Classification of stationary phases and other materials by gas chromatography

A Review of Mechanistic Models for Predicting Adverse Effects in Sediment Toxicity Testing

Since recognizing the importance of bioavailability for understanding the toxicity of chemicals in sediments, mechanistic modeling has advanced over the last 40 years by building better tools for estimating exposure and making predictions of probable adverse effects. Our review provides an up-to-date survey of the status of mechanistic modeling in contaminated sediment toxicity assessments. Relative to exposure, advances have been most substantial for non-ionic organic contaminants (NOCs) and divalent cationic metals, with several equilibrium partitioning-based (Eq-P) models having been developed. This has included the use of Abraham equations to estimate partition coefficients for environmental media. As a result of the complexity of their partitioning behavior, progress has been less substantial for ionic/polar organic contaminants. When the EqP-based estimates of exposure and bioavailability are combined with water-only effects measurements, predictions of sediment toxicity can be successfully made for NOCs and selected metals. Both species sensitivity distributions and toxicokinetic and toxicodynamic models are increasingly being applied to better predict contaminated sediment toxicity. Furthermore, for some classes of contaminants, such as polycyclic aromatic hydrocarbons, adverse effects can be modeled as mixtures, making the models useful in real-world applications, where contaminants seldomly occur individually. Despite the impressive advances in the development and application of mechanistic models to predict sediment toxicity, several critical research needs remain to be addressed. These needs and others represent the next frontier in the continuing development and application of mechanistic models for informing environmental scientists, managers, and decisions makers of the risks associated with contaminated sediments. Environ Toxicol Chem 2024;43:1778-1794. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Determination of solvation parameter model compound descriptors by gas chromatography

The solvation parameter model uses five system independent descriptors to characterize compound properties defined as excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity, A, hydrogen-bond basicity, B, and the gas-liquid partition constant at 25 °C on n-hexadecane, L, to model transfer properties in gas-condensed phase biphasic systems. The E descriptor for compounds liquid at 20 °C is available by calculation using a refractive index value while E for solid compounds at 20 °C and the S, A, B, and L descriptors are determined by experiment. As a single-technique approach, it is shown that with up to 20 retention factor measurements on four columns comprising a poly(siloxane) containing methyloctyl or dimethyldiphenylsiloxane monomers (SPB-Octyl or HP-5), a poly(siloxane) containing methyltrifluoropropylsiloxane monomers (Rtx-OPP or DB-210), a poly(siloxane) containing bis(cyanopropylsiloxane) monomers (HP-88 or SGE BPX-90), and a poly(ethylene glycol) stationary phase (DB-WAXetr or HP-INNOWAX) are suitable for assigning the S, A, and L descriptors. Using the descriptors in the updated WSU compound descriptor database as target values the average absolute error in the descriptor assignments for 52 varied compounds in the temperature range 60-140 °C was 0.072 for E, 0.016 for S, 0.008 for A, and 0.022 for L corresponding to 30 %, 3.5 %, and 0.6 % as a relative average absolute error for E, S, and L, respectively. For the higher temperature range of 160-240 °C and 34 varied compounds that are liquid at 20 °C the average absolute error for the S, A and L descriptors was 0.026, 0.020, and 0.031, respectively, with the largest relative average absolute error for S of 3.2 % (< 1 % for the L descriptor). For 35 varied compounds that are solid at 20 °C the relative absolute error for the E, S, A, and L descriptors in the higher temperature range was 0.068, 0.035, 0.020, and 0.020, respectively, with a relative average absolute error for E (6.5 %), S (3.5 %) and L (0.88 %). The S, A, and L descriptor can be accurately assigned on the four-column system over a wide temperature range. The E descriptor for solid compounds at 20 °C exhibits greater variability than desirable. The B descriptor cannot be assigned by the four-column system, which lack hydrogen-bond acid functional groups, and is only poorly assigned on the weak hydrogen-bond acid ionic liquid column SLB-IL100.

Simulation of gas chromatographic separations and estimation of distribution-centric retention parameters using linear solvation energy relationships

For method development in gas chromatography, suitable computer simulations can be very helpful during the optimization process. For such computer simulations retention parameters are needed, that describe the interaction of the analytes with the stationary phase during the separation process. There are different approaches to describe such an interaction, e.g. thermodynamic models like Blumberg's distribution-centric 3-parameter model (K-centric model) or models using chemical properties like the Linear Solvation Energy Relationships (LSER). In this work LSER models for a Rxi-17Sil MS and a Rxi-5Sil MS GC column are developed for different temperatures. The influences of the temperature to the LSER system coefficients are shown in a range between 40 and 200 °C and can be described with Clark and Glew's ABC model as fit function. A thermodynamic interpretation of the system constants is given and its contribution to enthalpy and entropy is calculated. An estimation method for the retention parameters of the K-centric model via LSER models were presented. The predicted retention parameters for a selection of 172 various compounds, such as FAMEs, PCBs and PAHs are compared to isothermal determined values. 40 measurements of temperature programmed GC separations are compared to computer simulations using the differently determined or estimated K-centric retention parameters. The mean difference (RSME) between the measured and predicted retention time is less than 8 s for both stationary phases using the isothermal retention parameters. With the LSER predicted parameters the difference is 20 s for the Rxi-5Sil MS and 38 s for the Rxi-17Sil MS. Therefore, the presented estimation method can be recommended for first method development in gas chromatography.

Determination of the hydrogen-bond basicity descriptor by reversed-phase liquid chromatography

Except for alkanes, most organic compounds are hydrogen-bond bases. The B° descriptor of the solvation parameter model provides a convenient measure of the effective (or summation) hydrogen-bond basicity of organic compounds. A fast and convenient method to assign the B° descriptor is required to support studies of hydrogen-bonding in separation systems. A two-column system with acetonitrile-water mobile phase compositions and the measurement of up to eleven isocratic retention factors is proposed for this purpose. Several reversed-phase column chemistries and mobile phases were evaluated with the two-column system consisting of a pentafluorophenylpropylsiloxane-bonded and octadecylsiloxane-bonded silica columns recommended for this purpose. To assess the accuracy of the method values for B° were taken from the Wayne State University (WSU) compound descriptor database, which were assigned using conventional multi-technique methods and large datasets. The two-column systems provided an unbiased assignment of B° with an average deviation of 0.008 and an average absolute deviation of 0.021 compared with the target value for 55 varied compounds. The two-column system is unsuitable for assigning the other descriptors used in the solvation parameter model and results in erroneous assignments of B° for nitrogen-containing compounds capable of electrostatic interactions on silica-based reversed-phase columns.

Determination of descriptors for the principal flavor compounds of the cinnamons of commerce by gas chromatography and liquid-liquid partition

Descriptors for fourteen semivolatile organic compounds associated with the authenticity, botanical origin, and flavor potential of the cinnamons of commerce were determined using the Solver method and experimental retention factors determined by gas chromatography at several temperatures on a minimum of seven selectivity-selected, open-tubular columns and liquid-liquid partition constants in up to twenty totally organic biphasic systems. The six descriptors that encode the solvation properties of the compounds were used to predict water-gas, octanol-gas, and octanol-water partition constants commonly employed to assess environmental distribution properties. For octanol-water partition constants, log KOW, the predicted partition constants exhibited an average absolute deviation of 0.12 for log KOW experimental - log KOW predicted (n = 14). Soil-water, soil-air, urban aerosol-air, skin-water permeation, and non-specific toxicity to the fathead minnow were predicted for the same compounds to assess their potential environmental impact. The product terms of the solvation parameter model provide a useful insight into the contribution of individual intermolecular interactions to the distribution properties of the cinnamon compounds and their environmental impact.

Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds

The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.

Multicapillary columns with ionic liquids as stationary liquid phase

The study demonstrates the possibility of using ionic liquids (IL) as a stationary liquid phase (SLP) for gas chromatographic (GC) multicapillary columns (MCC). Three types of IL of three classes were employed as SLP: Imidazolium, Pyridinium and Quinolinium. Dependences of the MCCs efficiency on the carrier gas flow rate were obtained. Highest efficiency was achieved on the column with 1,2-Dimethyl-3-propylimidazolium bis(trifluoromethylsulfonyl)imide (DiMPrIm). For this column, dependence of the efficiency on the sample volume has been investigated. Also the loading capacity of the MCC with DiMPrIm was determined. Separation of fatty acid esters and phenols served as an example to demonstrate that using ionic liquids as SLP for МСС make it possible to combine fast separations with high selectivity.

Selectivity evaluation of extraction systems

Extraction is the most common sample preparation technique prior to chromatographic analysis for samples which are too complex, too dilute, or contain matrix components incompatible with the further use of the separation system or interfere in the detection step. The most important extraction techniques are biphasic systems involving the transfer of target compounds from the sample to a different phase ideally accompanied by no more than a tolerable burden of co-extracted matrix compounds. The solvation parameter model affords a general framework to characterize biphasic extraction systems in terms of their relative capability for solute-phase intermolecular interactions (dispersion, dipole-type, hydrogen bonding) and within phase solvent-solvent interactions for cavity formation (cohesion). The approach is general and allows the comparison of liquid and solid extraction phases using the same terms and is used to explain the features important for the selective enrichment of target compounds by a specific extraction phase using solvent extraction, liquid-liquid extraction, and solid-phase extraction for samples in a gas, liquid, or solid phase. Hierarchical cluster analysis with the system constants of the solvation parameter model as variables facilitates the selection of solvents for extraction, the identification of liquid-liquid distribution systems with non-redundant selectivity, and evaluation of different approaches using liquids and solids for the isolation of target compounds from different matrices.

Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

The influence of descriptor database selection on the solvation parameter model for separation processes

The distribution of neutral compounds in biphasic separation systems can be described by the solvation parameter model using six solute properties, or descriptors. These descriptors characterize the size (McGowan's characteristic volume), V, excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity and basicity, A and B, and the gas-liquid partition constant on n-hexadecane at 298.15 K, L. McGowan's characteristic volume and the excess molar refraction for liquids are available by calculation (E requires and experimental refractive index). The other descriptors and excess molar refraction for solids are experimental quantities and subject to greater variation or are estimated using computational or empirical models. Solute descriptors for several thousand compounds are available in the Abraham descriptor database and for several hundred compounds in the WSU descriptor database. These publicly accessible databases were developed independently using different approaches and for many compounds provide different descriptor values. In this report we evaluate the effect of mixing descriptors from the two databases on modeling chromatographic retention factors and liquid-liquid partition constants. It is shown that the two descriptor databases are not interchangeable. The WSU descriptor database consistently demonstrates improved model quality as determined by statistical parameters. Model system constants exhibit a general dependence on database selection with an approximately linear trend as a function of the fraction of compounds assigned descriptors from either database. There is no general model performance advantage to using mixed descriptor datasets and no real cause for concern for relatively large datasets containing < 15 % of compounds with descriptors assigned from the other database. For small datasets, descriptor quality is an important variable for adequate model performance.

Reliable prediction of sensory irritation threshold values of organic compounds using new models based on linear free energy relationships and GC×GC retention parameters

The human sensory irritation threshold (SIT) is an important biochemical parameter for the exposure assessment of organic air pollutants. First, we recalibrated the Abraham solvation models (ASMs) for 9 SIT endpoints by curating 720 individual experimental SIT values to find an accurate and parsimonious ASM variant, which exhibited root mean square error (RMSE) = 0.174-0.473 log unit. Second, we report linear free energy relationships - henceforth called partition models (PMs) - which exploit the correlations of 9 SIT endpoints with the linear combinations of partition coefficients for octanol-water and air-water systems showing RMSE = 0.221-0.591 log unit. These PMs can easily be integrated into widely used EPI-Suite™ screening tool. The explanatory and predictive performance of PMs were like parameter-intensive ASMs. Third, we present GC × GC models that are based on the retention times of the nonpolar analytes on the comprehensive two-dimensional gas chromatography (GC × GC), which successfully described the SIT variance (R²=0.959-0.996) and depicted a strong predictive power (RMSE = 0.359-0.660 log unit) for an independent set of nonpolar analytes. Taken together, PMs allow easy SIT screening of organic chemicals compared to ASMs. Unlike ASMs, our GC × GC models can be applied to estimate SIT of complex nonpolar mixtures.

Recent advances for estimating environmental properties for small molecules from chromatographic measurements and the solvation parameter model

The transfer of neutral compounds between immiscible phases in chromatographic or environmental systems can be described by six solute properties (solute descriptors) using the solvation parameter model. The solute descriptors are size (McGowan's characteristic volume), V, excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity and basicity, A and B, and the gas-liquid partition constant on n-hexadecane at 298.15 K, L. V and E for liquids are accessible by calculation but the other descriptors and E for solids are determined experimentally by chromatographic, liquid-liquid partition, and solubility measurements. These solute descriptors are available for several thousand compounds in the Abraham solute descriptor databases and for several hundred compounds in the WSU experimental solute descriptor database. In the first part of this review, we highlight features important in defining each descriptor, their experimental determination, compare descriptor quality for the two organized descriptor databases, and methods for estimating Abraham solute descriptors. In the second part we focus on recent applications of the solvation parameter model to characterize environmental systems and its use for the identification of surrogate chromatographic models for estimating environmental properties.

Models Used to Predict Chemical Bioaccumulation in Fish from in Vitro Biotransformation Rates Require Accurate Estimates of Blood-Water Partitioning and Chemical Volume of Distribution

Methods for extrapolating measured in vitro intrinsic clearance to a whole-body biotransformation rate constant (kB ) have been developed to support modeled bioaccumulation assessments for fish. The inclusion of extrapolated kB values into existing bioaccumulation models improves the prediction of chemical bioconcentration factors (BCFs), but there remains a tendency for these methods to overestimate BCFs relative to measured values. Therefore, a need exists to evaluate the extrapolation procedure to assess potential sources of error in predicted kB values. We examined how three different approaches (empirically based, composition based, and polyparameter linear free energy relationships [ppLFERs]) used to predict chemical partitioning in vitro (liver S9 system; KS9W ), in blood (KBW ), and in whole fish tissues (KFW ) impact the prediction of a chemical's hepatic clearance binding term (fU ) and apparent volume of distribution (VD ), both of which factor into the calculation of kB and the BCF. Each approach yielded different KS9W , KBW , and KFW values, but resulted in fU values that were of similar magnitude and remained relatively constant at log octanol-water partition ratios (KOW ) greater than 4. This is because KBW and KS9W values predicted by any given approach exhibit a similar dependence on log KOW (i.e., regression slope), which results in a cancelation of "errors" when fU is calculated. In contrast, differences in KBW values predicted by the three approaches translate to differences in VD , and by extension kB and the BCF, which become most apparent at log KOW greater than 6. There is a need to collect KBW and VD data for hydrophobic chemicals in fish that can be used to evaluate and improve existing partitioning prediction approaches in extrapolation models for fish. Environ Toxicol Chem 2023;42:33-45. © 2022 SETAC.

Modeling the GCxGC Elution Patterns of a Hydrocarbon Structure Library To Innovate Environmental Risk Assessments of Petroleum Substances

Comprehensive two-dimensional gas chromatography (GCxGC) offers unrivaled separation of petroleum substances, which can contain thousands of constituents or more. However, interpreting substance compositions from GCxGC data is costly and requires expertise. To facilitate environmental risk assessments, industries provide aggregated compositional information known as "hydrocarbon blocks" (HCBs), but these proprietary methods do not transparently associate the HCBs with GCxGC chromatogram data. These obstacles frustrate efforts to study the environmental risks of petroleum substances and associated environmental samples. To address this problem, we developed a GCxGC elution model for user-defined petroleum substance compositions. We calibrated the elution model to experimental GCxGC retention times of 56 known hydrocarbons by fitting three tunable model parameters to two candidate instrument methods. With the calibrated model, we simulated retention times for a library of 15,447-15,455 hydrocarbon structures (plus 40-48 predicted as chromatographically unretained) spanning 11 classes of petroleum substance constituents in the C₁₀-C₃₀ range. The resulting simulation data reveal that GCxGC retention times are quantitatively associated with hydrocarbon class and carbon number information throughout the GCxGC chromatogram. These innovations enable the development of transparent and efficient technical methods to investigate the chemical compositions and environmental properties of petroleum substances, including in environmental and lab-weathered samples.

Immobilization of phosphonium-based ionic liquid stationary phases extends their operative range to routine applications in the flavor, fragrance and natural product fields

Phosphonium-based ionic liquids (ILs) have proven to be successful stationary phases (SPs) for gas chromatography (GC) in several fields of application because of their unique selectivity and good chromatographic properties. This study focuses on the use of two ILs as GC SPs that are based on the phosphonium derivatives trihexyl(tetradecyl)phosphonium chloride ([P₆₆₆₁₄⁺] [Cl^-]), and trihexyl(tetradecyl)phosphonium bis[(trifluoromethyl)sulfonyl]imide ([P₆₆₆₁₄⁺][NTf₂^-]), which have previously been shown to be complementary in terms of chromatographic selectivity and retention. Their application in routine analysis has been limited by their lower maximum allowable operating temperatures (MAOT) (200 °C for the [P₆₆₆₁₄⁺][Cl^-] IL and 180 °C for [P₆₆₆₁₄⁺][NTf₂^-]), which restricts their use to samples that consist of analytes with relatively high volatility. A previous study carried out in the Authors' laboratory focused on extending the use of the [P₆₆₆₁₄⁺][Cl^-] IL SP to the analysis of samples with analytes of medium-to-low volatility by optimizing column characteristics and operative conditions. This study addresses the immobilization of both the [P₆₆₆₁₄⁺][Cl^-] and [P₆₆₆₁₄⁺][NTf₂^-] ILs to the inner wall of fused silica columns to increase their MAOT under soft and hard reaction conditions. The resulting MAOT depended on more or less drastic immobilization conditions, and reached 220 °C for soft immobilization (So-Im) and 240 °C for hard immobilization (Ha-Im) in the [P₆₆₆₁₄⁺][Cl^-] IL columns, and 200 °C for So-Im and 220° for Ha-Im in columns coated with the [P₆₆₆₁₄⁺] [NTf₂^-] IL. The influence of immobilization on the separation power and performance of all the columns has been evaluated using i) the Grob test, ii) a model mixture of 41 compounds of different polarity, structure, and with different organic functional groups representative of the flavor and fragrance field, iii) a standard mixture of 37 fatty acid methyl esters, iv) the peppermint essential oil, v) two mixtures of sesquiterpenic alcohols (farnesols and santalols), and vi) a standard mixture of 16 pesticides. These test samples were also used to demonstrate the complementarity of the two phosphonium-based IL SPs in terms of selectivity and retention.

Development of a predictive model of the microbial inactivation of L. monocytogenes during low thermal treatment of fruit juices in combination with carvacrol as aroma compound

Mild heat treatment of fruit juices in combination with natural aroma compounds has been reported as an alternative to conventional pasteurization to better preserve their nutritional value. However, its antimicrobial efficiency varies from one juice to another. This study aims at developing a secondary predictive model of microbial inactivation scale during such combined process. Carvacrol was used as aroma compound and acid-adapted L. monocytogenes as target microorganism. The inactivation kinetics of this bacteria were followed in simulated fruit juices using a Central Composite Design with pH (2-6), °Brix (0–24), temperature (55–65 °C), and carvacrol concentration (0–60 μL/L) as independent variables. Curves were fitted to the Weibull inactivation model, and data collected used to generate two predictive models of the inactivation scale parameter through multiple regression analysis following an empirical and a mechanistic (based on Gamma concept) approach. The best of the two models was then validated using real fruit (orange, pineapple, and watermelon) juices. The empirical model where only the four variables tested were considered showed a lower statistical performance compared to the mechanistic model where octanol-water partition coefficient (Ko/w) and vapour pressure (Vp) of carvacrol at the treatment temperature were integrated (R² 0.6 and 0.9; Accuracy factor 1.5 and 1.3; Sum of Squared Error 3.6 and 1.1, respectively). No significant difference was observed between inactivation scale values obtained with real juices and the predicted values calculated using this mechanistic model. The Ko/w and Vp of the aroma compound used are key parameters that determine the efficiency of the above-described combined treatment.

pH and Brix of fruit juices determine L. monocytogenes mild heat inactivation

Carvacrol supplementation enhances mild heat tolerance of Listeria monocytogenes

This carvacrol effect is determined by its partition coefficient and vapour pressure

Consideration of these two physicochemical parameters increases model prediction.

Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases

In the present study, computational molecular descriptors of 90 saturated esters and seven poly(siloxane) stationary phases with different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP) were combined into quantitative structure-retention relationship (QSRR) models aimed at predicting the Kováts retention indices (RIs) of the solutes. The molecular descriptors (174) of the stationary phases included in the models were computed using Dragon software from poly(siloxane) oligomers made of 20 siloxane units reflecting the nominal composition of the stationary phase, whereas 439 molecular descriptors were adopted to represent the esters. Different QSRR models were generated by means of Partial Least Squares (PLS) regression to assess the accuracy of this approach in predicting the RIs of unexplored solutes both in known and external stationary phases. After calibration of each PLS model, the descriptors were selected/discarded according to their relevance, evaluated by Covariance Selection (CovSel), and the PLS models were re-built, which resulted in a noticeable improvement of their predictive ability. Firstly, all the available data were equally divided into a training and a test set; the model built on the calibration set was used to predict the RIs of the validation observations. Successively, seven diverse PLS models were created following a "leave-one-column-out" fashion procedure, each one finalized to the estimation of the RIs of the 90 esters associated with a single stationary phase, whereas the calibration model was calculated on the remaining data. All the estimated models provided successful results on the external stationary phase, and predictive performance further increased after variable selection based on CovSel analysis. The final models provided a Root Mean Square Error in Cross Validation (RMSECV) in the range 12-20, a Root Mean Square Error in Prediction (RMSEP) in the range 11-26, and Mean Absolute Percentage Errors in Prediction (MAMEPs) in the range 0.7-1.5, revealing accurate cross-column prediction. Eventually, to test the robustness of the proposed approach, the 90 solutes were equally partitioned into a calibration and a test set and two further QSSR strategies were applied. The first PLS model was calibrated on all the seven stationary phases and the RIs of the 45 external solutes in the same seven columns were simultaneously predicted. The last QSRR approach followed a "leave-one-column-out" scheme and RI of 45 test solutes on an external stationary phase was predicted by a PLS model calibrated with the data of the 45 remaining solutes and the six left stationary phases. After selection of the significant molecular descriptors, PLS regression provided RMSECV values in the range 6-19, RMSEPs in the range 10-14, and MAPEPs in the range 0.9-2.4, revealing the suitability of the approach to deduce the RI of unknown solutes in uncharted stationary phases.

Gas chromatography of essential oil: State-of-the-art, recent advances, and perspectives

This review is an overview of the recent advances of gas chromatography in essential oil analysis; in particular, it focuses on both the new stationary phases and the advanced analytical methods and instrumentations. A paragraph is dedicated to ionic liquids as gas chromatography stationary phases, showing that, thanks to their peculiar selectivity, they can offer a complementary contribution to conventional stationary phases for the analysis of complex essential oils and the separation of critical pairs of components. Strategies to speed-up the analysis time, thus answering to the ever increasing request for routine essential oils quality control, are also discussed. Last but not least, a paragraph is dedicated to recent developments in column miniaturization in particular that based on microelectromechanical-system technology in a perspective of developing micro-gas chromatographic systems to optimize the energy consumption as well as the instrumentation dimensions. A number of applications in the essential oil field is also included.

Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals

The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol–water and air–water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC × GC). The PPM successfully explained variability in the skin permeability data (n = 175) with R² = 0.82 and root mean square error (RMSE) = 0.47 log unit. In comparison, the US-EPA’s model DERMWIN™ exhibited an RMSE of 0.78 log unit. The Zhang model—a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs)—performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC × GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n = 79) with R² = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA’s Estimation Program Interface Suite (EPI Suite™). The GC × GC model can be applied to the complex mixtures of nonpolar chemicals.

Determination of physicochemical properties of ionic liquids by gas chromatography

Over the years room temperature ionic liquids have gained attention as solvents with favorable environmental and technical features. Both chromatographic and conventional methods afford suitable tools for the study of their physicochemical properties. Use of gas chromatography compared to conventional methods for the measurement of physicochemical properties of ionic liquids have several advantages; very low sample concentrations, high accuracy, faster measurements, use of wider temperature range and the possibility to determine physicochemical properties of impure samples. Also, general purpose gas chromatography instruments are widely available in most laboratories thus alleviating the need to purchase more specific instruments for less common physiochemical measurements. Some of the main types of physicochemical properties of ionic liquids accessible using gas chromatography include gas-liquid partition constants, infinite dilution activity coefficients, partial molar quantities, solubility parameters, system constants of the solvation parameter model, thermal stability, transport properties, and catalytic and other surface properties.

Polymeric Ionic Liquids Derived from L-Valine for the Preparation of Highly Selective Silica-Supported Stationary Phases in Gas Chromatography

A series of silica-supported polymeric ionic liquid (PIL)-based stationary phases derived from a vinylic L-valine ionic liquid monomer and divinylbenzene (DVB) as the crosslinking agent have been prepared and studied as gas chromatographic stationary phases. These coated gas chromatographic columns exhibited good thermal stabilities (230-300 °C) and high efficiencies (1700-2700 plates/m), and were characterized using a linear solvation parameter model in order to understand the effects of the amount of DVB on the features of the resulting composite systems. Their retention behavior and separation efficiencies were demonstrated using the Grob test. By tuning the crosslinking degree for the IL-derived stationary phase, the separation selectivity and resolution of different compounds were improved. The different retention behaviors observed for many analytes indicate that these stationary phases may be applicable as new types of GC stationary phases.

Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches

Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (K_POM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log K_POM-w with R²_adj of 0.825. The values of statistical parameters including R²_adj, Q²_ext, RMSE_tra and RMSE_ext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.

Yolk-Water Partitioning of Neutral Organic Compounds in the Model Organism Danio rerio

Yolk is the most important temporary biological compartment of the early life stages of fish embryos. The sorption strength of a chemical to yolk components may significantly influence the distribution of that chemical in the fish embryo. We determined yolk-water partition coefficients (K_yolk/water , in liters of water per kilogram of yolk, normalized to dry wt) for 70 neutral organic chemicals. The log K_yolk/water values range from 0.76 to 6.56. On the basis of these values, we developed polyparameter linear free energy relationship models to predict yolk-water partitioning for a broad range of neutral organic chemicals with a root mean squared error of 0.37 and r² of 0.919. These models can be applied for the prediction of internal concentrations at equilibrium (neglecting biotransformation and active transport) in the zebrafish embryo test system. Environ Toxicol Chem 2020;39:1506-1516. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model

Predicting plant cuticle-water partition coefficients (K_cw) and understanding the partition mechanisms are crucial to assess environmental fate and risk of organic pollutants. Up to now, experimental K_cw values are determined for only hundreds of compounds because of high experimental cost. For this reason, computational models, which can predict K_cw values based on chemical structures, are promising approaches to evaluate new compounds. In this study, a large dataset consisting of 279 logK_cw values for 125 unique compounds were collected and curated. A poly-parameter linear free energy relationship (pp-LFER) model was developed with stepwise multiple linear regression based on this dataset. The resulted pp-LFER model has good predictability and robustness as indicated by determination coefficient (R²_adj,tra) of 0.93, bootstrapping coefficient (Q²_BOOT) of 0.92, external validation coefficient (Q²_ext) of 0.94 and root mean square error of 0.52 log units. Contribution analysis of different interactions indicated that dispersion and hydrophobic interactions have the highest positive contribution (56%) to increase the partition of pollutants onto plant cuticles. In addition, for organic pollutions containing benzene ring (13-31%), double bond (9-17%) or nitrogen-containing heterocycles (9-17%), π/n-electron pairs interactions exhibit obvious positive contributions to logK_cw. In conclusion, the proposed pp-LFER model is beneficial for predicting logK_cw of potential organic pollutants directly from their molecular structures.

Application of chromatographic techniques in the analysis of total nitrosamines in water

The use of ozone, chloramine and chlorine dioxide for water treatment results in the formation N-nitrosamines in the treated water. These groups of chemicals and other nitrogen-containing compounds have been described as disinfection by-products (DBPs) which are known for their toxicity. Nitrosamines are a potential source of nitric oxide (NO) which can bind with metals present in the sample matrix leading to formation of metal - nitrosyl complexes and dissolved metals have the potential to increase the total nitrosamines in water. This phenomenon has not received the desired attention and determination of metal-nitrosyl complexes lack standard analytical technique. Chromatography linked to various detectors is the commonest of the techniques for nitrosamine analysis but it is beset with reduced sensitivity as a result of inappropriate choice of the column. Incidentally, chromatographic techniques have not been really adapted for the analysis of metal-nitrosyl complexes. Therefore, there is need for the survey of existing techniques vis-à-vis metal-nitrosamine analysis and to suggest possible areas for method optimization.

Correlations and prediction of adsorption capacity and affinity of aromatic compounds on activated carbons

Correlations capable of predicting organic compound adsorption by activated carbons (ACs) are essential to the applications of ACs as environmental adsorbents in water treatment. Adsorption isotherms of 21 aromatic compounds on 11 ACs both with various physicochemical properties were conducted and fitted by Dubinin-Ashtakhov model to develop the predictive correlations in this study. In addition to the correlations of adsorption capacity with total surface area of ACs, micropore surface area ratios (R_micro) of ACs and chemical molar volume reported in previous studies, the negative correlation of adsorption capacity with chemical melting point was newly observed in this study. This negative correlation could be attributed to expansion of chemicals adsorbed on the mesopore or external surface of ACs. Meanwhile, in addition to the positive correlations of adsorption affinity with R_micro of ACs, chemical polarity/polarizability and hydrogen bonding donor ability reported also in previous studies, the negative correlation of adsorption affinity with H/C of ACs was newly observed in this study, which should be attributed to that ACs with higher aromaticity could have stronger π-π interaction potential, hydrogen bonding interaction potential and hydrophobic effects for aromatic compounds. These observed correlations can be used to predict aromatic compound adsorption by ACs with readily available properties of both ACs (i.e., surface area, R_micro and H/C) and aromatic compounds (i.e., molar volume, melting point and solvatochromic parameters). Moreover, these predictive correlations, incorporating various adsorptive forces, steric hindrance effect and packing efficiency in adsorption and having clearly physicochemical significance, are important for exploring the adsorption mechanisms, and guiding the synthesis of ACs with desired physicochemical properties, and selecting ACs as adsorbents in water treatment applications.

Evolution and Evaluation of GC Columns

A chromatographic column is the fundamental element required for gas-chromatographic analysis. The separation of components coming from complex mixtures, prior to their detection was leading to a prominent revolution in different areas of science. Moreover, current advances in gas chromatographic (GC) columns technology and development have been providing almost unlimited possibilities for analysis employing diverse matrices. We aim through this review article to describe the evolution of chromatographic columns, by pointing the most important stages, as well as the new trends and future perspectives predicted for the new generation of GC columns. Furthermore, it was in our scope to present the main fundamentals regarding the theoretical relationships that describe the chromatographic separation, to introduce concepts related to columns selection in accordance with the required application as well as to discuss the available evaluation parameters for columns efficiency. Consequently, the early stages of first columns preparation up to the development of GC capillary columns used nowadays, together with examples of their applications are also reported and described in detail.

High-resolution performance of triptycene functionalized with polycaprolactones for gas chromatography

Developing highly selective stationary phases is essential to address the issues for separation of analytes with similar properties and various components in complex samples. Herein, we report a new triptycene-based material functionalized with polycaprolactone moieties (TP-PCL) as the stationary phase with high-resolution performance for gas chromatography (GC). The TP-PCL capillary column exhibited column efficiency of 5555 plates/m and moderate polarity. On the column, dozens of mixtures of positional and structural isomers can be well resolved, involving benzene derivatives with varying substituents (alkyl, halo, nitro, hydroxyl, amino), naphthalene derivatives, alkanes and alcohols. It exhibits advantageous performance for high resolution of the critical pairs of alkylbenzenes, phenols, anilines and alkanes over the PCL column and commercial DB-35 MS column with similar polarity. Moreover, the TP-PCL column showed excellent separation repeatability and reproducibility with RSD values of 0.02%-0.07% for run-to-run (n = 4), 0.11%-0.18% for day-to-day (n = 4) and 2.1%-4.7% for column-to-column (n = 4). In addition, it exhibited distinctly enhanced thermal stability in contrast to the PCL column. Its application to analysis of the essential oil from Artemisiae argyi proves its good potential for practical use.

Characterization of sorption properties of high-density polyethylene using the poly-parameter linearfree-energy relationships

High-density polyethylene (HDPE) is a known sorbent for non-ionic organic compounds in technical applications. Nevertheless, there is little information available describing sorption to industrial HDPE for a broad range of compounds. With a better understanding of the sorption properties of synthetic polymers, environmental risk assessment would achieve a higher degree of accuracy, especially for microplastic interactions with organic substances. Therefore, a robust methodology for the determination of sorbent properties for non-ionic organic compounds by HDPE is relevant for the understanding of molecular interactions for both technical use and environmental risk assessment. In this work, sorption properties of HDPE material used for water pipes were characterized using a poly-parameter linear free-energy relationship (ppLFER) approach. Sorption batch experiments with selected probe sorbates were carried out in a three-phase system (air/HDPE/water) covering an aqueous concentration range of at least three orders of magnitude. Sorption in the concentration range below 10^-2 of the aqueous solubility was found to be non-linear and the Freundlich model was used to account for this non-linearity. Multiple regression analysis (MRA) using the determined distribution coefficients and literature-tabulated sorbate descriptors was performed to obtain the ppLFER phase descriptors for HDPE. Sorption properties of HDPE were then derived from the ppLFER model and statistical analysis of its robustness was conducted. The derived ppLFER model described sorption more accurately than commonly used single-parameter predictions, based i.e., on log K_o/w. The ppLFER predicted distribution data with an error 0.5 log units smaller than the spLFERs. The ppLFER was used for a priori prediction of sorption by the characterized sorbent material. The prediction was then compared to experimental data from literature and this work and demonstrated the strength of the ppLFER, based on the training set over several orders of magnitude.

Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants

Retention in gas–liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure–retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas–liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress.

Amphiphilic triptycene-based stationary phase for high-resolution gas chromatographic separations

This work reports a new type of triptycene-based amphiphilic stationary phase (TP-2IL) for gas chromatography (GC). It is an integration of the 3D π-rich triptycene framework with ionic liquids. Its capillary column showed the efficiency of 3880 plates/m determined by n-dodecane at 120 °C (k = 2.79) and exhibited good performance for analytes from apolar to polar nature. Particularly, it has outstanding capability for resolving critical pairs of anilines and phenols with good peak shapes and shows distinct advantages over its composing counterparts (TP-2BO and O-IL) and widely-used commercial columns, namely 35% phenyl methyl polysiloxane (DB-35) and polyethylene glycol (INNOWAX). Moreover, the TP-2IL column exhibited good repeatability and reproducibility with the values of relative standard deviation in the range of 0.02%-0.07% for run-to-run, 0.10%-0.35% for day-to-day and 2.9%-5.1% for column-to-column, respectively, and good thermal stability up to 300 °C. Furthermore, its applications for determining isomer impurities in real samples demonstrate its feasibility for practical GC analysis. This work presents a facile strategy for constructing triptycene-based stationary phases with amphiphilic selectivity and provides alternatives of highly selective stationary phases for chromatographic analysis.

Aroma Profile Characterization of Mahi-Mahi and Tuna for Determining Spoilage Using Purge and Trap Gas Chromatography-Mass Spectrometry

Alcohols, aldehydes, ketones, amines, and sulfur compounds are essential aroma compounds related to fish flavor and spoilage. Gas chromatography-mass spectrometry (GC-MS) is an instrument that is widely used to identify and quantify volatile and semi-volatile compounds in fish products. In this research, a simple and accurate GC-MS method was developed to determine the aroma profile of mahi-mahi and tuna for chemical indicators of spoilage. In the developed GC-MS method, trichloroacetic acid (TCA) solution was used to extract analytes from homogenized fish samples. The purge and trap system was used for sample introduction, and the GC-MS with an RTX-Volatile Amine column was able to separate compounds without a derivatization procedure. The created purge and trap gas chromatography-mass spectrometry (PT-GC-MS) method could identify and quantify twenty aroma compounds in mahi-mahi (Coryphaena hippurus) and 16 volatile compounds in yellowfin tuna (Thunnus albacares) associated with fish spoilage. The amines (dimethylamine, trimethylamine, isobutylamine, 3-methylbutylamine, and 2-methylbutanamine), alcohols (2-ethylhexanol, 1-penten-3-ol and isoamyl alcohol, ethanol), aldehydes (2-methylbutanal, 3-methylbutanal, benzaldehyde), ketones (acetone, 2,3-butanedione, 2-butanone, acetoin), and dimethyl disulfide strongly statistically correlated with poorer quality tuna and mahi-mahi and were considered as the key spoilage indicators. PRACTICAL APPLICATION: A simplified and rapid purge and trap gas chromatography-mass spectrometry (PT-GC-MS) method developed in this research was able to identify and quantify important spoilage compounds in mahi-mahi and yellowfin tuna. This method is an efficient analytical method for determining volatile profiles of fish samples for industry analytical labs or the government. The identified analytical quality markers can be used to monitor the spoilage level of tuna and mahi-mahi.