<p>🎤 <strong>Presenter</strong>: Ryland T. Giebelhaus (University of Alberta, Edmonton, Canada)</p><p>💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/FL_15_giebelhaus_MDCW_talk_58bd3ee4b6.pdf"><strong>PDF presentation</strong></a></p><blockquote><p>💡 <strong>Book in your calendar: </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/events/787"><strong>17th Multidimensional Chromatography Workshop (MDCW)</strong></a><strong> 13 - 15. January 2026</strong></p></blockquote><h3><strong>Abstract</strong></h3><p><strong>With the 2018 legalization of cannabis in Canada, there is growing concern surrounding the impacts of fetal and neonatal cannabis exposures. The long-term outcomes of early cannabis exposures are still unknown; however, some teens who use cannabis have permanent brain changes and increased risk of mental health problems. Much less is known about second-hand cannabis exposure and whether this exposes children to biologically significant levels of cannabis.</strong></p><p>Our interdisciplinary study follows mothers from early pregnancy until their babies are 18 months old. Mothers complete surveys on cannabis and tobacco use, second-hand exposure, demographics, health, and home environment. We are collecting urine from the mothers and infants, and breastmilk shortly after birth and 3 months postpartum. Additionally, we are utilizing wristband-based passive samplers to profile the exposomes of participants. We are employing comprehensive two-dimensional gas-chromatography time-of-flight mass spectrometry (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=2-13"><strong>GC×GC-TOFMS</strong></a>) for the untargeted metabolomics of urine and breastmilk samples to determine how cannabis is metabolized, identify cannabis metabolites and biomarkers of other exposure events including tobacco smoke, and for exposome profiling.</p><p>Here, we present preliminary results from the initial batch of urine samples we analyzed from the first 100 participants enrolled. In this cohort, 72.8% reported some level of cannabis exposure (first or second-hand) in the prior 12 months, with 9.7% reporting daily or weekly use. In urine, we identified a number of exposure markers, including cannabinoid metabolites, dietary markers, and pharmaceutical metabolites. This research provides valuable information to assist families in making informed choices about cannabis use during pregnancy.</p>

GC×GC-TOFMS metabolomics and exposomics for cannabis exposures (Ryland T. Giebelhaus, MDCW 2025)

<p><strong>Cal State LA has completed a major instrumentation upgrade in its LECO Complex Chemical Compositional Analysis Lab, further advancing the university’s partnership with LECO Corporation and expanding opportunities for student research and training in analytical chemistry. The upgrade replaces the lab’s earlier-generation time-of-flight mass spectrometer with LECO’s newest model, the Pegasus® BTX, a benchtop detector designed for comprehensive two-dimensional gas chromatography (GC×GC) applications. The new system enhances analytical sensitivity, uptime, and throughput for complex chemical studies.</strong></p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Cal_State_LA_The_LECO_Pegasus_BTX_system_in_Cal_State_LA_s_LECO_Complex_Chemical_Compositional_Analysis_Lab_enhances_research_and_student_training_in_advanced_chemical_analysis_d8245334d6.jpg" alt="Cal State LA: The LECO Pegasus BTX system in Cal State LA’s LECO Complex Chemical Compositional Analysis Lab enhances research and student training in advanced chemical analysis." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Cal_State_LA_The_LECO_Pegasus_BTX_system_in_Cal_State_LA_s_LECO_Complex_Chemical_Compositional_Analysis_Lab_enhances_research_and_student_training_in_advanced_chemical_analysis_d8245334d6.jpg 223w," sizes="100vw" width="600" height="420"></p><p>The upgrade continues a collaboration that began with the launch of the LECO Complex Chemical Compositional Analysis Lab in 2023. Located in Rosser Hall, the lab serves as a hub for students, faculty, and industry partners to conduct advanced compositional studies of complex chemical samples.</p><h4><strong>The impact of the Pegasus BTX at Cal State LA</strong></h4><p>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=24&amp;search=Pegasus+BTX"><strong>Pegasus BTX</strong></a> is LECO’s latest benchtop time-of-flight mass spectrometry (<strong>TOFMS</strong>) detector engineered for both <strong>GC </strong>and <strong>GC×GC applications</strong>.&nbsp;</p><p><strong>Key capabilities include:</strong></p><ul><li>full-mass-range data collection (even during targeted analyses)</li><li>advanced deconvolution for cleaner spectra in dense matrices</li><li>and the StayClean® ion source that is designed to require no source cleaning, even after heavy-matrix exposure.&nbsp;</li></ul><p>The system offers <strong>femtogram-level detection</strong> with helium or hydrogen carrier gas, up to <strong>500 spectra/second</strong>, a <strong>10–1500 m/z mass range</strong>, and five orders of linear dynamic range, all orchestrated by <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=24&amp;search=ChromaTOF"><strong>ChromaTOF</strong></a><strong>® software</strong>.</p><p>These capabilities will support Cal State LA’s research and teaching in areas such as environmental analysis, petrochemicals, life sciences, pharmaceuticals, and food safety, broadening opportunities for undergraduate and graduate students to work hands-on with state-of-the-art GC×GC-TOFMS.</p><p>“<i><strong>This upgrade accelerates our ability to separate, detect, and identify thousands of compounds in complex samples while training students on industry-relevant workflows,</strong></i>” said <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/articles?search=petr+vozka"><strong>Dr. Petr Vozka</strong></a>, associate professor of chemistry and biochemistry at Cal State LA.</p><p>“<i><strong>We’re excited to support Cal State LA’s mission to prepare the next generation of analytical scientists</strong></i>,” said Farai Rukunda, Director of Separation science at LECO Corporation. “<i><strong>The Pegasus BTX gives students access to the same technology they’ll use in professional laboratories</strong></i>.”</p><h4><strong>A growing collaboration</strong></h4><p>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/article/3246"><strong>LECO Complex Chemical Compositional Analysis Lab</strong></a>, established in 2023 and housed in Rosser Hall, was created as a hub for students, faculty, and industry partners to conduct advanced compositional studies of complex chemical samples. The lab represents a continued collaboration between Cal State LA and LECO Corporation to advance chemical education and innovation in the Los Angeles region.</p><h4><strong>About LECO Corporation</strong></h4><p><a target="_blank" rel="noopener noreferrer" href="https://www.leco.com/products/pegasus-btx/"><strong>LECO</strong></a> develops analytical instruments and software solutions spanning mass spectrometry, elemental analysis, and more. The Pegasus BTX represents the company’s newest evolution in GC-TOFMS technology for GC and GC×GC applications. LECO Corporation</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Cal_State_LA_A_Cal_State_LA_student_conducts_analysis_using_the_LECO_Pegasus_BTX_system_in_the_LECO_Complex_Chemical_Compositional_Analysis_Lab_0df09db6ac.jpg" alt="Cal State LA: A Cal State LA student conducts analysis using the LECO Pegasus BTX system in the LECO Complex Chemical Compositional Analysis Lab." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Cal_State_LA_A_Cal_State_LA_student_conducts_analysis_using_the_LECO_Pegasus_BTX_system_in_the_LECO_Complex_Chemical_Compositional_Analysis_Lab_0df09db6ac.jpg 223w," sizes="100vw" width="600" height="420"></p><blockquote><p><a target="_blank" rel="noopener noreferrer" href="https://www.calstatela.edu/"><strong>California State University, Los Angeles</strong></a> is the premier comprehensive public university in the heart of Los Angeles. Cal State LA is ranked number one in the United States for the upward mobility of its students. The university is dedicated to engagement, service, and the public good, offering nationally recognized programs in science, the arts, business, criminal justice, engineering, nursing, education, and the humanities. Founded in 1947, Cal State LA serves more than 22,000 students and has more than 270,000 distinguished alumni.</p></blockquote>

Cal State LA and LECO enhance complex chemical analysis with new Pegasus BTX system

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 1st December 2025? Check out new documents from the field of the gas phase, especially GC and GC/MS techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT GCMS AND RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><strong>LabRulezLCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><strong>LabRulezICPMS</strong></a> libraries.</p></blockquote><p>This week we bring you application notes by <strong>Agilent Technologies, Shimadzu </strong>and<strong> Thermo Fisher Scientific </strong>and presentation by<strong> LECO /MDCW!</strong></p><h3><strong>1. Agilent Technologies: Analysis of 197 Pesticides in Durian by Agilent PAL 3 Autosampler and 7000E Triple Quadrupole GC/MSD</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_197_pesticides_durian_pal3_7000e_gc_msd_5994_8702en_agilent_e5a68eaf99.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Pesticide residue analysis in food products is critical to ensuring consumer safety and regulatory compliance. Durian (Durio zibethinus), known as the "king of fruits" and widely exported across Southeast Asia, China, and beyond, presents unique analytical challenges due to its sulfur-rich compounds, high fat content, and complex volatile matrix. Comprehensive pesticide analysis in such matrices demands highly sensitive, selective, and robust analytical solutions. The European Union Reference Laboratories (EURL) SANTE/11312/2021 guidelines set rigorous performance criteria for pesticide residue analysis, including strict requirements for method accuracy, precision, and sensitivity.1 Laboratories need to achieve recoveries between 70% to 120%, precision (RSD) below 20%, and quantification limits at or below regulatory maximum residue limits (MRLs), even in complex food matrices such as durian. This application note demonstrates a high-performance workflow for the analysis of 197 pesticides in durian using an Agilent PAL3 Autosampler combined with an Agilent 7000E Triple Quadrupole GC/MSD system. The PAL3 Autosampler provides high-throughput, precise sample handling, while the 7000E GC/MSD offers exceptional sensitivity and reproducibility for trace-level pesticide quantification. Optimized sample preparation and instrumental conditions ensure reliable performance, meeting and exceeding SANTE criteria for method validation.</p><h4><strong>Experimental</strong></h4><h5><strong>PAL 3 RTC and 7000E GC/TQ parameters&nbsp;</strong></h5><p>The analysis parameters are shown in Table 1. The target and ISTD compound MRM parameters are listed in Appendix&nbsp;1. Figure 1 shows a typical MRM chromatogram of targeted pesticides spiked in durian at a concentration of 100 ppb.</p><ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=PAL+3"><strong>Agilent PAL 3 (Liquid Tool)</strong></a></li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890"><strong>Agilent 8890 GC</strong></a></li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7000E"><strong>Agilent 7000E</strong></a></li></ul><h4><strong>Conclusion&nbsp;</strong></h4><p>This study successfully demonstrated a robust and sensitive method for the quantitative analysis of 197&nbsp;pesticides in durian using an Agilent PAL3 Autosampler combined with an Agilent&nbsp;7000E Triple Quadrupole GC/MSD system. The optimized workflow, including sample extraction with an Agilent Bond Elut QuEChERS EN kit followed by cleanup with the Highly Pigmented dSPE kit, provided excellent recovery and precision. All target pesticides showed analysis accuracy between 80% and 120% and RSD values below 10% at a spiked level of 10 ppb. The results confirm the method's suitability for routine high-throughput pesticide quantification in complex matrices such as durian. The use of matrix-matched calibration and matrix‑optimized MRMs further ensured accurate and reproducible quantitation, reinforcing the method's applicability for regulatory and food safety testing.</p><h3><strong>2. LECO / MDCW: Applying statistical data processing tools for GC×GC differentiation of alternative aviation fuels</strong></h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/FL_18_Sebastiano_b4cf417f82.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The presentation introduces LECO’s long-standing role in separation science and its evolution from elemental analyzers to advanced mass spectrometry platforms. A key emphasis is placed on LECO’s innovations in <strong>time-of-flight mass spectrometry (TOFMS)</strong>, including the Pegasus series and the new <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=24&amp;search=Pegasus+BTX"><strong>Pegasus BTX</strong></a>, a benchtop TOFMS offering high sensitivity, rugged detector technology, fast spectral acquisition (1–500 spectra/s), and broad mass range (10–1500 m/z). This technological foundation supports comprehensive <strong>GC×GC-MS</strong> workflows essential for analyzing chemically complex matrices such as aviation fuels.</p><p>A major focus of the talk is on <strong>software tools</strong> for non-targeted data processing. LECO’s software ecosystem—<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=24&amp;search=ChromaTOF"><strong>ChromaTOF</strong></a><strong>, ChromaTOF Tile, and </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=24&amp;search=ChromaTOF+Sync"><strong>ChromaTOF Sync</strong></a>—enables detailed deconvolution, quantitation, high-resolution spectral analysis, and especially <strong>statistical comparison of GC×GC datasets</strong>. ChromaTOF Tile uses a chromatographic “tiling” strategy to link datasets and detect differences between sample groups using three statistical metrics: <strong>Fisher Ratio</strong>, <strong>Coefficient of Variation (CoV)</strong>, and <strong>Fold-Change</strong>. Each metric serves a different experimental purpose, from comparing traditional vs. synthetic jet fuels (Fisher Ratio) to exploring variability among multiple alternative fuel processes (CoV), or identifying additive components in two-sample comparisons (Fold-Change).</p><p>The presentation demonstrates how raw GC×GC-MS data are curated into <strong>feature tables</strong>, statistically evaluated, and visualized in <strong>PCA plots, bar charts, and chromatographic overlays</strong>. For example, Fisher Ratio comparison highlights statistically significant chemical differences between conventional Jet A and synthetic fuels, while CoV analysis reveals clustering patterns among diverse synthetic fuel production pathways such as <strong>LT-FT SPK, HT-FT SPK, HEFA, SIP, ATJ, and CHJ</strong>. These workflows enable analysts to rapidly identify biomarkers or distinguishing compositional features within highly complex hydrocarbon matrices.</p><p>Ultimately, the talk demonstrates that choosing the correct statistical processing method can dramatically simplify interpretation of complex GC×GC-MS datasets. LECO’s integrated instrumentation—particularly the Pegasus BTX TOFMS—and software platforms together provide a powerful toolkit for fuel characterization, quality evaluation, and differentiation of emerging <strong>alternative aviation fuels</strong>. By combining comprehensive chromatography with robust statistical approaches, researchers can gain deeper insights into chemical variability, production signatures, and additive packages in modern aviation fuel technologies.</p><h3><strong>3. Shimadzu: Analysis of Formic Acid in Acetone Using GC/MS System</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00957_en_e8b083b86e.pdf"><strong>Full PDF for download</strong></a></li></ul><p>In research on artificial photosynthesis, the analysis of impurities in chemical products and raw materials has increased the demand for high-sensitivity analysis of formic acid. Previous examples of formic acid analysis using GC-BID and GC-FID (Jetanizer ) have been presented, but in this article high-sensitivity analysis of formic acid using the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-QP2050"><strong>GCMS-QP2050</strong></a> (see Fig. 1) isintroduced.</p><h4><strong>Insert, Column Phosphoric Acid Treatment&nbsp;</strong></h4><p>Formic acid is known to adsorb onto the injection port and column, making peak detection difficult. However, by performing phosphoric acid treatment on the glass insert and column before measurement, good peaks can be obtained. The procedure for phosphoric acid treatment of the glass insert is shown in Fig. 2, and the procedure for phosphoric acid treatment of the column isshown in Fig. 3.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>By performing phosphoric acid treatment on the insert and column, it became possible to analyze formic acid in SIM mode down to 0.2 ppm. Furthermore, good results could be obtained for both linearity and repeatability. Additionally, by analyzing in Scan mode, identification of unknown peaks other than formic acid was also achieved.</p><h3><strong>4. Thermo Fisher Scientific: Uninterrupted analysis of VOCs according to U.S. EPA Method 8260C using purge and trap and single quadrupole GC-MS technology</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_001018_gc_voc_analysis_gc_ms_epa_8260c_an001018_na_en_269857401c/an-001018-gc-voc-analysis-gc-ms-epa-8260c-an001018-na-en.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Volatile organic compounds, or VOCs, are human-made contaminants used and produced in the processing of, or as, paints, adhesives, petroleum products, pharmaceuticals, and refrigerants. Many of these compounds contaminate our environment and may cause negative health effects in humans and other living beings. Analytical laboratories must monitor a variety of sample types from the environment to ensure the public are not exposed to elevated levels of VOCs. The latest version of applicable method in the United States, U.S. EPA Method 8260C, is applicable when monitoring a variety of solid waste matrices for the presence of VOCs.</p><p>To perform U.S. EPA Method 8260C, all method acceptance criteria must be achieved. These criteria include calculating the mean response factor and the relative standard deviation (RSD) of the response factors for target analytes. The RSD should be &lt;20%, with minimum response factors (RF) and MDLs for a wide range of target compounds. The analytical method must produce consistent results and be reproducible from day to day, with a continuing calibration verification (CCV) analyzed every 12 hours while samples are run. As the method covers varying matrices, it is important that the performance criteria are met in all samples of interest. The following evaluation describes the use of the ISQ 7610 GCMS coupled to the Atomx XYZ P&amp;T for U.S. EPA Method 8260C.</p><h4><strong>Experimental</strong></h4><h5><strong>Instrument control and data processing&nbsp;</strong></h5><p>Data were acquired, processed, and reported using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/76"><strong>Chromeleon CDS software</strong></a>, version 7.2. This software can control both the GC-MS system and the Tekmar Atomx XYZ P&amp;T. This enables a single software solution to support the full workflow, simplifying the instrument operation. The optimized method used within this application note is available for download via the Thermo Scientific™ AppsLab library. AppsLab contains all the parameters needed to acquire, process, and report the analytical data for U.S. EPA Method 8260C.2</p><h5><strong>GC-MS parameters&nbsp;</strong></h5><p>A <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/486"><strong>TRACE 1610 GC</strong></a> was coupled to the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/484"><strong>ISQ 7610 MS </strong></a>equipped with the Thermo Scientific™ NeverVent™ vacuum probe interlock (VPI) and an ExtractaBrite™ ion source. Expanded method parameters for the GC-MS system are displayed in Table 3</p><h4><strong>Conclusion&nbsp;</strong></h4><p>The combined analytical solution with the TRACE 1610 GC coupled with the ISQ 7610 system and the Atomx XYZ P&amp;T system provides clear advantages for analytical testing laboratories that analyze environmental samples following the U.S. EPA Method 8260C requirements. The modularity of the TRACE 1610 GC as well as the ISQ 7610 VPI and ExtractaBrite ion source allows users to easily service the injection ports and to exchange ionization sources and analytical columns without venting the mass spectrometer, significantly reducing instrument downtime and minimizing sample analysis interruptions. The Atomx XYZ concentrator’s efficient trap cooling design reduces sample cycle time and allows for increased sample throughput. The moisture control system improves water vapor removal thereby reducing peak interference and increasing GC column life span.&nbsp;</p><p>The experiments performed clearly demonstrate the suitability of this analytical configuration for the analysis of VOCs in various environmental samples in accordance with U.S. EPA Method 8260C with the following performance parameters as evidence:&nbsp;</p><ul><li>The ISQ 7610 VPI coupled with the Tekmar Atomx XYZ P&amp;T exceeds all the requirements outlined in U.S. EPA Method 8260C for analysis of VOCs in wastewater and solid waste.</li><li>Linearity was achieved with &lt;20%,<span>&nbsp;</span>relative standard deviation for both water and soil calibration curves for the majority of compounds.&nbsp;</li><li>The MDL and precision were assessed using n=7 replicates of a 0.2 ppb water standard and n=7 replicates of a 0.5 ppb soil standard. Calculated MDLs were &lt;0.2ppb and RSDs of calculated results were &lt;10% for most compounds in both the soil and water matrices.</li><li>System robustness was tested by continuously acquiring 240 injections of environmental samples over three days with no user intervention at all. The average %RSD of the calculated concentration was 8.30% with an average compound recovery of 90%.</li></ul>

News from LabRulezGCMS Library - Week 49, 2025

<p><strong>🎤 Presenter:</strong> Sarah C. Foster (William &amp; Mary)</p><p>💾 <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/redirect?event=video_description&amp;redir_token=QUFFLUhqbFk5RFV6cGdVaThIVjRSQ0N3OVFkdzE5d2Y0QXxBQ3Jtc0trUHhzWGtLeTNKcFVhelU3X1E3U3A4Z1ZHUDBkME02MDl1ajk1QnpFc25ka3ZKUlhKOFVOdDZocnAwTEZZOTZEUE84cWJTUEVUVjgyY0FvT2xBcmpkenVYOVJTd3d0a1pQNVJkSENfZXFsVmF0dF9rMA&amp;q=https%3A%2F%2Fgcms.labrulez.com%2Flabrulez-bucket-strapi-h3hsga3%2FO_2_Foster_04b38f9b88.pdf&amp;v=e5w4zwhBzRY"><strong>PDF presentation</strong></a></p><h3><strong>Abstract</strong></h3><p><strong>Fermentation is a byproduct of microbial metabolism. When beverages become fermented, the microorganisms involved produce volatile organic compounds (VOCs). It is important to examine the VOC profile of fermented beverages to understand their consumer perception and health considerations. Traditionally, VOCs are detected by gas chromatography-mass spectrometry (GC-MS). However, more complex matrices, like fermented products, benefit from the enhanced separation of comprehensive two-dimensional gas chromatography (GC×GC). This study aimed to develop an understanding of the VOCs related to different fermented beverages through a nontargeted lens via comprehensive two-dimensional gas chromatography with dual detection using flame ionization detection and time-of-flight mass spectrometry (GC×GC-FID/TOFMS).&nbsp;</strong></p><p>Data analysis focused on developing a complete VOC profile of fermented products. Moreover, analysis aimed to identify differentiating VOCs to understand differences between products and distinguish the drink matrix from the microbial matrix. Results indicate that it is possible to attribute VOCs to one component of a complex sample, creating a microbial VOC profile and a base VOC profile. It was possible to discern products from one another, including contaminated products from non-contaminated products. Data visualization consisted of Principal Component Analysis (PCA) scores and loadings plots, bar chats, Hierarchical Cluster Analysis (HCA), and Partial Least Squares Discriminant Analysis (PLS-DA). Data analysis software was capable of processing and aligning samples with high levels of biological variation, making GC×GC a promising analytical tool for market research and regulation. Future studies will aim to correlate VOCs detected via GC×GC-FID/TOFMS to the microbial genetics of fermented products.</p><h3><strong>Video Transcription</strong></h3><h4><strong>1. Introduction</strong></h4><p>Sarah Foster introduces research focused on the application of comprehensive two-dimensional gas chromatography (GC×GC) for analyzing fermented beverages.</p><p>The laboratory primarily uses <strong>GC×GC</strong> for:</p><ul><li>Separation and identification of volatile organic compounds (VOCs)</li><li>Applications in forensic sciences and food sciences</li></ul><h5><strong>Forensic Applications</strong></h5><ul><li>Fingerprint residue</li><li>Gunshot residue</li><li>Pepper spray</li><li>Other trace evidence</li></ul><h5><strong>Food Science Applications</strong></h5><ul><li>Fermented beverages</li><li>Foods claiming health benefits</li></ul><p>An additional focus of the lab is transitioning GC×GC from proof-of-concept research toward real-world field applications, including improved data interpretation and presentation.</p><h4><strong>2. Challenges in Non-Targeted GC×GC Data</strong></h4><p>Working with complex, non-targeted datasets presents several challenges:</p><ul><li>Resolution of complex coelutions</li><li>Increased number of detectable features</li><li>Expansion of compound classes</li><li>Detection of low-level analytes previously masked by abundant compounds</li><li>Difficulty in longitudinal tracking due to increased data complexity</li></ul><p>While GC×GC reveals more chemical information, interpreting this data becomes more demanding.</p><h4><strong>3. Data Processing and Statistical Tools</strong></h4><h5><strong>Software Used</strong></h5><ul><li><strong>ChromaTOF</strong> – for single chromatogram peak detection and deconvolution</li><li><strong>ChromaTOF Sync 2D</strong> – alignment software for entire datasets</li><li><strong>R statistical software</strong> – for multivariate visualization<ul><li>vegan package</li><li>ComplexHeatmap package</li></ul></li></ul><h5><strong>Advantages of Sync 2D</strong></h5><ul><li>Performs peak detection and deconvolution across all chromatograms simultaneously</li><li>Generates a unified peak table for entire datasets</li><li>Facilitates trend analysis</li></ul><h5><strong>Dimensionality Reduction Techniques Used</strong></h5><ul><li>Principal Component Analysis (PCA)</li><li>Principal Coordinate Analysis (PCoA)</li><li>Hierarchical Cluster Analysis (HCA)</li></ul><p>Each data point includes:</p><ul><li>Primary retention time</li><li>Secondary retention time</li><li>Signal intensity</li></ul><h4><strong>4. Statistical Approaches Explained</strong></h4><h5><strong>PCA (Principal Component Analysis)</strong></h5><ul><li>Uses Euclidean distance</li><li>Assumes linear variance structure</li><li>Clustering based on shared analytes</li><li>Identifies key analytes driving variance</li><li>Loadings plots explain separation patterns</li></ul><h5><strong>PCoA (Principal Coordinate Analysis)</strong></h5><ul><li>Uses non-Euclidean dissimilarity (e.g., Bray–Curtis)</li><li>Emphasizes compositional differences</li><li>Better for non-linear variance structures</li></ul><h5><strong>HCA (Hierarchical Cluster Analysis)</strong></h5><ul><li>Uses Euclidean dissimilarity (in this study)</li><li>Clustering based on:<ul><li>Presence/absence</li><li>Relative abundance</li></ul></li><li>Output: dendrogram<ul><li>Short branches = more similar</li><li>Long branches = more dissimilar</li></ul></li></ul><h4><strong>5. Research Goals</strong></h4><ul><li>Apply GC×GC-TOFMS for volatile analysis and aroma profiling of mead</li><li>Investigate impact of:<ul><li>Yeast strain</li><li>Honey concentration</li></ul></li><li>Use a non-targeted analytical approach</li><li>Extract and compare chemical features across samples</li></ul><h4><strong>6. Mead Production Overview</strong></h4><p>Mead is produced by fermenting honey with filtered water. Fruits or spices may be added. It may also be barrel-aged.</p><h5><strong>Experimental Design (Silverhand Meadery Collaboration)</strong></h5><ul><li>1-gallon batches</li><li>5 yeast strains</li><li>3 honey concentrations:<ul><li>1.5 lb/gal (low alcohol, expected dry finish)</li><li>3 lb/gal (standard)</li><li>5 lb/gal (high alcohol, expected sweet finish)</li></ul></li></ul><h5><strong>Yeast Strains Tested</strong></h5><ul><li><strong>Kveik Voss</strong> – neutral with citrus notes</li><li><strong>Munich</strong> – banana, clove (esters &amp; phenols)</li><li><strong>EC1118</strong> – neutral (sparkling wine yeast)</li><li><strong>D47</strong> – tropical/white floral</li><li><strong>Abbaye</strong> – temperature-dependent:<ul><li>Low temp: banana, tropical, spicy</li><li>High temp: raisin, fig</li></ul></li></ul><p>Fermentation lasted 9 months. Stabilizers were added post-fermentation.</p><h4><strong>7. Sample Preparation</strong></h4><h5><strong>Mead Samples</strong></h5><ul><li>3 mL in 20 mL headspace vial</li><li>0.5 g salt</li><li>5 µL (500 ppm) 1-decanol internal standard</li></ul><h5><strong>Honey Samples</strong></h5><ul><li>1.5 g honey + 1.5 mL water</li><li>Salt + internal standard</li></ul><h5><strong>Water Controls</strong></h5><ul><li>3 mL water + salt + internal standard</li></ul><p>All samples were vortexed and analyzed by GC×GC-TOFMS.</p><h4><strong>8. Instrumentation</strong></h4><p>Instrument: <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=24&amp;search=Pegasus+BTX+4D"><strong>LECO Pegasus BTX 4D GC×GC-TOFMS</strong></a></p><p>Features:</p><ul><li>Dual TOFMS and FID detection</li><li>Reverse fill/flush modulator</li><li>Flow splitter</li></ul><p>The modulator:</p><ul><li>Fills loop in forward direction</li><li>Flushes to secondary column</li><li>High secondary flow split between TOFMS and FID</li></ul><h4><strong>9. Initial Observations</strong></h4><p>Key compounds identified:</p><ul><li>Furfurol (heated honey marker)</li><li>2-ethylhexanol (water)</li><li>3-methylbutanol (fermentation product)</li><li>Benzaldehyde (fermentation product)</li></ul><p>Total detected: <strong>96 volatile organic compounds</strong></p><h4><strong>10. Multivariate Results</strong></h4><h5><strong>PCA Findings</strong></h5><ul><li>Four groupings largely dictated by honey concentration</li><li>1.5 lb/gal group clustered together</li><li>3 lb/gal group tightly clustered</li><li>5 lb/gal showed pattern similar to 1.5 lb/gal</li><li>Abbey yeast formed a distinct cluster</li></ul><h5><strong>PCoA Findings</strong></h5><ul><li>Similar overall patterns</li><li>Abbey yeast more distinctly separated</li><li>Likely due to Bray–Curtis dissimilarity emphasizing compositional differences</li></ul><h5><strong>HCA + Heatmap Findings</strong></h5><ul><li>Clustering aligned with PCA/PCoA trends</li><li>Abbey yeast again formed distinct grouping</li><li>Appeared to contain similar compounds but in lower relative abundances</li></ul><p>Two major overall clusters:</p><ul><li>1.5 lb/gal group</li><li>All other meads</li></ul><h4><strong>11. Compound Trends vs Honey Concentration</strong></h4><p>Observed correlations:</p><ul><li><strong>Ethyl lactate</strong>: increased with honey concentration</li><li><strong>Furfurol</strong>: increased with honey concentration</li><li><strong>3-methylbutanol</strong>:<ul><li>Decreased with honey concentration (except Abbey strain)</li></ul></li><li><strong>Ethyl octanoate</strong>:<ul><li>Highest at 3 lb/gal</li><li>Lowest at 1.5 lb/gal</li><li>Intermediate at 5 lb/gal</li></ul></li></ul><p>Abbey strain showed unique behavior, possibly explaining its distinct clustering.</p><h4><strong>12. Conclusions</strong></h4><ul><li>GC×GC-TOFMS is highly effective for aroma profiling of mead</li><li>Dimensionality reduction techniques successfully highlighted relationships between samples</li><li>Honey concentration strongly influences volatile profile</li><li>Yeast strain (especially Abbey) significantly impacts composition</li></ul><h4><strong>13. Future Work</strong></h4><ul><li>Investigate microbial environment effects on VOC production</li><li>Link microbial kinetics data to volatile profiles</li><li>Explore compound-class-specific trends</li></ul><blockquote><p><strong>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.</strong></p></blockquote>

Application of GC×GC for the investigation of fermented beverages (Sarah C. Foster, MDCW 2026)

<p><strong>🎤 Presenter:</strong> David E. Alonso (LECO Corporation)</p><p>💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_3_Alonso_6f61e3ea43.pdf"><strong>PDF presentation</strong></a></p><h3><strong>Abstract</strong></h3><p><strong>Enormous quantities of Polyfluoro- and perfluoroalkyl substances (PFAS) have been produced through telomerization and electrochemical fluorination since the late 1940s. PFAS are a group of anthropogenic chemicals produced to improve the physical and chemical characteristics of products such as food packaging, cookware, textiles, paints, flame retardants, and stain-resistant clothing. PFAS increase heat, water, and oil resistance in these consumer products. Humans can be exposed to these harmful substances through ingestion and inhalation. Therefore, it is crucial that researchers screen these toxic chemicals on a regular basis. Unfortunately, monitoring hazardous chemicals such as persistent organic pollutants (POPs) is challenging due to the complexity of sample matrices. The goal of this study was to develop an analytical methodology for the comprehensive screening of complex samples for volatile and semi-volatile PFAS, as well as additional emerging contaminants.&nbsp;</strong></p><p>The protocol included the application of effective sample introduction techniques, as well as the design of general data acquisition and processing strategies for quick analysis of a wide variety of consumer goods. Different ionization modes: 1) Electron Ionization (EI), 2) Positive Chemical Ionization (PCI), and 3) Negative Chemical Ionization (NCI). The rich <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=2-13-3&amp;manufacturerItems=25"><strong>GCxGC-HRTOFMS</strong></a> data were used to annotate different classes of PFAS and additional harmful chemicals.</p><h3><strong>Video Transcription</strong></h3><h4><strong>1) Background and objective</strong></h4><p>The presentation focuses on <strong>screening and characterizing PFAS-related compounds (including PFOS/PFAS components and related xenobiotics)</strong> in <strong>consumer products and consumables</strong> using <strong>GC, GC×GC, and high-resolution time-of-flight mass spectrometry</strong>.</p><p>PFAS-type chemicals are widely used in industry and are increasingly recognized as environmentally and health-relevant contaminants. The speaker’s broader work includes screening materials such as <strong>ski wax</strong>, <strong>specialty lubricants</strong>, <strong>dental floss</strong>, <strong>chewing gum</strong>, and <strong>anti-fog solutions</strong>.</p><p>The overall goal is to show:</p><ul><li>why <strong>high-resolution accurate-mass TOF</strong> is powerful for <strong>unknown identification</strong>, and</li><li>how combining <strong>HRTOF-MS + GC×GC + advanced software tools</strong> enables <strong>fast, confident screening</strong> in complex matrices.</li></ul><h4><strong>2) Instrumentation and sample preparation approach</strong></h4><h5><strong>Instruments used</strong></h5><ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1097"><strong>LECO Pegasus BTX</strong></a> (initial experiments)</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/39"><strong>LECO Pegasus HRT</strong></a> (high-resolution TOF for accurate mass and improved confidence)</li></ul><h5><strong>Sample preparation philosophy</strong></h5><p>The speaker emphasizes <strong>minimal sample manipulation</strong>, using:</p><ul><li><strong>“Dilute and shoot”</strong> for liquid samples</li><li><strong>Thermal desorption</strong>, followed by <strong>pyrolysis</strong> (in separate steps) for solid/complex materials</li></ul><h4><strong>3) Case study A: Anti-fog solutions (PFAS concerns and unknowns)</strong></h4><h5><strong>Motivation</strong></h5><p>Anti-fog products are commonly used (lab work, sports such as hockey/skiing). Prior reports suggested some anti-fog solutions may contain <strong>PFAS-related components</strong>.</p><h5><strong>Initial GC-MS results (BTX)</strong></h5><p>A simple chromatogram can appear “clean,” but deeper evaluation shows:</p><ul><li>multiple organic classes (e.g., alkanes/alkenes)</li><li>a set of <strong>unknown major components</strong>, which became the key focus</li><li>limited library coverage for PFAS at the time (NIST version constraints were mentioned)</li></ul><p>The speaker identified a handful of compounds through automated processing, but many peaks remained <strong>unidentified</strong> (not found in NIST/Wiley/ChemSpider).</p><h5><strong>Why high resolution was needed</strong></h5><p>One compound of interest (“X”) produced:</p><ul><li>poor library match quality (“ridiculous” similarity score)</li><li>insufficient spectral clarity for confident identification</li></ul><h5><strong>HRTOF-MS advantages highlighted</strong></h5><p>With HRTOF-MS the speaker gains:</p><ul><li><strong>accurate mass</strong> (high precision mass values)</li><li>ability to compute <strong>elemental formulas</strong> for ions/fragments</li><li><strong>RDBE / ring double bond equivalence</strong> to support structural reasoning</li><li>multiple ionization modes:<ul><li><strong>EI</strong> (electron ionization)</li><li><strong>CI</strong> (chemical ionization), <strong>positive and negative</strong></li></ul></li></ul><h5><strong>Key step: Switching to CI for molecular ion information</strong></h5><p>The speaker notes EI spectra may be “skinny” (limited informative ions), while <strong>CI</strong> can provide:</p><ul><li>a strong <strong>protonated molecular ion</strong> with excellent mass accuracy</li><li>useful methane-CI adduct patterns (e.g., <strong>M+29</strong>, <strong>M+41</strong>)</li><li><strong>isotopic fidelity scoring</strong> (confidence metric; values above a high threshold indicate strong agreement between theoretical and observed isotope patterns)</li></ul><h5><strong>External database search to propose structure</strong></h5><p>Even with formula confidence, structure identification required:</p><ul><li>literature context (a specific paper was used as a clue)</li><li>searching the <strong>EPA CompTox</strong> library</li><li>narrowing to a small set of candidate structures</li><li>combining evidence from formula + fragmentation patterns (including signs of <strong>glycol-like</strong> substructures such as repeated <strong>CH₂CH₂O</strong> motifs)</li></ul><h5><strong>Extending logic to other unknowns</strong></h5><p>Once compound “X” was tentatively assigned, related unknowns (e.g., peaks 6–14) could be approached systematically by:</p><ul><li>analyzing recurring fragment motifs</li><li>applying complementary CI data</li><li>assigning consistent series behavior</li></ul><p>The speaker reports annotating the unknown series as <strong>polyfluorinated ethoxylate-type compounds</strong>, based on combined evidence and pattern relationships.</p><h4><strong>4) Case study B: Chewing gum (extreme matrix complexity and false positives)</strong></h4><h5><strong>Why chewing gum?</strong></h5><p>Chewing gum was unexpectedly complex and served as a strong example of:</p><ul><li>heavy thermal degradation (especially for sugar-containing gums)</li><li>a wide concentration dynamic range</li><li>analytes of interest present at <strong>very low levels</strong>, making screening difficult</li></ul><h5><strong>A key lesson: Library matches can mislead</strong></h5><p>The speaker reports seeing apparent PFAS hits with decent match scores (around the “confidence” range many users might accept), but calls them <strong>false positives</strong> due to:</p><ul><li>“skinny” spectra lacking molecular ion evidence</li><li>insufficient confirmation without accurate-mass + ionization support and advanced screening logic</li></ul><h4><strong>5) The “ultimate” workflow: HRTOF-MS + GC×GC + software-driven screening</strong></h4><p>The speaker describes the strongest approach as a combination of:</p><ul><li><strong>GC×GC</strong> (better separation and pattern recognition in complex mixtures)</li><li><strong>HR accurate mass</strong> (formula-level confidence)</li><li><strong>software tools</strong> that enable fast triage without manual peak-table review</li></ul><h4><strong>Screening strategy (needle-in-a-haystack)</strong></h4><p>Instead of relying purely on automated peak finding (often unreliable for trace levels), the speaker uses a multi-step spectral approach:</p><ol><li><strong>Sum ions</strong> across the GC×GC contour plot to capture the full complexity</li><li>Build a <strong>mass defect plot</strong>:<ul><li>y-axis: mass defect</li><li>x-axis: accurate mass</li></ul></li><li>Target entire <strong>PFAS fragment series</strong> at once:<ul><li>uses known spacing (e.g., <strong>CF₂ repeat patterns</strong> with consistent mass spacing)</li><li>targets both “tail” fragments and “head” fragments (e.g., acrylate-related head groups)</li></ul></li><li>Use specialized visualization (e.g., <strong>scaled mass defect plots</strong>) to make the PFAS-related ion series appear clearly aligned and easy to spot</li></ol><p>After locating candidate signals, the workflow moves to <strong>confirmation</strong>.</p><h4><strong>6) Confirmation logic: EI + CI (positive and negative) in “discovery mode”</strong></h4><p>The speaker’s standard operational mode is:</p><ul><li>acquire <strong>EI</strong>, then automatically acquire <strong>CI positive and CI negative</strong></li><li>if EI lacks a molecular ion, CI often provides:<ul><li><strong>protonated molecular ion</strong></li><li>isotope-pattern confirmation via <strong>isotopic fidelity</strong></li><li>accurate-mass formula confirmation</li></ul></li></ul><p>If needed, final confirmation can be achieved by:</p><ul><li>purchasing and running a <strong>reference standard</strong>, when available</li></ul><h4><strong>7) Results from chewing gum screening</strong></h4><p>Using the described approach, the speaker reports:</p><ul><li>identifying many gum constituents (sweeteners, flavor compounds such as terpenes in spearmint gum, softeners like glycerol and glycerides, and lipid-related compounds)</li><li>probing the gum base by increasing thermal conditions (e.g., higher temperatures revealing polymer degradation products)</li><li>evidence consistent with gum-base polymer components (example given: degradation products associated with <strong>polyisobutylene</strong>, such as isobutene and oligomers)</li></ul><h5><strong>PFAS conclusion for gum samples</strong></h5><p>For the gum samples tested (including gum, wrapper, and box):</p><ul><li><strong>PFAS-related compounds were not detected</strong> (good news)</li></ul><h4><strong>8) Take-home messages</strong></h4><ul><li><strong>HRTOF-MS is essential for unknown characterization</strong>, especially when libraries are incomplete.</li><li><strong>EI alone can be misleading</strong>, particularly for trace components and when spectra are information-poor (“skinny”).</li><li><strong>CI (positive/negative) adds decisive molecular-ion and formula confidence</strong>.</li><li><strong>GC×GC + HR accurate mass + mass defect workflows</strong> enable rapid screening for PFAS (and other POPs) without manually grinding through peak tables.</li><li>The workflow is broadly applicable for screening <strong>PFAS</strong> and other trace pollutant classes such as <strong>PCBs and persistent organic pollutants</strong>.</li></ul><blockquote><p><strong>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.</strong></p></blockquote>

Exploring PFAS in consumer goods using GCxGC-high-resolution mass spectrometry (David Alonso, MDCW 2026)

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