Application of a quantitative structure retention relationship approach for the prediction of the two-dimensional gas chromatography retention times of polycyclic aromatic sulfur heterocycle compounds

2016 ◽  
Vol 1437 ◽  
pp. 191-202 ◽  
Author(s):  
Rafal Gieleciak ◽  
Darcy Hager ◽  
Nicole E. Heshka
Keyword(s):  
Gas Chromatography ◽  
Two Dimensional ◽  
Quantitative Structure ◽  
Polycyclic Aromatic ◽  
Structure Retention ◽  
Sulfur Heterocycle ◽  
Quantitative Structure Retention Relationship

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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