Prediction of gas chromatographic retention indices of alkylbenzenes

Sutter, Jon M.; Peterson, Timothy A.; Jurs, Peter C.

doi:10.1016/s0003-2670(96)00578-8

Cited by 84 publications

(39 citation statements)

References 17 publications

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“…A regressive diagnosis for the model was used to calculate t and the respective variance inflation factor (VIF) of all five MEIV descriptors using the SPSS 13.0 software. VIF [10] was defined as VIF = (1-r 2 ) -1 , where r is the degree of correlation of an independent variable with the other variables. If VIF is 1.0, there is no correlation between different variables; when VIF ranged from 1.0 to 10.0, there is no obvious collinearity between variables and the equation is acceptable; if VIF is larger than 10, the equation is not acceptable.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…These descriptors introduced by Randić [32] and developed extensively by Kier and Hall [34,35] who suggested the use of d v instead of d values to show the presence of non-sp 3 carbon atoms and heteroatoms in the molecules [36,37]. Molecular connectivity indices can encode different structural features such as molecular size, degree of branching, topology of unsaturation, heteroatom content and cyclicity [38].…”

Section: Structural Interpretation Of Retention Indicesmentioning

confidence: 99%

Use of Topological Indices of Organic Sulfur Compounds in Quantitative Structure‐Retention Relationship Study

Safa

Hadjmohammadi

2005

QSAR Comb. Sci.

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Structure-gas chromatographic retention index models were developed for some organic sulfur compounds at four different temperatures (60, 80, 100 and 120 8C) using only topological descriptors. At first, regression models were generated for each temperature separately with high values of multiple correlation coefficient and Fisher-ratio statistics. The results of cross validation test using leave-one-out (Q 2 ! 0.956) and leave-two-out (Q 2 ! 0.953) methods showed good predictive ability of the models developed. Then, a single combined quantitative structure-retention relationship model, added temperature as a parameter, was also developed for all the temperatures, showing good statistical parameters (R ¼ 0.991 and F ¼ 728.474). The stability and validity of the combined model were verified by both internal (Q 2 > 0.970) and external validation (Q 2 prediction set ¼ 0.993) techniques. The results of the study indicated the efficiency of the classical topological descriptors in simultaneous prediction of retention index values of sulfur compounds at different temperatures. The topological descriptors well covered the molecular properties known to be relevant for gas chromatographic retention data, such as molecular size and degree of branching.

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“…Also, Karitzky et al have used QSPR techniques for correlation and prediction of GC retention indexes of methylbranched hydrocarbons produced by insects [8]. Some other works in this area are listed in [9][10][11][12][13][14].…”

Section: K'mentioning

confidence: 99%

Prediction of Retention Factors in Supercritical Fluid Chromatography Using Artificial Neural Network

Fatemi

Baher

2005

J Anal Chem

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In this study, a quantitative structure-property relationship technique has been used for the prediction of retention factors for some organic compounds in supercritical fluid chromatography using an artificial neural network. The best descriptors that appear in this model are the number of single bonds, the number of double bonds, and the hydrophilic factor. These descriptors were used as inputs for a generated artificial neural network. This network has a 3 : 3 : 1 topology that was trained using a back-propagation algorithm. The crossvalidation method was used to evaluate the predictive power of the generated network. The results obtained by artificial neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression technique. Comparison of these results shows the ability of the artificial neural network model to predict retention factors.

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Prediction of gas chromatographic retention indices of alkylbenzenes

Cited by 84 publications

References 17 publications

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

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

Use of Topological Indices of Organic Sulfur Compounds in Quantitative Structure‐Retention Relationship Study

Prediction of Retention Factors in Supercritical Fluid Chromatography Using Artificial Neural Network

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