Premium
Least median of squares and iteratively re‐weighted least squares as robust linear regression methods for fluorimetric determination of α‐lipoic acid in capsules in ideal and non‐ideal cases of linearity
Author(s) -
Korany Mohamed A.,
Gazy Azza A.,
Khamis Essam F.,
Ragab Marwa A. A.,
Kamal Miranda F.
Publication year - 2018
Publication title -
luminescence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 45
eISSN - 1522-7243
pISSN - 1522-7235
DOI - 10.1002/bio.3471
Subject(s) - ideal (ethics) , linearity , mathematics , linear regression , least squares function approximation , lipoic acid , regression , statistics , robust regression , chemistry , chromatography , analytical chemistry (journal) , physics , organic chemistry , philosophy , epistemology , quantum mechanics , estimator , antioxidant
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 (∆F 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.