Column Selection and Optimization for Comprehensive Two-Dimensional Gas Chromatography: A Review

Recent progress in the extraction of terpenoids from essential oils and separation of the enantiomers by GC-MS

Terpenoids possess significant physiological activities and are rich in essential oils. Some terpenoids have chiral centers and could form enantiomers with distinct physiological activities. Therefore, the extraction and separation of terpenoids enantiomers are very important and have attracted extensive attention in recent years. Meanwhile, the specific distribution and enantiomer excess results (the excess of one enantiomer over the other in a mixture of enantiomers) could be used as quality markers for illegitimate adulteration, origin identification, and exploring component variations and functional interrelations across different plant tissues. In this study, an overview of the progress in the extraction of terpenoids from essential oils and the separation of their enantiomers over the past two decades has been made. Extraction methods were retrieved by the resultant network visualization findings. The results showed that the predominant methods are hydrodistillation, solvent-free microwave extraction, headspace solid-phase microextraction and supercritical fluid extraction methods. GC-MS combined with chiral chromatography columns is commonly used for the separation of enantiomers, while 2D GC is found to have stronger resolution ability. Finally, some prospects for future research directions in the extraction and separation identification of essential oils are proposed.

Optimizing analytical precision in the identification of synthetic cathinones and isomers: a comparative assessment of diverse GC-MS operating parameters

Analyzing new psychoactive substances (NPSs) in forensic laboratories present a formidable challenge globally. Within illicit drug analysis, gas chromatography-mass spectrometry (GC-MS) emerges as a robust analytical tool. This study endeavors to assess and compare peak resolution in the analysis of illicit drugs, specifically focusing on 21 synthetic cathinones, encompassing 9 cathinone isomers. Varied GC-MS operating conditions, including distinct GC-MS columns and thermal gradients, were systematically employed for the simultaneous analysis of these synthetic cathinones. The study utilized HP-1 nonpolar and HP-5MS low-bleed columns to achieve optimal analyte resolution through modulation of GC-MS oven conditions. Mass spectra were meticulously recorded within a mass-to-charge (m/z) range spanning from 40 to 500 in full scan mode. The data showed that the cathinone isomers slightly differed in retention times and mass spectra. The GC oven conditions affected the peak resolution for chromatographic separation even with the same column. The peak resolution improved using a slower thermal gradient heat speed with a prolonged analysis time. Conclusively, the interplay of GC columns and thermal gradients emerged as pivotal factors impacting peak resolution in the analysis of illicit drugs. These empirical insights contribute to a nuanced understanding of peak resolution dynamics and facilitate the identification of synthetic cathinones, including their isomers, in seized materials through the judicious application of GC-MS methodologies.

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review

In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

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.

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.

Method development for optimizing analysis of ignitable liquid residues using flow-modulated comprehensive two-dimensional gas chromatography

The abundance and composition of matrix compounds in fire debris samples undergoing ignitable liquid residue analysis frequently leads to inconclusive results, which can be diminished by applying comprehensive two-dimensional gas chromatography (GC × GC). Method development must be undertaken to fully utilize the potential of GC × GC by maximizing separation space and resolution.. The three main areas to consider for method development are column selection, modulator settings and parameter optimization. Seven column combinations with different stationary phase chemistry, column dimensions and orthogonality were assessed for suitability based on target compound selectivity, retention, resolution, and peak shapes, as well as overall peak capacity and area use. Using Box-Behnken design of experimentation (DoE), the effect of modulator settings such as flow ratio and loop fill capacity were evaluated using carbon loading potential, dilution effect, as well as target peak amplitude and skewing effect. The run parameters explored for parameter optimization were oven programming, inlet pressure (column flow rate), and modulation period. Comparing DoE approaches, Box-Behnken and Doehlert designs assessed sensitivity, selectivity, peak capacity, and wraparound; alongside target peak retention, resolution, and shape evaluation. Certified reference standards and simulated wildfire debris were used for method development and verification, and wildfire debris case samples scrutinized for method validation. The final method employed a low polarity column (5% diphenyl) coupled to a semi-polar column (50% diphenyl) and resulted in an average Separation Number (SN) exceeding 1 in both dimensions after optimization. Separation Numbers of 18.16 for first and 1.46 for second dimension without wraparound for compounds with at least four aromatic rings signified successful separation of all target compounds from varied matrix compositions and allowed for easy visual comparison of extracted ion profiles. Mass spectrometry (MS) was required during validation to differentiate ions where no baseline separation between target compounds and extraneous matrix compounds was possible. The resulting method was evaluated against ASTM E1618 and found to be an ideal routine analysis method providing great resolution of target compounds from interferences and excellent potential for ILR classification within a complex sample matrix.

Latest Trends on the Future of Three-Dimensional Separations in Chromatography

Separation and characterization of complex mixtures are of crucial importance in many fields, where extremely high separation power is required. Three-dimensional separation techniques can offer a path toward achieving high peak capacities. In this Review, online three-dimensional separation systems are discussed, including three-dimensional gas chromatography, and hyphenated combinations of two-dimensional gas chromatography with liquid chromatography or supercritical-fluid chromatography. Online comprehensive two-dimensional liquid chromatography provides detailed information on complex samples and the need for higher peak capacities is pushing researchers toward online three-dimensional liquid chromatography. In this review, an overview of the various combinations are provided and we discuss and compare their potential performance, advantages, perspectives, and results obtained during the most recent 10-15 years. Finally, the Review will discuss a novel approach of spatial three-dimensional liquid separation to increase peak capacity.

Critical review and re-assessment of analyte protectants in gas chromatography

Despite nearly 80 years of advancements in gas chromatography (GC), indirect chemical matrix effects (MEs), known as the matrix-induced response enhancement effect, still occur to cause a high bias in the GC analysis of susceptible analytes, unless precautions are taken. Matrix-matched calibration is one common option used in GC to compensate for the MEs, but this approach is usually inconvenient, imprecise, and inefficient. Other options, such as the method of standard additions, surface deactivation techniques, chemical derivatizations, priming the GC, and/or use of internal standards, also have flaws in practice. When methods are accommodating, the use of analyte protectants (APs) can provide the best practical solution to not only overcome MEs, but also to maximize analyte signal by increasing chromatographic and detection efficiencies for the analytes. APs address the source of MEs in every injection by filling active sites in the GC inlet, column, and detector, particularly in GC-MS, rather than the analytes that would otherwise undergo degradation, peak tailing, and/or diminished response due to interactions with the active sites. The addition of an adequate amount of APs (e.g. sugar derivatives) to all calibration standards and final extracts alike often leads to lower detection limits, better accuracy, narrower peaks, and greater robustness than the other options to compensate for MEs in GC. This article consists of a critical review of the scientific literature, proposal of mechanisms and theory, and re-evaluation studies involving APs for the first time in GC-orbitrap and GC-MS/MS with a high-efficiency ion source design. The findings showed that 1 µg each of co-injected shikimic acid and sorbitol in the former case, and 1 µg shikimic acid alone in the latter case, led to high quality results in multi-residue analysis of pesticides and environmental contaminants.