<p>🎤 <strong>Presenter</strong>: Christopher Freye (Los Alamos National Laboratory)</p><p>💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_1_Freye_10783a9cf3.pdf"><strong>PDF presentation</strong></a></p><h3><strong>Abstract</strong></h3><p><strong>Two-dimensional gas chromatography (</strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=2"><strong>GC×GC</strong></a><strong>) when coupled with mass spectrometry (MS) especially high-resolution mass spectrometry (HRMS) provides the ideal technique to discover minute chemical changes. However, GC×GC analyses result in extremely large datasets (GBs of information) wherein the statistically significant chemical changes are buried within the background chemical matrix. To combat these issues, many chemometric techniques have been developed or applied. Recently we introduced a new chemometric technique for GC-MS termed alteration analysis (ALA) and two-dimensional correlation (2DCOR) which can be used to discover statistically significant chemical changes across a series of chromatograms and understand the relationship between the statistically relevant changes. ALA generates three sets of information, the basic alteration map (BAM) which is the magnitude of the change, the synchronous alteration map (SAM) which describes the linear change, and the asynchronous alteration map (AAM) which describes the non-linear change. 2DCOR can then be used to understand the relationship between the chemical changes but 2DCOR can also resolve the sequence of the changes. A workflow for GC×GC-MS will be demonstrated. Special attention will be given to what differentiates the GC×GC work flow from GC for both the ALA and 2DCOR.</strong></p><h3><strong>Video Transcription</strong></h3><p>The presentation focuses on how <strong>advanced mathematical and chemometric methods</strong> can be used to extract meaningful information from <strong>highly complex chromatographic data</strong>, especially in <strong>multidimensional chromatography coupled with mass spectrometry (GC×GC-MS, LC-MS)</strong>.</p><h4><strong>Why traditional data analysis falls short</strong></h4><p>Modern multidimensional chromatography generates <strong>extremely large datasets</strong>, often containing <strong>billions of data points</strong>. Visual inspection, peak tables, or classical identification-based workflows are impractical—especially when:</p><ul><li>Samples are highly complex (e.g. petroleum, metabolomics, high explosives)</li><li>Most compounds are <strong>unknown unknowns</strong></li><li>The key question is not <i>what</i> the compounds are, but <strong>whether samples differ and how</strong></li></ul><p>Program managers and decision-makers are primarily interested in <strong>detecting statistically significant changes</strong>, not exhaustive compound identification.</p><h4><strong>Introduction of Alteration Analysis (ALA)</strong></h4><p>To address subtle, trend-based changes across series of chromatograms, the speaker introduces <strong>Alteration Analysis (ALA)</strong>, a chemometric approach adapted from spectroscopy and applied to chromatography.</p><p>ALA quantitatively evaluates how <strong>individual data points change across multiple samples</strong>, allowing detection of:</p><ul><li>Extremely subtle trends</li><li>Overlapping peaks</li><li>Changes hidden in noise or baseline interference</li></ul><p>Key ALA outputs include:</p><ul><li><strong>BAM (Basic Alteration Map):</strong> magnitude of change</li><li><strong>SAM (Systematic Alteration Map):</strong> linear changes</li><li><strong>AM (Alteration Map):</strong> nonlinear changes</li></ul><p>Even changes as small as <strong>1% per sample</strong> can be detected reliably, including peaks that are heavily overlapped.</p><h4><strong>Beyond detection: understanding relationships with 2D Correlation</strong></h4><p>Once statistically significant changes are identified, <strong>two-dimensional correlation analysis (2D-COR)</strong> is used to understand <strong>relationships between changing compounds</strong>, including:</p><ul><li>Whether compounds are correlated or anti-correlated</li><li>The <strong>sequence of changes over time</strong> (which changes happen first)</li></ul><p>This is especially critical for <strong>degradation and aging studies</strong>, where intermediate species may appear and disappear before the final state—information that traditional methods like Fisher ratio or PCA often miss.</p><h4><strong>Solving GC×GC challenges with tiling</strong></h4><p>GC×GC data introduces additional challenges due to:</p><ul><li>Peak misalignment</li><li>Exponential data growth during correlation calculations</li></ul><p>The presenter explains how <strong>tile-based approaches</strong> reduce data dimensionality, overcome alignment issues, and make ALA and 2D correlation computationally feasible—even for massive GC×GC-MS datasets.</p><h4><strong>Real-world application: explosive aging studies</strong></h4><p>The methodology was applied to <strong>aging and decomposition of high explosives</strong>, where:</p><ul><li>Over <strong>250 chemical changes</strong> were identified</li><li>Many intermediate species would have been missed using traditional comparisons</li><li>ALA combined with 2D correlation enabled reconstruction of <strong>degradation pathways</strong></li><li>The most significant change was <strong>not necessarily the first change</strong>, a critical insight for risk assessment and stability studies</li></ul><h4><strong>Key takeaway</strong></h4><p>By combining <strong>Alteration Analysis and 2D correlation</strong>, the approach:</p><ul><li>Detects statistically guaranteed chemical changes</li><li>Handles subtle, overlapped, and noisy signals</li><li>Determines <strong>magnitude, type, interrelationships, and order of changes</strong></li><li>Shifts chromatographic data analysis from identification-driven to <strong>decision-driven</strong></li></ul><p>This math-driven workflow enables faster, more reliable interpretation of complex chromatographic datasets and provides actionable insights for applications such as <strong>explosive aging, pharmaceutical degradation, and complex mixture analysis</strong>.</p><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>

Discovery analysis for GCxGC trends using alteration and 2D correlation (Chris Freye, MDCW 2026)

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 9th February 2026? 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 technical note by <strong>Agilent Technologies, </strong>presentation by<strong> MDCW / University of Copenhagen </strong>and<strong> </strong>application notes by<strong> Shimadzu </strong>and<strong> Thermo Fisher Scientific!</strong></p><h3><strong>1. Agilent Technologies: What Causes Spots on </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=19&amp;manufacturerItems=1&amp;search=PLOT"><strong>PLOT Columns</strong></a><strong>? Will It Affect the Performance of the Column?</strong></h3><ul><li>Technical note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/te_what_causes_spots_plot_columns_affecting_performance_5994_8775en_agilent_3b7180512a.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Spots on porous layer open tubular (PLOT) columns are caused by small voids in the stationary phase coating inside the tube. These spots are areas where there is an uneven coating or lack of phase. It is normal to find some voids in the phase coating of PLOT columns. The stationary phase in PLOT columns is an opaque, solid, and porous material. In many Agilent PLOT columns such as the GS-Alumina phases, molecular sieves, porous polymers (Q and U), and the GS-OxyPLOT, the stationary phase is visible as a white powder. For many PLOT columns, the stationary phase fill solution is a viscous suspension of particles that are less than 10 microns in diameter. The phase is coated using a dynamic process in which a plug of fill solution is pushed through the tubing, and the plug’s speed and viscosity determines the thickness of the stationary phase layer. Once the PLOT’s stationary phase is deposited, it is held in place by electrostatic charge, chemical interactions between particles, and/or binding agents. In wall‑coated open tubular (WCOT) columns, stationary phases such as polysiloxanes or polyethylene glycols are immobilized by chemical bonding to the glass surface and cross-linking between polymer strands. Because this type of bonding and cross‑linking is not possible with PLOT phases, the result is a stationary phase layer that is both fundamentally different and inherently less stable in PLOT columns than in WCOT&nbsp;columns.</p><h3><strong>2. MDCW / University of Copenhagen: One-shot tensor decomposition of full-scale GC×GC-VUV datasets for resolving petrochemical groups</strong></h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_17_Schneide_67c8ff7866.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The presentation introduces a novel <strong>all-samples-at-once, shift-invariant tri-linear tensor decomposition approach</strong> for the analysis of full-scale <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?search=GC%C3%97GC-VUV"><strong>GC×GC-VUV</strong></a><strong> datasets</strong>, aimed at resolving and quantifying petrochemical groups. Traditional template-based or pixel-based methods struggle with retention-time shifts and co-eluting compounds, often requiring manual intervention and prior chemical knowledge. The proposed approach leverages the intrinsic low-rank structure of GC×GC-VUV data to enable robust, automated group-level analysis across multiple samples simultaneously.</p><p>At the core of the method is a <strong>shift-invariant tri-linear model</strong>, conceptually related to PARAFAC and multivariate curve resolution, but extended using frequency-domain (FFT-based) modeling to explicitly account for chromatographic peak shifts. By decomposing the entire dataset in one step, the model provides <strong>accurate estimates of peak landscapes, VUV spectra, and relative peak areas</strong>, while remaining agnostic to retention-time variability. Importantly, the approach requires only a small number of tunable parameters, making it practical for routine use.</p><p>The method was demonstrated on a <strong>case study involving 14 petrochemical gas-oil samples</strong>, including straight-run, hydrotreated, hydroconverted, coker, and light cycle oils. Extracted VUV spectra showed excellent agreement with reference spectra from a VUV spectral database, enabling confident assignment of components to chemical groups such as saturates, olefins, and mono- to poly-aromatics. Quantitative results exhibited <strong>high repeatability (RSD typically 3–9%)</strong> and strong correlation with established template-based reference methods.</p><p>Overall, the work demonstrates that <strong>one-shot, shift-invariant tensor decomposition</strong> provides a powerful and scalable alternative to conventional GC×GC data-analysis strategies. By combining chemical interpretability, robustness to chromatographic shifts, and strong quantitative performance, the approach opens new opportunities for automated, high-confidence group-level analysis of complex petrochemical samples and beyond.</p><h3><strong>3. Shimadzu: Py-Screener Ver. 3: Simultaneously Test for Substances Regulated Under RoHS, TSCA, and POPs</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_00949_en_4754e50164.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li>In just one analysis, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=Py-Screener"><strong>Py-Screener</strong></a> Ver. 3 can determine levels of phthalate esters, brominated flame retardants (PBBs, PBDEs), PIP (3:1), UV-328, dechlorane plus (DP), short-chain chlorinated paraffins (SCCPs), and medium-chain chlorinated paraffins (MCCPs).&nbsp;</li><li>It can also test for SCCPs and MCCPs by electron ionization (EI) instead of negative chemical ionization (NCI).&nbsp;</li><li>PIP (3:1), UV-328, DP, and SCCPs and MCCPs can be screened without the need for corresponding reference materials.</li></ul><p>Countries and regulatory bodies worldwide are introducing increasingly stringent regulations on the chemical substances used in electronic devices and industrial products. The RoHS Directive and REACH regulations in the EU, the Toxic Substances Control Act (TSCA) in the USA, and the Stockholm Convention on Persistent Organic Pollutants (POPs) are also targeting an increasingly broad range of substances. Therefore, manufacturers are required to accurately identify regulated substances in their products and comply with each regulation. While Py-Screener Ver. 2 can screen for seven phthalate esters and brominated flame retardants, including substances regulated under the RoHS Directive, version 3 can also screen for PIP (3:1), which is regulated under the TSCA, as well as UV328, DP, and SCCPs and MCCPs , which are regulated under the Stockholm Convention on POPs. A list of supported substances isshown in Table 1. This article introduces the simultaneous analysis of multiple regulated substances and a case study of testing specific regulated substances in samples, using Py-Screener Ver. 3.</p><h4><strong>Analysis Process</strong></h4><p>Py-Screener Ver. 3 can determine levels of phthalate esters and brominated flame retardants contained in products in accordance with the international analytical standards IEC62321-8 and IEC62321-3-3. It can also test for specific regulated substances (Table 1) under the same conditions and in the same analysis. In this test, background intensity and instrument sensitivity (S/N) were verified before analyzing samples. These parameters are prescribed by IEC62321-8 to ensure the accuracy of the methods used. Background intensity and instrument sensitivity were determined using a reference material (PN: 225-31003- 91) *1 that consisted of a plastic sheet that was spiked with seven phthalate esters. The results met the criteria for both parameters.</p><p>The testing workflow is shown in Fig. 1. Before testing samples, calibration curves were prepared by analyzing the 1000 mg/kg phthalate ester reference material and polypropylene spiked with brominated flame retardants (ERM-EC591). A hole puncher was used to punch out two discs at a time of the phthalate ester reference material, and a cutter was used to obtain pieces of the other materials. Each material was weighed in a sample cup for analysis. During the analysis of samples, phthalate esters and some of the regulated brominated flame retardants were quantitated using a one-point calibration curve. The remaining brominated flame retardants and specific regulated substances were quantitated using the correction factor database*2 that is included with Py-Screener Ver. 3.</p><h4><strong>Conclusion</strong></h4><p>Py-Screener Ver. 3 successfully determined in samples the concentrations of the regulated substances PIP (3:1), UV-328, DP, and SCCPs and MCCPs. Normally, SCCP and MCCP levels are tested using NCI, but Py-Screener Ver. 3 uses EI. This enables it to test for not only SCCPs and MCCPs but also phthalate esters, brominated flame retardants, and other regulated substances in just one analysis. In addition, by using the relative correction factors linked to DEHP in the phthalate ester reference material, the levels of these regulated substances were determined without the need for the corresponding reference materials. Py-Screener Ver. 3 can also create a screening program that addresses substance restrictions under the RoHS Directive, the US TSCA, and the POPs Convention.</p><h3><strong>4. Thermo Fisher Scientific: Improving low carbon steel production in specialty steel processes</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/prima_pro_process_mass_spectrometer_an1626_7fc840235f.pdf"><strong>Full PDF for download</strong></a></li></ul><p>World crude steel production reached 1,691 million tonnes in 2017, up by 5.3% compared to 2016.1 While much of this capacity is still produced in primary steel processes such as basic oxygen furnaces and electric arc furnaces, the need for refined steel with greater durability and resistance to heat and corrosion has led to the increased use of vacuum degassing processes, such as VOD and RH, in secondary steel production. These processes are able to achieve ultra-low levels of residual carbon, while at the same time retaining desired levels of other alloy materials. If these processes are to achieve the required level of steel product quality, they need fast, continuous gas analysis of the furnace exhaust gas. Without accurate information on the composition of the gas leaving the furnace, any variations in the decarburization process are only detected after the event, resulting in the production of out-of-specification steel.</p><h4><strong>Advantages of mass spectrometry&nbsp;</strong></h4><p>Traditional Non-Dispersive Infra-Red (NDIR) analyzers are used in many conventional steelmaking processes to measure CO and CO<sub>2</sub> , but they can only sample at atmospheric pressure. In vacuum steelmaking the process pressure changes dramatically, typically from atmospheric pressure down to less than 1 mbar, over the 20-30 minutes of the melt. So NDIR analyzers have to sample some distance downstream from the process. Analytical data is updated several minutes after the gas leaves the melt and the control system is forced to operate on historic rather than real-time data.&nbsp;</p><p>Paramagnetic analyzers can be used to measure O<sub>2</sub> , while thermal conductivity analyzers can be used to measure H<sub>2</sub> . These analyzers also suffer from slow response, while the need to operate three different types of analyzers adds to the plant maintenance burden. Moreover, the three analyzers cannot analyze inert gases, so N<sub>2</sub> is calculated by difference, a result that suffers from the sum of the errors of the three analytical techniques.&nbsp;</p><p>Mass spectrometry operates at high vacuum so it is ideal for monitoring vacuum processes. It is also able to monitor all seven components in Table 1 in seconds rather than minutes, ensuring the plant control model is frequently updated with accurate compositional data.</p><h4><strong>Precision of analysis&nbsp;</strong></h4><p>At the heart of the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=2&amp;search=Prima+PRO+Mass+Spectrometer"><strong>Thermo Scientific™ Prima PRO Mass Spectrometer</strong></a> (MS) is a magnetic sector analyzer which offers unrivalled precision and accuracy compared with other mass spectrometers. Thermo Fisher Scientific manufactures both quadrupole and magnetic sector mass spectrometers; over thirty years of industrial experience have shown the magnetic sector based analyzer offers the best performance for industrial on-line gas analysis.&nbsp;</p><p>Key advantages of magnetic sector analyzers include improved precision, accuracy, long intervals between calibrations, and resistance to contamination. Typically, analytical precision is between 2 and 10 times better than a quadrupole analyzer, depending on the gases analyzed and complexity of the mixture.&nbsp;</p><p>A unique feature of the Prima PRO magnet is that it is laminated. Its analysis times are similar to a quadrupole analyzer, offering the unique combination of rapid analysis and high stability. This allows the rapid and extremely stable analysis of an unlimited number of user-defined gases. The scanning magnetic sector is controlled with 24-bit precision using a magnetic flux measuring device for extremely stable mass alignment.&nbsp;</p><p>The ion source is an enclosed type for high sensitivity, minimum background interference, and maximum contamination resistance. This is a high-energy (1000 eV) analyzer that offers extremely rugged performance in the presence of gases and vapors that have the potential for contaminating the analyzer. Typical performance specifications for the Prima PRO MS are shown in Table 1. Analytical performance is demonstrated by analyzing the calibration bottle over 1 hour following calibration, with an analysis time of just 6 seconds. Standard deviations measured on the calibration cylinder will be equal to or better than the stated values.</p>

News from LabRulezGCMS Library - Week 07, 2026

<p><strong>Although modern agriculture needs to be based on accurate real-time data on soil, plant, and environmental conditions, affordable, sufficiently sensitive, and robust monitoring devices for this purpose are currently missing. According to scientists, low-cost sensors based on new smart materials and printing technologies offer a suitable solution. The results of multidisciplinary research thus far and proposals for the development of these devices have been presented by researchers from Czechia, Great Britain, Spain, and the USA in a review article in the prestigious journal </strong><a target="_blank" rel="noopener noreferrer" href="https://www.nature.com/articles/s41467-026-68778-6#Abs1"><i><strong>Nature Communications</strong></i></a><strong>.</strong></p><p>“<i>Sensor technologies for monitoring plant health are still in their infancy. Only simple sensors capable of monitoring basic parameters such as pH, soil moisture, or temperature are commonly available on the market. However, if farmers are to face current challenges, including climate change, and ensure sufficient food for humanity, they need much more effective tools that will enable them to monitor, for example, various pathogens, including bacteria and fungi. In our opinion, a suitable solution is a combination of new smart materials and printing for the production of inexpensive, durable, and scalable materials</i>,” said the article’s first author, <strong>David Panáček</strong> from CATRIN, who worked on the study during his internship at Imperial College London.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/CATRIN_Soucasne_senzory_nestaci_Vedci_navrhuji_novou_cestu_ke_sledovani_zdravi_rostlin_b4014952a3.jpg" alt="CATRIN: David Panáček" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_CATRIN_Soucasne_senzory_nestaci_Vedci_navrhuji_novou_cestu_ke_sledovani_zdravi_rostlin_b4014952a3.jpg 208w," sizes="100vw" width="1536" height="1152"></p><p>Sensors for monitoring plant health, for example, lag behind similar devices used in human medicine. According to the authors, advances in materials science, which will enable the development of scalable sensor technologies, play a key role in this potential shift. “<i>Printing techniques, including inkjet printing, screen printing, aerosol printing, 3D printing, and direct laser writing, offer flexible ways to produce flexible sensors, large-area sensors, and sensors integrated directly into plants. The review article summarizes recent advances in printable low-dimensional materials for agricultural sensors, analyzes their physico-chemical properties in relation to sensor performance, and discusses key challenges and future opportunities requiring interdisciplinary integration</i>,” said one of the corresponding authors, <strong>Michal Otyepka</strong>.</p><p>The research brought together materials scientists, chemists, biologists, sensing experts, and printing technology specialists. Materials research and the use of graphene-based smart materials were led by scientists from CATRIN at Palacký University and VŠB-Technical University of Ostrava. Plant biologists from CATRIN contributed their knowledge of plant development, their responses to stress conditions, and the monitoring of their properties. Researchers from Imperial College London took care of the electronics, while representatives from<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/companies/36"><strong> CEITEC – VUT</strong></a> focused on printing technologies. The team also included leading experts in sensing from the Catalan Institute of Nanoscience and Nanotechnology in Barcelona, and Joseph Wang of the University of California San Diego, the “father of sensing and electrochemistry,” also contributed his extensive knowledge.</p><p>“<i>This is a groundbreaking study on this topic, which has managed to attract some truly big names in their respective fields. Last but not least, the study demonstrates that interdisciplinarity at CATRIN gives rise to completely new and promising research topics, such as plant sensing and its use for studying plant-environment interactions and smart agriculture,</i>” added another corresponding author, <strong>Nuria De Diego</strong>.</p>

Current sensors are not good enough. Scientists propose a new way to monitor plant health

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The Multidimensional Chromatography (MDC) Workshop draws experts in this exciting field to share and discuss their research.

The Multidimensional Chromatography Workshop

The Czech Advanced Technology and Research Institute (CATRIN) at Palacký University is a cutting-edge scientific hub dedicated to advancing research in the fields of nanotechnology, biotechnology, and biomedicine. At its core, CATRIN boasts outstanding scientific teams, featuring international researchers. We emphasize interdisciplinarity, foster global collaborations, and work to translate our research findings into practical applications.

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VUV Analytics VGA-100 Gas Chromatography Detector for TRACE 1600 Series GC

The VGA-100 GC detector with VUV technology and TRACE 1600 GC delivers rapid, accurate analysis of isomers and complex mixtures with higher productivity and reliability.

<p><strong>The Thermo Scientific™ VGA-100™ Gas Chromatography Detector (developed by VUV Analytics, Inc.) introduces the next generation of GC detection through revolutionary vacuum ultraviolet (VUV) absorption technology. Delivering unmatched sensitivity, selectivity, and precision, the VGA-100 provides both qualitative and quantitative data with exceptional accuracy.</strong></p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_VGA_100_Gas_Chromatography_Detector_2_e45d1cf5c3.jpg" alt="Thermo Scientific VGA-100 Gas Chromatography Detector." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_VGA_100_Gas_Chromatography_Detector_2_e45d1cf5c3.jpg 165w," sizes="100vw" width="752" height="710"></p><h3><strong>Key Benefits</strong></h3><ul><li><strong>Seamless integration with TRACE 1600 Series GC</strong> – Together, the VGA-100 and<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/483"><strong> TRACE™ 1600 GC </strong></a>form a powerful, scalable, and intuitive system designed to meet the most demanding laboratory workflows.</li><li><strong>Enhanced productivity and speed</strong> – Faster analysis, higher throughput, and reduced operational costs thanks to automated spectral deconvolution and streamlined data processing.</li><li><strong>Superior selectivity and clarity</strong> – Compound-specific absorption spectra enable confident identification of isomers and co-eluting compounds without baseline separation.</li><li><strong>Universal and mass-sensitive detection</strong> – Provides comprehensive 3D data (time, wavelength, intensity) with library matching for reliable compound identification across a broad range of analytes.</li><li><strong>Ease of use</strong> – No vacuum pumps required, minimal maintenance, intuitive operation, and full compatibility with both R&amp;D and QC environments.</li><li><strong>VUV Analyze™ Software integration</strong> – Push-button workflows, automated Time Internal Deconvolution™ (TID), and ready-to-use spectral libraries deliver fast, reliable, and reproducible results.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_VGA_100_Gas_Chromatography_Detector_5_e387eb9360.jpg" alt="Thermo Scientific VGA-100 Gas Chromatography Detector: VUV spectra of hydrocarbons exhibit class-based similarities, simplifying complex fuel mixture analysis." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_VGA_100_Gas_Chromatography_Detector_5_e387eb9360.jpg 245w," sizes="100vw" width="1170" height="552"></p><h3><strong>Applications</strong></h3><p>The VGA-100 detector addresses critical analytical challenges such as accurate isomer identification, analyte co-elution, long run times, and complex data interpretation. It is especially impactful in:</p><ul><li><strong>Petrochemical analysis</strong> – Simplifies PIONA (paraffins, isoparaffins, olefins, naphthenes, aromatics) characterization in finished fuels, enabling rapid, automated fuel analysis.</li><li><strong>Environmental, food, and pharmaceutical testing</strong> – Reliable quantification of complex mixtures with improved accuracy and reduced calibration complexity.</li><li><strong>Quality control and research labs</strong> – Scalable, intuitive platform supporting compliance, throughput, and robust data integrity.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_VGA_100_Gas_Chromatography_Detector_3_ddcbe84bde.jpg" alt="Thermo Scientific VGA-100 Gas Chromatography Detector: Simplified PIONA compound analysis in finished gasoline." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_VGA_100_Gas_Chromatography_Detector_3_ddcbe84bde.jpg 245w," sizes="100vw" width="956" height="426"></p><h3><strong>Summary</strong></h3><p>The <strong>VGA-100 GC Detector</strong> sets a new benchmark in gas chromatography by combining VUV absorption technology with the robust performance of the TRACE 1600 Series GC. Laboratories gain a powerful “ready-to-use” solution that enhances throughput, accuracy, and confidence across diverse industries.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_VGA_100_Gas_Chromatography_Detector_4_5595ee9da1.jpg" alt="Thermo Scientific VGA-100 Gas Chromatography Detector." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_VGA_100_Gas_Chromatography_Detector_4_5595ee9da1.jpg 139w," sizes="100vw" width="816" height="918"></p>

VUV Analytics VUV Detector for TRACE 1600 Series GC

Thermo Scientific VGA-100 and LUMA VUV detectors for TRACE 1600 GC deliver accurate hydrocarbon speciation, impurity detection, and trace-level analysis with speed and reliability.

<div><div><div><div><div><h4><strong>Precision, clarity, and confidence from VUV spectroscopy in GC</strong></h4></div></div><div><div><p>VUV detectors paired with the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/483"><strong>Thermo Scientific TRACE 1600 Series GC </strong></a>conquer the challenge of complex mixtures, co-eluting compounds, and isomer differentiation&nbsp;for accurate hydrocarbon speciation, impurity detection, and trace-level detection.</p><p>Gas Chromatography (GC) is evolving, and VUV (Vacuum Ultraviolet) technology is at the forefront of this transformation. Now available on the TRACE 1600 Series GC, VUV spectroscopy, through VUV <strong>Analytics VGA-100 </strong>detector and the<strong> LUMA detector</strong>, provides accuracy in hydrocarbon speciation, impurity detection, and trace-level analysis.</p><h5><strong>Greater accuracy, automation, and confidence in your GC analysis</strong></h5><p>Traditional GC detectors struggle with complex mixtures, co-eluting compounds, and isomer differentiation. VUV spectroscopy overcomes these limitations by using absorption-based detection, capturing unique molecular fingerprints for each compound.</p><div><h3><strong>How VUV technology works</strong></h3></div><div><div><div><div><div><p>VUV detection provides qualitative and quantitative spectral information for most gas phase compounds.&nbsp;</p></div></div></div></div><div><div><div><div><div><p><strong>Broad-spectrum absorption:&nbsp;</strong>VUV detectors measure how molecules absorb light in the vacuum ultraviolet range (120–240 nm), revealing unique spectral signatures.</p><p><strong>Automated compound ID:&nbsp;</strong>To reduce errors and improve analysis speed, VUV software automatically deconvolutes overlapping peaks. unlike traditional GC detectors.</p><p><strong>Quantitative and qualitative insights:&nbsp;</strong>VUV enables compound identification and quantification in a single run by analyzing complete spectral data.</p></div></div></div></div></div><div><div><div><div><h4><strong>Principle of operations</strong></h4><div><div><div><div><ol><li><b style="font-size:1.0pc;"><strong>Sample injection in the GC: </strong></b>A sample is injected into the GC system and separated into its components as it travels through the GC column.</li><li><strong>Elution from the analytical column:&nbsp;</strong>The separated components elute from the GC column and enter the VUV detector.</li><li><strong>VUV light source:&nbsp;</strong>A deuterium lamp as VUV light source emits light in the vacuum ultraviolet range (typically 120-200 nm).</li><li><strong>Flow cell:&nbsp;</strong>The eluted components pass through a flow cell where they are exposed to the VUV light.</li><li><strong>Analytes absorption of VUV light:&nbsp;</strong>As components pass through the flow cell, they absorb VUV light at specific wavelengths. Each compound has a unique absorption spectrum in the VUV range.</li><li><strong>Analytes detection:&nbsp;</strong>Photodiode array detector measures the intensity of the transmitted VUV light. The decrease in light intensity corresponds to the absorption by the analytes.</li><li><strong>Data processing:&nbsp;</strong>Detector sends the signal to a data processing system, which converts the absorption data into a chromatogram.</li></ol><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_VUV_Detector_for_TRACE_1600_Series_GC_e3f5a229d6.jpg" alt="Thermo Scientific  VUV Detector for TRACE 1600 Series GC." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_VUV_Detector_for_TRACE_1600_Series_GC_e3f5a229d6.jpg 245w," sizes="100vw" width="2048" height="1128"></p><div><div><div><h3><strong>VUV for petrochemical and refining</strong></h3><ul><li><strong>PIONA analysis: </strong>Accurately classify paraffins, isomers, olefins, naphthenes, and aromatics with automated deconvolution for fast, confident hydrocarbon speciation and group type analysis in complex fuels.</li><li><strong>Fuel quality control (ASTM D8071):</strong> Ensure compliant, efficient fuel characterization using VUV’s spectral fingerprints, delivering fast, automated analysis for gasoline and other fuel blends.</li><li><strong>Sulfur &amp; nitrogen speciation:</strong> Detect and speciate sulfur and nitrogen compounds at trace levels in fuels, ensuring regulatory compliance and protecting product quality.</li></ul></div></div></div><div><div><div><h3><strong>VUV for pharmaceutical and chemicals</strong></h3><ul><li><strong>Residual solvent analysis: </strong>Faster, more sensitive detection of residual solvents, ensuring compliance with USP &lt;467&gt; and reducing reanalysis with precise, automated detection.</li><li><strong>Impurity profiling in APIs: </strong>VUV absorbance provides greater selectivity and lower detection limits than traditional GC detectors, ensuring reliable impurity profiling in even the most complex APIs.</li><li><strong>Trace-level contamination detection: </strong>Identify unknown contaminants at low levels in pharmaceuticals, chemicals, and environmental samples with enhanced selectivity and sensitivity.</li></ul></div><div><div><div><div><div><h4><strong>Benefits of upgrading to GC-VUV</strong></h4><div><div><div><div><div><p><strong>Hydrocarbon speciation</strong></p><ul><li>Distinguish paraffins, olefins, naphthenes, and aromatics (PONA) effortlessly</li><li>Eliminate misidentification errors common with FID and MS</li><li>Automate peak integration and classification</li></ul><div><div><div><div><div><p><strong>Trace impurity detection</strong></p><ul><li>Detect sulfur, nitrogen, and oxygen compounds at trace levels</li><li>Ensure compliance with regulatory standards in fuels and pharmaceuticals</li><li>Identify unknown contaminants in a single run</li></ul><div><div><div><div><div><p><strong>Analysis and workload</strong></p><ul><li>Decrease complex calibration standards requirement</li><li>Automated spectral deconvolution = less manual processing</li><li>Improved lab efficiency and throughput</li></ul><div><h3><strong>Which detector is right for you?</strong></h3><p>VGA-100 and LUMA detectors integrate seamlessly with the TRACE 1600 Series GC, offering plug-and-play installation, intuitive software, and superior analytical performance.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_VUV_Detector_for_TRACE_1600_Series_GC_2_f6a599c167.jpg" alt="Thermo Scientific  VUV Detector for TRACE 1600 Series GC." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_VUV_Detector_for_TRACE_1600_Series_GC_2_f6a599c167.jpg 245w," sizes="100vw" width="777" height="258"></p><blockquote><ul><li><strong>VGA-100 detector</strong></li><li><strong>LUMA detector</strong></li></ul></blockquote></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

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