A Chemometric Strategy for Simultaneous Determination of Cholesterol andCholestanol in Human Serum Samples

Abstract

In this study, we have developed a novel and efficient method based on spectrophotometry in combination with first-order multivariate calibration for simultaneous quantification of cholesterol (CHL) and cholestanol (CHN) in human serum samples. Several multivariate calibration (MVC) models including partial least squares-1 (PLS- 1), principal component regression (PCR), classical least squares (CLS), orthogonal signal correction-PLS-1, net analyte preprocessing-PLS-1 (NAP/PLS-1), and OSC-CLS were constructed based on first-order spectrophotometric data for simultaneous quantification of CHL and CHN under simulated physiological conditions to select the best algorithm for analyzing real samples. The compositions of the calibration mixtures were selected according to a central composite design (CCD) and validated with an external validation set. The results confirmed the more superiority of PCR to other algorithms. The results of applying PCR for simultaneous quantification of CHL and CHN in human serum samples as real samples were also encouraging. It is expected that the suitable features of the developed method make it potentially advantageous for biosensing, and clinical applications.