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/Author (Bouarra, Nabil and Nadji, Nawel and Nouri, Loubna and Boudjemaa, Amel and Bachari, Khaldoun and Messadi, Dejlloul)
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/Title ({Predicting retention indices of PAHs in reversed-phase liquid chromatography: A quantitative structure retention relationship approach})
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/Keywords (genetic algorithm,molecular descriptors,multiple linear regression,prediction)
/Subject (In this work, the liquid chromatography retention time in monomeric and polymeric stationary phases of PAHs was investigated. Quantitative structure retention relationship approach has been successfully performed. At first, 3224 molecular descriptors were calculated for the optimized PAHs structure using Dragon software. Afterwards, the modelled dataset was divided using the CADEX algorithm into two subsets for internal and external validation. The genetic algorithm-based on a multiple linear regression was used for feature selection of the most significant descriptors and the model development. The selected models included five descriptors: nCIR, GGI3, GGI4, JGT, and DP14 were used for the monomeric column and nR10, EEig01x, L1m, H5v, HATS6v were introduced for the polymeric column. Robustness and predictive performance of the suggested models were verified by both internal and external statistical validation. The good quality of the statistical parameters indicates the stability and predictive power of the suggested models. This study demonstrated that the suitability of the established models in the prediction of liquid chromatographic retention indices of PAHs.nema)
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In this work, the liquid chromatography retention time in monomeric and polymeric stationary phases of PAHs was investigated. Quantitative structure retention relationship approach has been successfully performed. At first, 3224 molecular descriptors were calculated for the optimized PAHs structure using Dragon software. Afterwards, the modelled dataset was divided using the CADEX algorithm into two subsets for internal and external validation. The genetic algorithm-based on a multiple linear regression was used for feature selection of the most significant descriptors and the model development. The selected models included five descriptors: nCIR, GGI3, GGI4, JGT, and DP14 were used for the monomeric column and nR10, EEig01x, L1m, H5v, HATS6v were introduced for the polymeric column. Robustness and predictive performance of the suggested models were verified by both internal and external statistical validation. The good quality of the statistical parameters indicates the stability and predictive power of the suggested models. This study demonstrated that the suitability of the established models in the prediction of liquid chromatographic retention indices of PAHs.nema
Nabil Bouarra
Nawel Nadji
Loubna Nouri
Amel Boudjemaa
Khaldoun Bachari
Dejlloul Messadi
bibtex/citationkey/Bouarra2021
bibtex/file/:C\:/Users/Dekanski/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bouarra et al. - 2020 - Predicting retention indices of PAHs in reversed-phase liquid chromatography A quantitative structure retention.pdf:pdf;:05_9146_5404.pdf:PDF
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bibtex/url/https://www.shd-pub.org.rs/index.php/JSCS/article/view/9146
bibtex/volume/86
10.2298/jsc200219019b
genetic algorithm
molecular descriptors
multiple linear regression
prediction
Serbian Chemical Society
{Predicting retention indices of PAHs in reversed-phase liquid chromatography: A quantitative structure retention relationship approach}
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