Gas Chromatographic Relative Retention Times Research Articles

Extracting Vapor Pressure Data from GLC Retention Times. Part 1: Analysis of Single Reference Approach

The vapor pressures of 52 compounds including n-alkanes and their monosubstituted derivatives, as well as chloro- and alkylbenzenes were determined exploring the gas chromatographic relative retention time (GLC-RT) technique in its simplest form, that is, using a single reference standard and neglecting the activity coefficient effect (GLC-RT1S). The selection of compounds was limited to those which are in the liquid state under the measurement conditions and for which high-quality vapor pressures obtained by direct methods are available at temperatures of GLC measurements. This approach should eliminate possible secondary error sources (such as data extrapolation or recalculation from supercooled liquid to solid phase or vice versa). On the basis of a thorough comparison of the GLC-RT1S-based and directly measured vapor pressures for the group of (functionalized) n-alkanes and by exploiting n-alkanes as reference solutes it is clear that deviations from direct data are significant (in many tens of percen...

Radial basis function neural networks based on projection pursuit approach and solvatochromic descriptors: single and full column prediction of gas chromatography retention behavior of polychlorinated biphenyls

The ultimate goal of projection methods is to search “interesting” projections in a low‐dimensional subspace that can uncover the natural structure of the data. The aim of this work is to compare the ability of projection pursuit (PP) and principal component analysis (PCA) in dimension reduction. For this purpose to be achieved, the scores of PP and PCA, by a different number of factors, were used as inputs of radial basis function (RBF) neural network. RBF neural network was used as a nonlinear regression method in a quantitative structure‐retention relationships study of 209 polychlorinated biphenyls (PCBs). The dependent variable was the high‐resolution gas chromatographic relative retention times of PCBs on 18 different stationary phases, and independent variables were solvatochromic solute descriptors. The results demonstrate that the dimension reduction ability of the PP is better than that of the PCA for both single and full column retention models.

Computational Model for Chromatographic Relative Retention Time of Polychlorinated Biphenyls Using Sub-structural Molecular Fragments

Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromato- graphic retention time to the chemical structure of a solute. Using the sub-structural molecular fragments (SMF) derived directly from the molecular structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were calculated. An eight-variable regression equation with the correlation coefficient of 0.9945 and the root mean square errors of 0.0134 was developed. Forward and backward stepwise regression variable selection and multi-linear regression analysis (MLRA) are combined to describe the effect of molecular structure on the RRT of PCB according to the QSRR method. To quantitatively relate RRT with the molecular structure MLR analysis is performed on the set of 163 sub-structural molecular fragments (SMF) provided by the ISIDA software. The eight fragments selected by variable subset selection, all belonging to the sub-fragments, adequately represent the structural factors influencing the affinity of PCB to SE-54 stationary phase in the separation process. Finally, a QSRR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 42 representative compounds excluded from model calibration. The prediction results from the MLR model are in good agreement with the experimental values. By applying the MLR method we can predict the test set with squared cross validated correlation coefficient (Q 2) of 0.9913 and root mean square error (RMSE) of 0.0169.

Prediction of GC Retention Times of PBDE Congeners Based on QSRR

Support vector machine (SVM) algorithm was applied to predict the gas chromatographic (GC) relative retention times (RRTs) for 126 polybrominated diphenyl ether (PBDE) congeners on 7 stationary phases. A total of 151 topological and connectivity indices descriptors were derived from E-dragon software. Genetic algorithm (GA) coupled with multiple linear regression (MLR) was used to select optimal subsets from large-size molecular descriptors. Overall support vector regression predicting training sets correlation coefficients R2 are greater than 0.996, except for the CP-Sil19 column, in which Q2loo (correlation coefficient of leave-one-out cross validation) and test sets correlation coefficients R2 are larger than 0.992. The excellent statistical parameters reveal that the models are robust and have high internal and external predictive capability.

Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids

The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure–retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101–109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available.

Investigation of retention behavior of polychlorinated biphenyl congeners on 18 different HRGC columns using molecular surface average local ionization energy descriptors

In this paper, based on the general interaction properties function (GIPF) family descriptors computed at the B3LYP/6-31G* level in Gaussian98 software, a significant quantitative structure–retention relationship (QSRR) models for the high resolution gas chromatographic relative retention time (HRGC-RRT) of all PCB congeners on 18 different HRGC capillary columns were constructed by using multiple linear regression (MLR) analysis, following the guidelines for development and validation of QSRR models. By means of the elimination selection stepwise regression algorithms, the molecular surface average local ionization energy was selected as one-parameter univariate linear regression to develop a QSRR model for prediction of GC-RRT of PCBs on each stationary phase. The accuracy of all developed models was confirmed using different types of internal and external procedures. A successful interpretation of the complex relationship between HRGC-RRTs of PCBs and the chemical structures was achieved by QSRR.

New Modes for the Prediction of Gas Chromatographic Relative Retention Times of Polybrominated Diphenyl Ethers

AbstractA series of quantitative structure‐retention relationship models were developed to predict gas chromatographic relative retention times (GC‐RRTs) for 209 polybrominated diphenyl ether (PBDE) congeners on 10 stationary phases. A genetic algorithm with twofold leave‐multiple‐out cross validation (LMOCV) was used to select optimal subsets from large‐size molecular descriptors. Overall multiple‐linear regression fitting correlation coefficients (R2) are greater than 0.988, except for the CP‐Sil 19 column, in which QLMOCV2 (correlation coefficient of LMOCV), QLOOCV2 (correlation coefficient of leave‐one‐out cross validation, LOOCV), and Rpre2 (correlation coefficients of prediction set) are larger than 0.98. The excellent statistical parameters reveal that the models are robust and have high internal and external predictive capability. According to the descriptors for constructing the models, the GC‐RRTs in various stationary phases are highly dependent on distances among atoms, branches of molecules, and molecular properties. PBDE congeners with 1, 9, and 10 bromines are major outliers based on the results of the application domain.

Semi-Empirical Topological Method for Prediction of the Relative Retention Time of Polychlorinated Biphenyl Congeners on 18 Different HR GC Columns.

High resolution gas chromatographic relative retention time (HRGC-RRT) models were developed to predict relative retention times of the 209 individual polychlorinated biphenyls (PCBs) congeners. To estimate and predict the HRGC-RRT values of all PCBs on 18 different stationary phases, a multiple linear regression equation of the form RRT = a o + a 1 (no. o-Cl) + a 2 (no. m-Cl) + a 3 (no. p-Cl) + a 4 (V M or S M) was used. Molecular descriptors in the models included the number of ortho-, meta-, and para-chlorine substituents (no. o-Cl, m-Cl and p-Cl, respectively), the semi-empirically calculated molecular volume (V M), and the molecular surface area (S M). By means of the final variable selection method, four optimal semi-empirical descriptors were selected to develop a QSRR model for the prediction of RRT in PCBs with a correlation coefficient between 0.9272 and 0.9928 and a leave-one-out cross-validation correlation coefficient between 0.9230 and 0.9924 on each stationary phase. The root mean squares errors over different 18 stationary phases are within the range of 0.0108–0.0335. The accuracy of all the developed models were investigated using cross-validation leave-one-out (LOO), Y-randomization, external validation through an odd–even number and division of the entire data set into training and test sets.Electronic supplementary materialThe online version of this article (doi:10.1365/s10337-010-1696-5) contains supplementary material, which is available to authorized users.

QSPR/QSAR models for prediction of the physico-chemical properties and biological activity of polychlorinated diphenyl ethers (PCDEs)

Polychlorinated diphenyl ethers (PCDEs) are a group of important persistent organic pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 209 PCDE congeners at the HF/6-31G * level of theory. A number of statistically-based parameters have been obtained. Linear relationships between gas-chromatographic relative retention time ( RRT), n-octanol/water partition coefficient (log K OW), 298 K supercooled liquid vapour pressures (log p L), aqueous solubilities (log S w,L) and the immunotoxicity values (log ED 50) of PCDEs and the structural descriptors have been established by multiple linear regression method. The result shows that the quantities derived from electrostatic potential V s,min , ∑ V s + , V s,av - , Π , σ tot 2 , σ + 2 , ν , and N v + , together with the number of the chlorine atoms on the two phenyl rings ( N Cl ) can be well used to express the quantitative structure–property (activity) relationships of PCDEs. Good predictive capabilities have also been demonstrated by leave-group(1/5)-out cross-validation and external test set. Based on these equations, the predicted values have been presented for those PCDE congeners whose experimentally determined physico-chemical properties are unavailable.

A quantitative structure–retention relationship study for prediction of chromatographic relative retention time of chlorinated monoterpenes

A novel quantitative structure–retention relationship model has been developed for the gas chromatographic relative retention times (tR) of 67 polychlorinated monoterpene congeners in a non-polar column. Modeling of the relative retention time of these compounds as a function of the theoretically derived descriptors was established by principal component and partial least squares regressions. The choice of optimal training sets is efficiently performed by Kohonen self-organizing map. The genetic algorithm was used for the selection of the variables resulted in the best-fitted models. Appropriate models with low standard errors and high correlation coefficients were obtained. Wiener index, Balaban index, and ideal gas thermal capacity are examples of the descriptors affected by the retention times of polychlorinated monoterpenes.

QSRR-Based Evaluating and Predicting of the Relative Retention Time of Polychlorinated Biphenyl Congeners on 18 Different High Resolution GC Columns

Quantitative structure–retention relationship (QSRR) studies have proved to be a valuable approach in the prediction of the high resolution gas chromatographic relative retention time (HRGC-RRT) of organic chemicals. Polychlorinated biphenyls (PCBs) are mainly ubiquitous pollutants due to their properties according to inflammability, chemical stability and solubility in most organic solvents. Moreover, these organochlorinated compounds are bioaccumulated because of their affinity to lipids and slow degradation rate. PCBs are also globally distributed in the environment and found in human, wildlife, air, water, soil and sediments. To estimate and predict the HRGC-RRT values of all 209 PCBs on 18 different stationary phases, 282 molecular descriptors from DRAGON program and the topological structures of PCBs molecules were calculated. By means of the final variable selection method which is genetic and elimination selection stepwise regression algorithms, four optimal descriptors were selected to develop a QSRR model for the prediction of GC-RRT of PCBs on each stationary phase. The accuracy of all the developed models was cross-validation using leave-one-out, Y-randomization and external validation through an odd-even number and division of the entire dataset into training and test sets.

Debromination of Decabrominated Diphenyl Ether by Resin-Bound Iron Nanoparticles

Nanoscale zerovalent iron, n-ZVI, was found to be highly effective in reductively debrominating decabromodiphenyl ether (BDE209) at ambient conditions and without the catalysis of noble metals. A method was developed to immobilize n-ZVI particles on a cation-exchange resin. The n-ZVI coated resin was then mixed with BDE209 in a water/ acetone (1:1) solution, and the reaction was allowed to proceed for up to 10 days. The first-order rate constant of BDE209 disappearance was estimated to be 0.28 - 0.04 h(-1). The debromination was found to be stepwise, and less-brominated congeners were produced with increasing reaction time. Dechlorination of decachlorobiphenyl (PCB209) was also investigated, but the reaction rate was much slower than the debromination of BDE209. Identification of the reaction products was highly challenging and was assisted by regression equations between experimental and reference gas chromatographic relative retention times, with confirmation by high-resolution mass spectrometry and reference to quantitative structure retention relationships. For randomly selected PBDE and PCB congeners, the net charges of individual atoms were calculated using the quantum chemical computation to explore the difference in relative vulnerability of halogens at different substitution positions between PBDEs and PCBs.

Semi-empirical topological method for prediction of the gas chromatographic relative retention times of Polybrominated Diphenyl Ethers (PBDEs)

Quantitative structure-retention relationship (QSRR) studies have proved to be a valuable approach in the prediction of the gas chromatographic relative retention time (GC-RRT) of organic chemicals. Polybrominated diphenyl ether (PBDE) congeners are now ubiquitous environmental pollutants. Of the 209 possible PBDE congeners, 126 have been synthesized and their retention-time data on seven different stationary phases has been determined [Korytár et al.:J Chromatography A 1065:239-249, (2005)]. To estimate and predict the GC-RRT values of all 209 PBDEs on different stationary phases, 17 molecular descriptors from the semi-experience algorithm in MOPAC program and the topological structures of PBDE molecules were calculated. By means of the VSMP (variable selection and modeling based on prediction) program [Liu et al.:J Chem Inf Comput Sci 43:964-969, (2003)], six optimal descriptors were selected to develop a QSRR model for the prediction of GC-RRT of PBDE. The descriptors contain some energy information (such as the energy of the lowest unoccupied molecular orbital and highest occupied molecular orbital) and topological information (the number of ortho-, meta-, and para- substituted bromine atoms) as well as the molecular weight (lnM (W)). All the models developed were cross-validated using leave-one-out (LOO). For seven GC stationary phases, the estimated correlation coefficients (r(2)) are all more than 0.985 but for the column CP-Sil 19 (r(2) = 0.9392) and LOO-validated correlation coefficients (q(2)) all more than 0.985 but for the column CP-Sil 19 (q(2) = 0.9345).

Predicting the Gas Chromatographic Relative Retention Time of Polybrominated Diphenyl Ethers by MEDV-13 Descriptors

Polybrominated diphenyl ethers (PBDEs) have been used extensively over the past two decades as flame or fire retardants added into many products such as textiles, electronic equipment and insulation material. PBDEs are now ubiquitous environmental pollutants and have been detected in biotic and abiotic matrices that include fish, birds, sediments, air, marine mammals, and human plasma and milk. The purpose of the paper is to develop some models which can predict the gas chromatographic relative retention time of PBDEs on seven stationary phases. The molecular electronegativity distance vector based on 13 atomic types (MEDV-13) were employed to describe the structural changes of PBDEs. Using the variables selection and modeling based on the prediction (VSMP), some four-variable linear equations from 80 PBDEs in the training sets with the correlation coefficient (r2) >0.98 and the root mean square errors of estimation <0.029 (except for CP-Sil19) were established. The standard regression coefficient indicated that MEDV descriptor x91 (interaction between the substructure –Br and –Br) was the most important descriptor to affect the RRT values of PBDEs. At the same time, it was found that the number of the ortho-substituent bromines and the number of bromine atoms in PBDE molecule may improve the predictive quality.

QSPR/QSAR models for prediction of the physicochemical properties and biological activity of polybrominated diphenyl ethers

Polybrominated diphenyl ethers (PBDEs) are a group of important persistent organic pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 209 PBDE congeners at the HF/6-31G ∗ level of theory. A number of statistically-based parameters have been obtained. Linear relationships between gas-chromatographic relative retention time (RRT), n-octanol/air partition coefficient (lg K OA), 298 K supercooled liquid vapour pressures (lg p L), Henry’s law constant (lg H) and Ah receptor binding affinity (−lg RBA) of PBDEs and the structural descriptors have been established by multiple regression method. The result shows that the quantities derived from electrostatic potential V s, max , V s, min , Π , ∑ V s + , V s - ¯ , ν , σ tot 2 , and N v - , together with the molecular volume ( V mc) can be well used to express the quantitative structure–property relationships of PBDEs, which proves the general applicability of this parameter set to a great extent. Good predictive capabilities have also been demonstrated. Based on these equations, the predicted values have been presented for those PBDE congeners whose experimentally determined physicochemical properties are unavailable. The QSAR model for the Ah receptor binding affinity is relatively poor, which can be ascribed to the complexity of factors which affect biological activity and the limitations of the present parameter set in describing steric characters of the molecule.

Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector

Using the molecular electronegativity distance vector (MEDV) descriptors derived directly from the molecular topological structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were predicted. A five-variable regression equation with the correlation coefficient of 0.9964 and the root mean square errors of 0.0152 was developed. The descriptors included in the equation represent degree of chlorination (nCl), nonortho index (Ino), and interactions between three pairs of atom types, i.e., atom groups -C= and -C=, -C= and >C=, -C= and -Cl. It has been proved that the retention times of all 209 PCB congeners can be accurately predicted as long as there are more than 50 calibration compounds. In the same way, the MEDV descriptors are also used to develop the five- or six-variable models of RRTs of PCBs on other 18 stationary phases and the correlation coefficients in both modeling stage and LOO cross-validation step are not lower than 0.99 except two models.

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.

Predicting gas chromatographic retention times for the 209 polybrominated diphenyl ether congeners

A gas chromatographic relative retention time (GC-RRT) model was developed to predict retention times of the 209 individual polybrominated diphenyl ether (PBDE) congeners. Using the available 46 PBDE standards with mono- to deca-bromination, a multiple linear regression equation of the form RRT=b 0+b 1( no. o- Br)+b 2( no. m- Br)+b 3( no. p- Br)+b 4(μ)+b 5( ln MW ) was used to predict the RRTs of the remaining 163 PBDE congeners. Molecular descriptors in the model included the number of ortho-, meta-, and para-bromine substituents (no. o-Br, m-Br and p-Br, respectively), the semi-empirically calculated dipole moment ( μ), and the natural logarithm of molecular weight (MW). A high level of predictability ( R 2=0.9972) was obtained for the model.

Development of a multiple-class high-resolution gas chromatographic relative retention time model for halogenated environmental contaminants.

A predictive model for the relative gas chromatographic retention times (GC-RRTs) of the following nine classes of halogenated environmental contaminants was developed: polybrominated diphenyl ethers (PBDEs); polychlorinated diphenyl ethers (PCDEs); polychlorinated biphenyls (PCBs); polychlorinated naphthalenes (PCNs); polychlorinated dibenzo-p-dioxins (PCDDs); polychlorinated dibenzofurans (PCDFs); polybrominated dibenzo-p-dioxins (PBDDs); polybrominated dibenzofurans (PBDFs); and organochlorine pesticides. MOPAC calculated physicochemical properties and structural descriptors in the model include molecular weight, square root of the number of halogen substituents, ionization potential, dipole moment, and the number of ortho, meta, and para halogen substituents. Using these variables, individual models for each of the contaminant classes were combined into a multiple class model incorporating the GC-RRTs of the 375 compounds of interest. The individual and multiclass GC-RRT models had acceptable fits between observed and predicted GC-RRTs (r2 = 0.9741-0.9990 for PBDEs, PCDEs, PCBs, PCNs, PCDD/Fs, and PBDD/Fs; r2 = 0.9250 for pesticides; and r2 = 0.9631 for the multiclass model) over a wide range of retention times and molecular structures. The combined model was tested on known GC-RRTs of hydroxylated PCBs and chlorinated phenoxyphenols and provided satisfactory results, demonstrating the strength of the model in predicting GC-RRT windows for contaminant classes not used in constructing the model. Such models will be useful in predicting the GC retention characteristics of novel environmental contaminants and their degradation products, for which analytical standards may not be available.

Use of self-training artificial neural networks in modeling of gas chromatographic relative retention times of a variety of organic compounds

A quantitative structure–activity relationship study based on multiple linear regression (MLR), artificial neural network (ANN), and self-training artificial neural network (STANN) techniques was carried out for the prediction of gas chromatographic relative retention times of 13 different classes of organic compounds. The five descriptors appearing in the selected MLR model are molecular density, Winer number, boiling point, polarizability and square of polarizability. A 5-6-1 ANN and a 5-4-1 STANN were generated using the five descriptors appearing in the MLR model as inputs. Comparison of the standard errors and correlation coefficients shows the superiority of ANN and STANN over the MLR model. This is due to the fact that the retention behaviors of molecules show non-linear characteristics. Inspection of the results of STANN and ANN shows there are few differences between these methods. However, optimization of STANN is much faster and the number of adjustable parameters for this technique is much less compared with those of the conventional ANN.