scholarly journals Quantitative structure-retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae)

2019 ◽  
Vol 84 (4) ◽  
pp. 405-416 ◽  
Author(s):  
Youssouf Driouche ◽  
Djelloul Messadi
Keyword(s):  
Essential Oils ◽  
Statistical Significance ◽  
Selection Procedure ◽  
Randomization Test ◽  
Retention Indices ◽  
Molecular Structures ◽  
Thymus Vulgaris ◽  
Quantitative Structure ◽  
Structure Retention ◽  
Quantitative Structure Retention Relationship

In this paper, a quantitative structure?retention relationship (QSRR) model was developed for predicting the retention indices (log RI) of 36 constituents of essential oils. First, the chemical structure of each compound was sketched using HyperChem software. Then, molecular descriptors covering different information of molecular structures were calculated by Dragon software. The results illustrated that linear techniques, such as multiple linear regression (MLR), combined with a successful variable selection procedure are capable of generating an efficient QSRR model for predicting the retention indices of different compounds. This model, with high statistical significance (R2 = 0.9781, Q2 LOO = 0.9691, Q2 ext = 0.9546, Q2 L(5)O = 0.9667, F = 245.27), could be used adequately for the prediction and description of the retention indices of other essential oil compounds. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-5-out cross-validation, bootstrap, randomization test and validation through the test set.

scholarly journals A Validation Model for Prediction of Kovats Retention Indices of Compounds Isolated from Origanum spp. and Thymus spp. Essential Oils

2021 ◽  
Vol 65 (4) ◽  
Author(s):  
Ivana Čabarkapa ◽  
Milica Aćimović ◽  
Lato Pezo ◽  
Vanja Tadić
Keyword(s):  
Essential Oils ◽  
Prediction Models ◽  
Retention Indices ◽  
Thymus Vulgaris ◽  
Quantitative Structure ◽  
Origanum Vulgare ◽  
Kováts Retention Indices ◽  
Structure Retention ◽  
Thymus Serpyllum ◽  
Quantitative Structure Retention Relationship

Abstract. This work aimed to obtain a validated model for the prediction of retention times of compounds isolated from Origanum heracleoticum, Origanum vulgare, Thymus vulgaris, and Thymus serpyllum essential oils. In total 68 experimentally obtained retention times of compounds, which were separated and detected by GC-MS were further used to build the prediction models. The quantitative structure–retention relationship was employed to foresee the Kovats retention indices of compounds acquired by GC-MS analysis, using eight molecular descriptors selected by a genetic algorithm. The chosen descriptors were used as inputs for the four artificial neural networks, to construct a Kovats retention indices predictive quantitative structure–retention relationship model. The coefficients of determination in the training cycle were 0.830; 0.852; 0.922 and 0.815 (for compounds found in O. heracleoticum, O. vulgare, T. vulgaris and T. serpyllum essential oils, respectively), demonstrating that these models could be used for prediction of Kovats retention indices, due to low prediction error and high r2.   Resumen. El objetivo de este trabajo es la obtención de modelos validados para la predicción del tiempo de retención de los compuestos aislados de aceites esenciales de Origanum heracleoticum, Origanum vulgare, Thymus vulgaris y Thymus serpyllum. Se han obtenido un total de 68 tiempos de retención de compuestos, separándose y detectándose por cromatografía de gases con detección por espectrometría de masas (GC-MS) con posterior desarrollo de modelos de predicción.  La relación cuantitativa estructura-retención ha sido utilizada para predecir el índice de retención Kovats de los compuestos obtenidos por análisis de GC-MS, utilizando ocho descriptores moleculares seleccionados mediante algoritmo genético. Los descriptores seleccionados han sido utilizados como entrada para las cuatro redes neuronales artificiales y así elaborar los índices predictivos del modelo de relación cuantitativa estructura-retención.  Los coeficientes de determinación en el ciclo de entrenamiento fueron de 0.830; 0.852; 0.922 y 0.815 (para los compuestos identificados en los aceites esenciales del O. heracleoticum, O. vulgare, T. vulgaris y T. serpyllum respectivamente) demostrando así que estos modelos son útiles en la predicción de los índices de retención de Kovats con un error de bajo predicción y alta r2.

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 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.

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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