title: A principal component analysis approach for developing retention models in liquid chromatography.
abstract: Three retention models for liquid chromatography are developed using principal component analysis (PCA). It is shown that they exhibit features similar to that of the model based on linear solvation energy relationship (LSER). However, the fitting performance of the PCA models is better than that of the LSER model, the performance of which can be considerably improved by the use of artificial neural networks. In addition, the possibility of using the proposed models as well as the LSER model to predict the retention times of solutes under chromatographic conditions at which these solutes have never been studied is also examined by means of three data sets of analytes consisting of non-polar compounds to polar compounds with a variety of functional groups.
authors: Laboratory of Physical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. Electronic address: nikitas@chem.auth.gr.
authors: P
authors: P
authors: Nikitas
authors: Laboratory of Physical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
authors: A
authors: A
authors: Pappa-Louisi
authors: Laboratory of Physical Chemistry, Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
authors: S
authors: S
authors: Tsoumachidou
authors: Drug Applied Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Iran.
authors: A
authors: A
authors: Jouyban
mesh_terms: D000465:Algorithms; D002853:Chromatography, Liquid / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis / Q000379:methods*; D013816:Thermodynamics