title: Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model.
abstract: Quantitative Structure-Retention Relationships (QSRRs) represent a popular technique to predict the retention times of analytes, based on molecular descriptors encoding the chemical structures of the analytes. The linear solvent strength (LSS) model relating the retention factor, k to the eluent concentration (log k=a-blog [eluent]), is a well-known and accurate retention model in ion chromatography (IC). In this work, QSRRs for inorganic and small organic anions were used to predict the regression parameters a and b in the LSS model (and hence retention times) for these analytes under a wide range of eluent conditions, based solely on their chemical structures. This approach was performed on retention data of inorganic and small organic anions from the "Virtual Column" software (Thermo Fisher Scientific). These retention data were recalibrated via a "porting" methodology on three columns (AS20, AS19, and AS11HC), prior to the QSRR modeling. This provided retention data more applicable on recently produced columns which may exhibit changes of column behavior due to batch-to-batch variability. Molecular descriptors for the analytes were calculated with Dragon software using the geometry-optimized molecular structures, employing the AM1 semi-empirical method. An optimal subset of molecular descriptors was then selected using an evolutionary algorithm (EA). Finally, the QSRR models were generated by multiple linear regression (MLR). As a result, six QSRR models with good predictive performance were successfully derived for a- and b-values on three columns (R2>0.98 and RMSE<0.11). External validation showed the possibility of using the developed QSRR models as predictive tools in IC (Qext(F3)2>0.7 and RMSEP<0.4). Moreover, it was demonstrated that the obtained QSRR models for the a- and b-values can predict the retention times for new analytes with good accuracy and predictability (R2of 0.98, RMSE of 0.89min, Qext(F3)2of 0.96 and RMSEP of 1.18min).
authors.affiliation: Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia.
authors.forename: Soo Hyun
authors.initials: SH
authors.lastname: Park
authors.affiliation: Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia. Electronic address: Paul.Haddad@utas.edu.au.
authors.forename: Paul R
authors.initials: PR
authors.lastname: Haddad
authors.affiliation: Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Talebi
authors.affiliation: Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia.
authors.forename: Eva
authors.initials: E
authors.lastname: Tyteca
authors.affiliation: Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia.
authors.forename: Ruth I J
authors.initials: RI
authors.lastname: Amos
authors.affiliation: Pfizer Global Research and Development, CT13 9NJ, Sandwich, UK.
authors.forename: Roman
authors.initials: R
authors.lastname: Szucs
authors.affiliation: LC Resources Inc., 1795 NW Wallace Rd., McMinnville, OR, 97128, USA.
authors.forename: John W
authors.initials: JW
authors.lastname: Dolan
authors.affiliation: Thermo Fisher Scientific, Sunnyvale, CA, USA.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Pohl
mesh_terms: D000465:Algorithms; D000838:Anions / Q000737:chemistry* / Q000302:isolation & purification*; D002853:Chromatography, Liquid / Q000379:methods*; D016014:Linear Models; D008956:Models, Chemical; D008970:Molecular Weight; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D012997:Solvents / Q000737:chemistry*
chemical_list: D000838:Anions; D012997:Solvents
keywords: Evolutionary algorithm; Ion chromatography; Linear solvent strength model; Multiple linear regression; Quantitative structure-retention relationships