Retention Indices Of Compounds Research Articles

Linear Correlations of the Gas Chromatographic Retention Indices of Compounds from Various Taxonomic Groups

Despite the constant improvement of complex computer algorithms for the calculation of gas chromatographic retention indices (RIs), the simplest methods for their evaluation based on linear correlations between the indices of structural analogs from different taxonomic groups, RI1 ≈ aRI2 + b, remain important. It is shown that symbatic variations of the first numerical differences of retention indices,= RIn + 1 – RIn (equivalent to the first derivatives of the retention indices with respect to the structural parameter varied in the group), are the conditions of correctness for such correlations in the simplest groups (substituted methanes). A monotonic variation ofin one of the groups with the presence of extrema in the other group is an unequivocal sign of the absence of a linear correlation between retention indices. If the values ofin one of the groups increase and decrease in the other, the ranking order of compounds in any one of them should be reversed. It is shown that the simplest relationship RI1 ≈ aRI2 + b is also applicable to more complex taxonomic groups (substituted ethanes, benzenes, and naphthalenes), and it allows one not only to estimate the RIs of compounds not yet characterized but also to refine known reference data.

Linear Correlations of the Gas Chromatographic Retention Indices of Compounds from Various Taxonomic Groups

Linear Correlations of the Gas Chromatographic Retention Indices of Compounds from Various Taxonomic Groups

A Validation Model for Prediction of Kovats Retention Indices of Compounds Isolated from Origanum spp. and Thymus spp. Essential Oils

Abstract. This work aimed to obtain a validated model for the prediction of retention times of compounds isolated from Origanum heracleoticum, Origanum vulgare, Thymus vulgaris, and Thymus serpyllum essential oils. In total 68 experimentally obtained retention times of compounds, which were separated and detected by GC-MS were further used to build the prediction models. The quantitative structure–retention relationship was employed to foresee the Kovats retention indices of compounds acquired by GC-MS analysis, using eight molecular descriptors selected by a genetic algorithm. The chosen descriptors were used as inputs for the four artificial neural networks, to construct a Kovats retention indices predictive quantitative structure–retention relationship model. The coefficients of determination in the training cycle were 0.830; 0.852; 0.922 and 0.815 (for compounds found in O. heracleoticum, O. vulgare, T. vulgaris and T. serpyllum essential oils, respectively), demonstrating that these models could be used for prediction of Kovats retention indices, due to low prediction error and high r2.
 
 Resumen. El objetivo de este trabajo es la obtención de modelos validados para la predicción del tiempo de retención de los compuestos aislados de aceites esenciales de Origanum heracleoticum, Origanum vulgare, Thymus vulgaris y Thymus serpyllum. Se han obtenido un total de 68 tiempos de retención de compuestos, separándose y detectándose por cromatografía de gases con detección por espectrometría de masas (GC-MS) con posterior desarrollo de modelos de predicción. La relación cuantitativa estructura-retención ha sido utilizada para predecir el índice de retención Kovats de los compuestos obtenidos por análisis de GC-MS, utilizando ocho descriptores moleculares seleccionados mediante algoritmo genético. Los descriptores seleccionados han sido utilizados como entrada para las cuatro redes neuronales artificiales y así elaborar los índices predictivos del modelo de relación cuantitativa estructura-retención. Los coeficientes de determinación en el ciclo de entrenamiento fueron de 0.830; 0.852; 0.922 y 0.815 (para los compuestos identificados en los aceites esenciales del O. heracleoticum, O. vulgare, T. vulgaris y T. serpyllum respectivamente) demostrando así que estos modelos son útiles en la predicción de los índices de retención de Kovats con un error de bajo predicción y alta r2.

Weather Conditions Influence on Hyssop Essential Oil Quality

This paper is a study of the chemical composition of Hyssopus officinalis ssp. officinalis grown during three years (2017–2019) at the Institute of Field and Vegetable Crops Novi Sad (Vojvodina Province, Serbia). Furthermore, comparisons with ISO standards during the years were also investigated, as well as a prediction model of retention indices of compounds from the essential oils. An essential oil obtained by hydrodistillation and analysed by GC-FID and GC-MS was isopinocamphone chemotype. The gathered information about the volatile compounds from H. officinalis was used to classify the samples using the unrooted cluster tree. The correlation analysis was applied to investigate the similarity of different samples, according to GC-MS data. The quantitative structure–retention relationship (QSRR) was also employed to predict the retention indices of the identified compounds. A total of 74 experimentally obtained retention indices were used to build a prediction model. The coefficient of determination for the training cycle was 0.910, indicating that this model could be used for the prediction of retention indices for H. officinalis essential oil compounds.

Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices

The aim of this study was the prediction model of retention indices of compounds from the aboveground parts of Achillea clypeolata Sibth. & Sm. essential oil, obtained by hydrodistillation and analysed by GC?MS. The quantitative structure?retention relationship analysis was applied in order to anticipate the retention time of the obtained compounds. The selection of the seven molecular descriptors was done by a genetic algorithm. The chosen descriptors were uncorrelated and were used to construct an artificial neural network. A total of 40 experimentally obtained retention indices was used to build this prediction model. The coefficient of determination for the training, testing and validation cycles were: 0.950, 0.825 and 1.000, respectively, indicating that this model could be used for prediction of retention indices for A. clypeolata, essential oil compounds.

DeepReI: Deep learning-based gas chromatographic retention index predictor

Retention index in gas chromatographic analyses is an essential tool for appropriate analyte identification. Currently, many libraries providing retention indices for a huge number of compounds on distinct stationary phase chemistries are available. However, situation could be complicated in the case of unknown unknowns not present in such libraries. The importance of identification of these compounds have risen together with a rapidly expanding interest in non-targeted analyses in the last decade. Therefore, precise in silico computation/prediction of retention indices based on a suggested molecular structure will be highly appreciated in such situations. On this basis, a predictive model based on deep learning was developed and presented in this paper. It is designed for user-friendly and accurate prediction of retention indices of compounds in gas chromatography with the semi-standard non-polar stationary phase. Simplified Molecular Input Entry System (SMILES) is used as the model’s input. Architecture of the model consists of 2D-convolutional layers, together with batch normalization, max pooling, dropout, and three residual connections. The model reaches median absolute error of prediction of the retention index for validation and test set at 16.4 and 16.0 units, respectively. Median percentage error is lower than or equal to 0.81% in the case of all mentioned data sets. Finally, the DeepReI model is presented in R package, and is available on https://github.com/TomasVrzal/DeepReI together with a user-friendly graphical user interface.

Principles of Controlling the Correctness of Gas-Chromatographic Retention Indices of Previously Uncharacterized Analytes (Based on an Example of 2-Aryl-1,3-Dioxolanes and 2-Aryl-1,3-Dioxanes)

The series of the interaction products of substituted benzaldehydes XC6H4CHO with several aliphatic diols (ethanediol, 1,2-propanediol, 1,3-propanediol, 1,3-butanediol, and racemic 2,3-butanediol) were characterized for the first time by analytical parameters required for their chromatography–mass spectrometric identification: electron ionization mass spectra and gas-chromatographic retention indices (RIs) on a standard nonpolar polydimethylsiloxane stationary phase. It was found that it is expedient to combine this characterization of new compounds with controlling the correctness of the RIs by checking their correlations with data for one or several series of simpler and, therefore, characterized in more detail structural analogs. The proposed algorithm made it possible not only to confirm the correctness of all of the newly determined retention indices but also to reveal an erroneous reference value of the retention index of 4-ethoxyphenol. An appropriate selection of structural analogs provides an opportunity to use this method for controlling the retention indices of compounds in whose molecules steric interactions of structural fragments are manifested.

QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition

A prediction model of retention indices of compounds from the aboveground parts of Satureja kitaibelii essential oil, obtained by hydrodistillation and analysed by Gas Chromatography coupled with Mass Spectrometry (GC-MS), was the aim of this study. The quantitative structure–retention relationship was employed to predict the retention time using five molecular descriptors selected by a genetic algorithm. The selected descriptors were used as inputs of an artificial neural network. Total of 53 experimentally obtained retention indices (log RI) were used to build a prediction model. The selected descriptors were used as inputs of an artificial neural network model, to build a prediction time predictive quantitative structure-retention relationship model. The coefficient of determination for the training cycle was 0.962, indicating that this model could be used for prediction of retention indices for S. kitaibelii essential oil compounds.

Characterization of Kewda volatile components by comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

Kewda (Pandanus fascicularis Lam.) is a well known medicinal and aromatic plant. The paper aims to precisely characterize volatile constituents present in Kewda flower oil using comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry (GCxGC-TOFMS). A total of 159 components were identified due to enhanced chromatographic separation and mass spectral deconvolution of GCxGC-TOFMS. On the basis of its chemical structure, the identified compounds were grouped into hydrocarbons, alcohols, ethers, ketones, esters, nitrogen compounds, aldehydes, acids, lactones, halides and sulfur containing compounds. Ethers were the major components. The predominant compounds identified by GCxGC-TOFMS were kewda ether, ortho-cymene and terpinen-4-ol. A database containing retention indices of compounds was created for the bi-dimensional column, thus proving to be a remarkable step for analysis of constituents using a GCxGC system. GCxGC-TOFMS separated a number of co-eluting components which were unresolved on a single GC column.

Quantitative structure-property relationships of retention indices of some sulfur organic compounds using random forest technique as a variable selection and modeling method.

In this work, a noble quantitative structure-property relationship technique is proposed on the basis of the random forest for prediction of the retention indices of some sulfur organic compounds. In order to calculate the retention indices of these compounds, the theoretical descriptors produced using their molecular structures are employed. The influence of the significant parameters affecting the capability of the developed random forest prediction power such as the number of randomly selected variables applied to split each node (m) and the number of trees (nt ) is studied to obtain the best model. After optimizing the nt and m parameters, the random forest model conducted for m = 70 and nt = 460 was found to yield the best results. The artificial neural network and multiple linear regression modeling techniques are also used to predict the retention index values for these compounds for comparison with the results of random forest model. The descriptors selected by the stepwise regression and random forest model are used to build the artificial neural network models. The results achieved showed the superiority of the random forest model over the other models for prediction of the retention indices of the studied compounds.

Terpinen-4-ol-rich Essential Oil from the Fresh Rhizomes of Zingiber pellitum of Vietnam

The hydrodistilled essential oil from the fresh rhizomes of Zingiber pellitum Gagnep. (Zingiberaceae) originating in Vietnam was analyzed by GC and GC-MS. The chemical constituents of the oil were identified using MassFinder program which allows two-dimensional search of mass spectra and retention indices of compounds. The oil was found to be rich of terpinen-4-ol (35.9 %), p-cymene (19.8 %), and sabinene (7.1 %). Monoterpenoids constituted 80.1 % of the oil. Sesquiterpenoids were detected as minor compounds of the oil and the identified compounds consisted of 2.5 % of the oil.

Gas-chromatographic identification of fluorine-containing organic compounds

The gas-chromatographic retention of a series of fluoroalkylarenes on the standard nonpolar polydimethylsiloxane stationary phase OV-101 was characterized, and the retention indices of these compounds were determined. It was found that large negative values of the homological increments of these constants can serve as a sign of fluorine derivatives that contain no less than three fluorine atoms per molecule. The retention indices on polar inorganic sorbents, the silica gels Silipor 75 and Silipor 600, were chosen as the second chromatographic parameter. On these sorbents (unlike partition separation), the nonadditivity effect of the retention indices of polyfunctional organic compounds with respect to their simpler structural analogs was found for the first time. It was found that a mutual correlation between indices for this combination of a phase and a sorbent was lower than that for the combinations of phases with different polarities; this fact is responsible for the higher information content of this combination for the identification of compounds from this class.

Simultaneous Modeling of the Kovats Retention Indices on Phenyl OV Stationary Phases with Different Polarity using MLR and ANN

AbstractA QSRR study was performed to develop a predictive model correlating the observed kovats retention indices of three congeneric aromatic series with their molecular structures in gas chromatography. The congeneric aromatic series included the substituted benzene, benzaldehyde and acethophenone compounds, which had been studied, previously, on six OV stationary phases with different phenyl percentages. At first, a model was generated for six columns separately, using only calculated descriptors and MLR technique. Then a combined model, added a polarity term of stationary phase (M), was also developed for all these columns, and the result was apparently satisfactory (R2=0.991, F=1009.828, SE=23.98). Since the intercept had a high value in this model, the neural network back propagation algorithm was applied for comparison, and it was found that the neural network could exceed the level of the multiple regression method. The stability and validity of both models were tested by cross‐validation technique and by predicted response values for the prediction set and test set. The results of the study indicated that a seven parameter equation can be utilized for prediction of retention indices of compounds on different OV stationary phases simultaneously using ANN technique, for which there are no empirical RI values.

Quantitative Structure‐Retention Relationships (QSRR) of Some CNS Agents Studied on DB‐5 and DB‐17 Phases in Gas Chromatography

AbstractA QSRR study was performed to develop a predictive model correlating molecular structures with their RIs in gas chromatography. CNS agents included some benzodiazepines, barbiturates and phenytoin, which had been studied, previously, on two DB stationary phases with different phenyl percentages. At first, a high quality model (R=0.988, F=253.89 for DB‐5 and R=0.983, F=164.721 for DB‐17 column) was generated for two columns separately, using only calculated descriptors and multiple linear regression techniques. Then a combined model, added a polarity term of stationary phase (M), was also developed for these columns, and the result was satisfactory (R=0.989, F=232.354). The results of leave‐one‐out cross and external validation techniques showed good predictive ability of the models (R$\rm{ {_{cv}^{2}}}$=0.967, R$\rm{ {_{Prediction\ set}^{2}}}$=0.954). The results of the study indicated that a six‐parameter equation can be utilized for prediction of retention indices of compounds on DB‐5 and DB‐17 stationary phases simultaneously, for which there are no empirical RI values.

Analysis of Mixtures Containing Unknown Components by Gas Chromatography: Determination of Molecular Mass

New methods are proposed for the gas-chromatographic determination of the molecular masses of unknown components in complex mixtures within one cycle of analysis. The methods are based on the use of complex chromatographic data in the form of linear retention indices of compounds for a column with a nonpolar stationary phase and sensitivity indices of a thermal conductivity detector and a flame ionization detector.

Sex pheromone components of male Tirathaba mundella (Lepidoptera: Pyralidae)

During peak calling activity by male oil palm bunch moths, Tirathaba mundella Walker (Lepidoptera: Pyralidae), their hairpencils, wings or entire body were extracted in hexane. Gas chromatographic-electroantennographic detection (GC-EAD) analyses of hair pencil extracts revealed four compounds that consistently elicited responses from female antennae. The NMR spectrum of isolated compound 1, and mass spectra and retention indices of compounds 1–4 suggested that they were (3S,6S)-2,2,6-trimethyl-6-vinyl-tetrahydro-pyran-3-ol (1), 4-hydroxy-3-methoxy-benzaldehyde (2, vanillin), 6,10,14-trimethyl-2-pentadecanone (3), and 6,10,14-trimethyl-2-pentadecanol (4). Comparative GC and GC-MS analyses of hair pencils extract and synthetic standards confirmed these structural assignments. Moreover, comparative chromatography of synthetic and hairpencil-isolated 1 on a Cyclodex-B column (which separated the four stereoisomers with baseline resolution) revealed that male T. mundella produce the SS-stereoisomer (SS-1). In field cage bioassay experiments in Palembang, Indonesia, synthetic SS-1 and vanillin in combination, but not singly, attracted female T. mundella. SS-1 plus vanillin were as effective as male T. mundella in attracting females. Compounds 3 and 4 did not enhance the blend's attractiveness.

Prediction of gas chromatographic retention indices by the use of radial basis function neural networks

A new method for the prediction of retention indices for a diverse set of compounds from their physicochemical parameters has been proposed. The two used input parameters for representing molecular properties are boiling point and molar volume. Models relating relationships between physicochemical parameters and retention indices of compounds are constructed by means of radial basis function neural networks. To get the best prediction results, some strategies are also employed to optimize the topology and learning parameters of the RBFNNs. For the test set, a predictive correlation coefficient R=0.9910 and root mean squared error of 14.1 are obtained. Results show that radial basis function networks can give satisfactory prediction ability and its optimization is less-time consuming and easy to implement.

Live retention database for identification of compounds in capillary gas chromatographic analysis—Principle and structure

AbstractA live retention database for compound identification in isothermal and any step temperature programmed capillary gas chromatography has been developed. The database utilizes the Kovats retention indices of compounds on a given stationary phase and the retention time of n‐alkanes measured at isothermal conditions on the column to be used, together with the programming parameters. Identification is performed by search operation that compares the calculated results with the retention values of unknown peaks. Cross‐reference of the search results of different operating conditions is performed automatically by the database in order to increase the reliability of the identification. The error of the database conversion is ≤ ± 0.5 index unit, or ≤ ± 1% on retention time. This paper describes the principle and the structure of the database in detail. The experimental results for different calsses of compounds tested at divers operating conditions will be presented in Part II.

Linear temperature programmed retention indices of gasoline range hydrocarbons and chlorinated hydrocarbons on cross‐linked polydimethylsiloxane

AbstractThis paper describes the experimentally determined linear temperature programmed gas chromatographic retention behavior of hundreds of gasoline range hydrocarbons and chlorinated hydrocarbons on a cross‐linked 100% polydimethylsiloxane stationary phase. The retention indices of additional compounds which were not available as pure standards were measured in a synthetic gasoline after they had been positively identified by matching mass and infrared spectra.The experimentally determined retention information has been used to estimate the retention indices of additional compounds by extrapolation or interpolation of plots of carbon number against retention index of homologs.Isothermally determined Kovats retention indices of compounds measured on 100 % polydimethylsiloxane have been taken from the literature and converted to linear temperature programmed retention indices using the method of Guan et al [11]. For many of these compounds, an experimentally determined linear temperature programmed retention index was measured: agreement between experimental and converted values was excellent.In total, retention indices are presented for 480 compounds, including all possible alkylbenzenes containing ten or fewer carbon atoms, and over 150 monoenes and dienes. The effect of column pressure and the average linear velocity of the helium carrier gas on retention index and order of elution is described and discussed.

HPLC Retention Index Scale for Nitrogen-Bridged Compounds

Abstract Retention indices of some nitrogen-bridged compounds having pharmacological activity have been determined. A retention index scale based on the relative retention of a homologous series of C3-C23 2-keto alkanes has been worked out. Linear relationships were found between RI and logP, allowing a prediction of retention indices. The relationships between the structures and the retention indices of these compounds have been interpreted.