Least median of squares and iteratively re‐weighted leastsquares as robust linear regression methods for fluorimetricdetermination of α‐lipoic acid in capsules in ideal and non‐idealcases of linearity
This study outlines two robust regression approaches, namely least median of squares (LMS) and
iteratively re‐weighted least squares (IRLS) to investigate their application in instrument analysis of
nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition).
These robust regression methods were used to calculate calibration data from the fluorescence
quenching reaction (ΔF and F‐ratio) under ideal or non‐ideal linearity conditions. For each
condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS
and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy
and precision were carefully studied for each condition. LMS and IRLS regression line fittings
showed significant improvement in correlation coefficients and all regression parameters for both
methods and both conditions. In the ideal linearity condition, the intercept and slope changed
insignificantly, but a dramatic change was observed for the non‐ideal condition and linearity intercept.
Under both linearity conditions, LOD and LOQ values after the robust regression line fitting
of data were lower than those obtained before data treatment. The results obtained after statistical
treatment indicated that the linearity ranges for drug determination could be expanded to
lower limits of quantitation by enhancing the regression equation parameters after data treatment.
Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric
OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions.
Luminescence The journal of biological and chemical Luminescence.,DOI: 10.1002/bio.3471, ISSN: 1522-7243, Volume: 33, Issue: 4, Pages Range: 742-750