Correlation between retention indices and quantum-chemical descriptors of ketones and aldehydes on stationary phases of different polarity

Evolution and Evaluation of GC Columns

A chromatographic column is the fundamental element required for gas-chromatographic analysis. The separation of components coming from complex mixtures, prior to their detection was leading to a prominent revolution in different areas of science. Moreover, current advances in gas chromatographic (GC) columns technology and development have been providing almost unlimited possibilities for analysis employing diverse matrices. We aim through this review article to describe the evolution of chromatographic columns, by pointing the most important stages, as well as the new trends and future perspectives predicted for the new generation of GC columns. Furthermore, it was in our scope to present the main fundamentals regarding the theoretical relationships that describe the chromatographic separation, to introduce concepts related to columns selection in accordance with the required application as well as to discuss the available evaluation parameters for columns efficiency. Consequently, the early stages of first columns preparation up to the development of GC capillary columns used nowadays, together with examples of their applications are also reported and described in detail.

Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review

With an enormous growth in the application of hydrophilic interaction liquid chromatography (HILIC), there has also been significant progress in HILIC method development. HILIC is a chromatographic method that utilises hydro-organic mobile phases with a high organic content, and a hydrophilic stationary phase. It has been applied predominantly in the determination of small polar compounds. Theoretical studies in computer-aided modelling tools, most importantly the predictive, quantitative structure retention relationship (QSRR) modelling methods, have attracted the attention of researchers and these approaches greatly assist the method development process. This review focuses on the application of computer-aided modelling tools in understanding the retention mechanism, the classification of HILIC stationary phases, prediction of retention times in HILIC systems, optimisation of chromatographic conditions, and description of the interaction effects of the chromatographic factors in HILIC separations. Additionally, what has been achieved in the potential application of QSRR methodology in combination with experimental design philosophy in the optimisation of chromatographic separation conditions in the HILIC method development process is communicated. Developing robust predictive QSRR models will undoubtedly facilitate more application of this chromatographic mode in a broader variety of research areas, significantly minimising cost and time of the experimental work.

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.

QSRR Models for Kováts' Retention Indices of a Variety of Volatile Organic Compounds on Polar and Apolar GC Stationary Phases Using Molecular Connectivity Indexes

Quantitative structure-retention relationship (QSRR) approaches, based on molecular connectivity indices are useful to predict the gas chromatography of Kováts relative retention indices (GC-RRIs) of 132 volatile organic compounds (VOCs) on different 12 (4 apolar and 8 polar) stationary phases (C₆₇, C₁₀₃, C₇₈, C_∞, POH, TTF, MTF, PCL, PBR, TMO, PSH and PCN) at 130 °C. Full geometry optimization based on Austin model 1 semi-empirical molecular orbital method was carried out. The sets of 30 molecular descriptors were derived directly from the topological structures of the compounds from DRAGON program. By means of the final variable selection method, which is elimination selection stepwise regression algorithms, three optimal descriptors were selected to develop a QSRR model to predict the RRI of organic compounds on each stationary phase with a correlation coefficient between 0.9378 and 0.9673 and a leave-one-out cross-validation correlation coefficient between 0.9325 and 0.9653. The root mean squares errors over different 12 phases were within the range of 0.0333–0.0458. Furthermore, the accuracy of all developed models was confirmed using procedures of Y-randomization, external validation through an odd–even number and division of the entire dataset into training and test sets. A successful interpretation of the complex relationship between GC RRIs of VOCs and the chemical structures was achieved by QSRR. The three connectivity indexes in the models are also rationally interpreted, which indicated that all organic compounds’ RRI was precisely represented by molecular connectivity indexes.

Prediction of the retention of β-diketonato complexes in TLC systems on silica gel by quantitative structure-retention relationships

Quantitative structure-retention relationships for a series of 30 mixed ?-diketonato complexes of cobalt(III), chromium(III) and ruthenium(III) were derived by multiple linear regression analyses using molecular descriptors obtained by quantum chemical calculations. The retention parameters were obtained by thin layer chromatography on silica gel using mono and two-component solvent systems. The molecular descriptors included in the multiple linear regression analysis were molecular weight, molecular volume, surface area, hydrophilic-lipophilic balance, percent hydrophilic surface area, dipole moment, polarizability, refractivity, energy of the highest occupied molecular orbital and energy of the lowest unoccupied molecular orbital. High agreement between the experimental and predicted retention parameters was obtained when polarizability and the hydrophilic-lipophilic balance were used as the molecular descriptors. Comparison of the models with those established on polyacrylonitrile showed that the structure of the sorbent is responsible for the chromatographic behaviour of the same compounds. The presented models can be used for the prediction of the retention of new solutes in screening chromatographic systems.

Prediction of quantitative calibration factors of some organic compounds in gas chromatography

A quantitative structure-property relationship (QSPR) methodology that involves multilinear (Hansch-type) and nonlinear (radial basis function neural network (RBFNN)) approaches was performed to correlate the quantitative molar calibration factors (f(M)) of 140 organic compounds against structural factors. The statistical characteristics provided by the multiple linear model (R(2) = 0.963; RMS = 0.089; AARD = 3.86% for test set) indicated satisfactory stability and predictive ability, while the predictive ability of the RBFNN model is somewhat superior (R(2) = 0.983; RMS = 0.075; AARD = 3.19% for test set). The multilinear model provided some insight into the main structure factors that modulate the quantitative calibration factor of the investigated compounds.

Theoretical prediction of the Kovat's retention index for oxygen-containing organic compounds using novel topological indices

For the retention index of polar compounds, polar groups in molecules would participate in polar interactions between eluents and stationary phases and thus would be expected to make large and separate contributions to the total retention index (RI). The characterization of the structural feature will help to elucidate the quantitative structure-retention relationship (QSRR). In this paper, on the basis of the PEI index previously developed by Cao, two novel molecular polarizability effect index, modified molecular polarizability index (MPEI(m)) and modified inner molecular polarizability index (IMPEI(m)) were proposed to predict the GC retention of a variety of oxygen-containing organic compounds with diverse chemical structures on OV-1 and SE-54 stationary phases. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. Simple linear regression equations between the RI and the topological indices were established for each stationary phase separately (R>0.99). Statistical analysis showed that the QSRR models have high internal stability and good predictive ability for external groups. The molecular properties known to be relevant for GC retention data, such as molecular size, branching and polar functional groups were well covered by the generated descriptors. The models with topological indices were compared with those based on quantum-chemical descriptors. It is observed that topological indices produce better correlations with Kovat's retention index. The results indicate the efficiency of presented indices in the structure-retention index correlations of complex compounds with polar multi-functional groups.

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.

Theoretical analysis on retention behavior of pigments in reversed-phase high-performance liquid chromatographic (HPLC)

Quantitative structure-retention relationship (QSRR) models have been used successfully to predict and explain retention behavior of pigments in reversed-phase high-performance liquid chromatography (HPLC). The semi-empirical quantum chemical method (PM3) in Gaussian98 was employed to calculate a set of molecular descriptors of pigments. Using multiple linear regression (MLR), we obtained empirical functions with high correlation coefficient between retention times and quantum-chemical descriptors. This analysis indicated that the proposed QSRR models were satisfactory.

Development of quantitative structure gas chromatographic relative retention time models on seven stationary phases for 209 polybrominated diphenyl ether congeners

Quantitative structure retention relationships (QSRRs) were developed to predict the gas chromatographic (GC) relative retention times (RRTs) for 209 polybrominated diphenyl ether (PBDE) congeners using the heuristic method included in the computer software Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA). A total of 445 constitutional, topological, geometrical, electrostatic, and semi-empirical quantum chemical descriptors were derived for all PBDEs. Using experimental RRT data for 126 PBDE congeners from the literature, predictive regression models were built for seven individual GC capillary columns differing in stationary phases. Each model includes four descriptors which included Wiener index, Randic index, polarity parameter, etc., selected by CODESSA. High predictability was obtained. High multiple correlation coefficients R(2) indicated that >98.5% (except for stationary phase CP-Sil 19) of the total variation in the predicted RRTs is explained by the fitted models. The models were subsequently used to predict the RRTs of the remaining 83 PBDE congeners on seven different stationary phases. The statistical results show that, compared with others, DB-XLB column not only produces the least number of peak overlaps but also results in shorter retention times.

Theoretical analysis of the retention behavior of alcohols in gas chromatography

Quantitative structure-retention relationship (QSRR) models for the chromatographic (GC) retention times of alcohols on Superox 20M-diglycerol polarity stationary phase have been developed. Semi-empirical quantum chemical method (AM1) in MOPAC and Hartree-Fock (HF) method in Gaussian 98 implemented were employed to calculate a set of molecular descriptors of alcohols and ethyl acetate. Using multiple linear regression (MLR), we obtained the empirical functions with high correlation coefficient between retention times and quantum-chemical descriptors. The retention mechanism of alcohols of separation operating in the gas chromatogram was discussed. The results indicated that the QSRR models proposed were satisfactory.

QSAR analysis of soil sorption coefficients for polar organic chemicals: substituted anilines and phenols

Based on descriptors of n-octanol/water partition coefficients (logKow), molecular connectivity indices, and quantum chemical parameters, several QSAR models were built to estimate the soil sorption coefficients (logKoc) of substituted anilines and phenols. Results showed that descriptor logKow plus molecular quantum chemical parameters gave poor regression models. Further study was performed to improve the QSAR model by using artificial neural networks (ANNs). It showed that ANN model with suitable network architecture could make a better agreement between predicted and measured values of the soil sorption coefficients. The quality of the QSAR models confirmed the suitability of ANN to predict the soil sorption coefficients for polar organic chemicals of substituted anilines and phenols.

Artificial neural network prediction of quantitative structure: Retention relationships of polycyclic aromatic hydocarbons in gas chromatography

A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature- programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al. [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (+-3 %).

Prediction of gas chromatographic retention indices of a diverse set of toxicologically relevant compounds

For a set of 846 organic compounds, relevant in forensic analytical chemistry, with highly diverse chemical structures, the gas chromatographic Kovats retention indices have been quantitatively modeled by using a large set of molecular descriptors generated by software Dragon. Best and very similar performances for prediction have been obtained by a partial least squares regression (PLS) model using all considered 529 descriptors, and a multiple linear regression (MLR) model using only 15 descriptors obtained by a stepwise feature selection. The standard deviations of the prediction errors (SEP), were estimated in four experiments with differently distributed training and prediction sets. For the best models SEP is about 80 retention index units, corresponding to 2.1-7.2% of the covered retention index interval of 1110-3870. The molecular properties known to be relevant for GC retention data, such as molecular size, branching and polar functional groups are well covered by the selected 15 descriptors. The developed models support the identification of substances in forensic analytical work by GC-MS in cases the retention data for candidate structures are not available.

Temperature dependence of Kováts indices in gas chromatography revisited

Temperature dependence of the Kováts retention index (I) was measured for some aliphatic ketones and aldehydes on a poly(dimethyl siloxane) (HP-1) stationary phase. An interesting minimum (non-linearity) was observed for the I versus isothermal column temperature (T) relationships. A novel empirical model is proposed: I=A+B/T+C ln T, where A, B and Care equation constants and B/C = T(min). A detailed statistical analysis clearly shows superiority of the extended model (i.e., of this containing the logarithm of the temperature (ln T) term) over the earlier established Antoine-type reciprocal equation. The minimum temperature (and the energy like quantity=RT(min), where R is the gas constant) changes in a systematic manner. The factors effecting the (RT(min)) term are as follows: (i) this term decreases with the increase of the molecular mass of the respective oxo compounds; (ii) ketones have higher absolute values of (RT(min) than aldehydes; (iii) branching of the carbon chain lowers the mentioned (RT(min)). This enthalpy term is unambiguously bound to the polarity of solutes.