<p><strong>In the week of November 3, the following webinars await you in the field of gas / solid phase and especially Raman, FTIR, GC and GC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><div><div><div><div><div><div><h5><strong>1. Wiley: Discover Hidden Trends in Spectral &amp; Chromatographic Datasets</strong></h5><ul><li>Tu, 4.11.2025 14:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5604">Registration</a></li></ul><p>Learn how KnowItAll’s new Trendfinder tool uses Principal Component Analysis (PCA) to uncover hidden relationships and trends in large spectral and chromatographic datasets.</p><h5><strong>2. Wasson-ECE Instrumentation: Rotary Valve Training</strong></h5><ul><li>Tu, 4.11.2025 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5525">Registration</a></li></ul><p>Gain hands-on insights into rotary valve operation, maintenance, and troubleshooting to ensure optimal performance in chromatographic and analytical systems.</p><h5><strong>3. Phenomenex: Clear Signals – Navigating Baseline Challenges in GC</strong></h5><ul><li>We, 5.11.2025 10:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5609">Registration</a></li></ul><p>Join Duilio Romanello to master GC baseline troubleshooting. Learn to diagnose noise and drift issues, improve stability, and optimize GC method performance.</p><h5><strong>4. Technology Networks: Enhancing Pharmaceutical Manufacturing Using Raman Spectroscopy</strong></h5><ul><li>We, 5.11.2025 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5563">Registration</a></li></ul><p>Discover how Raman spectroscopy enables fast, non-destructive pharmaceutical testing. Improve process efficiency, ensure compliance, and explore future innovations.</p><h5><strong>5. Thermo Fisher Scientific: Mastering Mass Spectrometry with Chromeleon CDS – Expert Tips and Tricks</strong></h5><ul><li>We, 5.11.2025 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5386">Registration</a></li></ul><p>Learn advanced Chromeleon 7.4 features to optimize mass spectrometry workflows. Boost data quality, streamline configuration, and explore new MS performance tools.</p><h5><strong>6. Agilent Technologies: Power Ahead with Ammonia</strong></h5><ul><li>Th, 6.11.2025 07:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5542">Registration</a></li></ul><p>Explore how Agilent GC systems ensure ammonia purity and support hydrogen generation through cracking for safe, efficient energy and mobility applications.</p><h5><strong>7. Waters Corporation: Analysis, Mitigation, and Monitoring in Modern Data Governance</strong></h5><ul><li>Th, 6.11.2025 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5658">Registration</a></li></ul><p>Learn how advanced data governance strategies improve compliance, quality, and resilience across analytical and regulatory systems.</p></div></div></div></div></div></div>

Webinars LabRulezGCMS Week 45/2025

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 3rd November 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 applicaton notes by <strong>Agilent Technologies, EST Analytical </strong>and <strong>Thermo Fisher Scientific </strong>and poster by<strong> Shimadzu!</strong></p><h3><strong>1. Agilent Technologies: Estimation of Methyl Bromide Residues in Food</strong></h3><p><strong>Using the Agilent 8890 GC and Agilent 7010 triple quadrupole GC/MS system with Agilent 8697 headspace sampler</strong></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_methyl_bromide_food_8890_gc_7010_tq_8697_hs_5994_8499en_agilent_35c0751a37.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Bromomethane (methyl bromide) is a halogenated hydrocarbon with a boiling point of 3.6 °C. It is widely used as a fumigant due to its strong fumigation and penetration effects, effectively controlling pests.1 However, methyl bromide is harmful to the ozone layer, causes environmental pollution, and is harmful to humans.2 By 2015, most countries phased out its production and application, except for essential uses such as port quarantine. China stopped agricultural use of methyl bromide by the end of 2018.2 Despite its high volatility, residues of methyl bromide can persist in foods such as nuts and seeds. Therefore, quantitative methods for determining these residues are important. Gas chromatography (GC) is commonly used for analysis, either directly with headspace sampling, or with sample pretreatment methods including solvent extraction and solid phase microextraction. Various detectors are used with GC, such as the flame ionization detector, electron capture detector, and electron ionization. Detection can also be performed by mass spectrometer, using an electron ionization (EI) source. The maximum residue limit for methyl bromide in the EU is 10&nbsp;ng/g, according to EU Regulation number 396/2005.&nbsp;</p><p>In this application note, an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1-10&amp;search=8697+headspace+"><strong>Agilent 8697 headspace sampler</strong></a> was used for sample introduction to an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1-10&amp;search=8890"><strong>Agilent 8890 GC system</strong></a> coupled with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7010"><strong>Agilent 7010 triple quadrupole GC/MS</strong></a> for quantification of methyl bromide residues.</p><h4><strong>Results and discussion&nbsp;</strong></h4><h5><strong>Calibration&nbsp;</strong></h5><p>Figures 1 through 3 show the linearity of the methyl bromide calibration curve in rice, cumin powder, and red chili powder, respectively. The R2 obtained was above 0.998.&nbsp;</p><h5><strong>Repeatability&nbsp;</strong></h5><p>The repeatability of this method was demonstrated by injecting six replicates of rice, cumin, and red chili powder spiked with 10 ng/g of methyl bromide. The area %RSD and retention time %RSD results are shown in Table 3.&nbsp;</p><h5><strong>Recovery&nbsp;</strong></h5><p>Methyl bromide was spiked in samples of rice, cumin, and red chili powder at concentration levels of 10 ng/g. Acceptable recoveries were obtained by quantification through prespike matrix-based calibration. Results are highlighted in Table 4.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>An accurate and rugged method was developed which meets the requirements of the EURL for the maximum residue limit of methyl bromide in foodstuffs. This ranges from 10 to 100 ng/g, depending on the commodity (Reg. (EC) number 396/2005) for the analysis of pesticides in rice, cumin powder, and red chili powder. The LOQ of the method was demonstrated at 5 ng/g for all tested matrices. Repeatable results were found for six successive replicates of matrix‑based standards at 10 ng/g concentration levels. Sufficient recoveries were obtained in all tested matrices at 10 ng/g spiked concentration levels. Thus, this study demonstrates the applicability of this method for routine analysis of food samples for methyl bromide at trace levels.</p><h3><strong>2. EST Analytical: Effect of Split Ratio on USEPA Method 8260 Compounds</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/Purge_and_Trap_Effect_Split_Ratio_on_USEPA_8260_Compounds_2b7339c7c7.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Injection size on a GC column is dictated by the diameter of the GC column. Packed columns are usually shorter in length and larger in diameter and are usually filled with a packing coated with stationary phase. Due to their large diameter, packed columns can handle much larger injections. However, since packed columns are shorter in length, compound resolution is more difficult. Capillary columns, on the other hand, are much longer with much smaller diameters. A capillary column has a stationary phase that is coated onto the column wall which in turn makes the diameter even smaller. Overloading is a common problem with capillary columns, thus smaller injections and higher dilutions are required when using a capillary column.&nbsp;</p><p>The benefit of a capillary column is that it has substantially more theoretical plates than a packed column. Increasing the number of theoretical plates enhances column efficiency because peaks are narrowed and peak resolution is improved. The disadvantage of the capillary column is column overloading. This is where a split injection is greatly beneficial. A split injection can limit sample overload while taking advantage of the efficiency of the capillary column.&nbsp;</p><h4><strong>Discussion:&nbsp;</strong></h4><p>Purge and trap sampling is the method used for purging volatile compounds out of a water matrix for USEPA Method 8260. Due to the large number of analytes and the lower detection limits required in this method, a capillary column is needed for detection and separation. Split injections provide a method to control moisture from the purge and trap sampling while preventing column overloading with the capillary column. However, due to the detection limits required of the method, if the split injection is too large, the detection limits of the method may not be met. On the other hand, if the split injection is too small, there can be moisture problems with the mass spectrometer. This application will provide a comparison of several different split rates and their effect on compound detection for USEPA Method 8260.</p><h4><strong>Experimental:&nbsp;</strong></h4><p>The sampling system used for this study was the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2160"><strong>EST Analytical Evolution concentrator</strong></a> and the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2159"><strong>Centurion WS autosampler</strong></a>. The concentrator was affixed with a Vocarb 3000 trap and connected to an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1-10&amp;search=7890A"><strong>Agilent 7890A GC</strong></a> and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1-10&amp;search=5975C"><strong>5975C </strong></a>inert XL MS. The GC was configured with a Restek Rxi-624 Sil MS 30m x 0.25mm x 1.4µm column. Three different split ratios were used for this study. Refer to Table 1 for the sampling method parameters and Table 2 for GC/MS parameters.</p><h4><strong>Conclusions:&nbsp;</strong></h4><p>As USEPA Methods require lower and lower detection limits, split ratios will play a large factor in reaching these new requirements. This study demonstrated that all three split ratios met USEPA Method 8260 requirements for linearity, compound response and method detection limits. However, there was a remarkable difference in compound detection limits as the split ratio was increased. The 40 to 1 split rate had much lower detection limits due to an increase in compound response. The Encon Evolution purge and trap concentrator has a unique feature for moisture control using an eight port valve to avoid desorbing through the moisture trap. This feature enables better moisture control when using a lower split rate, thus using a larger split rate to control moisture is unnecessary.</p><h3><strong>3. Shimadzu / AOAC: Simultaneous analysis of residual contaminants in drinking water using Programmed Temperature Vaporization (PTV) for Large Volume Injection (LVI) and GC-MS/MS</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/aoac_2025_w016_04bda0b617.pdf"><strong>Full PDF for download</strong></a></li></ul><p>This study aimed to develop a single analytical method for the simultaneous analysis of over 60 residual contaminants, including Poly-Aromatic Hydrocarbons (PAH), Poly-Chlorinated Biphenyls (PCB), and pesticides, in drinking water. Traditionally, multiple methods are being employed for analyzing these contaminants. Most common ones include, extraction using solvents in large separating funnel or SPE cartridge followed by volume concentration by evaporation. These methods require huge sample and solvent quantities. Due to these factors like large sample size handling, concentration steps etc., method may show poor reproducibility.&nbsp;</p><p>By utilizing Programmed Temperature Vaporization (PTV) for Large Volume Injection (LVI), the method significantly reduces the initial sample amount required for preparation, pre-treatment steps, and solvent consumption compared to conventional approaches, while eliminating the need for final sample concentration. The optimized procedure achieved limits of quantification (LOQs) as low as 0.005 ppb and 0.05 ppb, demonstrating high sensitivity without compromising efficiency. Validation parameters like specificity, linearity, recovery and precision were studied as per SANTE guidelines[1] . For linearity and recovery study, matrix match calibration was used.</p><h4><strong>Materials and methods&nbsp;</strong></h4><p>For this study, reference standard mixtures of over 60 contaminants regulated in drinking water were procured from Restek® Corporation. It included PAH, PCB and GC amenable pesticides. Drinking water sample was extracted and used to prepare matrix-matched calibration standards and spiked samples. This method was validated for criteria mentioned in SANTE Guidelines. <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-TQ8040+NX"><strong>GCMS-TQ8040 NX</strong></a> (Figure 2), manufactured by Shimadzu Corporation Japan, was used to quantify residual contaminants in drinking water sample.</p><h4><strong>Conclusion</strong></h4><ul><li>This study shows that the extraction of reduced sample quantity combined with LVI of 50 <span>μ</span>L on GC-MS/MS system is a reliable and efficient tool to simultaneously quantify residual contaminants like PAH, PCB and pesticides in drinking water sample.</li><li>Also, highly sensitive Shimadzu GC-MS/MS allows trace level detection even without the need of evaporating &amp; concentrating the sample. This helps in ruggedness resulting in reproducible detection of analytes.</li><li>The combination of sensitive instrument and reproducible extraction method enables its use in testing laboratories for residual contaminants analysis in drinking water.</li></ul><h3><strong>4. Thermo Fisher Scientific: Automated liquid-liquid extraction workflow for direct ultra-trace analysis of pesticides and PAHs in water matrices using GC-MS/MS</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_003943_gc_autosampler_triplus_rsh_smart_environmental_an003943_na_en_b9980f4e3e.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Ensuring high standards of water quality is a fundamental priority within the European Union. This is highlighted through stringent EU regulations, such as the Water Framework Directive and the Drinking Water Directive,1 where protection of the environment and safeguarding public health are of utmost importance. As such, regular monitoring of both surface and drinking water for hazardous semi-volatile organic compounds, such as polyaromatic hydrocarbons (PAHs) and pesticides, is needed.1,2&nbsp;</p><p>To meet regulatory compliance, laboratories are required to reach ultra-trace levels (i.e., parts per-trillion (ng/L)), while maintaining analytical method performance criteria outlined under EU Directive 2020/2184.3 To reach required concentration limits, laboratories typically need to extract large sample volumes using solvent (i.e., liquid-liquid) or sorbent-based (i.e., solid-phase) extraction techniques with additional preconcentration.</p><p>Such techniques involve manual operations, which are costly in terms of consumables and time. However, automated sample preparation and extraction can reduce such costs while helping to eliminate systematic errors to improve data quality and lab efficiency.&nbsp;</p><p>The aim of this work was to evaluate the sensitivity, precision, and robustness of the TriPlus RSH SMART autosampler in performing automated liquid-liquid extraction and on-line analysis of polyaromatic hydrocarbons (PAHs) and pesticides in drinking and surface water using triple quadrupole GC-MS/MS. This study will demonstrate that the developed automated workflow is compliant with EU regulation criteria and greatly increases laboratory productivity by reducing laboratory costs and time.</p><h4><strong>Experimental</strong></h4><h5><strong>Instrument and method setup&nbsp;</strong></h5><p>A <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/486"><strong>Thermo Scientific™ TRACE™ 1610 GC </strong></a>equipped with a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1744"><strong>Thermo Scientific™ TraceGOLD™ TG5-SilMS</strong></a> (30 m × 0.25 mm I.D. × 0.25 µm film) capillary column with a 5 m integrated SafeGuard retention gap (P/N 26096-1425) was used to perform the chromatographic separation. The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/422"><strong>TriPlus RSH SMART autosampler</strong></a> was configured to perform automated sample preparation, liquid-liquid extraction, and on-line injection for GC-MS/MS analysis. The automated workflow was designed using the Thermo Scientific™ TriPlus™ RSH Sampling Workflow Editor Software (P/N 1R77010-1200) and imported into the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/76"><strong>Thermo Scientific™ Chromeleon™ 7.3.2 Chromatography Data System (CDS) software</strong></a>. This integration allows sample preparation, acquisition, and data processing to be carried out within a single software. The automated workflow involved the following steps: internal standard addition, extraction solvent addition (pentane) and vortexing for extraction, addition of solvent for emulsion removal (isopropyl alcohol), addition of syringe standard prior to analysis, and large volume injection into the GC-MS. Parameter settings for the automated workflow are outlined in Table 1 with a visual overview provided in Figure 1. A list of accessories needed to configure the TriPlus RSH SMART autosampler is provided in the appendix (Table A2).&nbsp;</p><p>After sample extraction was completed, a 30 μL aliquot of the pentane layer was directly injected into a programmable temperature vaporization (PTV) inlet. Injection and oven program conditions can be found described in Tables 1 and 2. Data acquisition was carried out using selected reaction monitoring (SRM) with a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/485"><strong>Thermo Scientific™ TSQ™ 9610 triple quadrupole GC-MS</strong></a> equipped with an Advanced Electron Ionization (AEI) source. Method parameters for data acquisition are summarized in Table 2 with optimized SRM transitions outlined in the appendix (Table A3).</p><h4><strong>Conclusions&nbsp;</strong></h4><p>Maintaining water quality is of both human and environmental health importance. However, due to the physical chemical properties of various organic substances, analytical methods require ultra-trace sensitivity and accuracy to quantify hazardous risks within both drinking and environmental surface waters. The results presented demonstrate that the developed automated workflow for online liquid-liquid extraction and analysis of water samples can meet these requirements:&nbsp;</p><ul><li>Ultra-trace sensitivity combining large volume injection with highly sensitive triple quadrupole GC-MS/MS, with over 70% of targeted analytes having limits of quantification below 2 ng/L&nbsp;</li><li>Compliant extraction recovery at part per trillion concentrations&nbsp;</li><li>Robust method precision over extended analysis time&nbsp;</li><li>Improved laboratory efficiency and productivity while reducing environmental impact with limited solvent consumption&nbsp;</li><li>Workflow customization capability through the Sampling Workflow Editor software</li></ul>

News from LabRulezGCMS Library - Week 45, 2025

<p><strong>In the week of November 10, the following webinars await you in the field of gas / solid phase and especially Raman, FTIR, GC and GC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><div><div><div><div><div><div><h5><strong>1. Wasson-ECE Instrumentation: Calibration Techniques for Accurate Data</strong></h5><ul><li>Tu, 11.11.2025 09:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5526">Registration</a></li></ul><p>Learn best practices for GC calibration, including multi-point curve generation, standard preparation, and maintaining data integrity for precise analytical results.</p><h5><strong>2. Metrohm: Through-Container Raman for Chemicals, Raw Materials, and Field Response</strong></h5><ul><li>Th, 13.11.2025 15:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5665">Registration</a></li></ul><p>Discover how through-container Raman spectroscopy identifies chemicals directly through opaque containers, improving safety, compliance, and workflow efficiency.</p></div></div></div></div></div></div>

Webinars LabRulezGCMS Week 46/2025

Molecular Sensory Analysis Confirms Wood Smoke Exposure as a Source of Smoky Off-Flavors in Fermented Cocoa

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 10th November 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 applicaton notes by <strong>Agilent Technologies, EST Analytical </strong>and <strong>Shimadzu!</strong></p><h3><strong>1. Agilent Technologies: Improved Thermal Stability Leads to Retention Time Stability and Data Accuracy for the Analysis of Pesticides</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_pesticide_gc_ms_5994_8447en_agilent_773f1f135b.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The analysis of trace-level pesticides is required by many governmental regulatory authorities at varying concentrations and can include hundreds of target analytes. As many of the matrices tested are complex and the analytes active, a robust and inert gas chromatography method is required.<sup>1</sup> Heavy matrices can be difficult for analytical gas chromatography columns, and higher temperatures may be required to elute the matrix off of the column. To avoid damage from high temperatures and heavy matrix, a thermally stable and ultra inert analytical column phase is needed to successfully perform this analysis. The Agilent J&amp;W 5Q series of gas chromatography columns provide the most thermally stable column available and offer inertness for difficult analytes.<sup>2</sup> The increased thermal stability not only dramatically reduces column bleed but also allows for less spectral interference at higher temperatures, leading to less need for background subtraction when searching analyte spectra against commercially available libraries.&nbsp;</p><p>While column bleed is a normal phenomenon, the increase of the baseline due to column bleed is an indication of the column phase breaking down and leading to retention time shifts. Retention time shifting can be especially difficult with the analysis of pesticides, since sensitive detectors such as mass spectrometers and triple quadrupole systems collected in selected ion mode (SIM) or multiple reaction monitoring (MRM) mode are required to achieve lower limits of detection. In both SIM and MRM collection methods, time segment windows are stated for analytes of interest, and if a retention time shifts earlier, it can move out of the collection window and require time to update the analytical collection method. By using a GC column with increased thermal stability, such as the Agilent J&amp;W DB-5Q or the Agilent J&amp;W HP-5Q, the column will be more robust to thermal and matrix damage. These columns will also maintain consistent retention times over long periods of analytical stress.&nbsp;</p><p>Retention time locking is a useful tool in the analysis of pesticides, as it can adjust column flow or pressure to normalize retention times, so that the retention times of the analytes are the same from system to system or column to column.<sup>3,4</sup> When columns with identical selectivity are interchanged, after a single injection of a test mix, the analysis can be relocked and the column flow adjusted, based on the initial calibration, to be identical as in the original method. This eliminates the need to constantly update retention times in analytical methods for SIM or MRM windows or data processing methods when replacing analytical columns.&nbsp;</p><p>When analyzing pesticides in heavy matrix, a guard column can serve two additional benefits. The guard column can help protect the analytical column from heavy matrix damage and can improve solvent and sample focusing. It is recommended to use nonpolar solvents when working with nonpolar column phases, such as the DB-5Q and HP-5Q columns. Recently it has become more common to prepare heavy matrices for pesticide analysis using QuEChERs-type preparations, with a final solvent of acetonitrile. Solvent-column phase polarity mismatch can lead to improper solvent focusing and wettability leading to improper peak shape, especially when working with columns that have low free silanol content on the deactivated surface of the fused silica. Use of a guard column can help to refocus the solvent and analytes to maintain proper peak shape.<sup>5</sup>&nbsp;</p><p>The Agilent 7010D GC/TQ uses the new Agilent High Efficiency Source (HES) 2.0 equipped with a novel dipolar RF lens that redirects carrier gas and low mass ions by &gt;&nbsp;95%, helping to reduce noise and extend instrument robustness while maintaining sensitivity.<sup>6</sup> When using DB-5Q and HP‑5Q columns in conjunction with the HES 2.0, it is possible to increase the overall robustness of the workflow for the analysis of pesticides over a wide dynamic range of analyte&nbsp;concentrations.&nbsp;</p><h4><strong>Experimental&nbsp;</strong></h4><p>An <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890"><strong>Agilent 8890 GC</strong></a> coupled with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5977B"><strong>Agilent 5977B GC/MSD</strong></a> with an Inert Extractor Source was used for acquisition of data and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=MassHunter+10.0+Qualitative+Analysis"><strong>MassHunter 10.0 Qualitative Analysis software</strong></a> was used for data analysis. NIST20 library was used to confirm peak identification. A retention gap of one meter was used as a guard column to help protect the analytical column from heavy matrix and to aid in solvent focusing.</p><h4><strong>Results and discussion&nbsp;</strong></h4><p>The analysis of pesticides requires retention time stability as well as consistency from analytical column to analytical column. To compare the analytical performance of an Agilent J&amp;W DB-5Q to an Agilent J&amp;W DB-5ms UI GC column for the analysis of pesticides, a standard was first analyzed on a DB-5ms UI and the retention time locked for chlorpyrifosmethyl. The DB-5ms UI column was then replaced with a DB-5Q, and once the column was conditioned, the representative pesticide mix was again analyzed and relocked to the appropriate retention time for chlorpyrifos-methyl and analyzed again. Figure 1 demonstrates the identical selectivity of the DB-5ms UI to the DB-5Q phases and the ability to relock an existing analytical method when upgrading columns and compatibility with existing retention time locking libraries.&nbsp;</p><p>Additionally, when comparing the baseline of the DB-5Q to the DB-5ms UI column at the upper temperature limit of the chromatographic method, the increased thermal stability of the DB-5Q allows for a significant decrease in column bleed at upper temperature limits (Figure 1).</p><h4><strong>Conclusion&nbsp;</strong></h4><p>This application note demonstrated that the Agilent J&amp;W DB-5Q column has identical selectivity to the Agilent J&amp;W DB-5ms UI column, allowing for seamless adoption and use with existing retention time locking methods. The robust DB-5Q column phase maintained retention time stability even when analyzing heavy matrices and temperature cycling. The increased thermal stability not only decreased the baseline at higher temperatures but also allowed for less spectral interference and high library match factor scores. The analytical performance of the Agilent J&amp;W DB-5Q column coupled with the Agilent 7010D with the HES 2.0 ion source allows for optimal sensitivity and wide dynamic range of pesticide compounds.</p><h3><strong>2. EST Analytical: Automated Sampling of Methanol Extractions</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/Purge_and_Trap_Automated_Sampling_of_Methanol_Extractions_f521577cc4.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Environmental labs are required to perform methanol extractions on highly contaminated solid waste samples. Additionally, these extractions are used for difficult matrices, for example oily waste samples, that are also soluble in methanol. Due to the variety of matrices that can be extracted with methanol there are assorted hurdles to cross in order to automate the sampling process. One of these obstacles is how the matrix can absorb the extraction solvent. For example, many soil samples require more methanol; due to the soil expanding with the solvent addition. Adding more methanol aids in rectifying this issue however, when automating sampling, the added methanol coupled with the soil expansion needs to be accommodated. For this reason, EST Analytical created software for the Centurion WS that allows the user to program the needle depth to different distances. In doing this, laboratories are able to sample soils at higher depths in order to adjust for sample absorption and at lower depths for samples that do not require the added solvent. In order to test volatile compounds in methanol extractions, a portion of the extract needs to be sampled from the vial, diluted, and purged on a purge and trap concentrator. This examination will look at the automated sampling of three different soil matrices.&nbsp;</p><h4><strong>Experimental:&nbsp;</strong></h4><p>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2159"><strong>Centurion WS autosampler</strong></a> with the syringe option and the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2160"><strong>Evolution purge and trap concentrator</strong></a> were set up to run methanol extractions. Since this is a volatile analysis, a Vocarb 3000 (K) trap was used for the analytical trap. The sampling system was configured to an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1-10&amp;search=7890A"><strong>Agilent 7890A Gas Chromatograph (GC)</strong></a> and an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1-10&amp;search=5975C"><strong>Agilent 5975C inert XL Mass Spectrometer (MS)</strong></a>. The column selected for this analysis was a Restek Rxi®-624 Sil MS, with dimensions of 30m x 0.25mm I.D. x 1.4µm film thickness. Experimental parameters used for this analysis are listed in Tables 1 and 2.</p><h4><strong>Conclusions:&nbsp;</strong></h4><p>The system produced excellent results. The curve and the compound response factors met all of the method requirements. The overall precision was less than 5% RSD while the system showed an average recovery of 94%. When examining the expected analyte concentration to the extracted sand results, it was found that the average recovery was approximately 102%. Thus, the automated extraction performed extremely well. When comparing the three different matrices it was found that they all displayed similar recoveries, however the sand matrix did perform the best of the three as expected since sand tends not to absorb analytes as readily as other matrices. Finally, the ability to control the needle depth on extraction samples was a great benefit for the different matrices as the system exhibited no issues with needle clogging due to the higher needle depth when performing the potting soil extractions. As demonstrated from this study, the Centurion WS automated extraction capability would be an asset to any lab performing extractions.</p><h3><strong>3. Shimadzu: Fatty Acid Analysis of Edible Oils by GC</strong></h3><p><strong>Comparing Helium and Hydrogen as Carrier Gases</strong></p><p><strong>Comparing Analysis by GC with a Simple Screening Analysis by LC-MS</strong></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00852_en_e928ab40ea.pdf"><strong>Full PDF for download</strong></a></li></ul><p>There are a variety of edible oils that are sold commercially for a range of uses. They are almost entirely composed of compounds called triacylglycerols (neutral lipids), which consist of three fatty acids bound to a glycerol molecule. Because fatty acids affect the functional characteristics of food, determining the fatty acid composition of food is an important area of analysis. In this Application News, the Brevis GC-2050 (GC-FID) is used to determine the fatty acid composition of commercially available edible oils, and the use of hydrogen as a carrier gas is examined and compared with helium. This Application News also analyzes the same edible oils analyzed by LC-MS in a previous Application News1) and comparesthe results.</p><h4><strong>Equipment&nbsp;</strong></h4><p>GC-FID analysis was performed using the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=Brevis+GC-2050"><strong>Brevis GC-2050</strong></a> gas chromatograph (Fig.1) , which is designed to be highly compact, with an installation footprint 35 % smaller than the previous model. The Brevis GC-2050 is also equipped with a carrier gas saving mode that reduces carrier gas consumption. The carrier gas saving mode reduced the use of helium carrier gas by around 89 % and hydrogen carrier gas by around 79 % in the analyses described in this article. Hydrogen carrier gas also reduced analysis times by around 25 %.</p><h4><strong>Results from Real-World Sample Analysis&nbsp;</strong></h4><p>Edible oils were derivatized and analyzed using both helium and hydrogen carrier gases. Peaks that could not be identified with the calibration standard (37 FAME mixture) were analyzed by GC-MS to determine their carbon number and degree of unsaturation. (Labeled as isomer.) Fig. 4 shows the chromatograms (partial) obtained from analyzing linseed oil with helium and hydrogen carrier gases. Similar separation was achieved with both gases.</p><h4><strong>Comparing GC-FID and FIA-MS&nbsp;</strong></h4><p>Tables 5 and 6 compare the results of analyzing eight vegetable oils for fatty acids by FIA-MS (screening analysis) and GC-FID (a helium carrier). The results reveal characteristic features of the vegetable oils, such as the most abundant fatty acids, and that coconut oil contains fatty acids with relatively low carbon numbers. More information about the screening analysis performed with a single quadrupole mass spectrometer can be found in Shimadzu Application News 01-00674-EN. <sup>1)</sup></p><h4><strong>Conclusion&nbsp;</strong></h4><p>In this Application News, the Brevis GC-2050 was used to determine the fatty acid composition of edible oils. Fatty acid analysis is typically performed using helium as the carrier gas, but in this study, hydrogen was used. This reduced analysis times while producing results that compared favorably with helium. Given the current supply issues associated with helium, hydrogen should be considered as an alternative to helium.&nbsp;</p><p>The results also show that fatty acid compositions determined by FIA-MS correlate well with results obtained by GC-FID. And because the sample pretreatment only requires sample dilution, analysis by FIA-MS is rapid, taking just 1 minute. Therefore, FIAMS seems particularly well-suited to the screening of large numbers of samples or when there is an urgent need to verify quality or safety</p>

News from LabRulezGCMS Library - Week 46, 2025

The Plant of Many Scents: Unraveling the Odorant Composition of Selected CBD Hemp Cultivars

<p><strong>In the week of November 17, the following webinars await you in the field of gas / solid phase and especially Raman, FTIR, GC and GC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><div><div><div><div><div><div><h5><strong>1. GERSTEL: Innovative Fermentation Aroma Analysis using HS- and DI-SBSE as well as HS-SPME</strong></h5><ul><li>Tu, 18.11.2025 10:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5642">Registration</a></li></ul><p>Dr. Marina Rigling demonstrates how HS-SPME, HS-SBSE, and DI-SBSE sorptive extraction techniques reveal new aroma insights in complex fermentation matrices.</p><h5><strong>2. Separation Science: Snow DUMP-ing – Using Direct TD-GC-MS to Tackle Urban Snow and Microplastic Pollution</strong></h5><ul><li>Tu, 18.11.2025 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5615">Registration</a></li></ul><p>Explore how TD-GC-MS simplifies analysis of urban snow and microplastics, cuts solvent use, and supports advanced environmental monitoring.</p><h5><strong>3. Agilent Technologies: Carrier Gas Conundrum – Smart Solutions for Helium Alternatives</strong></h5><ul><li>Tu, 18.11.2025 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5654">Registration</a></li></ul><p>With helium shortages and price increases, learn practical guidance for switching to hydrogen or nitrogen carrier gases in GC workflows.</p><h5><strong>4. Bruker: A Guide to FT-IR-Based Gas Analysis</strong></h5><ul><li>Tu, 18.11.2025 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5631">Registration</a></li></ul><p>Gain an application-focused overview of FT-IR gas analysis, from setup to real-world examples, covering environmental and industrial monitoring.</p><h5><strong>5. Wasson-ECE Instrumentation: Single Cylinder Autosampler (SAS-100)</strong></h5><ul><li>Tu, 18.11.2025 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5527">Registration</a></li></ul><p>Discover how the SAS-100 automates gas sampling for greater accuracy, reproducibility, and lab efficiency in petrochemical and process applications.</p><h5><strong>6. Agilent Technologies: Sustainable Aviation Fuels – Path to Greener Skies</strong></h5><ul><li>We, 19.11.2025 07:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5543">Registration</a></li></ul><p>Learn how GC and LC techniques assess SAF quality by profiling hydrocarbons, detecting oxygenates, and ensuring compliance for sustainable aviation.</p><h5><strong>7. SciY/Mestrelab Research: Tablet Release Reinvented – Robotics, Vision, and Spectral Intelligence</strong></h5><ul><li>Th, 20.11.2025 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5698">Registration</a></li></ul><p>Explore the SciYbotic Tablet Testing system combining robotics, 3D vision, and spectroscopy for rapid, non-destructive tablet testing in Pharma 4.0 workflows.</p><h5><strong>8. Agilent Technologies: Raman Spectroscopy for Pharmaceutical Applications</strong></h5><ul><li>Th, 20.11.2025 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/4538">Registration</a></li></ul><p>Discover how Agilent’s SORS and TRS Raman technologies ensure accurate raw material ID and quality control in pharma production environments.</p><h5><strong>9. Agilent Technologies: UV-VIS and Raman Spectroscopy in BioPharma Protein Analysis</strong></h5><ul><li>Fr, 21.11.2025 15:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5679">Registration</a></li></ul><p>Learn how Raman and UV-Vis spectroscopy enable precise protein quantification, purity assessment, and compliance in biopharma processes.</p></div></div></div></div></div></div>

Webinars LabRulezGCMS Week 47/2025

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 17th November 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 applicaton notes by <strong>Agilent Technologies, EST Analytical </strong>and <strong>Shimadzu!</strong></p><h3><strong>1. Agilent Technologies: Accurate Identification and Quantification of Pesticide Residues in Ghee</strong></h3><p><strong>Analysis by QuEChERS with GC/MS/MS using the Agilent 8890 GC and Agilent 7010 GC/TQ</strong></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_pesticide_ghee_gc_5994_8399en_agilent_1cbd787002.pdf"><strong>Full PDF for download</strong></a></li></ul><p>India is one of the largest producers of milk, contributing about 23% of global milk production.<sup>1</sup> The dairy processing sector includes a wide range of products, such as milk, butter, cheese, ghee, yogurt, flavored milk, lactose-free dairy products, and value-added products like ice creams and yogurts. Amongst these, ghee production is significant because of its popularity, low cost of production, and versatile use. According to the Food Safety and Standards Authority of India (FSSAI), ghee is mentioned as a "product obtained exclusively from milk, cream or butter by a process that almost completely removes water and non-fat solids; it has a specially developed flavor and physical structure".<sup>2</sup> Milk‑producing animals can be exposed to pesticides through their diet, such as feed and fodder containing pesticide residues, or through the direct application of pesticides for controlling ectoparasites on their bodies, in animal sheds, and in milk processing areas. Due to their persistent and lipophilic nature, pesticides tend to accumulate in the fat-rich tissues of animals. The FSSAI has specified maximum residue levels (MRLs) for 55 pesticides, and a tolerance limit of 0.01&nbsp;mg/kg for the remaining pesticides for which an MRL has not been&nbsp;fixed.&nbsp;</p><p>Ghee consists of 98.9% fat, irrespective of the animal source. Among the short- and medium-chain saturated fatty acids, all samples contain butyric (C4:0), caproic (C6:0), caprylic (C8:0), and capric acid (C10:0).<sup>3</sup> Since its introduction, the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=QuEChERS"><strong>QuEChERS method</strong></a> has been widely used for the analysis of pesticides in several matrices. Some modifications to the original QuEChERS method have been introduced to ensure efficient extraction of pH-dependent compounds, to minimize degradation of susceptible compounds (such as base- and acid-labile pesticides), and to expand the spectrum of matrices covered. Ghee matrix has a very high lipid load, making it a difficult commodity for analysis pesticide residues. The primary challenge lies in obtaining an extract that contains the target analytes while minimizing the amount of fat. Co-extracted lipids in the extracts can be removed, to a high degree, by a freezing-out step or a C18 cleanup.&nbsp;</p><p>During sample extraction, the sample is often diluted. To obtain a quantification limit of 0.01 mg/kg, the instrument should be able to detect the residues at a lower concentration. This application note addresses these requirements by combining a sample preparation technique covering multiclass pesticides, target-specific multiple reaction monitoring (MRM) data acquisition, and efficient software to simplify quantitative analysis. The Agilent 7010 triple quadrupole GC/MS system enabled trace-level detection and confident quantitation of 150 pesticides in ghee.</p><h4><strong>Experimental&nbsp;</strong></h4><h5><strong>Equipment&nbsp;</strong></h5><p>An Agilent <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890+GC"><strong>8890 GC</strong></a> coupled to a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7010+GC%2FTQ"><strong>7010 GC/TQ</strong></a> system equipped with high efficiency source was used for the analysis. The GC system was equipped with a multimode inlet with air cooling and midcolumn backflush based on an Agilent purged Ultimate union controlled by a pneumatic switching device. The method parameters are displayed in Table 1.</p><h4><strong>Conclusion</strong></h4><p>The limits of quantitation (LOQs) for the target list of over 150 compounds ranged from 2.5 to 5 mg/kg for the ghee matrix. To minimize interferences from the ghee matrix, numerous multiple reaction monitoring (MRM) transitions were employed. This approach helped to accurately identify and quantify the target compounds despite the complex sample matrix. The samples were diluted five-fold to ensure that the analytes were within the required detection range of the instrument. This dilution also helped in reducing the matrix effects, leading to more reliable results. The average analyte recoveries were between 70 and 130%, indicating that the method has good accuracy and precision. This range of recoveries demonstrates the method’s robustness and suitability for routine analysis of these compounds.</p><h3><strong>2. EST Analytical: Analysis of Coffee Flavors by Purge and Trap Sampling</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/Purge_and_Trap_Analysis_of_Coffee_Flavors_by_Purge_and_Trap_Sampling_ca25beef60.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Most of us who drink coffee know our preferences. We know which blend we like, what strength and definitely what we like in our coffee. However, most of us do not think about the flavors that go into our coffee that help us define our preferences. Coffee manufacturers, on the other hand are working diligently to create a new blend or a more satisfying flavor that would make us want to choose their coffee over another maker.&nbsp;</p><p>This study will look at four different blends of coffee and some of the compounds that contribute to the flavor of the blend. Using purge and trap sampling, the flavor compounds were purged out of the coffee and trapped onto an adsorbent trap. The analytes were then transferred to a Gas Chromatograph/Mass Spectrometer (GC/MS) for analysis. The study focused on six flavor compounds. The spicy smoky aroma of 2-methylfuran, the spicy caramel, almond odor of 5-methylfurfural, the buttery scent provided by the addition of 2,3-butanedione and 2,3- pentanedione and the impact of the two and three methyl butanals giving the dark chocolate malty flavor to the coffee.</p><h4><strong>Experimental:&nbsp;</strong></h4><p>The EST Analytical <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2160"><strong>Encon Evolution Purge and Trap Concentrator</strong></a> and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2159"><strong>Centurion WS Autosampler </strong></a>were interfaced to a GC/MS. The Centurion WS was run in the soil mode and the purge and trap concentrator was configured with a Vocarb 3000 (K) analytical trap. The column selected for this study was an Rtx-1 60m x 0.25mm x 1µm. Listed in Tables 1 and 2 are the experimental parameters used for this study.</p><h4><strong>Conclusion:&nbsp;</strong></h4><p>The lightest roast had the most buttery and dark chocolate/malty flavors while the darkest roast had more of the smoky flavor. Surprisingly, the darkest roast and the medium roast had similar proportions of the flavor compounds with the exception of the smoky flavor. While the light roast had large amounts of the flavors tested for, the chromatography of the light roast was much simpler than that of the darker roasts. Overall, the Encon Evolution Purge and Trap Concentrator in conjunction with the Centurion WS Autosampler proved to be an excellent system for sampling coffee. The results were reproducible and provided a lot of information on the flavors of the respective coffees.</p><h3><strong>3. Shimadzu: Analysis of Ethanol in E10 Gasoline Using Polyarc Microreactor for GC</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_01033_en_d4ee66bd4f.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The flame ionization detector (FID) can detect almost all organic compounds, and the sensitivity of the obtained chromatograms displays a correlation with concentration and number of carbon atoms in the compound. However, as a weakness of FID, its sensitivity decreases when an organic compound contains oxygen atoms, asin the case of alcohols.&nbsp;</p><p>The Polyarc microreactor improvessensitivity for alcohols and other compounds containing oxygen atoms (hereinafter, oxygenated compounds), allowing calibration using a single compound. Research and use of biofuels has accelerated in recent years, and more sensitive quantitative analysis of oxygenated compounds in multiple compoundsis now required.&nbsp;</p><p>This Application News introduces an example of an analysis of a sample simulating E10 gasoline, in which 10 % ethanol by volume concentration was added to gasoline, demonstrating that enhanced sensitivity for oxygenated compounds can be achieved by adding Polyarc to the instrument configuration. The accuracy of the quantitative analysis was also verified using a general standard sample (alkane).</p><h4><strong>Polyarc Microreactor</strong></h4><p>Polyarc is a post-column type microreactor that is introduced between the column and the detector (FID). The organic compounds separated and eluted by the column are decomposed to methane by the Polyarc reactor in a two-stage oxidationreduction reaction (Fig. 1). Since the decomposed methane is detected by the FID, the uniform sensitivity for the carbon number can be obtained, regardless of the type of functional groups in a sample.</p><h4><strong>Effective Carbon Number</strong></h4><p>As mentioned above, the sensitivity of the FID detector varies depending on the types of functional groups in organic compounds. The detection sensitivity by the FID can be estimated based on the concept of the effective carbon number (ECN), which is an index showing how many carbon atoms in a compound function effectively. Depending on the type of functional groups contained in a compound, the carbon ratio in the molecule changes, and this causes changesin the sensitivity ofthe FID. For example, both ethane (C2H6) and ethanol (C2H5OH) contain the same number of carbon atoms of 2, but the sensitivity of the FID for ethanol is only 75 % of itssensitivity for ethane because the primary hydroxyl group (-OH) behaves as -0.5 carbon atoms in terms of its contribution to the ECN. This meansthe ECNof ethanol is 1.5. Table 1 shows several examples of other functional groups with ECNslowerthan the number of carbon atoms.</p><h4><strong>Analysis Conditions</strong></h4><p>In this Application News, sensitivity for ethanol when the Polyarc was added and when using only the FID was compared using an E10 sample, in which 10 v/v% ethanol was added to gasoline, simulating a biofuel which has attracted attention in recent years, and quantitativity for ethanol was confirmed. In the quantitative analysis, it was also carried out by the external standard method using a hydrocarbon (dodecane; C12) with a concentration of 1 w/v% as a standard sample. The analytical instrument was a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GC-2030"><strong>GC-2030</strong></a> (SPL/FID) model gas chromatograph with the Polyarc added (Fig. 2). Table 2 shows the analysis conditions.</p><h4><strong>Conclusion</strong></h4><p>E10 gasoline was analyzed using an FID detector and an added Polyarc microreactor, confirming that oxygenated compounds can be analyzed with high sensitivity. This study also confirmed that a uniform reaction of organic compounds can be obtained in the FID, and peak broadening can be suppressed by standardizing the response of sensitivity. Table 6 introduces several of the advantages that are possible by adding the Polyarc microreactor. The Polyarc converts all organic compounds to methane having the same number of carbon atoms (mol), enabling highsensitivity detection for compounds with low detection sensitivity by FID alone. In addition, since sensitivity is standardized, quantitation is possible using only one arbitrary calibration curve, regardless of the type of analysis compound. As a result, it is now possible to analyze compounds that were difficult to analyze by FID until now.</p>

News from LabRulezGCMS Library - Week 47, 2025

<p><strong>In the week of November 24, the following webinars await you in the field of gas / solid phase and especially Raman, FTIR, GC and GC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><div><div><div><div><div><div><h5><strong>1. Thermo Fisher Scientific: Chromeleon Processing Method Masterclass – From Integration to Calibration and Beyond</strong></h5><ul><li>We, 26.11.2025 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5682">Registration</a></li></ul><p>Join us for the "Chromeleon Processing Method Masterclass", where we will delve into the intricacies of integration, calibration, and beyond.</p><h5><strong>2. LECO: Beyond the Target – Analytical Strategies for the Non-Targeted Screening of Per- and Poly-Fluorinated Substances by GC-TOFMS</strong></h5><ul><li>We, 26.11.2025 14:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5618">Registration</a></li></ul><p>In this webinar, Prof. Flavio will present a dual analytical strategy combining one-dimensional GC and comprehensive two-dimensional GC (GC×GC) coupled with TOFMS.</p><h5><strong>3. Agilent Technologies: Quality Control in Focus – USP-Aligned Testing for Biopharmaceuticals</strong></h5><ul><li>Fr, 28.11.2025 15:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/webinar/5680">Registration</a></li></ul><p>Join Agilent for an insightful webinar focused on quality control strategies for the release testing of biopharmaceutical drugs in alignment with USP protocols.</p></div></div></div></div></div></div>

Webinars LabRulezGCMS Week 48/2025

<p><strong>Comprehensive GC×GC-TOFMS is widely used to characterize petroleum, but traditional targeted approaches can be slow and limited for large, complex data sets. A pixel-based preprocessing workflow enables rapid, untargeted chemometric exploration of two-dimensional chromatograms, allowing researchers to visualize subtle compositional differences in complex crude oils.</strong></p><p>Using this strategy, fifty Brazilian pre-salt crude oils from the Búzios field were analyzed to investigate variations in light hydrocarbons. After alignment, normalization, and multivariate analysis of total ion chromatogram matrices, PCA revealed minor yet meaningful differences among samples, while OPLS-DA successfully distinguished oils from the Barra Velha and Itapema formations—attributing lighter hydrocarbons to Barra Velha. These results demonstrate that pixel-level chemometrics is a powerful, high-throughput alternative for reservoir characterization, offering faster, more detailed insight than traditional GC×GC-TOFMS workflows.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243"><strong>Pixel-Based Chemometric Analysis of Pre-Salt Crude Oils: Advancing GC×GC-TOFMS for Reservoir Characterization</strong></a></h4><p>Mônica C. Santos*, Dayane M. Coutinho, Clarisse L. Torres, Thamara A. Barra, Victor G. K. Cardoso, Raquel V. S. Silva, Daniel S. Dubois, Joelma P. Lopes, Francisco R. Aquino Neto, and Débora A. Azevedo*</p><p>Energy Fuels 2025, 39, 15, 7204–7213</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.energyfuels.5c00243">https://doi.org/10.1021/acs.energyfuels.5c00243</a></p><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><p>Our research group has employed advanced chromatographic techniques to analyze complex matrices over the past two decades, mainly focusing on Brazilian crude oil. <a href="javascript:void(0);">(21−24)</a> GC×GC-TOFMS analysis has emerged as a pivotal tool in these studies. Vale et al. <a href="javascript:void(0);">(25)</a> demonstrated that significant improvements in crude oil sample characterization can be gained by integrating GC×GC-TOFMS with high-order chemometric methods, especially N-way partial least-squares analysis (N-PLS). Their innovative approach supports the precise calibration and prediction of properties such as American Petroleum Institute gravity (API gravity) and wax appearance temperature (WAT), achieving high accuracy and low error rates. In another work, <a href="javascript:void(0);">(26)</a> the authors used GC×GC-TOFMS with PCA analysis to assess the source- and maturity-related geochemical parameters of 11 Brazilian crude oil samples, yielding maturation indicators. These studies highlight the potential of GC×GC-TOFMS combined with chemometrics in advancing crude oil characterization; enhancing our understanding of molecular composition, biodegradation processes, source information, and maturity through enhanced data resolution and reduced data set dimensionality.</p><p>As part of the ongoing stream of research on advancing analytical methods for crude oil characterization, <a href="javascript:void(0);">(7,27)</a> this work explores a novel and rapid untargeted approach to GC×GC-TOFMS data treatment with high sensitivity to detect slight differences between complex samples. This method integrates GC×GC-TOFMS analysis with pixel-based data processing and multivariate statistical analysis to differentiate the light hydrocarbon compositions (C<sub>5</sub>–C<sub>15</sub>) of 50 presalt crude oil samples from the Búzios field in Santos Basin, Brazil. This promising analytical tool can be used to support reservoir geochemistry in oil–oil correlation studies.</p><h3><strong>2. Materials and Methods</strong></h3><h4><strong>2.4. GC-FID</strong></h4><p>GC-FID analyses were performed using a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=6890N"><strong>6890N Network Gas Chromatograph </strong></a>equipped with a fused silica capillary column and a 5%-phenyl-95%-methyl-siloxane HP5-MS film (Agilent Technologies; 30 m × 0.25 mm di; 0.25 μm d<sub>f</sub>). Previous studies have described the experimental conditions we adopted. <a href="javascript:void(0);">(33,34)</a> The samples were prepared by dissolving 10 mg of crude oil in 500 μL of a mixture containing deuterated internal standards (<i>n</i>-hexadecane-<i>D</i><sub>34</sub> and <i>n</i>-tetracosane-<i>D</i><sub>50</sub>) at a final concentration of 100 μg mL<sup>–1</sup>. Whole oil chromatogram profiles were used to assess contamination by drilling fluids.</p><h4><strong>2.5. GC×GC-TOFMS</strong></h4><p>The analyses were carried out using comprehensive two-dimensional gas chromatography (GC×GC) coupled with a time-of-flight mass spectrometry (TOFMS) system (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=24&amp;search=Pegasus+4D"><strong>Pegasus 4D</strong></a>, Leco, St. Joseph, MI). The experimental parameters were optimized in accordance with previous research. <a href="javascript:void(0);">(7)</a> The column set consisted of a DB-5 ms (30 m, 0.25 mm ID, 0.25 μm) in the first dimension and a DB-17ht (1.0 m, 0.25 mm ID, 0.15 μm) in the second dimension (Agilent Technologies) to enhance the separation and detection of light hydrocarbons, particularly in the C<sub>5</sub>–C<sub>15</sub> range. <a href="javascript:void(0);">(35)</a></p><p>The GC oven temperature was programmed from 35 °C (hold 3 min) to 40 °C with a heating rate of 1.0 °C min<sup>–1</sup>. The oven was then heated to 160 °C at 3.0 °C min<sup>–1</sup>. Finally, a heating rate of 20 °C min<sup>–1</sup> was employed up to 330 °C. Helium was the carrier gas with a constant flow rate of 1.0 mL min<sup>–1</sup>. The transfer line temperature was set to 280 °C, with the ion source temperature at 230 °C, electron energy at 70 eV, mass scanning range from <i>m</i>/<i>z</i> 25 to 600 Da, and acquisition rate at 100 spectra/second. The cold-jet–hot-jet ratio between the stages was set to 2:1. The modulation periods applied were variable. Detailed information on these modulation periods, time intervals, and variable intervals can be found in the <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.energyfuels.5c00243/suppl_file/ef5c00243_si_001.pdf">Supporting Information</a>.</p><p>The samples were prepared by solubilizing 10.0 mg of the crude oil in 200 μL of a carbon disulfide solution containing the internal standards (IS) 2,4,4-trimethyl-1-pentene, dodecane-<i>D</i><sub>26</sub>, methylcyclohexane-<i>D</i><sub>14</sub>, Bicyclo[4.4.0]decane-<i>D</i><sub>18</sub> (decalin-<i>D</i><sub>18</sub>), toluene-<i>D</i><sub>8</sub>, <i>p</i>-xylene-<i>D</i><sub>10</sub>, and naphthalene-<i>D</i><sub>8</sub> at a concentration of 100 μg mL<sup>–1</sup>. The selected IS represent a variety of hydrocarbon classes, including <i>n</i>-alkanes, <i>iso</i>-alkanes, naphthenes, and aromatics, covering different molecular weights. Each crude oil was weighed and solubilized in a carbon disulfide solution containing the mixture of internal standards. Immediately after, the vial was capped and kept in the refrigerator at 4 °C until analysis to avoid the loss of most volatile compounds.</p><h3><strong>3. Results and Discussion</strong></h3><h4><strong>3.2. Untargeted Pixel-Based GC×GC-TOFMS Approach</strong></h4><p>GC×GC-TOFMS crude oil analysis provided information-rich chromatographic profiles of compounds in the range of C<sub>5</sub>–C<sub>15</sub>. Different hydrocarbon classes were separated in the two-dimensional chromatogram space according to their volatility and polarity properties: <i>n</i>-alkanes, <i>iso</i>-alkanes, aromatic, and naphthenic compounds. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig1">Figure 1</a> shows the characterization of light hydrocarbons by class for two samples: #12 (from the Barra Velha Formation) and #1 (from the Itapema Formation). Depending on the signal-to-noise ratio applied in the conventional processing method, around 1,000 peaks may be detected and need to be checked manually by an analyst, which takes considerable time. Additionally, most crude oil samples exhibit similar GC × GC-TOFMS chromatogram profiles, as illustrated by <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig1">Figure 1</a>.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Energy_Fuels_2025_39_15_7204_7213_Figure_1_Total_ion_chromatograms_of_samples_12_Barra_Velha_Formation_and_1_Itapema_Formation_obtained_by_GC_GC_TOFMS_analysis_deba18bb6c.jpg" alt="Energy Fuels 2025, 39, 15, 7204–7213: Figure 1. Total ion chromatograms of samples #12 (Barra Velha Formation) and #1 (Itapema Formation) obtained by GC×GC-TOFMS analysis." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Energy_Fuels_2025_39_15_7204_7213_Figure_1_Total_ion_chromatograms_of_samples_12_Barra_Velha_Formation_and_1_Itapema_Formation_obtained_by_GC_GC_TOFMS_analysis_deba18bb6c.jpg 178w," sizes="100vw" width="992" height="870"></p><p>The high compositional homogeneity among the studied samples illustrates the complexity of identifying subtle differences in crude oil samples from the Búzios field. This shows how challenging grouping crude oil according to their molecular composition can be, especially when low-concentration compounds are relevant to the differentiation. In this regard, untargeted approaches followed by chemometric analyses have become promising tools for achieving deeper crude oil correlations without assessing peaks individually.</p><h5><strong>3.2.1. PCA and OPLS-DA Analyses</strong></h5><p>Unsupervised PCA analysis was used as an exploratory model for the 50 GC×GC-TOFMS crude oil profiles that employed ROI 1 (range: <i>n</i>-C<sub>6</sub>–<i>n</i>-C<sub>13</sub> in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.energyfuels.5c00243/suppl_file/ef5c00243_si_001.pdf">Supporting Information</a>). The PCA score plot (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig2">Figure 2</a>A) showed a slight tendency to separate samples by GC×GC-TOMFS chromatogram profile, roughly grouping the test samples according to their geological formation of origin. The supervised OPLS-DA method was then used for sample classification, with the Itapema and Barra Velha formations incorporated as labels in the analysis (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig2">Figure 2</a>B). The main advantage of OPLS-DA is that class information can be used to maximize covariance with chromatographic profiles. <a href="javascript:void(0);">(45)</a> An orthogonalization procedure in which the variance is related to class discrimination in the first latent variable (LV 1) facilitates more rigorous model interpretation. <a href="javascript:void(0);">(46)</a></p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Energy_Fuels_2025_39_15_7204_7213_Figure_2_Score_plot_of_A_PCA_model_applied_to_distinguish_crude_oils_4154bb5c88.jpg" alt="Energy Fuels 2025, 39, 15, 7204–7213: Figure 2. Score plot of (A) PCA model applied to distinguish crude oils showing the identification of the three highly contaminated samples (#29, #41, and #47). (B) OPLS-DA model applied for sample classification. Itapema formation is represented by orange squares and Barra Velha by blue circles." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Energy_Fuels_2025_39_15_7204_7213_Figure_2_Score_plot_of_A_PCA_model_applied_to_distinguish_crude_oils_4154bb5c88.jpg 245w," sizes="100vw" width="1750" height="797"></p><p>Therefore, when comparing OPLS-DA and PCA analysis, it was observed that the capacity to differentiate between the geological formations was improved using the supervised model. Nevertheless, based on the OPLS-DA loadings of the reconstructed chromatogram per pixel using MATLAB (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#fig3">Figure 3</a>), this separation was partly influenced by the presence of olefins, which are absent from crude oil but known to be a common constituent of drilling fluids. <a href="javascript:void(0);">(47)</a> By checking the PCA score plot, three crude oil samples from the Itapema formation with high PC1 values (#29, #41, and #47) were strongly contaminated with drilling fluid (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.energyfuels.5c00243#tbl1">Table 1</a>). This result shows that interference related to drilling fluid (an external source of compounds) may prevent the identification of slight differences in light hydrocarbons. Therefore, samples #29, #41, and #47 were removed from the original set. Additionally, a new ROI (ROI 2, <i>n</i>-C<sub>6</sub>-<i>n</i>-C<sub>12</sub> range in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.energyfuels.5c00243/suppl_file/ef5c00243_si_001.pdf">Supporting Information</a>) was established in the GC×GC chromatogram to avoid any remaining contaminant peaks without missing relevant information.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Energy_Fuels_2025_39_15_7204_7213_Figure_3_Loadings_of_the_OPLS_DA_model_comprising_the_most_important_variables_f897eb0c88.jpg" alt="Energy Fuels 2025, 39, 15, 7204–7213: Figure 3. Loadings of the OPLS-DA model comprising the most important variables (compounds) that express the differences between Itapema and Barra Velha formations. Itapema formation is enriched in olefins related to contamination by drilling fluids." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Energy_Fuels_2025_39_15_7204_7213_Figure_3_Loadings_of_the_OPLS_DA_model_comprising_the_most_important_variables_f897eb0c88.jpg 245w," sizes="100vw" width="992" height="474"></p><h3><strong>4. Conclusions</strong></h3><p>This analytical strategy enabled the identification of subtle differences in fluid composition that facilitated the differentiation of samples from the Barra Velha and Itapema formations. Supervised OPLS-DA analyses indicated the critical light hydrocarbons that enabled this discrimination.</p><p>The results show that the Barra Velha formation is enriched with lighter hydrocarbons than the Itapema formation. Additional PCA analysis revealed potential differences between crude oil samples within each geological formation. In the Barra Velha Formation, three samples were highlighted as distinct from the main group (#43, #7, and #9), while in the Itapema Formation, only sample #4 was distinct. This information shows that even when samples are very similar, it is still possible to identify slight differences that provide a better understanding of the fluid distribution and its relationship with the reservoir characterization.</p><p>Pixel-based chemometrics proves to be a rapid and innovative complement to crude oil characterization, representing a faster alternative to the current methodologies used to process and analyze GC×GC-TOFMS data. In sum, a complex oil–oil correlation case study encompassing 47 presalt crude oil samples from Búzios Field was addressed by identifying sample-distinguishing analytes, which can be further explored with targeted chemometric tools.</p>

Pixel-Based Chemometric Analysis of Pre-Salt Crude Oils: Advancing GC×GC-TOFMS for Reservoir Characterization

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Pixel-Based Chemometric Analysis of Pre-Salt Crude Oils: Advancing GC×GC-TOFMS for Reservoir Characterization

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