Gas Chromatographic Retention Research Articles

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.

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.

Quantitative structure-property relationship for gas-chromatographic retention indices of volatile organic compounds in colored-quinoa seeds

This study outlines the development of a predictive in silico model for the retention index (RI) of 61 volatile organic compounds (VOCs) present in diverse colored-quinoa seeds. The analysis was performed using gas chromatography-ion mobility spectrometry (GC-IMS) with the FS-SE-54-CB-1 capillary column. The chemical information in the database was carefully curated, and the molecular geometries of the VOCs were properly optimized to calculate diverse molecular descriptors. The database was then divided into a training set (48 compounds) and a test set (13 molecules). For model development, unsupervised and supervised chemometric approaches were integrated to obtain a four-descriptor quantitative structure-property relationship (QSPR). The negligible differences in the coefficient of determination and residual standard deviation for the training set (R2 = 0.957, s = 34.16) and test set (R2 = 0.954, s = 35.46) indicate a stable and predictive model. The QSPR model was also subjected to cross-validation using diverse strategies, along with an applicability domain assessment. A thorough explanation of the mechanistic effects of these descriptors in predicting the RI is provided, ensuring the model accomplished all the criteria defined by the Organization for Economic Co-operation and Development. This model is simple and can assist chemists working on the analysis of VOCs in quinoa seeds using GC-IMS and to gain a deeper understanding of the molecular mechanisms involved in the retention index phenomenon. This QSPR model was also applied as a tool to predict the retention index of an external set of 89 new VOCs identified in quinoa seeds through GC-IMS analysis.

Elucidation of radiation-induced molecular fragmentation in triphenylmethane aromatic dye using mass spectrometric tool

This study investigates the potential of triphenylmethane dye as a gamma radiation dosimeter using gas chromatography-mass spectrometry (GC-MS) through detection of radiation-induced derivatives produced at low concentrations in the irradiated material. Pristine triphenylmethane established a reference baseline for the dye’s dosimetric properties to establish a differentiated signature of the radiation-synthesized derivatives. The two key radiation-induced derivatives identified and quantified in the present work were 2-Propanone,1,1-diphenyl- and diphenylcarbinol Benzenemethanol, α, α-diphenyl- which was found in trace amounts in pristine material due to possible natural chemical or radiation processes. Gamma irradiation significantly accelerates their formation in which their relative concentration increases linearly with increasing radiation dose up to 100 kGy. Unavoidable radiation-induced synthesis of low-mass radiation fragments was observed at higher gas chromatography retention time; and also exhibit a linear increase with dose within the investigated range.

Directly measuring octanol-air partition ratios using a gas chromatography retention time (GC-RT) method

The octanol-air partition ratio (KOA) is a fundamental property used for screening chemicals for concern, depending on their potential to bioaccumulate and harm living systems. With millions of chemicals used in commerce, unfortunately, less than 800 compounds currently have experimentally measured KOA values due to limitations in traditional measurement techniques. We aimed to develop a direct gas chromatography retention time (GC-RT) method using a custom-packed column, with octanol as the stationary phase, for rapidly measuring KOA. We installed the column into a GC-mass spectrometry system and isothermally measured retention times of 15 volatile organic compounds. We calculated KOA values at experimental temperatures from the retention times and extrapolated the results to values at 25 °C using the Van't Hoff equation. Our directly measured log KOA values at 25 °C spanned 3.66 log units (0.820–4.48). Except for alcohols, our measured values agree with literature log KOA values (root mean square error, RMSE = 0.50). The discrepancy probably arose from differences in how well our method versus the literature methods capture measurement artifacts, specifically, gas-phase adsorption to the octanol phase and hydrogen bonding interactions. This proof-of-concept study demonstrates that our direct GC-RT method is promising for rapidly measuring KOA to support chemical risk assessment.

Critical evaluation of the NIST retention index database reliability with specific examples.

The NIST gas chromatographic retention index database is widely used in gas chromatography-mass spectrometry analysis. For many compounds, the NIST database contains many entries that are presumably obtained independently of each other. We showed with specific examples that there are cases in the NIST database where several entries exist for the same compound, and all of them are equally erroneous (an error of more than 100 units). In particular, we demonstrated that all retention index values for such an important compound as imidazole for non-polar stationary phases in theNISTdatabase are erroneous. In addition to imidazole, a similar situation is observed for four more nitrogen-containing heterocyclic compounds. For certainty, measurements were performed under several conditions, using various temperature programs, and using two specimens of columns. The structures were confirmed using nuclear magnetic resonance and mass spectrometry. It was shown with specific examples that many values are not reliable: either data were obtained using standard samples of undescribed origin without confirmation (without even using mass spectrometry) or, in some cases, standard samples were not used at all, and the retention index was obtained for a mixture component identified using a mass spectral library search. Some "independent" values are not such but are repeated publications of the same data (secondary sources), or simply several values taken from the same source. In the work, an analysis was carried out and assumptions were made about how several equally incorrect retention index values could appear in the NIST database.

Recurrent Relationships as an Important Class of Mathematical Equations in Chemistry

The unique potential and different areas of applications of recurrent (synonymous: recursive) relationships in chemistry and chromatography are considered. Recurrent relations can be used in two forms: as functions of integer arguments, y(x+ 1) = ay(x) + b, and as functions of equidistant argument values, A(x+ Δx) = aA(x)+ b, Δx= const. The first form applies to all physicochemical properties of homologs in organic chemistry, because the number of carbon (and other) atoms in a molecule can be integer only. The second one applies to chemical variables depending on temperature, pressure, concentrations, etc., when the chemists should provide equal “steps” of their variations. Recurrent relations combine the properties of arithmetic and geometric progressions, which accounts for their unique approximation abilities. This was illustrated by approximating the number of isomers of alkanes, the boiling points of homologs (nonlinear dependencies), the melting points of homologs (alternation effects), the temperature dependence of the solubility of inorganic salts in water, and by revealing the anomalies of gas chromatographic retention indices and retention times in reversed-phase high performance liquid chromatography.

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: https://github.com/mtshn/chereshnya.

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.

Mutual interference between 3,6-dichlorinated carbazole and p,p'-dichlorodiphenyltrichloroethane in gas chromatography mass spectrometry analysis.

The widespread contamination of the environment by polyhalogenated carbazoles (PHCZs) has been increasingly observed during the past decade. Among numerous PHCZ congeners, 3,6-dichlorocarbazole (36-CCZ) is often among the most frequently detected at higher concentrations. Although the environmental level of the legacy pesticide p,p'-dichlorodiphenyltrichloroethane (p,p'-DDT) has been declining, it continues to be ubiquitously detected. These two compounds were found to interfere with each other during analyses using gas chromatography (GC) coupled with single- or triple-quadrupole low-resolution mass spectrometry (MS or MS/MS). The base peak in the mass spectra was that of m/z 235 for both compounds. In MS/MS with multiple reaction monitoring (MRM), the same transitions (235 → 200 and 235 → 165) were often used. Under the same GC operating conditions, the SH-I-5MS capillary column used in this work did not resolve the two compounds at baseline. Pre-treatment using cleanup column chromatography can fractionate the sample extract, with the two compounds separated in different fractions before instrumental analyses. Reversed-phase HPLC columns also work for resolving 36-CCZ and p,p'-DDT. Possible overlaps in GC retention and similarity in MS spectra might have caused data inaccuracy for 36-CCZ as well as p,p'-DDT in some studies published to date, and steps to avoid the interference should be taken into quality control protocols in future research and environmental monitoring.

The effect of the assigned descriptors for phthalate esters on the characterization of their separation properties using the solvation parameter model

Revised descriptors are determined for fifteen phthalate esters for use in the solvation parameter model and form part of the Wayne State University (WSU) compound descriptor database. For thirteen phthalate esters a comparison is made with the same compounds in the Abraham descriptor database. Gas chromatographic retention factors on poly(methyloctylsiloxane), SPB-Octyl, and poly(cyanopropylphenyldimethylsiloxane), DB-225, stationary phases are used to facilitate an assessment of the contribution of cavity formation and dispersion interactions, L descriptor, and dipole-type interactions, S descriptor, to the experimental retention factors (log k) for the phthalate esters with minimum interference from competing intermolecular interactions. The results indicate a systematic overprediction of the cavity and dispersion interaction term and underprediction of dipole-type interactions for the Abraham descriptors compared with the WSU descriptors for the phthalate esters. The average absolute deviation (AAD) for 13 phthalate esters on SPB-Octyl is 0.039 (WSU descriptors) compared with 0.252 (Abraham descriptors) and for 9 phthalate esters on DB-225 0.030 (WSU descriptors) compared with 0.167 (Abraham descriptors). The results for dipole-type interactions are confirmed and extended to include the hydrogen-bond basicity of the phthalate esters, B descriptor, by evaluation of partition constants in aqueous biphasic systems and the n-heptane-2,2,2-trifluoroethanol biphasic system. Differences in the contribution of the hydrogen-bond basicity of the phthalate esters to the experimental partition constants are largely random with respect to database selection but important for the accurate prediction of the partition constants. The AAD for the partition constant for 15 phthalate esters is 0.063 (WSU descriptors) compared with 0.320 (Abraham descriptors) for the heptane-2,2,2-trifluoroethanol biphasic system and 0.13 (WSU descriptors) compared with 0.25 (Abraham descriptors) for 9 phthalate esters in the octanol-water biphasic system. The WSU descriptors for the phthalate esters exhibit a better fit with the experimental data for separation systems and are free of the extreme values predicted for the Abraham descriptors for several phthalate esters.

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).

Detailed Comparison of Xenon APPI (9.6/8.4 eV), Krypton APPI (10.6/10.0 eV), APCI, and APLI (266 nm) for Gas Chromatography High Resolution Mass Spectrometry of Standards and Complex Mixtures.

Photoionization schemes for mass spectrometry, either by laser or discharge lamps, have been widely examined and deployed. In this work, the ionization characteristics of a xenon discharge lamp (Xe-APPI, 9.6/8.4 eV) have been studied and compared to established ionization schemes, such as atmospheric pressure chemical ionization, atmospheric pressure photoionization with a krypton discharge lamp (Kr-APPI, 10.6/10 eV) and atmospheric pressure laser ionization (266 nm). Addressing the gas-phase ionization behavior has been realized by gas chromatography coupled to high-resolution mass spectrometry without the usage of a dopant. For standard substances, it has been found that Xe-APPI is able to ionize a broad range of polycyclic aromatic hydrocarbons as well as their heteroatom-containing and alkylated derivatives. However, thiol and ester compounds could not be detected. Moreover, Xe-APPI revealed a high tendency to generate oxygenated artifacts, most likely due to a VUV absorption band of oxygen at 148 nm. Beneficially, almost no chemical background, commonly caused by APCI or Kr-APPI due to column blood, plasticizers or impurities, is observed. This advantage is noteworthy for evolved gas analysis without preseparation or for chromatographic coelution. For the complex mixtures, Xe-APPI revealed the predominant generation of radical cations via direct photoionization with a high selectivity toward aromatic core structures with low alkylation. Interestingly, both Xe-APPI and Kr-APPI could sensitively detect sterane cycloalkanes, validated by gas chromatographic retention. The narrowly ionized chemical space could let Xe-APPI find niche applications, e.g., for strongly contaminated samples to reduce the background.

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

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.

Using Thermodynamics and Simulations to Understand Selectivity and Retention in Gas Chromatography

Retention and selectivity in gas chromatography (GC) occur because of the thermodynamics of the phase transition from the vapor phase following sample injection to the solution phase of being dissolved in the stationary phase. Analytes then move along the column because the vapor phase is flowing. In this installment, we quickly review the basic thermodynamics underlying separation in GC, and we see how this impacts selectivity and retention. Last month, we saw how seemingly large differences in column polarity may still generate little, or even the opposite, effect on retention and generate confusion and difficulty in method development. This month, we explore this challenge more in-depth using thermodynamics and a freely available online GC simulator.

Determination of Abraham Model Solute Descriptors for 62 Additional C10 through C13 Methyl- and Ethyl-Branched Alkanes

Abraham model solute descriptors are reported for the first time for 62 additional C10 through C13 methyl- and ethyl-branched alkanes. The numerical values were determined using published gas chromatographic retention Kováts retention indices for 157 alkane solutes eluted from a squalane stationary phase column. The 95 alkane solutes that have known descriptor values were used to construct the Abraham model KRI versus L-solute descriptor correlation needed in our calculations. The calculated solute descriptors can be used in conjunction with previously published Abraham model correlations to predict a wide range of important physico-chemical and biological properties. The predictive computations are illustrated by estimating the air-to-polydimethylsiloxane partition coefficient for each of the 157 alkane solutes.

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.

Comparison of Gas-Chromatographic Retention Parameters of Aliphatic Enyne Alcohols with the Data for Their Structural Analogues

We studied the regularities of gas-chromatographic retention indices of aliphatic enyne alcohols containing С=С and С≡С bonds in the molecule (14 secondary and two tertiary homologues) on standard nonpolar polydimethylsiloxane stationary phases, which have not been characterized in sufficient detail earlier. Using homologous increments of retention indices iRI = RI – 100x, where х = int(M/14), М is the molecular weight number, and 14 is the mass number of the CH2 homological difference, one can compare data for structural analogs, including alkanols, alkenols, and alkynols. The features of comparing iRI values for compounds belonging to different homologous groups y ≡ M(mod14), for example, CnH2n+2O (y = 4), CnH2nO (y = 2), CnH2n–2O (y = 0), and CnH2n–4O (y = 12), are discussed. The main feature is that when the boundary value of y = 0 is passed, the relation between the parameter x and the number of carbon atoms in the molecule changes due to an increase in the formal unsaturation (the condition n = x – 1 is replaced by the condition n = x). Therefore, in the latter case, 100 should be subtracted from the iRI homologous increments to compare them with the data for structural analogs of other series. Taking this correction into account, we found that the iRI values for secondary enyne alcohols (12 ± 16) and previously characterized sec-alkynols (23 ± 21) virtually coincide, and for tertiary enyne alcohols, they are consistent with the data for all considered groups of structural analogues (alkanols, alkenols, and alkynols).