Concepts, selectivity options and experimental design approaches in multidimensional and comprehensive two-dimensional gas chromatography

A deep learning-based simulator for comprehensive two-dimensional GC applications

Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation = 8.56%, R2  = 0.9202) and 0.014 (relative average deviation = 1.67%, R2  = 0.8009) in the prediction of the first- and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users.

Multidimensional chromatography in environmental analysis: Comprehensive two-dimensional liquid versus gas chromatography

Analysis of complex environmental matrices poses an extreme challenge for analytical chemists due to the vast number of known and unknown compounds, with very diverse chemical and physical properties. The need for a holistic characterisation of this complexity has sparked the development of effective tools to unravel the chemical composition of such environmental samples. Multidimensional chromatographic methods, namely comprehensive two-dimensional (2D) gas and liquid chromatography (GC × GC and LC × LC, respectively), coupled to different detection systems have emerged as powerful tools with the capability to address this challenge. While GC × GC has steadily gained popularity in environmental analysis, LC × LC is surprisingly less attractive in this research field. This critical review article explores the potential reasons why LC × LC is not the dominant technique used in environmental analysis as compared to GC × GC, while simultaneously highlighting the quite unique role of LC × LC for the target and untargeted analysis of complex environmental matrices. The possible combinations of stationary phases, the important role of the interfacing valve as the heart of an LC × LC assembly, the existing optimization strategies for improving the separation power in the 2D chromatographic space, and the need for user-friendly mathematical tools for multidimensional data handling are also discussed. Finally, a set of practical measures are suggested to increase the use and secure the success of LC × LC in environmental analysis.

Development and Application of a Novel Multiloop Splitter-Based Non-cryogenic Artificial Trapping Modulation System in Comprehensive Two-Dimensional Gas Chromatography

A multiloop splitter-based non-cryogenic artificial trapping (M-SNAT) modulation technique was established, which applied the first (¹D) nonpolar and the second (²D) polar columns, deactivated fused silica (DFS) columns, a microfluidic Deans switch (DS), and splitters located between the ¹D column outlet and the DS. The splitters were connected into multiple loops with a progressively doubled perimeter of the next loop. This enabled a duplex splitting mechanism within each loop consisting of splitting of analyte pulses, the pulse delay, and their combination which led to equally split peaks of the same analytes with the number of split peaks (n_split) equal to 2^m (m = number of loops). This system resulted in local profiles of artificially split-and-trapped analytes prior to their selective transfers onto the ²D column by means of periodic multiple heart-cuts (H/C). The developed SNAT approach can be successful, providing that the ratio of modulation period to sampling time (P_M/t_samp) is equal to n_split. The approach with n_split = 16 was further developed into a single device platform and applied for the modulation of a wide range of compounds in waste tire pyrolysis samples with the RSD of ≤0.01 and <10% for the one-dimensional modulated peak times and peak areas, respectively (n = 50). The method enabled an artificial modulation mechanism without cryogen consumption and enhanced the ²D peak capacity (²n_c) and ²D separation by use of a longer ²D column.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

A method for the determination of 70 pesticides in extra virgin olive oil based on a limited-volume solvent extraction step prior to comprehensive two-dimensional gas chromatography-tandem mass spectrometry

The goal of the present research was to develop a method based on a miniaturized solvent extraction step (using only 500 μL of acetonitrile, with no further clean-up or concentration processes) prior to cryogenic-modulation comprehensive two-dimensional gas chromatography-tandem mass spectrometry for the determination of seventy pesticides in extra virgin olive oil, exploiting the enhanced specificity and sensitivity of this technique. Limits of quantification were always below European legislation residue limits, intra-day precision was between 0.3 and 4.9% (at the 50 and 100 μg kg^-1 concentration levels), inter-day precision was between 1.6 and 6.1% (at the 100 μg kg^-1 concentration level), recovery (at the 20, 50, and 100 μg kg^-1 concentration levels) was in the 14-120% range, accuracy at the initial stage of the work (within repeatability conditions) was between 79 and 110%, while accuracy after 3 months (within intermediate precision conditions) was between 91 and 121% (at the 50 and 100 μg kg^-1 concentration levels). Finally, the matrix effect was always positive, between 16 and 197%. The method was applied to the investigation of twenty samples, and eleven of these were found to be contaminated.

Authentication of fish oil (omega-3) supplements using class-oriented chemometrics and comprehensive two-dimensional gas chromatography coupled to mass spectrometry

Food supplement authentication is an important concern worldwide due to the ascending consumption related to health benefits and its lack of effective regulation in underdeveloped countries, making it a target of fraudulent activities. In this context, this study evaluated fish oil supplements by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) to obtain fingerprints, which were used to build predictive models for automated authentication of the most popular products sold in Brazil. The authentication process relied on a one-class classifier model using data-driven soft independent modeling of class analogy (DD-SIMCA). The output of the model was a binary classifier: certified IFOS fish oils and non-certified ones - regardless of the source of adulteration. The compositional analysis showed a significant variation in the samples, which validated the need for reliable statistical models. The DD-SIMCA algorithm is still incipient in GC×GC studies, but it proved to be an excellent tool for authenticity purposes, achieving a chemometric model with a sensitivity of 100%, specificity of 98.6%, and accuracy of 99.0% for fish oil authentication. Finally, orthogonalized partial least square discriminant analysis (OPLS-DA) was used to identify the features that distinguished the groups, which ascertained the results of the DD-SIMCA model that IFOS-certified oils are positively correlated to omega-3 fatty acids, including eicosapentaenoic acid (EPA, C20:5 n-3) and docosahexaenoic acid (DHA, C22:6 n-3).

Design of experiment techniques for the optimization of chromatographic analysis conditions: A review

Design of experiment (DoE) techniques have been widely used in the field of chromatographic parameters optimization as a valuable tool. A systematic literature review of the available DoE techniques applied to the development of a chromatographic analysis method is presented in this paper. First, the most common available designs and the implementation steps of DoE are comprehensively introduced. Then the studies in recent 10 years for the application of DoE techniques in various chromatographic techniques are discussed, such as capillary electrophoresis, liquid chromatography, gas chromatography, thin-layer chromatography, and high-speed countercurrent chromatography. Current problems and future outlooks are finally given to provide a certain inspiration of research in the application of DoE techniques to the different chromatographic techniques field. This review contributes to a better understanding of the DoE techniques for the efficient optimization of chromatographic analysis conditions, especially for the analysis of complex systems, such as multicomponent drugs and natural products.

Unveiling Chemical Cues of Insect-Tree and Insect-Insect Interactions for the Eucalyptus Weevil and Its Egg Parasitoid by Multidimensional Gas Chromatographic Methods

Multidimensional gas chromatography is, presently, an established and powerful analytical tool, due to higher resolving power than the classical 1D chromatographic approaches. Applied to multiple areas, it allows to isolate, detect and identify a larger number of compounds present in complex matrices, even in trace amounts. Research was conducted to determine which compounds, emitted by host plants of the eucalyptus weevil, Gonipterus platensis, might mediate host selection behavior. The identification of a pheromone blend of G. platensis is presented, revealing to be more attractive to weevils of both sexes, than the individual compounds. The volatile organic compounds (VOCs) were collected by headspace solid phase microextraction (HS-SPME), MonoTrapTM disks, and simultaneous distillation-extraction (SDE). Combining one dimensional (1D) and two-dimensional (2D) chromatographic systems—comprehensive and heart-cut two-dimensional gas chromatography (GC×GC and H/C-MD-GC, respectively) with mass spectrometry (MS) and electroantennographic (EAD) detection, enabled the selection and identification of pertinent semiochemicals which were detected by the insect antennal olfactory system. The behavioral effect of a selected blend of compounds was assessed in a two-arm olfactometer with ten parallel walking chambers, coupled to video tracking and data analysis software. An active blend, composed by cis and trans-verbenol, verbenene, myrtenol and trans-pinocarveol was achieved.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Analytical Separation of Carcinogenic and Genotoxic Alkenylbenzenes in Foods and Related Products (2010-2020)

Alkenylbenzenes are potentially toxic (genotoxic and carcinogenic) compounds present in plants such as basil, tarragon, anise star and lemongrass. These plants are found in various edible consumer products, e.g., popularly used to flavour food. Thus, there are concerns about the possible health consequences upon increased exposure to alkenylbenzenes especially due to food intake. It is therefore important to constantly monitor the amounts of alkenylbenzenes in our food chain. A major challenge in the determination of alkenylbenzenes in foods is the complexity of the sample matrices and the typically low amounts of alkenylbenzenes present. This review will therefore discuss the background and importance of analytical separation methods from papers reported from 2010 to 2020 for the determination of alkenylbenzenes in foods and related products. The separation techniques commonly used were gas and liquid chromatography (LC). The sample preparation techniques used in conjunction with the separation techniques were various variants of extraction (solvent extraction, liquid-liquid extraction, liquid-phase microextraction, solid phase extraction) and distillation (steam and hydro-). Detection was by flame ionisation and mass spectrometry (MS) in gas chromatography (GC) while in liquid chromatography was mainly by spectrophotometry.

A multi-location peak parking approach for calculation of second dimensional retention indices for improved volatile compound identification with cryogen-free comprehensive heart-cut two-dimensional gas chromatography

Comprehensive heart-cut multidimensional gas chromatography (CH/C MDGC) without a cryogenic trapping device was developed with an established approach for calculation of first and second dimensional retention indices (¹I and ²I) for improved compound identification. A first dimensional (¹D) DB-1MS column (60 m) and a second dimensional (²D) DB-WAX column (60 m) were applied with a Deans switch (DS) using a constant H/C window of 0.2 min and a periodic multiple heartcut strategy comprising 225H/C throughout the CH/C. ¹I was calculated based on comparison of the middle of the heartcut time with the alkane retention times on the ¹D column. A multi-location peak parking approach using sixteen sets of automated injections of alkane references was also established with the least square curve fitting method for construction of the alkane isovolatility curves which were applied for ²I calculation. The untargeted compound analysis of a perfume sample was then performed according to comparison with the libraries of mass spectra, ¹I and ²I. The CH/C MDGC system with a 25 h analysis time showed a peak capacity (n_c) of 9198 and 128 separated peaks with 71 compounds successfully identified according to MS, ¹I and ²I library match under the established error approximation criteria. Furthermore, relationship between the analysis time and number of separated peaks was proposed based on the set of 84 identifiable compounds. With the compensation of lower separation performance and greater I errors, the analysis time could be reduced by applying a 2.5 min H/C window with a total analysis time of 2 h and n_c of 1134.

Strategies towards simpler configuration and higher peak capacity with comprehensive multidimensional gas chromatography

Experimental and data analysis approaches in multidimensional gas chromatography (MDGC) comprising comprehensive multiple heart-cut (H/C) and comprehensive two dimensional GC (GC × GC) were developed with an example application illustrated for analysis of a technical glycol precursor sample. The GC × GC system employed a long ¹D (30 m) and a short ²D (5 m) column with a flow modulator and a Deans switch (DS) as a splitter; meanwhile. The H/C system was applied solely as a DS located between long ¹D (30 m) and ²D (60 m) columns without use of cryogenic trapping devices. The effects of injection time and ²D column flow in GC × GC and the impacts of H/C window and number of injections (total analysis time) in H/C analysis were investigated. The analysis performance for each condition was evaluated according to peak capacity and number of separated compounds. The continuum between the two techniques was then established via the relationship between analysis time and analysis performance. The separation performances were improved with longer analysis time so that the suitable condition was selected within this compromise. Under the selected conditions, volatile compounds in the technical glycol precursor sample were identified according to the match between the experimental MS spectra and first dimensional retention indices (¹I) with that from the NIST2014 database and literature. An hour analysis with GC × GC resulted in a total peak capacity of 798, number of separated peaks of 61 and average MS match score of 887 ± 35; meanwhile, the corresponding numbers were improved to be 9198, 107 and 898 ± 24, respectively, with the 25 h comprehensive H/C analysis.

Experimental and data analysis approaches in MDGC comprising comprehensive H/C and GC × GC were developed with an example application illustrated for analysis of a technical glycol precursor sample.

Techniques and application in comprehensive multidimensional gas chromatography - mass spectrometry

In contrast to the well-known comprehensive two-dimensional gas chromatography (GC×GC) method, it is possible to define comprehensive multidimensional gas chromatography. 'Comprehensiveness' relates to analysis of the whole sample. Two-dimensional and multidimensional here refer to the use of at least two separation stages for analysis, however comprehensive 2DGC now appears to be reserved for the GC×GC method. This may be differentiated from comprehensive MDGC (CMDGC) simply by the analysis time assigned to the second (²D) column, although there does not appear to be a specific definition that relates to this analysis time parameter. A number of different implementation protocols for comprehensive MDGC are described here, that may involve either a single, or multiple, injection(s). In all cases, independent retention must be achieved on each dimension to ensure the probability of enhanced separation. An original application of a crude oil sample is presented to illustrate development of the MDGC approach that incorporates two Deans switches (DS) and a cryogenic trapping approach, performed using a sequential heart-cut (H/C) event method incremented by 0.5 min for each injection; a total of 40 injections is used to analyse the total sample. The higher peak capacity and consequently greater resolution on the long ²D column is illustrated, compared with that expected for conventional GC×GC, with tentative identification in order to classify chemical classes. Incorporating an approach to acquiring retention indices may be implemented, although its utility for petroleum hydrocarbons is limited. Structured groupings of different chemical classes, as exemplified by mono and diaromatics for the crude oil sample, were noted.