Selection of molecular descriptors used in quantitative structure-gas chromatographie retention relationships

Three-dimensional topographic index applied to the prediction of acyclic C5–C8 alkenes Kováts retention indices on polydimethylsiloxane and squalane columns

A novel approach is described for the prediction of gas chromatographic Kováts retention indices of 150 acyclic C5-C8 alkenes on two stationary phases (polydimethylsiloxane, PDMS, and squalane, SQ). The heuristic method was used to build multiple linear regression models using descriptors calculated by MODLESLAB software and CODESSA program. The resulting quantitative structure-retention relationship (QSRR) models were well-correlated, with predictive R2 values of 0.970 and 0.958 for retention indices on PDMS and SQ columns, respectively. 1Omegap, a three-dimensional (3D) topographic index, was found to play the most important role in the description of the chromatographic retention behavior of the alkenes in these two stationary phases. Moreover, this index could completely distinguish different isomers of alkene. Therefore, it can also be extended to distinguish different isomers of other compounds so that can well describe their quantitative structure-retention relationships.

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.

Artificial neural network modeling of Kováts retention indices for noncyclic and monocyclic terpenes

A quantitative structure-property relationship study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques was carried out to investigate the retention behavior of some terpenes on the polar stationary phase (Carbowax 20 M). A collection of 53 noncyclic and monocyclic terpenes was chosen as data set that was randomly divided into two groups, a training set and a prediction set consist of 41 and 12 molecules, respectively. A total of six descriptors appearing in the MLR model consist of one electronic, two geometric, two topological and one physicochemical descriptors. Except for the geometric parameters the remaining descriptors have a pronounced effect on the retention behavior of the terpenes. A 6-5-1 ANN was generated by using the six descriptors appearing in the MLR model as inputs. The mean of relative errors between the ANN calculated and the experimental values of the Kováts retention indexs for the prediction set was 1.88%. This is in aggreement with the relative error obtained by experiment.

QSPR correlation and predictions of GC retention indexes for methyl-branched hydrocarbons produced by insects

A successful interpretation of the complex manner by which the GC retention indexes of methylalkanes produced by insects are related to chemical structure was achieved using the quantitative structure-property relationship (QSPR) method. A general QSPR model including mainly topological descriptors was obtained for 178 data points. The error of the model is similar to the experimental error. The model was supported by (i) leave-one-out cross validation and (ii) division into three sets and prediction of each set from the other two. As a further test of the utility of the model, retention indexes were successfully predicted for an external set of 30 methyl-branched hydrocarbons not involved in the deduction of the correction equation from the main data set. General trends of the structural variation of compounds in any given range of retention index are discussed. The average error was 4.6 overall and 4.3 for the 165 compounds remaining after leaving out small monomethyl alkanes.

Semi-empirical topological method for the prediction of the chromatographic retention of cis- and trans-alkene isomers and alkanes

A new index is proposed for the prediction of the chromatographic retention of the cis- and trans-n-alkene isomers and alkanes. This index is based on the hypothesis that the chromatographic retention of the molecule is due to the interaction of each carbon atom with the stationary phase, and consequently the index is reduced by its neighbours' steric effects. The topological values are obtained by a numerical approximation considering the general behaviour of the chromatographic retention of the compounds. The simple linear regressions between the chromatographic retention and the index proposed for all branched alkanes and also isomers of the studied straight-chain C5 and C14 alkenes (1-ene, cis- and trans-2-, 3-, 4-, 5-, 6- and 7-enes) is very good (the correlation coefficient is r = 0.9999), and the elution sequence is correct for most of them. The models have a high predictive ability, as established by cross-validation values (r2cv). Thus, this new method, different from those already existent, can be used as complementary tool for the elucidation of the molecular structure, or prediction of the chromatographic retention of the cis- and trans-alkene isomers and branched alkanes. It could be extended with success, in the future, to the other types of compounds.

Gas chromatography

This review of the fundamental developments in gas chromatography (GC) includes articles published from 1996 and 1997 and an occasional citation prior to 1996. The literature was reviewed principally using CA Selects for Gas Chromatography from Chemical Abstracts Service, and some significant articles from late 1997 may be missing from the review. In addition, the online SciSearch Database (Institute for Scientific Information) capability was used to abstract review articles or books. As with the prior recent reviews, emphasis has been given to the identification and discussion of selected developments, rather than a presentation of a comprehensive literature search, now available widely through computer-based resources. During the last two years, several themes emerged from a review of the literature. Multidimensional gas chromatography has undergone transformation encompassing a broad range of activity, including attempts to establish methods using chromatographic principles rather than a totally empirical approach. Another trend noted was a comparatively large effort in chromatographic theory through modeling efforts; these presumably became resurgent with inexpensive and powerful computing tools. Finally, an impressive level of activity was noted through the themes highlighted in this review, and this was particularly true with detectors and field instruments.