Retention prediction using quantitative structure‐retention relationships combined with the hydrophobic subtraction model in reversed‐phase liquid chromatography
Sign Up to like & getrecommendations! Published in 2019 at "ELECTROPHORESIS"
DOI: 10.1002/elps.201900022
Abstract: The hydrophobic subtraction model (HSM) combined with quantitative structure‐retention relationships (QSRR) methodology was utilized to predict retention times in reversed‐phase liquid chromatography (RPLC). A selection of new analytes and new RPLC columns that had never… read more here.
Keywords: subtraction model; quantitative structure; structure retention; retention relationships ... See more keywords
Quantitative structure-retention relationships of the chromatographic retentions of phthalic acid ester contaminants in foods.
Sign Up to like & getrecommendations! Published in 2019 at "Journal of separation science"
DOI: 10.1002/jssc.201900372
Abstract: The harmful health effects caused by phthalic acid esters have been supported from the increasing scientific evidence, developing the efficient methodologies to monitor the levels of phthalic acid esters in various foods become especially important… read more here.
Keywords: acid esters; quantitative structure; structure retention; phthalic acid ... See more keywords
Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets.
Sign Up to like & getrecommendations! Published in 2021 at "Chemosphere"
DOI: 10.1016/j.chemosphere.2021.130036
Abstract: Predicting chromatographic retention times of pesticides has become more and more important for suspect and non-target screening. Indeed, high-resolution mass spectrometry hyphenated (HRMS) to liquid chromatography (LC) are of growing interest for research and monitoring… read more here.
Keywords: retention relationship; quantitative structure; molecular descriptors; structure retention ... See more keywords
Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure-Retention Relationships Studies.
Sign Up to like & getrecommendations! Published in 2017 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.7b00346
Abstract: Quantitative structure-retention relationship (QSRR) models are powerful techniques for the prediction of retention times of analytes, where chromatographic retention parameters are correlated with molecular descriptors encoding chemical structures of analytes. Many QSRR models contain geometrical… read more here.
Keywords: creation; quantitative structure; structure retention; computational methods ... See more keywords
Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Molecular Sciences"
DOI: 10.3390/ijms21062053
Abstract: This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the… read more here.
Keywords: column; quantitative structure; analysis; structure retention ... See more keywords
Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy
Sign Up to like & getrecommendations! Published in 2023 at "Molecules"
DOI: 10.3390/molecules28041696
Abstract: Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming… read more here.
Keywords: structure retention; retention relationship; retention; quantitative structure ... See more keywords