<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 3rd August 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 application notes by <strong>Agilent Technologies </strong>and<strong> Shimadzu </strong>and presentation by<strong> MDCW / University of Alberta!</strong></p><h3><strong>1. Agilent Technologies: Analysis of Trace Impurities in Monocyclic Aromatic Solvents Following ASTM D7504</strong></h3><p><strong>Dual simultaneous analysis using the Agilent 8860B gas chromatograph</strong></p><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_trace_impurity_aromatic_solvents_analysis_astm_d7504_8860_gc_5994_9316en_agilent_c9fa6868e6.pdf"><strong>Full PDF for download</strong></a></li></ul><section><div><div><div><div><div><div><p>The production of high-purity monocyclic aromatic hydrocarbons, such as benzene, toluene, ethylbenzene, and xylenes (BTEX), is a cornerstone of the modern petrochemical industry. These compounds serve as essential feedstocks for the synthesis of plastics, resins, and textiles. Because even trace impurities can adversely affect downstream chemical reactions and product quality, rigorous analytical testing is required to ensure these intermediates meet strict commercial specifications.&nbsp;</p><p>ASTM D7504 is the industry-standard test method for determining the purity of, and trace impurities in, monocyclic aromatic hydrocarbons using gas chromatography (GC) with flame ionization detector (FID).1 This method is favored for its streamlined workflow; it utilizes effective carbon number (ECN) correction factors for area normalization. This eliminates the need for labor-intensive, multipoint calibrations for every individual impurity, allowing laboratories to calculate purity and quantify trace nonaromatics and aromatics with high precision.&nbsp;</p><p>The application scope of ASTM D7504 covers a wide array of aromatic liquids, including benzene, toluene, mixed xylenes, and styrene. To comply with the method's stringent performance requirements, the GC system must&nbsp;demonstrate:&nbsp;</p><ul><li><strong>High sensitivity: </strong>A limit of quantitation (LOQ) of at least 0.0006% by mass.</li><li><strong>Good resolution: </strong>Maintaining a valley-to-peak ratio of no more than 50% for critical separations (such as p-xylene and m-xylene).&nbsp;</li><li><strong>Strict precision: </strong>Highly reproducible peak areas to ensure accurate purity calculations.</li></ul><p>Implementing ASTM D7504 on a dual-channel GC/FID platform—where two independent analytical channels configured with identical injectors, columns, and detectors operate within a single GC oven—provides significant operational advantages:&nbsp;</p><ol><li>Enhanced laboratory throughput: By performing two injections simultaneously, the test lab can double its sample capacity per instrument, effectively reducing the cost-per-analysis and maximizing hardware ROI.&nbsp;</li><li>Method flexibility and redundancy: A dual-channel set up allows a lab to run different sample types (such as toluene on the front channel and xylene on the back) in a single oven cycle. Furthermore, it provides an immediate "back up" channel to maintain uptime during routine&nbsp;maintenance.&nbsp;</li></ol><p>For the system to be considered reliable, both channels should meet ASTM D7504 sensitivity, resolution, and precision requirements. In addition, both channels should yield consistent quantitative results for the same sample. The reproducibility limit (R) demonstrated in ASTM D7504 interlaboratory testing will be used to evaluate the result consistency between the two channels.</p><p>ASTM D7504 recommends helium and hydrogen as carrier gas. In China, major Chinese petrochemical companies often align their internal quality control standards directly with ASTM D7504 when testing for export of high-purity chemical intermediates. Because nitrogen is commonly used in China, they commonly adapt the ASTM D7504 method to use nitrogen carrier gas.&nbsp;</p><p>This application note demonstrates how dual-channel analysis on the Agilent 8860B GC, following ASTM D7504, delivers consistent and high-precision data using three types of carrier gas.</p><h4><strong>Experimental</strong></h4><p>An <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8860B"><strong>Agilent 8860B GC</strong></a><strong> </strong>was configured with dual split/splitless inlets and dual flame ionization detectors (FIDs). The sample introduction was made by dual <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7693A"><strong>Agilent 7693A automatic liquid samplers (ALS)</strong></a> with high density turrets (16-vial). The analytical parameters and consumables are summarized in Tables 1 and 2. Data acquisition and analysis were conducted using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=OpenLab+CDS"><strong>Agilent OpenLab CDS version 2.8</strong></a></p><h4><strong>Conclusion&nbsp;</strong></h4><p>The Agilent 8860B gas chromatography system configured with two identical analytical channels are applied for impurity analysis in monocyclic aromatic solvents using three carrier gases. Each analytical channel was evaluated according to the ASTM D7504 method in terms of resolution, sensitivity, quantitation precision, and recovery with satisfactory results. The quantitation results between the two channels are consistent with each other.&nbsp;</p><ul><li>Column resolution compounds pair demonstrated good separation with H/V no less than 5:1 for 300 ppm m-xylene and p-xylene solvent.&nbsp;</li><li>The LODs achieved for benzene on each analytical channel are less than 0.0001%, exceeding the 0.0002% LOD required by ASTM D7504.&nbsp;</li><li>The response and mass concentration precisions of most impurities in check standards are below 1%, meeting or exceeding the precision requirements in D7504.&nbsp;</li><li>Excellent recovery results, ranging from 93 to 105%, was achieved for added impurities, indicating good quantification accuracy based on the 8860B GC system.&nbsp;</li><li>The quantitation difference of all probe impurities between two channels is less than the reproducibility thresholds (R), demonstrating excellent across channel consistency</li></ul><p>Besides great instrument performance, the Agilent OpenLab CDS software can realize ECN based normalization for easy and fast quantitation and report generation. The 8860B dual channel GC system coupled with the OpenLab CDS software provides an accurate and reliable analysis of impurities in monocyclic aromatic solvents with satisfactory precision. Lab productivity can be improved significantly compared to the single channel analytical system.</p><h3><strong>2. MDCW / University of Alberta: Leveraging Local Libraries for Positively Parsimonious Peak Tables</strong></h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_6_Harynuk_f005bdafaf.pdf"><strong>Full PDF for download</strong></a></li></ul><section><div><div><div><div><div><div><p>James J. Harynuk’s presentation, <strong>“Leveraging Local Libraries for Positively Parsimonious Peak Tables,”</strong> explores how custom mass spectral libraries can simplify and improve compound identification in complex GC×GC-(HR)MS datasets. GC×GC-MS is widely applied in petroleum, environmental, food/flavour, and omics research, where samples can contain hundreds or thousands of peaks. While expanded separation space, cleaner spectra, retention patterns, and high-resolution accurate-mass data provide strong identification capabilities, converting large datasets into coherent peak tables with consistent compound assignments across multiple samples remains challenging.</p><p>The presentation demonstrates the approach using a field pea study investigating how nitrogen availability influences plant metabolism and volatile organic compounds. A conventional workflow based on peak finding and library searching can generate very large peak tables and inconsistent assignments. Incorporating retention indices (RI) and LECO’s Identification Grading System (IGS) substantially reduces the number of questionable peaks. In one example, a pea sample containing <strong>789 detected peaks was reduced to 146 retained peaks</strong> after RI filtering and IGS scoring, producing a much more manageable dataset. However, conventional libraries still struggle with isomers and with compounds that repeatedly occur in a laboratory’s samples but cannot yet be confidently identified—the presentation calls these <strong>“unknown knowns.”</strong></p><p>Harynuk therefore proposes building a <strong>custom local HRMS library</strong> containing not only standards and confidently identified compounds, but also reproducible unknown features. To qualify for inclusion, a peak should have a high-quality spectrum, S/N &gt;100, chromatographic purity, occurrence in multiple technical replicates, an assigned retention index, and a unique identifier. Crucially, the exact chemical identity does not need to be known: an unknown but reproducible feature can receive an internal identifier such as “JJH-001” and be updated later as more information becomes available. This allows laboratories to retain analytically meaningful recurring signals instead of discarding them simply because a definitive library identification is unavailable.</p><p>The results show that combining a custom library with local RI data and IGS filtering improves both the consistency and efficiency of peak-table generation. Compounds with IGS scores ≥3.0 were generally assigned the same identification across samples when the custom library was used, while reliance on NIST and IGS alone produced less consistent results. After correcting only a small number of problematic identifications per peak table, retention-index errors for custom-library entries dropped dramatically—in the evaluated samples, typically to around <strong>0.5–1.9 RI units</strong>. The key recommendation is therefore straightforward: <strong>build a custom MS library, include recurring “unknown knowns,” collect local retention-index data, and apply identification-quality filters</strong> to obtain cleaner and more consistent GC×GC-MS peak tables with substantially less manual effort.</p><h3><strong>3. Shimadzu: Simultaneous Analysis of Pesticide Residues in Food Using Triple Quadrupole GC-MS/MS with SPL Mode of a Multimode injection Unit (MMI)</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_01234_en_d8cff47c74.pdf"><strong>Full PDF for download</strong></a></li></ul><h4><strong>User benefits</strong></h4><ul><li>The MMI’s rapid heating and cooling technology significantly reduces waiting time during inlet maintenance, improving instrument uptime.&nbsp;</li><li>The Smart Pesticides Database enables automatic creation of analytical methods without the need for pesticide standards.&nbsp;</li><li>Peakintelligence for GCMS uses AI-based peak processing to deliver highly accurate analysis with no parameter settings required</li></ul><p>GC-MS/MS is an indispensable tool for the simultaneous analysis of pesticide residues in food. However, improving productivity in high-throughput workflows remains a persistent challenge. With the increasing use of simplified sample preparation methods such as QuEChERS, contamination of the inlet by matrix components is unavoidable, and frequent inlet maintenance, including liner replacement, is often required. Because this maintenance involves both cooling the inlet and waiting for temperature re-stabilization afterward, it can become a major source of instrument downtime.&nbsp;</p><p>The newly developed Multi-Mode Inlet (MMI) incorporates Shimadzu’s proprietary thermal insulation technology, providing excellent temperature stability together with rapid heating and cooling performance. While maintaining high data compatibility with conventional split/splitless inlets, the MMI significantly reduces the waiting time associated with inlet maintenance.</p><p>In this Application News, simultaneous multiresidue analysis in SPL mode is demonstrated for four food matrices with different characteristics - spinach, orange, avocado, and ginger - using the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-TQ+8040+RX"><strong>GCMS-TQ 8040 RX triple quadrupole</strong> </a>GC-MS/MS equipped with the MMI.</p><h4><strong>Data Analysis&nbsp;</strong></h4><p>LabSolutions Insight, a quantitative data processing software package designed for high-throughput sample analysis, was used for data analysis, with Peakintelligence for GCMS applied as the peak integration algorithm (Fig. 6). Peakintelligence is an AI-based waveform processing algorithm that uses machine learning to emulate the peak processing performed by experienced analysts. It requires no operator-defined parameter settings and provides peak integration performance comparable to that of skilled users.&nbsp;</p><p>For more information on improving the efficiency of pesticide data analysis using Peakintelligence for GCMS, please refer to Application News 01-00585-EN, “<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00585_en_7e311a3c77/an_01-00585-en.pdf"><strong>Time-Saving Data Processing for Pesticide Residues with Peakintelligence for GCMS</strong></a>.”</p><h4><strong>Conclusion&nbsp;</strong></h4><p>In this Application News, an example of simultaneous multiresidue pesticide analysis in food using a triple quadrupole GC-MS/MS equipped with a Multi-Mode Inlet (MMI) was presented. By adopting the MMI, instrument downtime associated with inlet maintenance can be significantly reduced while maintaining high quantitative performance. In addition, by combining automated method creation with the Smart Pesticides Database and efficient data analysis using the AIbased waveform processing algorithm Peakintelligence for GCMS, highly accurate results were obtained even for complex food matrices. This system optimizes the overall analytical workflow, from method development and maintenance to data analysis, and can make a substantial contribution to improving productivity in high-throughput pesticide residue analysis.</p></div></div></div></div></div></div></section></div></div></div></div></div></div></section>

News from LabRulezGCMS Library - Week 32, 2026

<p><strong>Gas Chromatography–Mass Spectrometry (GC-MS) is a powerful analytical technique widely used for compound identification, quantification, and structural elucidation. By coupling the separation efficiency of gas chromatography (GC) with the detection and identification capabilities of mass spectrometry (MS), GC-MS enables highly sensitive and specific chemical analysis.&nbsp;</strong></p><p>Understanding how GC-MS works, and its underlying principles, helps scientists optimize analytical workflows, improve accuracy, and troubleshoot effectively whether in environmental testing, drug development, or forensic investigations.</p><h3><strong>What is GC-MS?</strong></h3><p>GC-MS is a hybrid analytical technique that combines the separation power of gas chromatography with the molecular identification capability of mass spectrometry.</p><p>In gas chromatography, a vaporized sample is carried by an inert gas (such as helium or hydrogen) through a capillary column coated with a stationary phase. Each compound interacts differently with the stationary phase depending on its volatility and polarity, causing them to separate as they elute at distinct retention times.</p><p>As compounds exit the column, they enter the mass spectrometer, where they are ionized and fragmented. The mass-to-charge ratio (m/z) of these ions is measured, producing a mass spectrum, a unique molecular fingerprint that enables unambiguous compound identification and quantification.</p><p>This seamless coupling of GC and MS enhances analytical performance by combining chromatographic separation with mass-based detection, allowing even trace-level compounds to be analyzed in complex matrices.</p><h3><strong>Principle of GC-MS</strong></h3><p>The principle of gas chromatography–mass spectrometry involves two fundamental processes:</p><h4><strong>Separation by Gas Chromatography</strong></h4><p>Compounds are vaporized and separated in the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products?category=7&amp;manufacturerItems=50"><strong>GC column</strong></a> based on their boiling points and interaction strength with the stationary phase. Less volatile compounds or those with stronger affinity for the column elute later, giving each component a unique retention time.</p><h4><strong>Detection by Mass Spectrometry</strong></h4><p>As compounds elute, they are introduced into the MS, where they undergo ionization commonly by electron impact or chemical ionization. Ionization produces charged fragments that are separated in a mass analyzer according to their mass-to-charge (m/z) ratio. The resulting mass spectra serve as fingerprints that enable identification and quantitation through comparison with spectral libraries.</p><p>This dual process allows GC-MS to distinguish structurally similar molecules and perform trace-level analyses in complex mixtures with exceptional accuracy.</p><h3><strong>Step-by-Step Working of GC-MS</strong></h3><ul><li><strong>Sample Introduction:</strong> A liquid or gaseous sample is introduced into the GC, typically with a split or splitless inlet, where it is vaporized immediately.</li><li><strong>Separation in GC Column:</strong> The carrier gas propels the vaporized sample through a long capillary column. Compounds interact with the stationary phase, and those with greater affinity or lower volatility move slower, thus separating from other compounds.</li><li><strong>Transfer to MS:</strong> As separated analytes elute, they are transferred into the mass spectrometer via a heated interface.</li><li><strong>Ionization and Fragmentation:</strong> In the MS, an ion source generates charged ions and fragments. These fragments reflect the compound’s structure.</li><li><strong>Mass Analysis:</strong> The mass analyzer (commonly a quadrupole, but also ion trap or TOF) separates ions by m/z. Each analyte produces a unique mass spectrum.</li><li><strong>Detection and Data Output:</strong> Detectors (e.g., electron multipliers) record signal intensities at each m/z, producing a total ion chromatogram (TIC) and mass spectra, which the data system interprets for compound identification and quantification.</li></ul><h3><strong>Applications of GC-MS</strong></h3><ul><li><strong>Forensic Science:</strong> Used in toxicology, drug identification, and arson investigations, GC-MS provides highly specific detection of trace evidence and controlled substances.</li><li><strong>Pharmaceuticals:</strong> Supports drug purity testing, residual solvent analysis, and metabolite profiling during formulation and quality control.</li><li><strong>Environmental Testing:</strong> Monitors pesticides, volatile organic compounds (VOCs), and pollutants in air, water, and soil samples to ensure regulatory compliance.</li><li><strong>Food Safety:</strong> Detects contaminants, additives, and flavor compounds to maintain product safety and authenticity.</li><li><strong>Clinical and Biomedical Research:</strong> Used in metabolomics and clinical diagnostics to identify volatile biomarkers and metabolites relevant to disease states.</li></ul><h3><strong>Key Benefits of Using GC-MS</strong></h3><ul><li><strong>High Sensitivity and Specificity:</strong> Detects trace-level compounds in complex matrices.</li><li><strong>Comprehensive Structural Information:</strong> Mass spectra provide molecular fingerprints for precise identification.</li><li><strong>Separation and Identification Combined:</strong> Minimizes matrix interference and enhances quantitation accuracy.</li><li><strong>Wide Application Range:</strong> Suitable for environmental, pharmaceutical, food, and forensic testing.</li><li><strong>Extensive Spectral Libraries:</strong> Enables rapid identification of known and unknown compounds.</li></ul><p>A solid understanding of the <a target="_blank" rel="noopener noreferrer" href="https://www.phenomenex.com/resources/knowledge-center/gc-knowledge-center/understanding-the-essential-components-of-gas-chromatography"><strong>components of gas chromatography</strong></a> such as injectors, columns, and detectors helps analysts optimize GC-MS system performance and ensure accurate, reproducible results.</p>

GC-MS: Principles of How it Works?

<p><strong>In late February 2025, a train carrying benzene derailed in Hustopeče nad Bečvou, and within hours, a race was on to save the groundwater and surface water. One of the most toxic industrial substances began seeping into the gravel-sand subsoil. While fieldwork was underway to prevent the spread of contamination, decisions were already being made in the CXI TUL laboratories regarding how to subsequently clean up the contaminated rock environment.</strong></p><p>The accident ranks among the <strong>most serious environmental incidents in the Czech Republic in recent years</strong>. After a tanker train derailed, hundreds of<strong> metric tons of benzene leaked</strong>; due to the highly permeable geological subsoil, it quickly seeped into deeper soil layers and into the groundwater. The combination of the substance’s high toxicity and local conditions complicated both the emergency response and the subsequent remediation. The key was to stop further spread, determine the extent of the contamination, and establish a remediation strategy for the entire site.</p><p>It was at this stage that CXI TUL became involved in the solution. At the request of the renowned remediation company DEKONTA, a.s., a team led by <strong>RNDr. Jan Němeček, Ph.D.</strong>, began testing the feasibility of the<strong> in situ chemical oxidation (ISCO) method</strong>. This technology is designed to<strong> break down benzene directly within the rock matrix</strong>, without the need for extensive excavation of contaminated soil or the pumping out of large volumes of groundwater. The goal was simple: to find a method that would work quickly, safely, and under the actual conditions of the affected site.</p><h4><strong>Benzene Remediation: Technically Simple, Complicated in Practice</strong></h4><p>Benzene is a highly toxic and carcinogenic substance that rapidly penetrates into groundwater in permeable subsoil, where it dissolves and spreads further as the water flows toward surface water bodies. Benzene also binds to rock particles and migrates in the form of vapors into overlying unconsolidated layers.</p><p>Scientists in Liberec therefore tested <i>in situ </i>chemical oxidation (ISCO) technology under laboratory conditions. The goal was to find an oxidant capable of breaking down benzene directly underground into less hazardous substances.</p><p>The tests evaluated two main options:<strong> activated hydrogen peroxide and sodium peroxodisulfate</strong>. Both substances removed up to<strong> 99.99% of the benzene</strong>, but there was a difference in their kinetics and impacts on the natural environment. Activated hydrogen peroxide achieved nearly complete degradation of the contaminant within a single day. Activated sodium peroxodisulfate was slower and also significantly altered the chemistry of the water; it acidified the environment and increased the sulfate content in the water, which poses an additional environmental burden.</p><p>For field applications, the researchers therefore unequivocally recommended <strong>activated hydrogen peroxide</strong>.</p><h4><strong>Dosage Matters</strong></h4><p>It was not just the oxidant’s effectiveness that was key. It was equally important to determine how the chemical reaction would behave in a real geological environment. The team of scientists therefore also analyzed what is known as <strong>soil oxidant consumption (SOD)</strong>. It turned out that a significant portion of the oxidizing agent does not react solely with benzene but is also consumed to a significant extent by the rock environment itself, particularly by natural organic substances present in the fine-grained fraction of the soil.</p><p>An oxidant consumption value was determined specifically for the local composition of the aquifers, which is essential for the correct dosing of chemicals in the field so that the remediation is not only effective but also economically viable and safe for surrounding ecosystems.</p><h4><strong>One Year After the Accident: Research Turns into Real-World Assistance</strong></h4><p>While the public often perceives university science as a distant world of laboratories and grant projects, it is precisely situations like this that demonstrate its practical impact. The results of the work by researchers at CXI TUL did not remain confined to academic studies but directly influenced decisions regarding the technology used to clean up one of the largest environmental accidents in recent years.</p>

Science on the Front Lines: Cleanup of a Benzene Spill

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LECO ChromaTOF® Tile analytical software

ChromaTOF Tile enables rapid, statistically robust GC×GC-TOFMS data comparison using tiled heatmaps, reducing false positives and cutting data mining from days to minutes.

<p><strong>Turn Your Data into Chemistry</strong></p><p><strong>The volume of GC×GC-TOFMS data can quickly become overwhelming. Thousands of analytes across multiple tables may require months of pixel-level data mining, often complicated by false positives caused by normal chromatographic variation.</strong></p><p><strong>ChromaTOF Tile</strong> fundamentally changes how GC×GC data are compared and interpreted. Instead of evaluating individual pixels, the software statistically compares tiled regions of chromatograms. Truly significant differences rise immediately to the surface—allowing you to focus on chemical meaning rather than data hunting.</p><blockquote><p><i>“ChromaTOF Tile is an enabling technology that has reduced our peak picking time from 10 days to 10 minutes.”</i></p></blockquote><h3><strong>Designed for Sample Group Differentiation</strong></h3><p>Integrated heatmap tables provide instant visual feedback on relative intensities and variance. ChromaTOF Tile highlights differences between two or more sample classes, including:</p><ul><li>Biomarkers distinguishing healthy vs. diseased samples</li><li>Off-odor detection in food quality control</li><li>Environmental samples upstream/downstream of contamination sources</li><li>Petroleum samples from different geographic origins</li><li>Any complex GC×GC comparison where class differentiation matters</li></ul><h3><strong>Key Features</strong></h3><ul><li><strong>Stand-alone software</strong> designed to work in conjunction with <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/41"><span><strong>ChromaTOF</strong></span></a></li><li><strong>Rapid statistical comparison</strong> of GC×GC data sets</li><li><strong>Exportable tables and plots </strong>for efficient reporting</li><li><strong>Clean, library-searchable spectra generated</strong> from tiling</li><li><strong>Approximate trend quantification using</strong> tile areas</li><li><strong>Reduced false positives </strong>from chromatographic misalignment</li></ul><h3><strong>Theory of Operation</strong></h3><p><strong>The challenge:</strong> Comparing large sets of GC×GC chromatograms is slow and alignment-sensitive, often taking weeks or months even with automated tools.</p><p><strong>The solution:</strong> In collaboration with <span>Robert Synovec</span>, ChromaTOF Tile applies Fisher ratios to partition chromatograms into regions (“tiles”). Instead of pixel-by-pixel comparison, corresponding tiles are averaged and statistically evaluated.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_Chroma_TOF_Tile_analytical_software_816a8a7705.jpg" alt="LECO ChromaTOF® Tile analytical software" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_Chroma_TOF_Tile_analytical_software_816a8a7705.jpg 245w," sizes="100vw" width="854" height="462"></p><p>The result is a set of intuitive heatmaps that immediately direct attention to regions of high variance and high analytical relevance—without altering the underlying data.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_Chroma_TOF_Tile_analytical_software_2_c3c8abd476.jpg" alt="LECO ChromaTOF® Tile analytical software" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_Chroma_TOF_Tile_analytical_software_2_c3c8abd476.jpg 245w," sizes="100vw" width="1045" height="566"></p>

LECO Simply GC×GC®

Simply GC×GC from LECO guides users through the steps of creating an optimized GC×GC method or converting an existing 1D GC method for complex samples.

<p><strong>Simply GC×GC from </strong><span><strong>LECO</strong></span><strong> is a free tool designed to walk users through the steps of creating an optimized GC×GC method for complex samples.</strong></p><p>The tool allows users to <strong>create a GC×GC method from scratch</strong> or <strong>convert an existing 1D GC method to GC×GC</strong>.</p><p>Simply GC×GC provides <strong>logical, step-by-step instructions</strong>, for example for:</p><ul><li>setting the <strong>secondary oven offset</strong>,</li><li>setting the <strong>second-dimension column length</strong>,</li><li><strong>experimentally evaluating stationary phases and peak capacity</strong>.</li></ul><p>Simply GC×GC takes a simple approach to GC×GC and helps users avoid unnecessary testing while streamlining the method development cycle.</p>

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