Recent applications of retention modelling in liquid chromatography

Abstract

Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.

Analysis of adsorption energy distribution in selected hydrophilic‐interaction chromatography systems with amide, amine, and zwitterionic stationary phases

“…The sorption mechanism in HILIC has been a subject of intense investigations by many research groups (e.g. [1–6] and references cited therein). One of the first proposed mechanisms assumes that the aqueous part of a mobile phase is adsorbed on the surface of a polar stationary phase forming a thin stable layer and then analyte particles split between the layer and the organic part of a mobile phase (this is so called hydrophilic partitioning mechanism) [6].…”

“…[1–6] and references cited therein). One of the first proposed mechanisms assumes that the aqueous part of a mobile phase is adsorbed on the surface of a polar stationary phase forming a thin stable layer and then analyte particles split between the layer and the organic part of a mobile phase (this is so called hydrophilic partitioning mechanism) [6]. Many authors also emphasize that the course of this process, especially in the case of eluents with a relatively low water content, may also be significantly influenced by various types of adsorption as well as hydrophobic and electrostatic interactions [1,7–9].…”

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data

“… 33 The mzXML files were then imported into julia programing language using MS_Import.jl package (see Code Availability). Peaks in the mass domain were extracted from these scans to generate the dataset used consisting of 2.7 × 10 11 m / z values, intensities, retention factors, 34 , 35 and mass peak width at half-height. The first three parameters were used as independent variables (i.e., predictors) while the fourth was used as a dependent variable (i.e., to be predicted).…”

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data

“…To mitigate the issues related to the identification of known and unknown chemicals, the use of retention times and retention indices (r i ) have been, previously tested. [51][52][53][54][55][56][57][58] For r i measurements, a series of calibrants (i.e. chemicals with known retention behavior) are necessary.…”