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 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.

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.

Three-dimensional holographic vector of atomic interaction field for quantitative structure-retention relationship of purine bases

2006 ◽  
Vol 51 (13) ◽  
pp. 1557-1562 ◽  
Author(s):  
Jianbo Tong ◽  
Peng Zhou ◽  
Shengwan Zhang ◽  
Yuan Zhou ◽  
Hu Mei ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Atomic Interaction ◽  
Quantitative Structure ◽  
Purine Bases ◽  
Interaction Field ◽  
Structure Retention ◽  
Relationship Of ◽  
Quantitative Structure Retention Relationship

scholarly journals Prediction of the GC-MS retention time for terpenoids detected in sage (Salvia officinalis L.) essential oil using QSRR approach

2020 ◽  
Vol 85 (1) ◽  
pp. 9-23
Author(s):  
Branimir Pavlic ◽  
Nemanja Teslic ◽  
Predrag Kojic ◽  
Lato Pezo
Keyword(s):  
Essential Oil ◽  
Supercritical Fluid Extraction ◽  
Supercritical Fluid ◽  
Retention Time ◽  
Quantitative Structure ◽  
Fluid Extraction ◽  
Prediction Of Retention ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship ◽  
Essential Oil Compounds

This work aimed to obtain a validated model for prediction of retention time of terpenoids isolated from sage herbal dust using supercritical fluid extraction. In total 32 experimentally obtained retention time of terpenes, which were separated and detected by GC?MS were further used to build a prediction model. The quantitative structure?retention relationship was employed to predict the retention time of essential oil compounds obtained in GC?MS analysis, using six molecular descriptors selected by a genetic algorithm. The selected descriptors were used as inputs of an artificial neural network, to build a retention time predictive quantitative structure?retention relationship model. The coefficient of determination for training cycle was 0.837, indicating that this model could be used for prediction of retention time values for essential oil compounds in sage herbal dust extracts obtained by supercritical fluid extraction due to low prediction error and moderately high r2. Results suggested that a 2D autocorrelation descriptor AATS0v was the most influential parameter with an approximately relative importance of 25.1 %.

Quantitative structure-retention relationship (QSRR) models for predicting the GC retention times of essential oil components

2010 ◽  
Vol 22 (3) ◽  
pp. 357-373 ◽  
Author(s):  
E. Pourbasheer ◽  
S. Riahi ◽  
M. R. Ganjali ◽  
P. Norouzi
Keyword(s):  
Essential Oil ◽  
Quantitative Structure ◽  
Structure Retention ◽  
Oil Components ◽  
Quantitative Structure Retention Relationship
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