Quantitative structure–retention relationships of polycyclic aromatic hydrocarbons gas-chromatographic retention indices

Drosos, Juan Carlos; Viola-Rhenals, Maricela; Vivas‐Reyes, Ricardo

doi:10.1016/j.chroma.2010.04.038

Cited by 23 publications

(13 citation statements)

References 26 publications

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“…Levene's test suggests the inhomogeneity of variances ( P = 0.0000). Models denoted by M17 to M24 stem from Drosos et al , M1 to M6 from Liu et al ., and the remaining ones from Skrbic et al . SRD, sum of ranking differences…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of validation variants by sum of ranking differences and ANOVA

Héberger

Kollár-Hunek

2019

Journal of Chemometrics

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The old debate is revived: Definite differences can be observed in suggestions of estimation for prediction performances of models and for validation variants according to the various scientific disciplines. However, the best and/or recommended practice for the same data set cannot be dependent on the field of usage. Fortunately, there is a method comparison algorithm, which can rank and group the validation variants; its combination with variance analysis will reveal whether the differences are significant or merely the play of random errors. Therefore, three case studies have been selected carefully to reveal similarities and differences in validation variants. The case studies illustrate the different significance of these variants well. In special circumstances, any of the influential factors for validation variants can exert significant influence on evaluation by sums of (absolute) ranking differences (SRDs): stratified (contiguous block) or repeated Monte Carlo resampling and how many times the data set is split (5‐7‐10). The optimal validation variant should be determined individually again and again. A random resampling with sevenfold cross‐validations seems to be a good compromise to diminish the bias and variance alike. If the data structure is unknown, a randomization of rows is suggested before SRD analysis. On the other hand, the differences in classifiers, validation schemes, and models proved to be always significant, and even subtle differences can be detected reliably using SRD and analysis of variance (ANOVA).

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Section: Resultsmentioning

confidence: 99%

“…Retention indices of polycyclic aromatic hydrocarbons (PAHs) in the so‐called Lee's scale were predicted by different authors . Predicted retention indices for 46 PAHs have been extended with the experimental values (Iexp); see the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

Comparison of validation variants by sum of ranking differences and ANOVA

Héberger

Kollár-Hunek

2019

Journal of Chemometrics

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“…For example, Drosos et al have studied the retention of 209 polycyclic aromatic compounds (PAHs) using quantum mechanics and other source descriptors estimated by different QSRR approaches [17]. A good linear relationship between gas-chromatographic retention index and electronic and topologic descriptors of these compounds was found by stepwise linear regression analysis.…”

Section: Mohammad Hossein Fatemi Maryam Elyasimentioning

confidence: 99%

Prediction of gas chromatographic retention indices of some amino acids and carboxylic acids from their structural descriptors

Fatemi

Elyasi

2011

J of Separation Science

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In this work, quantitative structure-retention relationship (QSRR) approaches were applied for modeling and prediction of the retention index of 282 amino acids (AAs) and carboxylic acids (CAs). Descriptors that were used to encode structural features of molecules in a data set were calculated by using the Dragon software. The genetic algorithm (GA) and stepwise multiple linear regression (MLR) methods were used to select the most relevant descriptors. Then support vector machine (SVM), artificial neural network (ANN) and multiple linear regression were utilized to construct nonlinear and linear quantitative structure-retention relationship models. The obtained results using these techniques revealed that nonlinear models were much better than other linear ones. The GA-ANN model has the average absolute relative errors (AARE) of 0.054, 0.059 and 0.100 for training, internal and external test set. Applying the tenfold cross-validation procedure on GA-AAN model obtained the statistics of Q(2)=0.943, which revealed the reliability of this model.

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“…They found that the topological descriptor (1/ ( 3 χ c − 3 χ cv )) is one of the best in the QSRR model [11]. QSRR relationships have also been built with variables like Kier and Hall n-order connectivity topological indices ( n χ), [12][13][14][15][16]. Polyakova et al [17] found that Harary index, associated to other descriptors gives good correlation with the retention index.…”

Section: Introductionmentioning

confidence: 98%