Linearity of chromatographic systems in drug analysis part II: a Monte Carlo justification for the use of nonlinear regressions

Abstract

In Part I of this series of communications an alternative approach was outlined to quantify the deviation from linearity by estimation of the change in true sensitivity of several mathematically defined concentration–response relationships. In this paper, Monte Carlo techniques were used to compare the strengths and limitations of linear and nonlinear regressions in assay calibrations. Nonlinear regressions can define a calibration curve as efficiently as a linear regression. Provided that the nonlinear regression is matched to the actual nonlinear concentration–response relationship, the use of nonlinear regressions can improve the accuracy of the assay and increase the percentage of calibrations meeting acceptance criteria.