scholarly journals Gas chromatographic retention indices for N-substituted amino s-triazines on capillary columns. Part V. Temperature dependence of the retention index

2003 ◽  
Vol 68 (11) ◽  
pp. 825-831 ◽  
Author(s):  
Dusan Mijin ◽  
Dusan Antonovic ◽  
Bratislav Jovanovic
Keyword(s):  
Temperature Dependence ◽  
Retention Index ◽  
Linear Equations ◽  
Capillary Columns ◽  
Retention Indices ◽  
Chromatographic Retention ◽  
Column Temperature ◽  
Temperature Range ◽  
Gas Chromatographic Retention Indices ◽  
Chromatographic Retention Indices

The temperature dependence of the retention index was studied for N-substituted amino s-triazines on DB-1, DB-5 and DB-WAX capillary columns within the temperature range 190?230 ?C. Two linear equations with the column temperature and its reciprocal as variables were studied. The first one shows a slightly better precision for 2,4-bis(alky lamino)-6-chloro-s-triazines and 2-alkylamino-4,6-dichloro-s-triazines while the second one shows a better precision for 2,4-bis(cycloalkylamino)-6-chloro-s-triazines.

scholarly journals Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

2021 ◽  
Vol 22 (17) ◽  
pp. 9194
Author(s):  
Dmitriy D. Matyushin ◽  
Anastasia Yu. Sholokhova ◽  
Aleksey K. Buryak
Keyword(s):  
Deep Learning ◽  
Retention Index ◽  
Stationary Phases ◽  
Absolute Error ◽  
Retention Indices ◽  
Chromatographic Retention ◽  
Data Sets ◽  
Data Set ◽  
Gas Chromatographic Retention Indices ◽  
Chromatographic Retention Indices

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

scholarly journals Gas chromatographic retention indices for N-substituted amino s-triazines on capillary columns - Part IV: Influence of column polarity on retention index

2003 ◽  
Vol 68 (7) ◽  
pp. 557-564 ◽  
Author(s):  
Dusan Mijin ◽  
Dusan Antonovic ◽  
Gordana Boncic-Caricic ◽  
Bratislav Jovanovic ◽  
Olga Rajkovic
Keyword(s):  
Linear Regression ◽  
Retention Index ◽  
Retention Factor ◽  
Capillary Columns ◽  
Retention Indices ◽  
Chromatographic Retention ◽  
Analyte Molecule ◽  
Alkyl Groups ◽  
Gas Chromatographic Retention Indices ◽  
Temperature Programmed

The retention index increment for the addition of a methylene group to the alkyl group of an analyte molecule is shown to be lower than 100 i.u. for N-substituted amino s-triazines. In temperature programmed gas chromatography, a linearly interpolated retention index I, determined from the linear regression equation, I = AZ + (GRF)z, with the number of atoms (Z) in the molecule as variable, was used to describe the retention of 25 N-substituted amino s-triazines, on DB-1, DB-5 and DB-WAX capillary columns divided into five series according to the similarity of the alkyl groups in the particular series. In the above equation, A is the linear regression coefficient or the retention index increment per atom addition, Z the number of C,N and Cl atoms in the molecule, and (GRF)z the group retention factor or functionality constant for functional groups in the molecule, based on the number Z. It is possible to estimate the retention indices of an unknown member of the series from the Z, A and (GRF) values.

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