Quantitative Structure-Retention Relationship (QSRR) Study for Predicting Gas Chromatographic Retention Times for Some Stationary Phases

2008 ◽  
Vol 27 (8) ◽  
pp. 996-1005 ◽  
Author(s):  
Adrian Beteringhe ◽  
Ana C. Radutiu ◽  
Daniela C. Culita ◽  
Alice Mischie ◽  
Florica Spafiu
Keyword(s):  
Stationary Phases ◽  
Chromatographic Retention ◽  
Quantitative Structure ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

scholarly journals Quantitative Structure-Retention Relationship Studies as an Analytical Tool in the Determination and Modeling of Pesticide Residues in Plant Organisms

2010 ◽  
Vol 93 (6) ◽  
pp. 1703-1714 ◽  
Author(s):  
Bogusaw Buszewski ◽  
Monika Michel
Keyword(s):  
Physicochemical Properties ◽  
Pesticide Residues ◽  
Solid Phase ◽  
Biochemical Properties ◽  
Chromatographic Retention ◽  
Separation Mechanism ◽  
Quantitative Structure ◽  
Crop Models ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

Abstract Crop models use mathematical equations to simulate the physical and chemical processes that generally control the uptake, translocation, and sorption of pesticides in all parts of plants. Our interest is focused on method optimization to determine the new compounds using stationary and mobile phases with different physicochemical properties. The work deals with five fungicides composed of nitrogen-containing heterocycles, 1,2,4-triazoles. The sample preparation liquid extraction and solid-phase-based methods are used to determine and model the pesticide residues in plants organisms. Analysis of these compounds is generally carried out by GC or HPLC coupled to different detectors, especially to mass spectrometers, in hyphenated techniques that have been extremely developed in recent years. The relationships between the chromatographic retention factor (k) and those physicochemical properties that are relevant in quantitative structure-retention relationship (QSRR) studies were investigated. The accuracy of the simple linear regressions between the chromatographic retention and the descriptors for all of the compounds was satisfactory (correlation coefficient 0.83 ≥ R2 ≥ 0.99). The QSRR models of these nitrogen-containing heterocyclic compounds could be predicted with a multiple linear regression equation having the statistical index R2 = 1.00. Evaluation of chromatographic properties of the new stationary phases and description of the molecular separation mechanism using the QSRR method, including molecular modeling, were performed. A universal model is presented that links the physicochemical parameters describing the fungicide compounds with the anatomical, physiological, and biochemical properties of the plant.

Quantitative structure–retention relationship modelling of esters on stationary phases of different polarity

2009 ◽  
Vol 28 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Érica Silva Souza ◽  
Carlos Alberto Kuhnen ◽  
Berenice da Silva Junkes ◽  
Rosendo Augusto Yunes ◽  
Vilma Edite Fonseca Heinzen
Keyword(s):  
Stationary Phases ◽  
Quantitative Structure ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document