Methodological Approaches to the Calculation and Prediction of Retention Indices in Capillary Gas Chromatography

Zhokhov, A. K.; Loskutov, A. Yu.; Rybal’chenko, I. V.

doi:10.1134/s1061934818030127

Cited by 25 publications

(17 citation statements)

References 102 publications

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“…Among the various available methods for predicting physicochemical properties of molecules and for seeking combinatorial libraries to find molecular structures that are generally comparative to a target structure, the technique involving molecular descriptors is one of the simplest and most widely used such methods. [1][2][3][4] According to Todeschini and Consonni, [5] "a molecular descriptor is the final result of a logical and mathematical procedure which transforms chemical information encoded within a symbolic representation of a molecule into an useful number or the result of some standardized experiment".…”

Section: Introductionmentioning

confidence: 99%

Alkanes with the First Three Maximal/Minimal Modified First Zagreb Connection Indices

Ali

Trinajstić

2019

Molecular Informatics

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The modified first Zagreb connection index (ZC Ã 1 ) is a molecular descriptor, which was initially appeared within a formula of the total electron energy of alternant hydrocarbons in 1972. In a recent paper [A. Ali, N. Trinajstić, A novel/old modification of the first Zagreb index, Mol. Inform. 37 (2018) 1800008], it was observed that the molecular descriptor ZC Ã 1 correlates well with the entropy and acentric factor of octane isomers. In this article, the molecules with the first three maximal ZC Ã 1 values as well as the first three minimal ZC Ã 1 values are determined from the family of all alkanes with n carbon atoms, for n ! 6. This extends the main results of the aforementioned paper.

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Section: Introductionmentioning

confidence: 99%

Alkanes with the First Three Maximal/Minimal Modified First Zagreb Connection Indices

Ali

Trinajstić

2019

Molecular Informatics

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“…Gas chromatographic retention indices represent vital information needed in the identification of (semi‐)volatile compounds, most frequently employed in the analysis of essential oils and related natural products . When experimental and reliable retention data are not available, prediction of retention indices appears attractive . Such prediction is usually associated with quantitative structure‐property relationships (QSPR) and is envisaged to be as precise as possible but calculated from parameters that are readily available from either other experimental data or directly from the analyte structures .…”

Section: Introductionmentioning

confidence: 99%

“…Topological indices and other properties are often straightforwardly obtainable from numerous software in a matter of seconds. The choice of parameters, or among a certain type of parameters (for example, which topological index to use), is mostly intuitive and is quite frequently left to trial and error . Up to now, a number of researchers have successfully used predicted retention data of an array of compound classes; branched alkanes and alkenes, substituted aromatics, unsaturated esters, coumarins, and sulfur‐containing compounds are just a few examples, and such findings have been reviewed on several occasions …”

Section: Introductionmentioning

confidence: 99%

“…2 When experimental and reliable retention data are not available, prediction of retention indices appears attractive. 3,4 Such prediction is usually associated with quantitative structure-property relationships (QSPR) and is envisaged to be as precise as possible but calculated from parameters that are readily available from either other experimental data or directly from the analyte structures. 2 The latter approach is often more easily adoptable since experimental data on unknown compounds or new ones are inherently inaccessible.…”

Section: Introductionmentioning

confidence: 99%

“…3 Up to now, a number of researchers have successfully used predicted retention data of an array of compound classes; branched alkanes and alkenes, substituted aromatics, 11 unsaturated esters, 12 coumarins, 13 and sulfur-containing compounds 14 are just a few examples, and such findings have been reviewed on several occasions. 3,4 Building on the already accumulated data on the esters of eugenol and its regioisomers, we were motivated to enlarge the existing library and explore the gas chromatographic behavior, that is, retention index trends on the column of the most widely employed polarity (5% phenylmethylsiloxane) within and between series of regioisomeric allylmethoxyphenyl esters. Additional 85 esters of 4-allyl-2-methoxyphenol (eugenol), 2-allyl-3-methoxyphenol, 2-allyl-4-methoxyphenol, 2-allyl-5-methoxyphenol, and 2-allyl-6-methoxyphenol (o-eugenol) with 17 acids were prepared, their retention data on a slightly polar column (DB-5MS) were determined, and mass spectra were recorded.…”

Section: Introductionmentioning

confidence: 99%

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A synthetic library of allylmethoxyphenyl esters: spectral characterization and gas chromatographic behavior

Mladenović

Radulović

2019

Flavour & Fragrance J

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A recent identification of eugenyl (allylmethoxyphenyl) esters as Anthemis segetalis Ten. (Asteraceae) essential oil constituents, accomplished by means of organic synthesis, motivated us to expand the existing synthetic library with an aim to examine the generality of structure‐chromatographic property relationships within this series of volatiles. Herein, the design, synthesis, gas–chromatographic and spectral characterization of members of a library of 85 allylmethoxyphenyl esters (64 are completely new) were described. Analysis of experimentally obtained RI data showed that esters of the same acid and different allylmethoxyphenols always elute in the same order from an apolar gas chromatography (DB‐5MS) column: 2‐allyl‐6‐methoxyphenyl < 2‐allyl‐3‐methoxyphenyl < 2‐allyl‐5‐methoxyphenyl < 2‐allyl‐4‐methoxyphenyl < 4‐allyl‐2‐methoxyphenyl esters. Based on the obtained RI values, several quantitative structure–property relationship models were built up that correlated several easily available parameters (e.g. Wiener [WI], Balaban [BI], and molecular topological [MTI] indices) of the mentioned esters and their RI data. The generated various quantitative structure–property relationships models (equations that predict RIs of esters of different regioisomers of allylmethoxyphenols with the same acids and the ones that predict RIs of esters of different acids with the same allylmethoxyphenols) were tested on data of previously known related esters. RI data of esters of allylmethoxyphenol regioisomers could be successfully predicted based on 1D topological indices, such as the Balaban, Wiener, and molecular topological indices. Together with RI and MS data accumulated in this work, these models represent a new and simple tool for the identification of allylmethoxyphenyl esters that might potentially represent new or rare natural products.

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Ready‐to‐use Models Built Using a Diverse Set of 266 Aroma Compounds for the Estimation of Gas Chromatographic Retention Indices for the 50%‐Cyanopropylphenyl‐50%‐Dimethylpolysiloxane Stationary Phase

Sholokhova,

Matyushin

2024

J of Separation Science

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Retention index prediction based on the molecule structure is not often used in practice due to low accuracy, the need to use paid software to calculate molecular descriptors (MD), and the narrow applicability domain of many models. In recent years, relatively accurate and versatile deep learning (DL)‐based models have emerged. These models are now used in practice as an additional criterion in gas chromatography‐mass spectrometry identification. The DB‐225ms stationary phase (usually described as 50%‐cyanopropylphenyl‐50%‐dimethylpolysiloxane in available sources) is widely used, but ready‐to‐use retention index estimation models are not available for it. This study presents such models. The models are linear and use simple constitutional MD and retention indices predicted by DL for the DB‐WAX and DB‐624 stationary phases as MD (we show that it is their use that allows us to achieve satisfactory accuracy). The accuracy obtained for a completely unseen hold‐out test set: root mean square error 73.2; mean absolute error 45.7; median absolute error 22.0. The models were trained using a retention data set of 266 volatile compounds. All calculations can be performed using the convenient open‐source software CHERESHNYA. The final equations are implemented as a spreadsheet and a code snippet and are available online: https://doi.org/10.6084/m9.figshare.26800789.

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Methodological Approaches to the Calculation and Prediction of Retention Indices in Capillary Gas Chromatography

Cited by 25 publications

References 102 publications

Alkanes with the First Three Maximal/Minimal Modified First Zagreb Connection Indices

Alkanes with the First Three Maximal/Minimal Modified First Zagreb Connection Indices

A synthetic library of allylmethoxyphenyl esters: spectral characterization and gas chromatographic behavior

Ready‐to‐use Models Built Using a Diverse Set of 266 Aroma Compounds for the Estimation of Gas Chromatographic Retention Indices for the 50%‐Cyanopropylphenyl‐50%‐Dimethylpolysiloxane Stationary Phase

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