Chromatographic Retention Indices Research Articles

Quantitative structure-retention relationships for pyridinium-based ionic liquids used as gas chromatographic stationary phases: convenient software and assessment of reliability of the results

Ionic liquids, i.e., organic salts with a low melting point, can be used as gas chromatographic liquid stationary phases. These stationary phases have some advantages such as peculiar selectivity, high polarity, and thermostability. Many previous works are devoted to such stationary phases. However, there are still no large enough retention data sets of structurally diverse compounds for them. Consequently, there are very few works devoted to quantitative structure-retention relationships (QSRR) for ionic liquid-based stationary phases. This work is aimed at closing this gap. Three ionic liquids with substituted pyridinium cations are considered. We provide large enough data sets (123 - 158 compounds) that can be used in further works devoted to QSRR and related methods. We provide a QSRR study using this data set and demonstrate the following. The retention index for a polyethylene glycol stationary phase (denoted as RI_PEG), predicted using another model, can be used as a molecular descriptor. This descriptor significantly improves the accuracy of the QSRR model.. Both deep learning-based and linear models were considered for RI_PEG prediction. The ability to predict the retention indices for ionic liquid-based stationary phases with high accuracy is demonstrated. Particular attention is paid to the reproducibility and reliability of the QSRR study. It was demonstrated that adding/removing several compounds, small perturbations of the data set can considerably affect the results such as descriptor importance and model accuracy. These facts have to be considered in order to avoid misleading conclusions. For the QSRR research, we developed a software tool with a graphical user interface, which we called CHERESHNYA. It is intended to select molecular descriptors and construct linear equations connecting molecular descriptors with gas chromatographic retention indices for any stationary phase. The software allows the user to generate several hundred molecular descriptors (one-dimensional and two-dimensional). Among them, predicted retention indices for popular stationary phases such as polydimethylsiloxane and polyethylene glycol are used as molecular descriptors. Various methods for selecting (and assessing the importance of) molecular descriptors have been implemented, in particular the Boruta algorithm, partial least squares, genetic algorithms, L1-regularized regression (LASSO) and others. The software is free, open-source and available online.

Validation of the identification reliability of known and assumed UDMH transformation products using gas chromatographic retention indices and machine learning

Thirty two commercially available standards were used to determine chromatographic retention indices for three different stationary phases (non-polar, polar and mid-polar) commonly used in gas chromatography. The selected compounds were nitrogen-containing heterocycles and amides, which are referred to in the literature as unsymmetrical dimethylhydrazine (UDMH) transformation products or its assumed transformation products. UDMH is a highly toxic compound widely used in the space industry. It is a reactive substance that forms a large number of different compounds in the environment. Well-known transformation products may exceed UDMH itself in their toxicity, but most of the products are poorly investigated, while posing a huge environmental threat. Experimental retention indices for the three stationary phases, retention indices from the NIST database, and predicted retention indices are presented in this paper. It is shown that there are virtually no retention indices for UDMH transformation products in the NIST database. In addition, even among those compounds for which retention indices were known, inconsistencies were identified. Adding retention indices to the database and eliminating erroneous data would allow for more reliable identification when standards are not available. The discrepancies identified between experimental retention index values and predicted values will allow for adjustments to the machine learning models that are used for prediction. Previously proposed compounds as possible transformation products without the use of standards and NMR method were confirmed.

Hologram quantitative structure-activity relationship on the gas chromatographic retention index of plant essential oil constituents

The gas chromatography retention index (RI) is an important parameter for the identification of different types of compounds in the field of chromatographic analysis; however, the experimental collection of RI values is a extremely cumbersome process. Thus, there is an urgent need for the establishment of a simple, efficient, and accurate model for the prediction of the RI values of compounds. In this study, first, the experimental RI values for 60 plant essential oil constituents were obtained. Next, a model describing the hologram quantitative structure-activity relationship (HQSAR) between the structural properties of the essential oil constituents and their RI values was investigated and constructed. The optimal HQSAR model was established by setting the model parameters "fragment size", "fragment distinction", "hologram length" and "principal components" to "1-4", "C, Ch", "199", and "4", respectively. Finally, the predictive ability of the model was verified using external test set validation and leave-one-out cross-validation (LOO-CV). The experimental results were as follows, the root mean square error of prediction (RMSEP), predictive determination coefficient ([Formula: see text]), concordance correlation coefficient (CCC), and mean relative error (MRE) for external test set validation were 40.45, 0.984, 0.968, and 2.20%, respectively. Meanwhile, the root mean square error of cross validation (RMSECV) and MRE for LOO-CV were 72.56 and 4.17%, respectively. These findings demonstrate that the established HQSAR model has a good predictive ability and can accurately predict the RI values of plant essential oil constituents. In addition, the molecular contribution maps of the HQSAR model revealed that the RI values of aromatic compounds increase when hydroxyl groups are connected to their alkyl chains. Aliphatic compounds feature long chain alkyl groups, which can lead to an increase in RI values. The above phenomena highlight the promising application prospects of HQSAR for studying the RI values of plant essential oil constituents. Therefore, this study provides a reliable theoretical basis for predicting the RI values of other essential oil constituents.

A simple and reliable QSPR model for prediction of chromatography retention indices of volatile organic compounds in peppers.

Worldwide, various types of pepper are used in food as an additive due to their unique pungency, aroma, taste, and color. This spice is valued for its pungency contributed by the alkaloid piperine and aroma attributed to volatile essential oils. The essential oils are composed of volatile organic compounds (VOCs) in different concentrations and ratios. In chromatography, the identification of compounds is done by comparing obtained peaks with a reference standard. However, there are cases where reference standards are either unavailable or the chemical information of VOCs is not documented in reference libraries. To overcome these limitations, theoretical methodologies are applied to estimate the retention indices (RIs) of new VOCs. The aim of the present work is to develop a reliable QSPR model for the RIs of 273 identified VOCs of different types of pepper. Experimental retention indices were measured using comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC × GC/qMS) using a coupled BPX5 and BP20 column system. The inbuilt Monte Carlo algorithm of CORAL software is used to generate QSPR models using the hybrid optimal descriptor extracted from a combination of SMILES and HFG (hydrogen-filled graph). The whole dataset of 273 VOCs is used to make ten splits, each of which is further divided into four sets: active training, passive training, calibration, and validation. The balance of correlation method with four target functions i.e. TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.5 & WCII = 0), TF2 (WIIC = 0 & WCII = 0.3) and TF3 (WIIC = 0.5 & WCII = 0.3) is used. The results of the statistical parameters of each target function are compared with each other. The simultaneous application of the index of ideality of correlation (IIC) and correlation intensity index (CII) improves the predictive potential of the model. The best model is judged on the basis of the numerical value of R2 of the validation set. The statistical result of the best model for the validation set of split 6 computed with TF3 (WIIC = 0.5 & WCII = 0.3) is R2 = 0.9308, CCC = 0.9588, IIC = 0.7704, CII = 0.9549, Q2 = 0.9281 and RMSE = 0.544. The promoters of increase/decrease for RI are also extracted using the best model (split 6). Moreover, the proposed model was used for an external validation set.

On the challenge of unambiguous identification of fentanyl analogs: Exploring measurement diversity using standard reference mass spectral libraries.

Fentanyl analogs are a class of designer drugs that are particularly challenging to unambiguously identify due to the mass spectral and retention time similarities of unique compounds. In this paper, we use agglomerative hierarchical clustering to explore the measurement diversity of fentanyl analogs and better understand the challenge of unambiguous identifications using analytical techniques traditionally available to drug chemists. We consider four measurements in particular: gas chromatography retention indices, electron ionization mass spectra, electrospray ionization tandem mass spectra, and direct analysis in real time mass spectra. Our analysis demonstrates how simultaneously considering data from multiple measurement techniques increases the observable measurement diversity of fentanyl analogs, which can reduce identification ambiguity. This paper further supports the use of multiple analytical techniques to identify fentanyl analogs (among other substances), as is recommended by the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG).

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

Intelligent Workflow and Software for Non-Target Analysis of Complex Samples Using a Mixture of Toxic Transformation Products of Unsymmetrical Dimethylhydrazine as an Example.

Unsymmetrical dimethylhydrazine (UDMH) is a widely used rocket propellant. Entering the environment or being stored in uncontrolled conditions, UDMH easily forms an enormous variety (at least many dozens) of transformation products. Environmental pollution by UDMH and its transformation products is a major problem in many countries and across the Arctic region. Unfortunately, previous works often use only electron ionization mass spectrometry with a library search, or they consider only the molecular formula to propose the structures of new products. This is quite an unreliable approach. It was demonstrated that a newly proposed artificial intelligence-based workflow allows for the proposal of structures of UDMH transformation products with a greater degree of certainty. The presented free and open-source software with a convenient graphical user interface facilitates the non-target analysis of industrial samples. It has bundled machine learning models for the prediction of retention indices and mass spectra. A critical analysis of whether a combination of several methods of chromatography and mass spectrometry allows us to elucidate the structure of an unknown UDMH transformation product was provided. It was demonstrated that the use of gas chromatographic retention indices for two stationary phases (polar and non-polar) allows for the rejection of false candidates in many cases when only one retention index is not enough. The structures of five previously unknown UDMH transformation products were proposed, and four previously proposed structures were refined.

Construction of Chemical Libraries of Volatile Compounds by Combinatorial Synthesis of Homologous Mixtures: Alk-4-en-1-ols, Alk-4-enals and Methyl Alk-4-enoates.

A compound library of sixty six linear compounds, eleven representatives of six molecular families: (E)- and (Z)-isomers of alk-4-en-1-ols, alk-4-enals, and methyl alk-4-enoates, was prepared by combinatorial syntheses to allow the creation of a mass spectral database directly usable for their identification in GC/MS analyses. We demonstrate here that compound libraries can be prepared by combinatorial syntheses using long linear synthetic sequences, i. e., eight step in the case of 4-enals. The resulting mixtures of homologues are still perfectly exploitable to deliver the requested information such as clean mass spectra and good gas chromatographic retention indices.

Volatiles of the Apicomplexan Alga Chromera velia and Associated Bacteria.

Volatiles released by the apicomplexan alga Chromera velia CCAP1602/1 and their associated bacteria have been investigated. A metagenome analysis allowed the identification of the most abundant heterotrophic bacteria of the phycosphere, but the isolation of additional strains showed that metagenomics underestimated the complexity of the algal microbiome, However, a culture-independent approach revealed the presence of a planctomycete that likely represents a novel bacterial family. We analysed algal and bacterial volatiles by open-system-stripping analysis (OSSA) on Tenax TA desorption tubes, followed by thermodesorption, cryofocusing and GC-MS-analysis. The analyses of the alga and the abundant bacterial strains Sphingopyxis litoris A01A-101, Algihabitans albus A01A-324, "Coraliitalea coralii" A01A-333 and Litoreibacter sp. A01A-347 revealed sulfur- and nitrogen-containing compounds, ketones, alcohols, aldehydes, aromatic compounds, amides and one lactone, as well as the typical algal products, apocarotenoids. The compounds were identified by gas chromatographic retention indices, comparison of mass spectra and syntheses of reference compounds. A major algal metabolite was 3,4,4-trimethylcyclopent-2-en-1-one, an apocarotenoid indicating the presence of carotenoids related to capsanthin, not reported from algae so far. A low overlap in volatiles bouquets between C. velia and the bacteria was found, and the xenic algal culture almost exclusively released algal components.

Comparative Prediction of Gas Chromatographic Retention Indices for GC/MS Identification of Chemicals Related to Chemical Weapons Convention by Incremental and Machine Learning Methods

During on-site verification activities conducted by the Technical Secretariat of Organization for the Prohibition of Chemical Weapons, identification by gas chromatography retention indices (RI) data, in addition to mass spectrometry data, increase the reliability of factual findings. However, reference RIs do not cover all the possible chemical structures. That is why it is important to have models to predict RIs. Applicable only for narrow data sets of chemicals with a fixed scaffold (G- and V-series gases as example), the non-learning incremental method demonstrated predictive median absolute and percentage errors of 2–4 units and 0.1–0.2%; these are comparable with the experimental bias in RI measurements in the same laboratory with the same GC conditions. It outperforms the accuracy of two reported machine learning methods–median absolute and percentage errors of 11–52 units and 0.5–2.8%. However, for the whole Chemical Weapons Convention (CWC) data set of chemicals, when a fixed scaffold is absent, the incremental method is not applicable; essential machine learning methods achieved accuracy: median absolute and percentage errors of 29–33 units and 0.5–2.2%, depending on the machine learning method. In addition, we have developed a homology tree approach as a convenient method for the visualization of the CWC chemical space. We conclude that non-learning incremental methods may be more accurate than the state-of-the-art machine learning techniques in particular cases, such as predicting the RIs of homologues and isomers of chemicals related to CWC.

QSRR modelling for the investigation of gas chromatography retention indices of flavour and fragrance compounds on Carbowax 20 ​M glass capillary column with the index of ideality of correlation and the consensus modelling

The goal of the present study was to use a quantitative structure-retention relationship (QSRR) for the retention indices of 1179 flavour and fragrance organic compounds using the Monte Carlo algorithm of CORAL software. All the organic compounds were represented by SMILES notation for computation of descriptor of correlation weight (DCW). The dataset of 1179 flavour and fragrance organic compounds was used to make nine splits, each of which was further segmented into four sets: training, invisible training, calibration, and validation. The task of the index of ideality correlation (IIC) was analysed in-depth and it was found that the QSRR models generated by the use of IIC were more robust and significant. Two target functions i.e. TFA (IICweight=0.0), TFB (IICweight=0.2) were applied to build 18 QSRR models. The established QSRR model with TFB having Rvalidation2=0.9015 for split 6 was considered as the prime model. The reliability and robustness of the prime model was also confirmed by the numerical value of Qvalidation2=0.9000 and Qcalibration2=0.8919. The common promoters of increase and decrease of endpoint were also extracted from three splits 5, 6 and 9. Moreover, consensus modelling employing the split 6 layout of dataset distribution improves the prediction performance by enhancing the numerical value of Rvalidation2 from 0.9015 to 0.9241.

Characteristics of the Anomalous Temperature Dependence of Gas Chromatographic Retention Indices of Polar Compounds on Packed Columns with a Nonpolar Phase

Characteristics of the Anomalous Temperature Dependence of Gas Chromatographic Retention Indices of Polar Compounds on Packed Columns with a Nonpolar Phase

Descriptors for some compounds with pharmacological activity; calculation of properties

Abraham model solute descriptors have been determined for nisoldipine, nizatidine, loratadine, zonisamide, oxaprozin, rebamipide, domperidone, temozolomide, ‘florfenicol’, florfenicol A, dapsone, chrysin, benorilate, β-lapachone, and Ipriflavone based on published partition coefficients, molar solubilities and gas chromatographic retention indices. The calculated solute descriptors, combined with our previously published Abraham model correlations, are used to predict several important physicochemical and biological properties, such as air–water, air-blood, air-lung, air-fat, air-skin, water–lipid, water-membrane and water-skin partition coefficients, as well as permeation from water through skin.

Analytical aspects of organic compounds retention indices dependence in reversed phase HPLC on the content of methanol in eluent

As a result of the determination of retention indices (RI) of 11 organic compounds of various polarity in reversed phase (RP) HPLC using methanol as organic solvent in elu­ent, the coefficients of the dependence of RI vs. content of methanol, dRI/dС were de­ter­mi­ned. Formally, these parameters are equivalent to the temperature coefficients of gas chromatographic retention indices, b = dRI/dT. It is shown that the ranges of variations of these coefficients are rather large (from -2.0 up to +4.6 in the current work), which requires re­ve­­a­ling the factors influencing their signs and absolute values. The correlations of coeffi­ci­­ents dRI/dC with retention indices themselves, as well as with hydrophobicity factors, logP (the logarithms of partition coefficients in the system 1-octanol/water) seem to be negligible. The best correlations of dRI/dC were observed with such polarity mea­su­res of organic compounds as homologous increments of hydrophobicity factors (ilogP), and re­ten­tion indices (iRI). Such transformations of pro­perties A as their homologous increments characterize different homologues despite of their locations within the homologous series. Thus, the parameters ilogP and iRI appeared to be the measures of the polarity of the corres­pon­­ding series. This approach per­mits us to reveal that the dependence of retention indices in RP HPLC on the content of me­tha­nol in eluent is determined just by the analytes¢ po­la­rity and their hydrophobic – hydrophilic proper­ties. The ratio dRI/dC > 0 is typi­cal for non-polar analytes, while for polar compo­unds the negative signs of these coeffi­ci­ents are typical, dRI/dC < 0. Hence, deter­mining even the sign of this coefficient provi­des the important information on the chemical origin of analytes.

A QSRR model for predicting gas chromatography retention indices of essential oils using an improved chemical reaction optimization algorithm

A QSRR model for predicting gas chromatography retention indices of essential oils using an improved chemical reaction optimization algorithm

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

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.

Measuring evaporation rate constants of highly volatile compounds for use in predictive kinetic models

A kinetic model was previously developed in our laboratory to predict evaporation of compounds as a function of gas chromatographic retention index (IT). To define the initial model, evaporation rate constants were experimentally determined for compounds in the range IT = 800–1400 at temperatures from 5 to 35 °C. While the predictive accuracy was demonstrated, broader application of the model requires extension of the IT range to include more volatile compounds. However, such extension requires experimental determination of rate constants, which is challenging due to the explosive hazard and rapid evaporation of volatile compounds.In this work, rate constants of highly volatile compounds were experimentally determined and used to extend the kinetic model to predict evaporation. Prior to experimental evaporations, theoretical calculations were performed to optimize experimental parameters and to ensure that the vapor generated remained below the lower flammability limit for each compound. Compounds were then experimentally evaporated at three different temperatures (10, 20, and 30 °C) and analyzed by gas chromatography-mass spectrometry. The evaporation rate constants for each compound, corrected for condensation, were determined by regression to a first-order rate equation. These rate constants were combined with previously collected data to extend the kinetic model at each temperature. Comparison of predicted and experimentally determined chromatograms of an evaporated validation mixture indicated good model performance, with correlation coefficients ranging from 0.955 to 0.997 and mean absolute percent errors in predicting abundance ranging from 3 to 26%.

Determination of Anomalies of Gas Chromatographic Retention Indices for Homologs Based on Their Homologous Increments

Determination of Anomalies of Gas Chromatographic Retention Indices for Homologs Based on Their Homologous Increments

Statistical Evaluation of the Standard Deviation of Chromatographic Retention Time in the Capillary Column Temperature Programming Mode

In the qualitative analysis of complex mixtures of unknown composition, a promising direction for increasing the reliability of the identification of substances is the joint use of several simultaneously determined analytical parameters. A combination of chromatographic methods with FTIR spectroscopy, mass spectrometry, or other methods is used. In solving complex analytical problems of industrial nature, it is economically expedient to use only chromatographic methods. In analyzing complex multicomponent mixtures with a high density of peaks in the chromatogram, the precision of the determination of retention time determines the reliability of the identification of substances and of the result of analysis, using identification both directly by the absolute retention time and by relative parameters derived from it (relative retention times, retention indices). The standard deviation of a measurement of the chromatographic retention time is a measure of precision. Experimental chromatographic retention times were obtained for analyzing a mixture by the following scheme: initial isotherm, temperature programming, final isotherm. A sharp increase (by one order of magnitude) in the numerical value of the standard deviation was noted at the step of temperature programming. A regression equation was obtained linking the point estimate of the standard deviation of the retention time and column temperature. The performance characteristic of the regression equation is obtained as a function that enables calculating the total standard uncertainty in the estimate of the standard deviation of the chromatographic retention time. The results can be used to refine chromatographic retention time and indices that ensure the reliable identification of substances.

Application of a kinetic model to predict extracted ion profiles for the identification of evaporated ignitable liquids

A kinetic model was previously developed in our laboratory to predict evaporation rate constants of compounds in ignitable liquids as a function of gas chromatographic retention index. The model is applied to the chromatogram of an unevaporated liquid and used to predict chromatograms corresponding to any evaporation level of the liquid. In previous work, accuracy in predicting total ion chromatograms (TICs) for gasoline and petroleum distillates was demonstrated. In this work, the model is applied for the first time to predict extracted ion profiles (EIPs) corresponding to different compound classes.Fifteen ignitable liquids representing five classes (isoparaffinic, naphthenic-paraffinic, aromatic, petroleum distillate, and gasoline) defined in ASTM E1618 were experimentally evaporated to 50–90% (v/v). The kinetic model was applied to predict TICs and EIPs corresponding to the alkane, cycloalkane, aromatic, indane, and polynuclear aromatic compound classes for each evaporated liquid. Strong correlation between experimental and predicted TICs and EIPs indicated the predictive accuracy of the model. Reference collections were then generated containing predicted TICs and EIPs for each liquid corresponding to evaporation levels ranging from 10 to 90%. The reference collections were used to identify the ignitable liquid class in samples of increasing complexity, based on maximum correlation between the experimental and predicted chromatograms. In all cases, the correct liquid class was identified, even in the presence of substrate interferences. Overall, this work demonstrates the potential of a kinetic model to generate TICs and EIPs as a practical tool for identification of ignitable liquids in fire debris samples.