Liquid chromatography coupled with photodiode array and a multivariate curve resolution - Alternating least square algorithm for identification and quantification of co-eluting impurities in pharmaceutical analysis

Abstract

This paper systematically investigated and reported for the first time the identification and quantification of co-eluting impurities as low as 0.05 area% by PDA with i-PDeA II deconvolution software in the LabSolutions Chromatographic Data System (CDS) using an integrated multivariate curve resolution-alternating least squares (MCR-ALS) algorithm with a bidirectional exponentially modified Gaussian (BEMG) model function. The algorithm was able to consistently identify 0.05% impurities when co-eluting with the main component (Rs ≥ 0.8) as well as when co-eluting with another impurity (Rs ≥ 0.5). In the case of two co-eluting impurities from 0.05% to 1% (Rs ≥ 0.5), the quantification error ranged from +10.6% to -16.7%. In the case of an impurity co-eluting with the main component (Rs ≥ 0.8), the quantification error was 4.4-8.9% for 1% impurity and 109-184% for 0.05% impurity. The precision was excellent for the range of 0.05-1.0% impurities with the RSD being 1.4-3.0% for 1% impurity and 4.0-8.7% for 0.05% impurity. The identification rate and quantitation accuracy were not affected by the spectral similarity of the molecules, as comparable results were obtained by analyzing two molecules with low similarity (4,4-difluorobenzophenone and valerophenone) and two molecules with high similarity (diazepam and oxazepam) based on simulated data. This peak resolution by MCR-ALS approach provides fast and robust identification and quantification of co-eluting impurities even when method development efforts do not provide complete separation of the target peaks, and could therefore find a wide range of applications in pharmaceutical and other types of analyses.

Keywords: Co-elution; Impurities; MCR-ALS; Pharmaceutical analysis; i-PDeA II.