Quantitative Structure–Retention Relationship Modeling of Morphine and Its Derivatives on OV-1 Column in Gas–Liquid Chromatography Using Genetic Algorithm

2017 ◽  
Vol 80 (4) ◽  
pp. 629-636 ◽  
Author(s):  
Asrin Bahmani ◽  
Saadi Saaidpour ◽  
Amin Rostami
Keyword(s):  
Genetic Algorithm ◽  
Liquid Chromatography ◽  
Gas Liquid Chromatography ◽  
Quantitative Structure ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

scholarly journals A quantitative structure-retention relationship for the prediction of retention indices of the essential oils of Ammoides atlantica

2011 ◽  
Vol 76 (6) ◽  
pp. 891-902 ◽  
Author(s):  
Aberomand Azar ◽  
Mehdi Nekoei ◽  
Siavash Riahi ◽  
Mohamad Ganjali ◽  
Karim Zare
Keyword(s):  
Genetic Algorithm ◽  
Essential Oil ◽  
Essential Oils ◽  
Retention Indices ◽  
Quantitative Structure ◽  
Data Set ◽  
Prediction Of Retention ◽  
Structure Retention ◽  
Oil Components ◽  
Quantitative Structure Retention Relationship

A simple, descriptive and interpretable model, based on a quantitative structure-retention relationship (QSRR), was developed using the genetic algorithm-multiple linear regression (GA-MLR) approach for the prediction of the retention indices (RI) of essential oil components. By molecular modeling, three significant descriptors related to the RI values of the essential oils were identified. A data set was selected consisting of the retention indices for 32 essential oil molecules with a range of more than 931 compounds. Then, a suitable set of the molecular descriptors was calculated and the important descriptors were selected with the aid of the genetic algorithm and multiple regression method. A model with a low prediction error and a good correlation coefficient was obtained. This model was used for the prediction of the RI values of some essential oil components which were not used in the modeling procedure.

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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