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Introduction to modern liquid chromatography

3rd Edition, 2010

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Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review

Analytica chimica acta, 2018-02, Vol.1000, p.20-40

2018

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Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures

Journal of Chromatography A, 2017-02, Vol.1486, p.59-67

2017

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Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure–Retention Relationships and Design of Experiments

Analytical chemistry (Washington), 2017-02, Vol.89 (3), p.1870-1878

2017

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Temperature selectivity in reversed-phase high performance liquid chromatography

Journal of Chromatography A, 2002-08, Vol.965 (1-2), p.195-205

Dolan, John W.

2002

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Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model

Journal of Chromatography A, 2018-03, Vol.1541, p.1-11

2018

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Error measures in quantitative structure-retention relationships studies

Journal of Chromatography A, 2017-11, Vol.1524, p.298-302

2017

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Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

Analytical chemistry (Washington), 2018-08, Vol.90 (15), p.9434-9440

2018

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Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio

Journal of Chromatography A, 2017-02, Vol.1486, p.50-58

2017

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Optimizing selectivity during reversed-phase high performance liquid chromatography method development: Prioritizing experimental conditions

Journal of Chromatography A, 2013-08, Vol.1302, p.45-54

2013

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Use of dual-filtering to create training sets leading to improved accuracy in quantitative structure-retention relationships modelling for hydrophilic interaction liquid chromatographic systems

Journal of Chromatography A, 2017-07, Vol.1507, p.53-62

2017

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High-performance gradient elution: the practical application of the linear-solvent-strength model.

1, 2007

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Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model

Journal of Chromatography A, 2017-02, Vol.1486, p.68-75

2017

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Retention prediction using quantitative structure‐retention relationships combined with the hydrophobic subtraction model in reversed‐phase liquid chromatography

Electrophoresis, 2019-09, Vol.40 (18-19), p.2415-2419

2019

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Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, logP, and retention factor ratio to identify optimal analyte training sets for ion chromatography

Journal of Chromatography A, 2017-10, Vol.1520, p.107-116

2017

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Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography

Journal of Chromatography A, 2017-11, Vol.1523, p.173-182

2017

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Inside Back cover: Retention prediction using quantitative structure‐retention relationships combined with the hydrophobic subtraction model in reversed‐phase liquid chromatography

Electrophoresis, 2019-09, Vol.40 (18-19), p.NA

2019

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Measurement of paclitaxel in biological matrices: high-throughput liquid chromatographic–tandem mass spectrometric quantification of paclitaxel and metabolites in human and dog plasma

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences, 2003-03, Vol.785 (2), p.253-261

2003

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Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure–Retention Relationships Studies

Journal of chemical information and modeling, 2017-11, Vol.57 (11), p.2754-2762

2017

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Enhanced methodology for porting ion chromatography retention data

Journal of Chromatography A, 2016-03, Vol.1436, p.59-63

2016

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Column selectivity in reversed-phase liquid chromatography: IV. Type-B alkyl-silica columns

Journal of Chromatography A, 2003-06, Vol.1000 (1), p.757-778

2003

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Lloyd Robert Snyder (1931-2018)

Journal of Chromatography A, 2018-12, Vol.1581-1582, p.180-181

2018

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Column selectivity in reversed-phase liquid chromatography: V. Higher metal content (type-A) alkyl-silica columns

Journal of Chromatography A, 2004-02, Vol.1026 (1), p.77-89

2004

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Molecular modeling and prediction accuracy in Quantitative Structure-Retention Relationship calculations for chromatography

TrAC, Trends in analytical chemistry (Regular ed.), 2018-08, Vol.105, p.352-359

2018

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The Hydrophobic-Subtraction Model of Reversed-Phase Column Selectivity

Advances in Chromatography, Volume 50, 2012, p.305-384

2012

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Introduction to modern liquid chromatography

Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review

Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures

Rapid Method Development in Hydrophilic Interaction Liquid Chromatography for Pharmaceutical Analysis Using a Combination of Quantitative Structure–Retention Relationships and Design of Experiments

Temperature selectivity in reversed-phase high performance liquid chromatography

Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model

Error measures in quantitative structure-retention relationships studies

Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio

Optimizing selectivity during reversed-phase high performance liquid chromatography method development: Prioritizing experimental conditions

Use of dual-filtering to create training sets leading to improved accuracy in quantitative structure-retention relationships modelling for hydrophilic interaction liquid chromatographic systems

High-performance gradient elution: the practical application of the linear-solvent-strength model.

Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model

Retention prediction using quantitative structure‐retention relationships combined with the hydrophobic subtraction model in reversed‐phase liquid chromatography

Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, logP, and retention factor ratio to identify optimal analyte training sets for ion chromatography

Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography

Inside Back cover: Retention prediction using quantitative structure‐retention relationships combined with the hydrophobic subtraction model in reversed‐phase liquid chromatography

Measurement of paclitaxel in biological matrices: high-throughput liquid chromatographic–tandem mass spectrometric quantification of paclitaxel and metabolites in human and dog plasma

Benchmarking of Computational Methods for Creation of Retention Models in Quantitative Structure–Retention Relationships Studies

Enhanced methodology for porting ion chromatography retention data

Column selectivity in reversed-phase liquid chromatography: IV. Type-B alkyl-silica columns

Lloyd Robert Snyder (1931-2018)

Column selectivity in reversed-phase liquid chromatography: V. Higher metal content (type-A) alkyl-silica columns

Molecular modeling and prediction accuracy in Quantitative Structure-Retention Relationship calculations for chromatography

The Hydrophobic-Subtraction Model of Reversed-Phase Column Selectivity

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