Evaluation of the retention profile in flow-modulated comprehensive two-dimensional gas chromatography and independent component analysis of weathered heavy oils

Are We Ready for It? A Review of Forensic Applications and Readiness for Comprehensive Two-Dimensional Gas Chromatography in Routine Forensic Analysis

Comprehensive two-dimensional gas chromatography (GC×GC) has been explored in forensic research to provide advanced chromatographic separation for forensic evidence, including illicit drugs, fingerprint residue, chemical, biological, nuclear, and radioactive (CBNR) substances, toxicological evidence, odor decomposition, and petroleum analysis for arson investigations and oil spill tracing. In GC×GC, the separation and analysis of analytes is similar to one-dimensional GC, but the primary column is connected to a secondary column via a modulator to provide two independent separation mechanisms, thus increasing the peak capacity of the analysis. The goal of implementing GC×GC in forensic studies is often to increase the separation and detectability of analytes and has most often been applied in nontargeted forensic applications where a wide range of analytes must be analyzed simultaneously. To date, there has been no summary of the current state of forensic research that evaluates both analytical and legal readiness for routine use. For these analytical methods to be adopted into forensic laboratories and be used in evidence analysis, they must meet rigorous analytical standards. In addition, new analytical methods for evidence analysis must adhere to standards laid out by the legal system, including the Frye Standard, Daubert Standard, and Federal Rule of Evidence 702 in the United States and the Mohan Criteria in Canada. Current research on GC×GC use for forensic applications was summarized and reviewed for analytical advances and technology readiness to provide a comprehensive view of GC×GC use for future routine implementation. A technology readiness scale, with levels from 1 to 4, was used to characterize the advancement of research in each individual application area. Seven forensic chemistry applications are discussed related to courtroom criteria and categorized into technology readiness levels based on current literature as of 2024. Future directions for all applications should place a focus on increased intra- and inter-laboratory validation, error rate analysis, and standardization.

Comprehensive two-dimensional gas chromatography- A discussion on recent innovations

Although comprehensive 2-D GC is an established and often applied analytical method, the field is still highly dynamic thanks to a remarkable number of innovations. In this review, we discuss a number of recent developments in comprehensive 2-D GC technology. A variety of modulation methods are still being actively investigated and many exciting improvements are discussed in this review. We also review interesting developments in detection methods, retention modeling, and data analysis.

Automated method using direct-immersion solid-phase microextraction and on-fiber derivatization coupled with comprehensive two-dimensional gas chromatography high-resolution mass spectrometry for profiling naphthenic acids in produced water

Naphthenic acids (NAs) are naturally occurring organic acids in petroleum and are found in waste waters generated during oil production (produced water, PW). Profiling this class of compounds is important due to flow assurance during oil exploration. Compositional analysis of PW is also relevant for waste treatment to reduce negative impacts on the environment. Here, comprehensive two-dimensional gas chromatography coupled with high-resolution mass spectrometry (GC×GC-HRMS) was applied as an ideal platform for qualitative analysis of NAs by combining the high peak capacity of the composite system with automated scripts for group-type identification based on accurate mass measurements and fragmentation patterns. To achieve high-throughput profiling of NAs in PW samples, direct-immersion solid phase microextraction (DI-SPME) was selected for extraction, derivatization and preconcentration. A fully automated DI-SPME method was developed to combine extraction, fiber rinsing and drying, and on-fiber derivatization with N-methyl-N‑tert-butyldimethylsilyltrifluoroacetamide (MTBSTFA). Data processing was based on filtering scripts using the Computer Language for Identifying Chemicals (CLIC). The method successfully identified up to 94 NAs comprising carbon numbers between 6 and 18 and hydrogen deficiency values ranging from 0 to -4. The proposed method demonstrated wider extraction coverage compared to traditional liquid-liquid extraction (LLE) - a critical factor for petroleomic investigations. The method developed also enabled quantitative analysis, exhibiting detection limits of 0.5 ng L^-1 and relative standard deviation (RSD) at a concentration of NAs of 30 µg L^-1 ranging from 4.5 to 25.0%.

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).

Multidimensional Gas Chromatography: Benefits and Considerations for Current and Prospective Users

Two-dimensional gas chromatography (GC×GC) offers improved separation power for complex samples containing hundreds to thousands of analytes. However, several considerations must be made to determine whether multidimensional gas chromatography (MDGC) is the logical instrument choice to answer a particular scientific question, including, but not limited to, whether the analysis is targeted or non-targeted, the number of analytes of interest, and the presence of interferences that are coeluted, as well as any potential regulatory or industrial constraints. Currently, MDGC remains daunting for many users because of data complexity and the limited tools commercially available, which are critical for improving the accessibility of MDGC. Herein, we discuss considerations that may assist analysts, laboratory managers, regulatory agents, instrument and software vendors, and those interested in understanding the applicability of 2D-GC for the scientific question being investigated.

Advanced tuning of the ion management parameters in GC × GC-HRMS using a Fourier transform Orbitrap mass analyzer for pixel-based data handling and multivariate analysis

GC × GC investigations are well known to generate a substantial amount of information-rich and structurally complex data, requiring advanced data processing strategies like chemometrics. Many workflows are available for data handling and processing, such as the peak-table and pixel-based approaches. The goal of this work is to present a solution based on method development to solve the missing pixel problem that may be encountered in experiments performed with GC and GC × GC coupled to the Fourier transform orbital ion trap (FT-Orbitrap) mass analyzer. Data input is vital for pixel-based chemometric analyses, as some post-processing solutions may lead to significant loss of chemical information in the data set. Hence, a key requisite is that the chemical information is consistently indexed in the data arrays for proper pixel-based data handling and analysis. In this study, we carefully evaluated the ion management parameters to preserve the intrinsic structure and information of the data arrays of the GC × GC-FT-Orbitrap for future pixel-oriented chemometric analysis. The most acceptable conditions yielded acquisition rates up to 42.6 spectra per s, while a routine setting of 24.7 Hz was successfully employed in analyses of different petroleum fractions, producing both consistent tensor sizes and acceptable peak reconstructions. A data acquisition rate of 24.7 spectra per s and a mass resolving power of 15 000 allowed the resolution of a mass split of only 0.004 Da - which is an interesting configuration for challenging applications in petroleomics. Using such advanced settings, the missing pixel problem was reduced from up to 30% to much less than 0.04% of the data array dimension. Thus, the proposed configuration can be employed in studies that require pixel-oriented multivariate data analysis.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing

The timeliness, precision, and low cost of search data have great potential for projecting tourist volume. Obtaining valuable information for decision-making, particularly for predicting, is hampered by the vast amount of search data. A systematic investigation of keyword selection and processing has been conducted. Using Beijing tourist volume as an example, 11 different feature extraction algorithms were selected and combined with long short-term memory (LSTM), random forest (RF) and fuzzy time series (FTS) for forecasting tourist volume. A total of 1612 keywords were retrieved from Baidu Index demand mapping using the direct word extraction method, range word extraction method and empirical selection method. The remaining 813 keywords were subjected to feature extraction. Based on the forecasting results of medium and short-term (1-day, 7-days and 10-days), the forecasting results of Kernel principal component analysis (KPCA) and locally linear embedding (LLE) are relatively stable when the dimensionality is reduced to 5 dimensions. The forecasting results of t-stochastic neighbor embedding (t-SNE), isometric mapping (IsoMap) and locally linear embedding (LLE), locality preserving projections (LPP), independent component correlation (ICA) are relatively stable when the dimensionality is reduced to 10 dimensions. Accurately forecasting many factors (transportation, attraction, food, lodging, travel, tips, tickets, and weather) provides a solid foundation for tourism demand optimization and scientific management and a resource for tourists' holistic vacation planning.