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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

Citation

Park, SH and Haddad, PR and Amos, RIJ and Talebi, M and Szucs, R and Pohl, CA and Dolan, JW, 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, 1520 pp. 107-116. ISSN 0021-9673 (2017) [Refereed Article]


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DOI: doi:10.1016/j.chroma.2017.09.016

Abstract

Retention prediction for unknown compounds based on Quantitative Structure-Retention Relationships (QSRR) can lead to rapid "scoping" method development in chromatography by simplifying the selection of chromatographic parameters. The use of retention factor ratio (or k-ratio) as a chromatographic similarity index can be a potent method to cluster similar compounds into a training set to generate an accurate predictive QSRR model provided that its limitation that the method is impractical for retention prediction for unknown compounds is successfully addressed. In this work, we propose a localised QSRR modelling approach with the aim of compensating the critical limitation in the otherwise successful k-ratio filter-based QSRR modelling. The approach is to combine a k-ratio filter with both Tanimoto similarity (TS) and a ΔlogP index (i.e., logP-Dual filter). QSRR models for two retention parameters (a and b) in the linear solvent strength (LSS) model in ion chromatography (IC), logk = ablog[eluent], were generated for larger organic cations (molecular mass up to 506) on a Thermo Fisher Scientific CS17 column. The application of the developed logP-Dual filter resulted in the production of successful QSRR models for 50 organic cations out of 87 in the dataset. The predicted a- and b-values of the models were then applied to the LSS model to predict the corresponding retention times. External validation showed that QSRR models for a-, b- and tR- values with excellent accuracy and predictability (Qext(F2)2 of 0.96, 0.95, and 0.96, RMSEP of 0.06, 0.02, and 0.38 min) were created successfully, and these models can be employed to speed up the "scoping" phase of method development in IC.

Item Details

Item Type:Refereed Article
Keywords:QSRR, ion chromatography, Tanimoto similarity, retention factor ratio, LogP
Research Division:Chemical Sciences
Research Group:Analytical Chemistry
Research Field:Separation Science
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Chemical Sciences
Author:Park, SH (Miss Soo Hyun Park)
Author:Haddad, PR (Professor Paul Haddad)
Author:Amos, RIJ (Dr Ruth Amos)
Author:Talebi, M (Dr Mohammad Talebi)
ID Code:122439
Year Published:2017
Funding Support:Australian Research Council (LP120200700)
Deposited By:Chemistry
Deposited On:2017-11-14
Last Modified:2017-11-14
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