Quantitative structure–property relationship study of chromatographic retention indices and normal boiling points for oxo compounds using the semi-empirical topological method

Quantitative structure-retention relationships applied to liquid chromatography gradient elution method for the determination of carbonyl-2,4-dinitrophenylhydrazone compounds

A usual method for the determination of aldehydes and ketones in different matrices consists of a derivatization with 2,4-dinitrophenylhydrazine (DNPH) followed by HPLC-UV analysis. In the present work, a HPLC-UV gradient elution method has been applied to the analysis of 13 aldehydes and ketones-DNPH in automotive emission samples. In addition to these 13 compounds-DNPH, several carbonyl-DNPH compounds (linear, ramified and cyclic, saturated and unsaturated compounds) have been analyzed by HPLC-UV. Quantitative structure-retention relationships (QSRR) methods have been applied to predict the logarithm of capacity factor (logk') of carbonyl-DNPH compounds. According to its physicochemical meaning, combinations of 2 and 3 molecular descriptors have been proposed in order to achieve higher correlation with logk'. Using linear and non-linear QSRR methodologies, the resulting prediction models allowed the screening of the most probable carbonyl-DNPH derivative candidates that correspond to unknown compounds detected in automotive emission samples. This information has been useful for their identification by UPLC(®)-MS/MS. In addition, the chromatographic retention of different carbonyl-DNPH compound families was studied using two HPLC isocratic methods working with two orthogonal stationary phases (octadecylpolyethoxysilane and cyanopropyl). Differences between the retention indexes obtained for each column were used for classifying carbonyl-DNPH into compounds families.

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.

Semi-empirical topological index: a tool for QSPR/QSAR studies

The semi-empirical topological index (I(ET)), developed to predict the chromatographic retention of a series of organic compounds, is extended to predict other properties and biological activities of aliphatic alcohols. This topological index takes into account the contribution of each individual atom type to the property considered and is able to encode information about structural features of the molecules. The efficiency of this index is verified by high quality Structure - Property and structure - Activity Relationships (QSPR/QSAR) models obtained for several representative physicochemical properties, biological activities and toxicities of aliphatic alcohols. Most of the properties investigated are well modeled (r > 0.98) employing the I(ET). Cross-validation using the more general leave-one-out method demonstrates that these models are highly statistically reliable. The proposed I(ET) index promises to be a useful descriptor in the QSPR/QSAR studies.

Two-step multivariate adaptive regression splines for modeling a quantitative relationship between gas chromatography retention indices and molecular descriptors

The relationship between retention indices and molecular descriptors of alkanes is established by two-step multivariate adaptive regression splines (TMARS). TMARS combines linear regression with multivariate adaptive regression splines (MARS). It is demonstrated for the present data set that using linear regression or MARS modeling alone causes lack of fit. TMARS avoids lack of fit and appreciably improves the prediction ability for the model. The use of this combined approach permits the development of additional understanding of the adaptive nature in MARS modeling.