Quantitative structure-retention relationship for chromatographic behaviour of anthraquinone derivatives through considering organic modifier features in micellar liquid chromatography

2019 ◽  
Vol 1599 ◽  
pp. 46-54 ◽  
Author(s):  
Amir M. Ramezani ◽  
Saeed Yousefinejad ◽  
Azin Shahsavar ◽  
Afshan Mohajeri ◽  
Ghodratollah Absalan
Keyword(s):  
Liquid Chromatography ◽  
Micellar Liquid Chromatography ◽  
Quantitative Structure ◽  
Organic Modifier ◽  
Chromatographic Behaviour ◽  
Anthraquinone Derivatives ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

scholarly journals Predicting retention indices of PAHs in reversed-phase liquid chromatography: A quantitative structure retention relationship approach

2020 ◽  
pp. 19-19
Author(s):  
Nabil Bouarra ◽  
Nawel Nadji ◽  
Loubna Nouri ◽  
Amel Boudjemaa ◽  
Khaldoun Bachari ◽  
...  
Keyword(s):  
Liquid Chromatography ◽  
External Validation ◽  
Reversed Phase ◽  
Retention Indices ◽  
Chromatographic Retention ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Statistical Validation ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

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.

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