Robust multivariate diagnostics for PLSR and application on high dimensional spectrally overlapped drug systems

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.

KEYWORDS:

• Cook's distance
• influential observations
• leverage points
• partial least squares regression
• SIMPLS
• weighted likelihood

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