Gas chromatographic retention times prediction for components of petroleum condensate fraction

2009 ◽  
Vol 63 (5) ◽  
Author(s):  
Nagy Moustafa
Keyword(s):  
Molecular Descriptors ◽  
Predictive Accuracy ◽  
Linear Regression Analysis ◽  
Retention Indices ◽  
Chromatographic Retention ◽  
Condensate Fraction ◽  
Quantitative Structure ◽  
Multi Linear Regression ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

AbstractA new and simple quantitative structure-retention relationship (QSRR) was tested as a predictive model for adjusted retention times in complex petroleum condensate fractions. This relationship adopted the form of a non-linear collective retention-variables model. The adjusted retention times were correlated with the components molecular descriptors, e.g. total path counts and boiling temperatures, by multi-linear regression analysis. The obtained two QSRR models show an acceptable predictive accuracy with R 2 of 0.9949 and 0.9856, respectively. Stability and validity of the models were tested by comparing the calculated and the experimental retention indices.

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.

scholarly journals The quantitative structure-retention relationship of the GC-MS profile of yarrow essential oil

2021 ◽  
pp. 123-132
Author(s):  
Milica Acimovic ◽  
Lato Pezo ◽  
Jovana Stankovic-Jeremic ◽  
Marina Todosijevic ◽  
Milica Rat ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Essential Oil ◽  
Molecular Descriptors ◽  
Retention Indices ◽  
Achillea Millefolium ◽  
Caryophyllene Oxide ◽  
Quantitative Structure ◽  
Structure Retention ◽  
Relationship Of ◽  
Quantitative Structure Retention Relationship

In the essential oil of yarrow (Achillea millefolium L. sensu lato) collected from natural population on Mt. Rtanj (Serbia) and distilled by Clevenger apparatus 104 compounds were detected, and the most abundant were camphor (9.8%), caryophyllene oxide (6.5%), terpinen-4-ol (6.3%) and 1,8- cineole (5.6%). The quantitative structure-retention relationship (QSRR) model was employed to predict the retention indices, using four molecular descriptors selected by factor analysis and a genetic algorithm. The coefficients of determination reached the value of 0.862, demonstrating that this model could be used for prediction purposes.

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.

scholarly journals Chemical composition of the essential oils of Citrus sinensis cv. valencia and a quantitative structure-retention relationship study for the prediction of retention indices by multiple linear regression

2011 ◽  
Vol 76 (12) ◽  
pp. 1627-1637 ◽  
Author(s):  
Aberoomand Azar ◽  
Mehdi Nekoei ◽  
Kambiz Larijani ◽  
Sakineh Bahraminasab
Keyword(s):  
Gas Chromatography ◽  
Chemical Composition ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Retention Indices ◽  
Quantitative Structure ◽  
Cold Press ◽  
Prediction Of Retention ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

The chemical composition of the volatile fraction obtained by head-space solid phase microextraction (HS-SPME), single drop microextraction (SDME) and the essential oil obtained by cold-press from the peels of C. sinensis cv. valencia were analyzed employing gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The main components were limonene (61.34 %, 68.27 %, 90.50 %), myrcene (17.55 %, 12.35 %, 2.50 %), sabinene (6.50 %, 7.62 %, 0.5 %) and ?-pinene (0 %, 6.65 %, 1.4 %) respectively obtained by HS-SPME, SDME and cold-press. Then a quantitative structure-retention relationship (QSRR) study for the prediction of retention indices (RI) of the compounds was developed by application of structural descriptors and the multiple linear regression (MLR) method. Principal components analysis was used to select the training set. A simple model with low standard errors and high correlation coefficients was obtained. The results illustrated that linear techniques such as MLR combined with a successful variable selection procedure are capable of generating an efficient QSRR model for prediction of the retention indices of different compounds. This model, with high statistical significance (R2 train = 0.983, R2 test = 0.970, Q2 LOO = 0.962, Q2 LGO = 0.936, REP(%) = 3.00), could be used adequately for the prediction and description of the retention indices of the volatile compounds.

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