🎤 Presenter: Masaaki Ubukata (JEOL Ltd.)💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_7_Masaaki_Ubukata_2_56a67fdf00.pdf">PDF presentation</a><h3>Abstract</h3>Since aroma components in food comprise numerous compounds, comprehensive two-dimensional gas chromatography–mass spectrometry (GCxGC-MS) is an effective technique. In GCxGC-MS, compound identification is commonly performed by searching mass spectral databases using electron ionization (EI). However, many compounds are often not registered in these databases. These require manual structural analysis, which can be difficult. To address the difficulty of manual structural analysis, we developed a structural elucidation method using machine learning-based mass spectral prediction. The predicted mass spectra can be used as a database, enabling structural estimation in the same way as conventional database searching. Furthermore, in this method, molecular formulas obtained from soft ionization (SI) data are used to narrow down candidate structures. However, a limitation was that multiple molecular formula candidates often remained. To overcome this, we also developed a technique to rank candidate formulas using a machine learning model. In this study, we report an application of the above method to the analysis of aroma compounds in spices by SPME-GCxGC-TOFMS.In total, 518 compounds were detected by GCxGC-TOFMS. Integrated analysis using both EI and SI data enabled reliable identification of key aroma constituents of cardamom, including monoterpenes, sesquiterpenes, and furans. Compound annotation was based on combined evidence from NIST database searching, retention index, molecular ion and isotope information from SI, and accurate fragment masses from EI. When multiple candidate formulas were obtained, our machine learning model ranked them, allowing selection of the most plausible result. Even for database-unregistered compounds, molecular formulas and tentative structures could be estimated.<h3>Video Transcription</h3>Hello everyone, my name is Masaki Ubukata from Tokyo, Japan. Thank you for the opportunity to present our latest developments. In my previous presentation, I introduced our artificial intelligence–based structure analysis functionalities integrated into our software platform. Today, I will provide an updated overview of these AI-driven technologies, including a newly developed model for chemical formula recommendation, followed by a demonstration of metabolomics applications enabled by these tools.Metabolomics focuses on the comprehensive analysis of small molecules generated through biological processes. In this context, the term “small molecules” represents a key analytical focus. Both GC-MS and LC-MS are widely used in metabolomics; however, GC-MS continues to offer significant advantages for analyzing low-molecular-weight metabolites. Comprehensive separation strategies such as GC×GC are particularly beneficial for resolving complex metabolomic samples. Furthermore, trimethylsilyl (TMS) derivatization remains a gold-standard sample preparation approach in GC-MS-based metabolomics workflows.Qualitative GC-MS analysis typically involves multiple stages, including sample introduction, chromatographic separation, compound extraction, and final identification. Database searching, particularly using the NIST spectral library, is standard practice. However, a major challenge arises when compounds are not present in available databases. Manual interpretation of mass spectra can address this issue but is time-consuming and requires substantial expertise. This limitation motivated the development of our AI-based identification technologies, which aim to facilitate the characterization of unknown compounds beyond conventional database coverage.To address this need, we developed a machine-learning-driven predicted electron ionization (EI) mass spectral database. Our high-resolution GC time-of-flight platform provides resolving power exceeding 30,000 and mass accuracy down to 1 ppm, combined with multiple ionization techniques including EI, chemical ionization (CI), photoionization (PI), and field ionization (FI). The latter three approaches provide softer ionization conditions, enabling reliable detection of molecular ions. The accompanying AI software incorporates a database containing over 200 million predicted EI spectra, generated through deep-learning models. Recent software updates also support comprehensive GC×GC data processing.The workflow integrates both EI and soft-ionization data. Initial steps involve database matching, fragment-ion analysis using accurate-mass spectral information, and molecular-ion determination from soft-ionization measurements. When these results are consistent, compound identification can be achieved with high confidence. If discrepancies occur, AI-based structure elucidation is performed. In this stage, molecular-formula information is used to narrow candidate structures among millions of possible compounds. The system simultaneously evaluates substructural features, predicted spectra, and retention indices, enabling identification even for previously unknown compounds.Two AI models underpin the spectral library: one predicts EI mass spectra from molecular structures, while the other predicts chromatographic retention indices. By combining predicted spectra and retention information for more than 200 million compounds—including structures sourced from public databases and in-silico-generated chemical spaces—we created a comprehensive AI-assisted identification framework. Validation studies conducted since 2022 demonstrate continuous improvements in model performance, with average cosine similarity between predicted and measured spectra increasing from 0.72 in early versions to 0.86 in the latest release.Despite these advances, determining the correct molecular formula can still present challenges, particularly when multiple candidate compositions are possible. To address this, we developed an additional AI model that estimates probability distributions for elemental composition directly from mass spectral data. This model evaluates isotopic patterns, fragment masses, and elemental likelihoods, thereby guiding users toward the most probable molecular formula prior to library searching. As a result, analytical workflows can proceed efficiently without manual bottlenecks.Finally, we demonstrate the application of these technologies in metabolomics. Using GC×GC-TOFMS with combined EI and FI ionization, we analyzed human cancer cell metabolites in collaboration with Professor Tugawa at Tokyo University of Agriculture and Technology. A novel metabolite, not listed in conventional databases, was successfully identified using our AI-assisted workflow. From more than 8,000 detected compounds, the system narrowed candidates to structural isomers with identical molecular formulas, correctly ranking the true compound as the top candidate. This example highlights the capability of AI-driven identification for real-world unknown metabolite analysis.In conclusion, our platform integrates large-scale predicted spectral libraries, molecular-formula estimation models, and advanced chromatographic separation techniques to enhance confidence in GC-MS qualitative analysis. These combined technologies offer significant benefits for metabolomics and other applications requiring robust unknown-compound identification.This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.

Structural elucidation using GC×GC-TOFMS and machine learning (Masaaki Ubukata, MDCW 2026)

Visit the LabRulez booth at <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/events/871">analytica 2026</a> in Munich, 24. - 27. March 2026! You can find us in Hall A1, Booth A1.506. At the booth, you will be able to explore live demonstrations of how the LabRulez portals work and see selected analytical instruments from our pre-owned marketplace section.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Lab_Rulez_at_Analytica_2026_Floor_plan_55e1057e03.jpg" alt="LabRulez at Analytica 2026: Hall A1 Floor Plan and Booth Location" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Lab_Rulez_at_Analytica_2026_Floor_plan_55e1057e03.jpg 245w," sizes="100vw" width="1516" height="881"><h3>Would you like to learn how the LabRulez portals work?</h3>➡︎ <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/">LabRulezLCMS</a> ➡︎ <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/">LabRulezGCMS</a> ➡︎ <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/">LabRulezICPMS</a><ul><li>A unique online solution for your marketing</li><li>A targeted audience from the analytical chemistry community</li><li>Modern technologies and a unique concept</li><li>Design optimized for most smart devices – PC, mobile, and tablet</li><li>Advanced statistical tools fully available to users</li></ul><h3>Services Offered</h3><h4>News + Company Updates</h4><ul><li>Publish news related to your instrumentation, applications, services, events, job openings, and more</li><li>News is shared both on our portals and across our social media channels</li></ul><h4>Library – All Documents in One Place</h4><ul><li>Applications, posters, presentations, technical articles, brochures, specifications</li><li>Fast and advanced search capabilities</li><li>Over 32,000 documents currently available in our database</li></ul><h4>Webinars</h4><ul><li>Upcoming and on-demand webinars from the field of analytical chemistry</li><li>More than 5,500 webinars covering chromatography, mass spectrometry, and spectroscopy</li></ul><h4>Careers</h4><ul><li>Targeted job listings in analytical chemistry</li><li>Clear filtering by specialization or location</li><li>Increased visibility for job postings</li></ul><h4>Companies</h4><ul><li>Company profile with key information and contacts</li><li>Integration of all your activities on our portal: News, Library documents, Webinars, Upcoming Events, Careers, and mentions by partners or customers</li><li>Connection to your Products listed on LabRulez portals</li></ul><h4>Products and Services</h4><ul><li>Comprehensive overview of instrumental techniques and services</li><li>Targeting the analytical chemistry community and customers in the growing online environment</li></ul><h4>Marketplace</h4><ul><li>Buying and selling pre-owned analytical equipment</li><li>We purchase analytical instrumentation of all brands and ages</li><li>Sell your equipment through LabRulez</li><li>Request and purchase complete systems, modules, or spare parts with warranty and service</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Lab_Rulez_at_analytica_2026_bazar_7edf4a0872.jpg" alt="LabRulez at analytica 2026" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Lab_Rulez_at_analytica_2026_bazar_7edf4a0872.jpg 88w," sizes="100vw" width="1600" height="2844"><h3>What Will Be on Display at Our Booth?</h3>Come and explore selected spare parts and modules from our marketplace section. These include both new, tested, and pre-owned items.<h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/bazaar">LabRulezLCMS Marketplace</a></h4>Spare parts for LC/MS:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/bazaar?category=378">Vacuum turbo pumps for LC/MS</a></li></ul>Spare parts for HPLC:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/bazaar?category=397">Degassers for HPLC/UHPLC</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/bazaar?category=368&amp;search=valve">Automatic valves for HPLC</a></li><li>HPLC components for Agilent 1100, 1200, and 1260 Infinity II series</li></ul>Consumables:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/bazaar?category=377">HPLC lamps</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/bazaar?category=48">HPLC columns</a></li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/bazaar">LabRulezGCMS Marketplace</a></h4>Spare parts for GC/MS:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/bazaar?category=367&amp;search=turbo">Turbo pumps for GC/MS</a></li></ul>Spare parts for GC:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/bazaar?category=366&amp;search=EPC">EPC modules</a></li></ul>Sample injection<ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/bazaar/432">Autosampler 7693A for GC</a></li></ul>Consumables:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/bazaar?category=7">GC columns </a>and selected accessories</li></ul>

LabRulez at analytica 2026

Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 23rd March 2026? Check out new documents from the field of the gas phase, especially GC and GC/MS techniques!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT GCMS AND RELATED TECHNIQUES</a></blockquote><blockquote>👉 Need info about different analytical techniques? Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library">LabRulezLCMS</a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library">LabRulezICPMS</a> libraries.</blockquote>This week we bring you application notes by Agilent Technologies, Shimadzu, and Thermo Fisher Scientific and presentation by MDCW / Mount Royal University!<h3>1. Agilent Technologies: Analysis of Epichlorohydrin in Drinking Water</h3>Using the Tekmar Atomx XYZ P&amp;T, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890">Agilent 8890 GC</a>, and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5977C">Agilent 5977C GC/MSD</a><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_epichlorohydrin_analysis_drinking_water_5994_8787en_agilent_9708db9a54.pdf">Full PDF for download</a></li></ul>The Tekmar Atomx XYZ is Teledyne LABS' most advanced P&amp;T system and is based on the time-tested Atomx instrument platform. The concentrator's efficient trap cooling design reduces sample cycle time by as much as 14% over the previous model. Combined with its 84-position soil and water autosampler, the result is more samples tested per 12-hour period. An innovative moisture control system (MCS) improves water vapor removal, thereby reducing peak interference and increasing GC column life span. In addition to other refinements, the Atomx XYZ incorporates a precision-machined valve manifold block to reduce potential leak sources and ensure the system is both reliable and robust.<h4>Conclusion&nbsp;</h4>This study demonstrates the capability of the Teledyne LABS Tekmar Atomx XYZ P&amp;T concentrator to process low‑level ECH in drinking water samples, with detection performed using an Agilent&nbsp;8890 GC and Agilent 5977C GC/MSD. The linearity of the calibration curve from 10 to 500&nbsp;ppt passed method requirements, including verification of the initial calibration curve. The 10&nbsp;ppt standard passed the lower limit of quantitation (LLOQ) within ± 50% of its true value, while all other calibration levels (&gt; LLOQ) passed within ± 30%. The blank following the highest point in the calibration curve passed method carryover requirements by remaining below half pf the LLOQ. Furthermore, the application proved robust during a long‑term study with 40 samples of a 30&nbsp;ppt ECH standard, achieving 4.3% precision and 92% accuracy of the&nbsp;recovery.<h3>2. MDCW / Mount Royal University: Selection, Optimization, and Validation of Thermal Desorption for analysis of VOCs and PAHs in Combustion Emissions</h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_21_Marx_a94d951603.pdf">Full PDF for download</a></li></ul>The study focused on optimizing and validating thermal desorption (TD) as a sampling technique for volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) generated during combustion emissions, particularly from wildland fires. Smoke from these fires contains chemically complex mixtures of pollutants that can persist in the atmosphere and impact air quality over large geographic regions. Traditional sampling methods often emphasize particulate-bound compounds, while gas-phase organics—critical for understanding secondary organic aerosol formation—may be underrepresented. Thermal desorption was evaluated as a modern alternative capable of capturing both gas- and particle-phase compounds.To determine optimal sampling conditions, multiple sorbent tube configurations were compared using a mixture of 41 standard VOC and PAH compounds across various concentration levels. Performance metrics included analyte recovery, linearity, and detection limits. The results showed that multibed sorbent tubes significantly expanded analyte coverage and improved retention of combustion-related species. Additionally, factorial studies investigating conditioning and desorption parameters revealed that reducing temperature or duration did not improve artefact removal or analyte recovery, highlighting the importance of robust standardized conditions.The methodology was further evaluated through storage stability trials and real smoke emission sampling experiments. Samples demonstrated stable analyte retention for up to 35 days, supporting the suitability of TD sampling for field studies requiring delayed analysis. Validation experiments involving combustion of white spruce and analysis by <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=6&amp;search=unity-xr">TD</a>-GC×GC-ToFMS produced highly detailed chromatographic data, identifying over 1 600 peaks per sample. Approximately 78 % of detected compounds fell within the C6–C14 range, while 19 of the 41 target analytes—including all PAHs and BTEX species—were successfully identified.Overall, the study confirmed that thermal desorption combined with comprehensive two-dimensional gas chromatography offers a powerful approach for characterizing complex combustion emissions. The optimized multibed sorbent strategy and validated sampling workflow enable more accurate profiling of smoke-derived VOCs and PAHs, providing valuable insights into air quality impacts and atmospheric chemistry processes associated with large-scale wildfires.<h3>3. Shimadzu: Analysis of Base Material and Additives in Tire Rubber</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00901_en_7200aa4e86.pdf">Full PDF for download</a></li></ul><h5>User Benefits</h5><ul><li>With a split FPD(S) and MS detector system, sulfur compounds can be detected selectively and with high sensitivity by the FPD detector while being qualitatively analyzed simultaneously by the MS detector, which significantly reduces the time and effort required.</li><li>The LabSolutions GCMS system makes it easy to configure settings for complex detector splitting.</li><li>With evolved gas analysis, polymer materials can be inferred from thermogram average mass spectra.</li></ul>The rubber used in tires for automobiles, bicycles, and other types of tires needs to have high resistance to the weather, heat, and wear for long periods of use. But as the rubber wears, it mixes with mineral particles from the road to generate “tire and road wear particles” (TRWP). TRWP matter is known to be a type of microplastic that causes marine plastic pollution.&nbsp;TRWP can contain a large variety of substances, such as vulcanizing accelerators, antioxidants (such as 6PPD), and plasticizers, and when released into the environment, TRWP substances can be harmful to ecosystems. Therefore, to reduce the quantity of TRWP matter released, it is important to increase the wear resistance of tires. To achieve this, an assessment of the base materials and additives in rubber is essential. Among additives, vulcanizing accelerators in particular improve the physical properties of tires by promoting sulfur vulcanization reactions.&nbsp;This article analyzes the base materials in tire rubber and additives, such as vulcanizing accelerators and other sulfur components.<h4>Analysis Process Flow</h4>A pyrolysis gas chromatograph mass spectrometer (Py-GC-MS) system was used, as specified in the international standard ISO/TS 21396. 1) Simply placing cut-up samples in a sample cup (Fig. 1) enables direct analysis of samples without pretreatment. The analytical process flow isindicated in Fig. 2.<ul><li>Pyrolyzer Unit: <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=42&amp;search=EGA%2FPY-3030D">EGA/PY-3030D</a> Multi-Shot Pyrolyzer AS-1020E Auto-Shot Sampler (Frontier Laboratories Ltd.)</li><li>GC-MS: <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-QP2020+NX">GCMS-QP2020 NX</a></li></ul><h4>Inferring Polymers</h4>The evolved gas analysis (EGA-MS) method uses pyrolyzer (Py) and mass spectrometer (MS) units linked via a deactivated metal tube to heat samples to continuously increasing temperatures to generate gases from the samples, which are then detected by the MS.&nbsp;Samples (0.2 mg) of tire rubber fragments were placed in sample cups, with fiberglass wool inserted to prevent them from dropping, and then analyzed. Analysis conditions are indicated in Table 1.&nbsp;Thermograms obtained from that analysis are shown in Fig. 5. Originally, the horizontal axis of the thermogram is the time, but the heating temperature of the pyrolyzer can be calculated from the time. Peaks from polymer thermal decomposition products appear across the range of 370 to 500 °C. The average MS spectrum for that peak range and the results from inferring the polymer are shown in Fig. 6. Based on the F-Search EGA-MS Polymer Library (Frontier Laboratories Ltd.), the base material in the tire rubber was inferred to be styrene-butadiene rubber (SBR).&nbsp;Enlarging the thermogram shows a shoulder peak between 240 and 370 °C. Assuming that the shoulder peak originated from an additive, a thermal desorption analysis was carried out with the temperature range from 60 to 370 °C.<h4>Conclusion</h4>By combining FPD (S) that selectively and sensitively detects sulfur compounds, with Py-GC-MS, sulfur compounds in tire rubber were easily detected and simultaneously analyzed qualitatively by the MS.<h3>4. Thermo Fisher Scientific: Natural Gas Analysis in Hazardous Locations Using Raman Spectroscopy and Chemometric Modeling</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/marqmetrix_all_in_one_x_process_raman_analyzer_an1350_en_0ba9fa1165.pdf">Full PDF for download</a></li></ul>Natural gas is a fossil fuel composed primarily of methane, with smaller amounts of other hydrocarbons and impurities. Formed from ancient marine organisms under heat and pressure, natural gas is used as a combustion source for heating, electricity generation, vehicle fuel, and as a feedstock in chemical production. It is considered to be a cleanerburning alternative to other fossil fuels, contributing to reduced greenhouse gas emissions and air pollutants.&nbsp;The measurement and analysis of natural gas is a vital process that demands both speed and precision. Accurate natural gas measurements have far-reaching implications. They ensure revenue accuracy and facilitate fair financial transactions between producers, which is crucial for the economic integrity of the industry. Additionally, these measurements are essential for regulatory compliance, as they ensure adherence to stringent government and industrial standards.&nbsp;Operational efficiency of gas-using industries is also impacted by accurate natural gas measurement. By obtaining trustworthy and precise data, operators can optimize their processes, reduce waste, and ultimately enhance the overall efficiency of their operations. Furthermore, the measurement of natural gas plays a significant role in safety assurance and environmental protection. By accurately measuring methane content and detecting any irregularities in the natural gas stream, operators can address potential safety hazards and mitigate environmental issues, thereby promoting safer and more sustainable practices within the industry.&nbsp;Raman spectroscopy is an exceptionally powerful tool that can effectively address the myriad challenges associated with natural gas measurements. The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2208">MarqMetrix All-In-One X Process Raman Analyzer</a>, when equipped with a flow cell or an immersion probe, offers the capability for non-invasive, in-situ measurements of natural gas directly within pipelines or process equipment. This innovative approach eliminates the need for traditional sampling systems or loops, thereby significantly reducing both the cost and operational disruptions typically associated with gas sampling.&nbsp;The versatility of the MarqMetrix All-In-One X analyzer is underscored by its ability to operate reliably across a wide range of conditions, performing consistently under varying temperature and pressure regimes. Moreover, the hazardous location configuration enhances the instrument’s capabilities even further, enabling the analyzer to function safely and effectively in some of the harshest and most hazardous operating environments. This robust configuration ensures that accurate and reliable natural gas measurements can be obtained even in extreme conditions, providing a critical tool for maintaining operational efficiency, safety, and regulatory&nbsp;compliance.<h4>Results&nbsp;</h4>The spectra produced, along with the reference data obtained from the certificates of analysis accompanying the gas cylinders, were imported into SOLO software. Using this dataset, Partial Least Squares (PLS) regression models were constructed for each of the hydrocarbons present in the natural gas standards. The models underwent rigorous optimization processes, focusing on variable selection, determination of latent variables, and appropriate preprocessing techniques.&nbsp;Variable selection emerged as a crucial step in the modeling process due to the significant covariance observed among the different hydrocarbons within the natural gas standards. By carefully selecting specific regions of the spectra that correspond exclusively to the hydrocarbons of interest in each model, the independence and relevance of the information utilized in the model were significantly enhanced. This careful selection process ensures that the PLS regression models are robust and accurate, leading to more reliable predictions and analyses of the hydrocarbon content in the natural gas&nbsp;samples.&nbsp;Given the lower concentration and relatively lower intensities of the butane and pentane peaks, a wider range of variables were selected to build the models.<h4>Conclusion&nbsp;</h4>In conclusion, the MarqMetrix All-In-One X Process Raman Analyzer along with a Thermo Scientific™ MarqMetrix™ FlowCell™ Sampling Optic, both developed to be operated in ATEX Zone 0, proved to be a highly effective solution for collecting and analyzing the spectra of natural gas standards. The resulting data produced robust PLS regression models for each hydrocarbon component. Careful variable selection and optimization enhanced the independence and relevance of the models, resulting in accurate and reliable predictions. The performance of these models demonstrated excellent linearity, resolution, and sensitivity across the range of natural gas components. This study underscores the viability and applicability of Raman spectroscopy, coupled with advanced chemometric modeling, for precise and reliable natural gas analysis in hazardous&nbsp;environments.

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LabRulez at analytica 2026

News from LabRulezGCMS Library - Week 13, 2026

Structural elucidation using GC×GC-TOFMS and machine learning (Masaaki Ubukata, MDCW 2026)