scholarly journals Quantitative structure-gas chromatographic retention index relationship of long-chain esters: The case of Scandix pecten-veneris L. essential-oil constituents

2016 ◽  
Vol 14 (2) ◽  
pp. 97-104 ◽  
Author(s):  
Marko Mladenovic ◽  
Niko Radulovic
Keyword(s):  
Essential Oil ◽  
Retention Index ◽  
Retention Indices ◽  
Chromatographic Retention ◽  
Retention Data ◽  
Chromatographic Property ◽  
Gas Chromatographic Retention Indices ◽  
Chromatographic Retention Indices ◽  
Relationship Of ◽  
Long Chain

Motivated by a recent identification of two homologous series of branched butanoates and pentanoates in S. pecten-veneris essential oil, with an apparently regular change of their retention index (RI) values, we decided to examine the generality of such structure-chromatographic property relationship. Based on the experimentally obtained retention data (RI values of in total 20 compounds) of hexyl, decyl, tridecyl, tetradecyl, pentadecyl, hexadecyl, heptadecyl, octadecyl, heneicosyl and tricosyl isobutanoates and 3-methylbutanoates and selected 2-methylbutanoates, a model was built up that correlates the total number of carbon atoms, Wiener (WI), Balaban (BI) and molecular topological (MTI) indices of the mentioned esters and their RI data (RI = 240.5 + 91.2 x C + 2.94 x WI + 4.6 x 10-5 x BI - 0.381 x MTI). The obtained equation represents a new and simple tool for the prediction of gas chromatographic (retention indices) data for esters of straight long-chain fatty alcohols and branched short aliphatic acids.

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 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.

Sign in / Sign up

Export Citation Format

Share Document