Quantitative structure–retention relationship studies for predicting the gas chromatography retention indices of polycyclic aromatic hydrocarbons

Kang, Jijun; Cao, Chenzhong; Li, Zhiliang

doi:10.1016/s0021-9673(97)01025-x

Cited by 39 publications

(13 citation statements)

References 22 publications

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“…SMR was implemented by SPSS 13.0; each standard regression coefficient in regression process is presented in Supporting Table 1 (online version). The obtained original variable matrix by SMR was then subject to a partial least square (PLS) regression modeling and the optimal model was determined when cross-validation correlative coefficients ( ) in leave one out cross-2 cum Q 5,11,17,20,24, and the maximum value appeared when it was 17, several PLS models were built by combining the above variables and the related statistics data are presented in Table 2. 3D-HoVAIF descriptors were employed to study QSRR of 33 purine bases, and the resulting correlative statistics from models and some reported results [15] are listed in Table 2.…”

Section: Constructing Modelmentioning

confidence: 99%

“…In the past significant work has been done for QSRR [1,2] researches, for retention index predictions, separation condition selections and retention mechanism exploration [3] . For example, QSRR models [4][5][6][7][8][9][10] were established by introducing such descriptors as molecular geometrical characteristics, topological structures and diversities of physicochemical parameters. However, it was difficult to construct QSRR model for organic and biological molecules because they were based on 2D structures without considering interactions among compounds, fixed phase and fluxion phase.…”

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confidence: 99%

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Three-dimensional holographic vector of atomic interaction field for quantitative structure-retention relationship of purine bases

et al. 2006

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A newly developed descriptor, threedimensional holographic vector of atomic interaction field (3D-HoVAIF), was used to describe the chemical structures of purine bases. After variable screening by stepwise multiple regression (SMR) technique, a partial least square (PLS) regression model was built with 3D-HoVAIF. The model was satisfactory comparing to reference since correlation coefficients of molecular modeling ( ), cross-validation ( ) and standard deviation of estimation (SD) were 0.966, 0.860 and 0.112, respectively, showing that the model had favorable estimation and prediction capabilities. It was illustrated that information related to retention data of purine bases could preferably be expressed by 3D-HoVAIF with definite physicochemical meanings and easy structural interpretation for purine bases. It was illustrated that 3D-HoVAIF was to preferably express retention data of purine bases and had definite physicochemical significance. So 3D-HoVAIF was a useful structural expression technique for quantitative structure activity (or property or retention) relationships (QSAR/QSPR/QSRR) study, such as structural characterization and chromatographic retention prediction. 2 cum R 2 cum Q Keywords: three-dimensional holographic vector of atomic interaction field (3D-HoVAIF), quantitative structure-retention relationship (QSRR), purine bases.It is necessary to introduce structural descriptors when creating quantitative structure-retention relationship. In the past significant work has been done for QSRR [1,2] researches, for retention index predictions, separation condition selections and retention mechanism exploration [3] . For example, QSRR models [4][5][6][7][8][9][10] were established by introducing such descriptors as molecular geometrical characteristics, topological structures and diversities of physicochemical parameters. However, it was difficult to construct QSRR model for organic and biological molecules because they were based on 2D structures without considering interactions among compounds, fixed phase and fluxion phase. Under such circumstances, 3D descriptors have been gradually developed in structural representation. Recently, there have been only few reports on 3D descriptors. Gramatica [11] used weighted holistic invariant molecular index (WHIM) proposed by Todeschini [12][13][14] to calculate chromatogram retention index of polychlorobiphenyl and Luo [15] used comparative molecular field analysis (CoMFA) proposed by Cramer [16] to calculate purine bases' capacity factor. Since WHIM index arises from rotation and translation of constants via weighted transform of physical variables, the calculation process is too intricate to be generalized. While CoMFA requires a series of complex procedures, such as overlap of molecular steric structure and configuration, partition of spatial grids, control of variable amounts and selection of potential probes. Combinating advantages of both 3D methods in explicit, accurate and explainable representation and 2D methods in easy and quick characterization, 3D...

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Section: Constructing Modelmentioning

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Three-dimensional holographic vector of atomic interaction field for quantitative structure-retention relationship of purine bases

et al. 2006

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“…The values for these five variables have been collected from the GRAND PAH-spectrum database developed at Stockholm University [29]. Electronic properties such as conjugated p-electron surface [28], electron density or electron density distribution [24] have also been suggested as descriptor for GC chromatographic separation. We thus retained parameters describing the hexacyclic PAH p-electron system, such as Hückel p-electron energy (Ep), the difference between coefficients of the highest occupied molecular orbital and lowest unoccupied molecular orbital (Dx), and Kekule counts.…”

Section: Gc Retention Datamentioning

confidence: 99%

Approaches to identifying and quantifying polycyclic aromatic hydrocarbons of molecular weight 302 in diesel particulates

Sauvain¹,

Duc

2004

J of Separation Science

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Among the PAH class of compounds, high molecular weight PAH are now considered as relevant cancer inducers, but not all of them have the same biological activity. However, their analysis is difficult, mainly due to the presence of numerous isomers and due to their low volatility. Retention indices (Ri) for 13 dibenzopyrenes and homologues were determined by high-resolution capillary gas chromatography (GC) with four different stationary phases: a 5% phenyl-substituted methylpolysiloxane column (DB-5 ms), a 35% phenyl-substituted methylpolysiloxane column (BPX-35), a 50% phenyl-substituted methylpolysiloxane column (BPX-50), and a 35% trifluoropropylmethyl polysiloxane stationary phase (Rtx-200). Correlations for retention on each phase were investigated by using 8 independent molecular descriptors. Ri has been shown to be linearly correlated to PAH volume, polarisability alpha, Hückel-pi energy on the four examined columns. Ionisation potential Ip is a fourth variable which improves the regression model for DB-5ms, BPX-35, and BPX-50 column. Correlation coefficients ranging from r2 = 0.935 to r2 = 0.952 are then observed. Application of these indices to the identification and quantification of PAH with MW 302 in certified diesel particulate matter SRM 1650a is presented and discussed.

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“…For example, the connection features of molecular geometry, the molecular topology structure, various physical and chemical parameters, etc. were used to describe molecular structures, and multiple linear regression (MLR) was used to establish QSRRs to predict retention values of compounds [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure–Retention Relationship Study on Nitrogen-Containing Polycyclic Aromatic Compounds by Using Molecular Electronegativity Interaction Vector

Liu

2011

Chromatographia

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The molecular structures of 117 nitrogen-containing polycyclic aromatic compounds (N-PACs) were described by a method of molecular structural characterization (MSC) called molecular electronegativity interaction vector (MEIV). The samples were divided into a training set and a test set. For the training set, a quantitative structure-retention relationship (QSRR) model was built up by multiple linear regression (MLR) and the model was evaluated by performing the cross validation with the leave-one-out (LOO) procedure. The correlation coefficient (R) and the cross-verification correlation coefficient (R CV ) of the model were 0.992 and 0.991, respectively. Moreover, the model was evaluated by the test set and satisfactory results with a correlation coefficient (R test ) of 0.993 were obtained. The results suggested good stability and predictability of the model.

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Quantitative structure–retention relationship studies for predicting the gas chromatography retention indices of polycyclic aromatic hydrocarbons

Cited by 39 publications

References 22 publications

Three-dimensional holographic vector of atomic interaction field for quantitative structure-retention relationship of purine bases

Three-dimensional holographic vector of atomic interaction field for quantitative structure-retention relationship of purine bases

Approaches to identifying and quantifying polycyclic aromatic hydrocarbons of molecular weight 302 in diesel particulates

Quantitative Structure–Retention Relationship Study on Nitrogen-Containing Polycyclic Aromatic Compounds by Using Molecular Electronegativity Interaction Vector

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