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Metabolites 2017, 7(1), 7; doi:10.3390/metabo7010007

QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression

1
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, G12 8LT Glasgow, United Kingdom
3
Helmholtz Zentrum München, Research Unit Analytical BioGeoChemistry, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany
4
Department of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Meikle
Received: 21 December 2016 / Accepted: 5 February 2017 / Published: 9 February 2017
(This article belongs to the Special Issue Metabolomics 2016)
View Full-Text   |   Download PDF [178 KB, uploaded 17 February 2017]

Abstract

Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, tR(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor tR(R) was considered. View Full-Text
Keywords: quantitative structure retention relationship models; HPLC retention; metabolites identification quantitative structure retention relationship models; HPLC retention; metabolites identification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zisi, C.; Sampsonidis, I.; Fasoula, S.; Papachristos, K.; Witting, M.; Gika, H.G.; Nikitas, P.; Pappa-Louisi, A. QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression. Metabolites 2017, 7, 7.

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