Quantitative structure–property relationship study of GC retention indices for PCDFs by DFT and relative position of chlorine substitution

Quantitative structure-retention relationship for the Kovats retention indices of a large set of terpenes: a combined data splitting-feature selection strategy

A data set consisting of a large number of terpenoids, the widely distributed compounds in nature that are found in abundance in higher plants, have been used to develop a quantitative structure property relationship (QSPR) for their Kovats retention index. QSPR models are usually obtained by splitting the data into two sets including calibration (or training) and prediction (or validation). All model building steps, especially feature selection procedure, are performed using this initial splitting, and therefore the performances of the resulted models are highly dependent on the initial data splitting. To investigate the effects of data splitting on the feature selection in the current article we proposed a combined data splitting-feature selection (CDFS) methodology for QSPR model development by producing several different training/validation/test sets, and repeating all of the model building studies. In this method, data splitting is achieved many times and in each case feature selection is performed. The resulted models are compared for similarity and dissimilarity between the selected descriptors. The final model is one whose descriptors are the common variables between all of resulted models. The method was applied to QSPR study of a large data set containing the Kovats retention indices of 573 terpenoids. A final 8-parametric multilinear model with constitutional and topological indices was obtained. Cross-validation indicated that the model could reproduce more than 90% of variances in the Kovats retention data. The relative error of prediction for an external test set of 50 compounds was 3.2%. Finally, to improve the results, structure-retention relationships were followed by nonlinear approach using artificial neural networks and consequently better results were obtained.

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.

Speciation of alkylated dibenzothiophenes through correlation of structure and gas chromatographic retention indexes

The gas chromatographic (GC) speciation of polycyclic aromatic sulfur heterocycles (PASH) in deeply desulfurized fuels is difficult without recourse to authentic standard compounds. Here we investigate the GC retention indexes for 30 alkylated dibenzothiophenes on two methylphenylsiloxane stationary phases of different selectivity (5% and 50% phenyl groups) with a view to avoid the synthesis of all congeners. The influence of the substitution pattern on the retention indexes is discussed. With the measured data it is possible to carry out a multiple linear regression (MLR) to calculate parameters for predicting the retention indexes of unknown polymethylated isomers based only on their structural features. Nine trimethyldibenzothiophene in a synthesis product are identified through their retention indexes. The retention indexes for a total of 43 alkyldibenzothiophenes are listed.