Prediction of Retention Time of Morphine and Its Derivatives Without Using Computer-Encoded Complex Descriptors

2020 ◽  
Author(s):  
Mohammad Hossein Keshavarz ◽  
Zeinab Shirazi ◽  
Mohammad Ali Rezayat
Keyword(s):  
Retention Time ◽  
Prediction Of Retention

Robust estimation of thermodynamic parameters (ΔH, ΔS and ΔCp) for prediction of retention time in gas chromatography – Part II (Application)

2015 ◽  
Vol 1425 ◽  
pp. 258-264 ◽  
Author(s):  
Carlos Alberto Claumann ◽  
André Wüst Zibetti ◽  
Ariovaldo Bolzan ◽  
Ricardo A.F. Machado ◽  
Leonel Teixeira Pinto
Keyword(s):  
Gas Chromatography ◽  
Thermodynamic Parameters ◽  
Robust Estimation ◽  
Retention Time ◽  
Prediction Of Retention

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 %.

Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification

2016 ◽  
Vol 916 ◽  
pp. 8-16 ◽  
Author(s):  
Giuseppe Marco Randazzo ◽  
David Tonoli ◽  
Stephanie Hambye ◽  
Davy Guillarme ◽  
Fabienne Jeanneret ◽  
...  
Keyword(s):  
Liquid Chromatography ◽  
Retention Time ◽  
Reversed Phase ◽  
Reversed Phase Liquid Chromatography ◽  
Prediction Of Retention ◽  
Phase Liquid

scholarly journals Prediction of retention time of cutinases tagged with hydrophobic peptides in hydrophobic interaction chromatography

2007 ◽  
Vol 1154 (1-2) ◽  
pp. 460-463 ◽  
Author(s):  
M.E. Lienqueo ◽  
O. Salazar ◽  
K. Henriquez ◽  
C.R.C. Calado ◽  
L.P. Fonseca ◽  
...  
Keyword(s):  
Hydrophobic Interaction ◽  
Retention Time ◽  
Hydrophobic Interaction Chromatography ◽  
Hydrophobic Peptides ◽  
Prediction Of Retention

QSPR in forensic analysis – The prediction of retention time of pesticide residues based on the Monte Carlo method

Talanta ◽  
2018 ◽  
Vol 178 ◽  
pp. 656-662 ◽  
Author(s):  
Miodrag Zdravković ◽  
Aleksandra Antović ◽  
Jovana B. Veselinović ◽  
Dušan Sokolović ◽  
Aleksandar M. Veselinović
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Retention Time ◽  
Pesticide Residues ◽  
Forensic Analysis ◽  
The Monte Carlo Method ◽  
Prediction Of Retention

scholarly journals PREDICTION OF RETENTION TIME FOR GRADIENT ELUTION USING STEPWISE ELUTION EQUATION

1991 ◽  
Vol 7 (Supple) ◽  
pp. 1363-1366
Author(s):  
SUN HO HAN ◽  
SEUNG SOO KIM ◽  
KIH SOO JOE ◽  
MOO YUL SUH ◽  
TAE YOON EOM
Keyword(s):  
Retention Time ◽  
Gradient Elution ◽  
Prediction Of Retention

Robust estimation of thermodynamic parameters (ΔH, ΔS and ΔCp) for prediction of retention time in gas chromatography – Part I (Theoretical)

2015 ◽  
Vol 1425 ◽  
pp. 249-257 ◽  
Author(s):  
Carlos Alberto Claumann ◽  
André Wüst Zibetti ◽  
Ariovaldo Bolzan ◽  
Ricardo A.F. Machado ◽  
Leonel Teixeira Pinto
Keyword(s):  
Gas Chromatography ◽  
Thermodynamic Parameters ◽  
Robust Estimation ◽  
Retention Time ◽  
Prediction Of Retention
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