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

JALALVAND A; GHOLIVAND M; GOICOECHEA H C; ASMUND RINNAN; SKOV T

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2015 vol. 146 p. 437 - 446

0169-7439

The most challenges arising from voltammetric data are interactions among analytes and the backgroundinterferents which may cause signal changes in comparison with pure analyte profiles, and sample-to-samplepotential shifts in the analyte profiles. These disadvantages can be tackled by baseline- and shift corrections.Regarding the above commented problems performances of asymmetric least-squares spline regression (AsLSSR)algorithm for baseline correction and two well-known chemometric algorithms including interval correlationoptimised shifting (icoshift) and correlation optimised warping (COW) for potential shift correction wereexamined. Finally, the COW was chosen for potential shift correction before applying recursive weighted partialleast squares (rPLS) for simultaneous quantification of dopamine (DP), serotonin (ST), acetaminophen (AC) andnoradrenaline (NA). In contrast to many other variable selection methods, the rPLS method has the advantage thatonly the number of latent factors used in the PLS needs to be estimated. A multivariate calibration (MVC) modelwas developed as a quaternary calibration model in a blank human serum sample (drug-free) provided by a healthyvolunteer to regard the presence of a strong matrix effect which may be caused by the possible interferents presentin the serum, and it was validated and tested with two independent sets of analytes mixtures in the blank and actualhuman serum samples, respectively. Fortunately, the COW-rPLS approach was successful in simultaneousquantification of DP, ST, AC, and NAD in both blank and actual human serum samples