ABSTRACT
Statistical methods are effectively used in the evaluation of pharmaceutical formulations instead of laborious liquid chromatography. However, signal overlapping, nonlinearity, multicollinearity and presence of outliers deteriorate the performance of statistical methods. The Partial Least Squares Regression (PLSR) is a very popular method in the quantification of high dimensional spectrally overlapped drug formulations. The SIMPLS is the mostly used PLSR algorithm, but it is highly sensitive to outliers that also effect the diagnostics. In this paper, we propose new robust multivariate diagnostics to identify outliers, influential observations and points causing non-normality for a PLSR model. We study performances of the proposed diagnostics on two everyday use highly overlapping drug systems: Paracetamol–Caffeine and Doxylamine Succinate–Pyridoxine Hydrochloride.
Acknowledgments
Authors thank Ismail Fasfoud, PhD (Chemistry Department, Hashemite University, Jordan) for revising the text and for his contribution on the preparation of PAR/CAF mixtures. Authors also thank the anonymous referee for detailed revision and comments that greatly improved the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Aylin Alin http://orcid.org/0000-0002-2977-331X
Claudio Agostinelli http://orcid.org/0000-0001-6702-4312
Plamen Katsarov http://orcid.org/0000-0002-9158-6208
Yahya Al-Degs http://orcid.org/0000-0002-9555-7594