Quantitative structure‐retention relationships in reversed‐phase liquid chromatography using several stationary and mobile phases

Abstract

Quantitative structure-retention relationships in reversed-phase liquid chromatography using several stationary and mobile phases Quantitative Structure-Retention Relationships (QSRRs) have been employed to study retention mechanism of Reversed-Phase Liquid Chromatography (RPLC). Two C 18 and two C 8 columns were used with mobile phases containing methanol, acetonitrile, and tetrahydrofuran in concentrations ranging from 40 to 90 (v/v)% in water. QSRR equations derived are generally characterized by a satisfactory quality of fit, although missing data or a low average retention can decrease the goodness-of-fit. In agreement with literature the most important retention descriptors appeared to be the analyte's molecular volume and its hydrogen-bond accepting basicity, but also the polarity/polarizability, hydrogen-bond donating acidity, and excess molar refraction showed statistical significance in several cases. Relatively small, but significant differences between the two C 18 stationary phases were observed. The differences between C 8 and C 18 column types were more pronounced. The influence of the mobile phase on regression coefficients is more difficult to interpret, because of adsorption of mobile phase constituents by the stationary phase. However, the importance of this partitioning of water and modifier is illustrated by the practically identical trends in b values for the three modifiers studied, while the trends in v values are significantly different. Principal component analysis of the QSRR regression coefficients demonstrated the prevailing role of the mobile phase for chromatographic properties of the RPLC systems studied as compared to the stationary phase.

A novel method for characterization and comparison of reversed-phase column selectivity

“…Additionally, the effect of organic modifier on coefficients of LSERs equations can be investigated. It can be seen from Table S3 values were the substantially same as those reported in literatures [14,16,44]. Ln k w , the extrapolation of ln k to a hypothetical 0% ACN in the mobile phase [45,46], properly reflected the characteristics of stationary phases.…”

Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds

“…In our previous QSRR studies we have defined variety of models built by different linear or nonlinear modeling techniques, including multiple linear regression (MLR) [14], principal component analysis [15], partial least-squares [16], and artificial neural networks [17]. The aim of this study, being a part of our structure retention relationship models design project, is modeling by the MLR technique the RP-LC-HRMS and HILIC-LC-HRMS gradient retention time data of 146 drugs and metabolites of widely diverse chemical structures.…”

Application of solvation model to prediction of the solute retention in liquid chromatography over a wide range of mobile-phase compositions

“…Works by Vonk and coworkers [25], Chu and coworkers [26], and Coym [27] have also paid attention to the selection of the compound set. Based on a broad spread of the descriptor values, the normalization of them has been carried out and statistically evaluated.…”