Gas chromatographic retention indices of biologically and environmentally important organic compounds on capillary columns with low-polar stationary phases

2009 ◽  
Vol 1216 (51) ◽  
pp. 8998-9007 ◽  
Author(s):  
Valery A. Isidorov ◽  
Lech Szczepaniak
Keyword(s):  
Organic Compounds ◽  
Stationary Phases ◽  
Capillary Columns ◽  
Retention Indices ◽  
Chromatographic Retention ◽  
Gas Chromatographic Retention Indices ◽  
Chromatographic Retention Indices

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.

A new approach to the prediction of gas chromatographic retention indices from physico-chemical constants

1991 ◽  
Vol 56 (10) ◽  
pp. 2042-2054 ◽  
Author(s):  
Igor G. Zenkevich ◽  
Lyudmila M. Kuznetsova
Keyword(s):  
Molecular Weight ◽  
Stationary Phases ◽  
Retention Indices ◽  
Molar Refraction ◽  
Boiling Temperature ◽  
Chromatographic Retention ◽  
New Approach ◽  
Physico Chemical ◽  
Gas Chromatographic Retention Indices ◽  
Chromatographic Retention Indices

A general approach is proposed to the calculation of gas chromatographic retention indices (RI) of organic compounds on standard polydimethylsiloxane stationary phases based on their principal physico-chemical constants such as the boiling temperature, molar refraction or molecular weight. A combination of logical criteria was established for comparing functions of the above parameters for the identification of substances whose RI values are only determined by their boiling temperatures. It is demonstrated that within homologous series, the dependence of the RI value on the boiling temperature or any additive molecular parameter (molecular weight, molar refraction, number of carbon atoms in the molecule, etc.) is nonlinear. If this dependence is taken into account, the RI value of any organic compound can be predicted with a precision comparable to the standard deviation of the statistically processed values determined in nonequivalent conditions.

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