Comparison of prediction power between theoretical and neural-network models in ion-interaction chromatography-Reference-Cited by-同舟云学术

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Comparison of prediction power between theoretical and neural-network models in ion-interaction chromatography

Published:1998-03 Issue:1-2 Volume:799 Page:35-45
ISSN:0021-9673
Container-title:Journal of Chromatography A
language:en
Short-container-title:Journal of Chromatography A

Author:

Sacchero Giovanni,Concetta Bruzzoniti Maria,Sarzanini Corrado,Mentasti Edoardo,Metting Harm J,Coenegracht Pierre M.J

Publisher

Elsevier BV

Subject

Organic Chemistry,General Medicine,Biochemistry,Analytical Chemistry

Reference36 articles.

1. P.R. Haddad, P.E. Jackson, in: Ion Chromatography—Principles and Applications, Journal of Chromatography Library, Vol. 46, Elsevier, Amsterdam, 1990, Ch. 6, p. 165.

2. Retention model for singly and doubly charged analytes in ion-interaction chromatography

3. Retention Model for Anionic, Neutral, and Cationic Analytes in Reversed-Phase Ion Interaction Chromatography

4. Separation of acidic compounds by high-pressure liquid-liquid chromatography involving ion-pair formation

5. Reversed-phase ion-pair partition chromatography of carboxylates and sulphonates

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1. Artificial Neural Network Modelling of the Retention of Acidic Analytes in Strong Anion-Exchange HPLC: Elucidation of Structure-Retention Relationships;Chromatographia;2012-05-22

2. Retention Mechanism for Ion-Pair Chromatography with Chaotropic Reagents;Advances in Chromatography;2011-03-04

3. Computer-assisted Development of ad hoc Methodology for Monitoring of Inorganic Cations in Surface Water Using Gradient Elution Ion Chromatography;Croatica Chemica Acta;2011

4. Application of artificial neural networks in the prediction of product distribution in electrophoretically mediated microanalysis;ELECTROPHORESIS;2009-07

5. Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase;Journal of Separation Science;2009-02

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