A Retention Model for Polar Selectivity in Reversed Phase Chromatography as a Function of Mobile Phase Organic Modifier Type

Zhuang, Ping; Thompson, Richard A.; O'Brien, Thomas P.

doi:10.1081/jlc-200054828

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…As an example, an LC-MS method was required to monitor labeled cortisol/cortisone in the presence of unlabeled cortisone and other possible interfering metabolites (Figure 4-13). Separations using YMC-ODS-AQ column exhibited differences in selectivity that could be further enhanced by using different organic modifiers to separate the eight components of interest (Figure 4-14) [51]. Selectivity differences can also be obtained by using small percentages (<10%) of a third solvent component for more challenging separations.…”

Section: Selectivity As a Function Of Type And Concentration Of Organmentioning

confidence: 99%

Reversed‐Phase HPLC

LoBrutto

Kazakevich

2006

HPLC for Pharmaceutical Scientists

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Section: Selectivity As a Function Of Type And Concentration Of Organmentioning

confidence: 99%

Reversed‐Phase HPLC

LoBrutto

Kazakevich

2006

HPLC for Pharmaceutical Scientists

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“…This range includes some of the most toxic and carcinogenic PAHs, such as benzo[a]pyrene, as well as less toxic compounds, such as naphthalene, as reported in Figure 1 . The number of molecules used in a QSRR study may depend on several factors, including the structural homogeneity of the molecules being studied [ 33 , 34 , 35 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography

et al. 2023

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A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

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Quantitative structure–(chromatographic) retention relationships

Héberger

2007

Journal of Chromatography A

369

188

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Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

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A Retention Model for Polar Selectivity in Reversed Phase Chromatography as a Function of Mobile Phase Organic Modifier Type

Cited by 5 publications

References 44 publications

Reversed‐Phase HPLC

Reversed‐Phase HPLC

Quantitative Structure–Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography

Quantitative structure–(chromatographic) retention relationships

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