Robust multivariate diagnostics for PLSR and application on high dimensional spectrally overlapped drug systems |
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| Authors: | Aylin Alin Claudio Agostinelli Georgi Gergov Plamen Katsarov Yahya Al-Degs |
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| Institution: | 1. Department of Statistics, Dokuz Eylul University, Izmir, Turkeyaylin.alin@deu.edu.tr;3. Department of Mathematics, University of Trento, Trento, Italy;4. Faculty of Pharmacy, Medical University, Sofia, Bulgaria;5. Department of Pharmaceutical Sciences, Faculty of Pharmacy, Medical University – Plovdiv, Plovdiv, Bulgaria;6. Department of Chemistry, Hashemite University, Az-Zarqa, Jordan |
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| Abstract: | ABSTRACTStatistical 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. |
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| Keywords: | Cook's distance influential observations leverage points partial least squares regression SIMPLS weighted likelihood |
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