Developing quantitative structure–retention relationship model to prediction of retention factors of some alkyl-benzenes in nano-LC

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 %.

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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

Journal of the Serbian Chemical Society ◽

10.2298/jsc101218141a ◽

2011 ◽

Vol 76 (12) ◽

pp. 1627-1637 ◽

Cited By ~ 20

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

Journal of the Serbian Chemical Society ◽

10.2298/jsc100219076a ◽

2011 ◽

Vol 76 (6) ◽

pp. 891-902 ◽

Cited By ~ 7

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.

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