Quantitative structure-retention relationships in doping control

Quantitative structure-(chromatographic) retention relationships

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.

Application of artificial neural networks for prediction of retention factors of triazine herbicides in reversed-phase liquid chromatography

In this paper a quantitative structure-retention relationship (QSRR) method is used to model reversed-phase high-performance liquid chromatography (HPLC) behaviour of a series of triazine herbicides and their metabolites. Accurate description of the retention factors in terms of four descriptors related to the analytes and to the mobile phase is achieved by means of an artificial neural network (ANN). For comparison, a QSRR model is derived by multilinear regression (MLR). Validation of the two models shows a better ability in prediction of the ANN as compared with the MLR method. A solid-phase extraction (SPE) procedure allowing the simultaneous determination of the five triazinic compounds in groundwater analysis is also presented. The observed recoveries from water samples range between 85 and 100% for ng/ml concentration levels of all analytes.

Quantitative structure-enantioselective retention relationships for chromatographic separation of arylalkylcarbinols on Pirkle type chiral stationary phases

Quantitative structure-retention (QSRR, retention factors log k1 and log k2 for the first and second eluted enantiomer) as well as enantioselective retention relationships (QSERR, separation factor log a) for a series of 42 chiral arylalkylcarbinols on four brush-type chiral stationary phases are derived by multiple linear regression analyses and artificial neuronal network calculations using 2D and 3D molecular descriptors including those obtained by quantum chemical calculations. Separation factors are in addition modeled by the 3D-QSAR method of comparative molecular field analysis (CoMFA). For the retention factors the LUMO energy turns out to be the most important descriptor, whereas for log a it is the hydrophobicity of the analytes. With CoMFA both the steric and electrostatic field are found to be of almost comparable significance.