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For: Morgan PE, Barlow DJ, Hanna-brown M, Flanagan RJ. Artificial Neural Network Modelling of the Retention of Acidic Analytes in Strong Anion-Exchange HPLC: Elucidation of Structure-Retention Relationships. Chromatographia 2012;75:693-700. [DOI: 10.1007/s10337-012-2251-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Number Cited by Other Article(s)
1
Larbi H, Didaoui L, Righezza M. Characterization of stationary phases based on monosubstituted benzene retention indices using correspondence factor analysis and linear solvation energy relationships in RPLC. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2018. [DOI: 10.1007/s13738-018-1418-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
2
Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography. J Chromatogr A 2017;1523:173-182. [DOI: 10.1016/j.chroma.2017.02.054] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 02/20/2017] [Accepted: 02/23/2017] [Indexed: 11/19/2022]
3
Park SH, Haddad PR, Amos RI, Talebi M, Szucs R, Pohl CA, Dolan JW. Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, log P , and retention factor ratio to identify optimal analyte training sets for ion chromatography. J Chromatogr A 2017;1520:107-116. [DOI: 10.1016/j.chroma.2017.09.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 09/02/2017] [Accepted: 09/06/2017] [Indexed: 11/17/2022]
4
Park SH, Haddad PR, Talebi M, Tyteca E, Amos RI, Szucs R, Dolan JW, Pohl CA. Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model. J Chromatogr A 2017;1486:68-75. [DOI: 10.1016/j.chroma.2016.12.048] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 12/14/2016] [Accepted: 12/16/2016] [Indexed: 10/20/2022]
5
Computer-assisted simulation and optimisation of retention in ion chromatography. Trends Analyt Chem 2016. [DOI: 10.1016/j.trac.2015.07.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
6
Miller TH, Musenga A, Cowan DA, Barron LP. Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks. Anal Chem 2013;85:10330-7. [DOI: 10.1021/ac4024878] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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