetry. The most challenges arising from voltammetric data are interactions among analytes and the background interferents which may cause signal changes in comparison with pure analyte profiles, and sample-to-sample potential shifts in the analyte profiles. These disadvantages can be tackled by baseline- and potential shift-correction Regarding the above commented problems, performances of asymmetric least squares spline regression (AsLSSR) algorithm for baseline correction and two well-known chemometric tools including interval correlation optimized shifting (icoshift) and correlation optimized warping (COW) for potential shift correction were examined. Finally, the COW was chosen for potential shift correction before applying recursive weighted partial least squares (rPLS) for simultaneous quantification of dopamine (DP), serotonin (ST), acetaminophen (AC) and noradrenaline (NA). In contrast to many other variable selection methods, the rPLS method has the advantage that only the number of latent factors used in the PLS needs to be estimated. A multivariate calibration (MVC) model was developed as a quaternary calibration model in 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 blank and actual human serum samples, respectively. Fortunately, the AsLSSR–COW–rPLS approach was successful in simultaneous quantification of DP, ST, AC, and NAD in both blank and actual human serum samples.