<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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Elevate your analytical data processes—automate with Mnova Gears!</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_Design_your_data_workflows_and_let_Mgears_do_the_rest_5771003702.jpg" alt="Mestrelab Research: Mnova Gears - Design your data workflows and let Mgears do the rest!"></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-03-25/3nwsl4"><strong>Visit Mestrelab Research Mnova Gears product page to learn more!</strong></a></h4></blockquote><h4><strong>What’s new in Mnova Gears 2.5?</strong></h4><p><strong>Mnova Gears 2.5 has been released with Mnova 15 introducing a range of new capabilities:</strong></p><ul><li>New file copy option that copies the input files to the output folder without the need to open, process, or analyze them.</li><li>Ability to add chromatogram blind regions.</li><li>Improved data selection and auto-detection.</li><li>Capability to manage input data organized in multiple subfolders and generate sorted reports for each subfolder.</li><li>Heat map data visualization.</li><li>And other improvements that you can check in the complete changelog.</li></ul><p><strong>Upgrade to Mgears 2.5 now to explore these features and to unlock the full potential of data automation.</strong></p><p>Also be sure you check our newest Mgears brick designed for <a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-03-25/3nwskt"><strong>Affinity Selection Mass Spectrometry</strong></a> analyses.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_Increase_your_efficiency_using_automated_analysis_workflows_c9d3fb13e6.jpg" alt="Mestrelab Research: Mnova Gears - Increase your efficiency using automated analysis workflows!"></p><h3><strong>Mnova Gears products and how they relate to one another!</strong></h3><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-03-25/3nwsl4"><strong>Mnova Gears</strong></a></h4><ul><li>Automation of multiple sample analysis</li><li>Batch and real time mode to generate documents for analysis</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2020-10-22/25htxg"><strong>Mnova MyGears</strong></a></h4><ul><li>Automation of single sample analysis</li><li>Automation workflows on an open Mnova document</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-04-10/3p2gsd"><strong>Mnova Gears Bricks</strong></a></h4><ul><li>Build automation workflows using the following Gears bricks</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_1_b99a72ab8c.jpg" alt="Mestrelab Research: Mnova Gears products and how they relate to one another."></p><blockquote><h4><strong>Want to run a 45-day FREE trial?</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-04-10/3p2gsh"><strong>CONTACT AN EXPERT</strong></a></h4></blockquote><h3><strong>Intuitive 5-Step workflow!</strong></h3><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_Intuitive_5_Step_workflow_9191e59a96.jpg" alt="Mestrelab Research: Mnova Gears - Intuitive 5-Step workflow."></p><h4><strong>Mnova Gears Benefits</strong></h4><ul><li><strong>Automate</strong> routine <strong>processes and workflows</strong>, saving valuable specialist time.</li><li>Create and manage <strong>end-to-end workflows</strong> seamlessly within a single tool.</li><li>Streamline <strong>multiple analytical workflows</strong> on a common, user-friendly platform.</li><li><strong>Customize workflows</strong> by selecting components tailored to specific needs.</li><li>Enjoy <strong>flexibility and configurability</strong>, allowing seamless integration with existing infrastructure.</li><li>Collaborate with <strong>different systems in an unified environment</strong> due to vendor independence.</li><li>Leverage the familiarity with the <strong>Mnova interface</strong> to deploy and adopt <strong>automation</strong> solutions by your team <strong>without an extensive learning curve</strong>.</li><li>Benefit from <strong>expedited product support and development</strong>, consolidating all your automation analyses in one software suite.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Gears_Mnova_Gears_Benefits_3186347cb7.jpg" alt="Mestrelab Research: Mnova Gears - Mnova Gears Benefits"></p><h4><strong>Mnova Gears Features</strong></h4><h5><strong>Powerful Automation Capabilities for Comprehensive Data Tasks</strong></h5><ul><li>A user-friendly platform equipped with all essential controls for customizing data processes, covering input data, processing and analysis methods, design, and output generation.</li><li>A wide array of default features and options is available to maximize adaptation to customer needs.</li><li>Acceptance of templates and scripts for further customization; you can even construct your own automation bricks using scripting.</li><li>Details of your customization are stored in a settings file, easily shareable and importable.</li></ul><h5><strong>Automatic Reporting and Archiving</strong></h5><ul><li>Diverse output formats, including CSV, PDF, HTML, JSON, and Mnova formats.</li><li>Seamless adaptation to data organization standards by specifying requirements for correct data archiving (in a folder, database, ELN, etc.).</li><li>Easy workflows to automatically update historic data to new standards, ensuring consistency across years and sites.</li><li>Continuous support and updates: Contact the support team for specific import/export format requests.</li></ul><h5><strong>A User-Friendly Result Viewer with Graphical Dashboards for Rapid and Easy Data Review</strong></h5><ul><li>Effortless exploration of results with the Mgears Results Viewer, featuring graphical dashboards.</li><li>At-a-glance and by-exception reviews for quick and effective data analysis.</li><li>Smooth navigation through sample lists or well plates, facilitating the review of data and analyzed spectra.</li><li>Identification of samples not meeting specifications using graphics and warning flags, saving time on extensive scrutiny.</li><li>Swift recalculations and result updates.</li></ul><h4><strong>Multi-Technique and Multi-Application Analytical Workflows</strong></h4><ul><li>Multiple techniques supported: NMR, LC-MS, GC-MS, Chromatography, etc.</li><li>Mnova Gears has already proven its prowess with a broad range of applications spanning from discovery, R&amp;D, to manufacturing and quality control.</li><li>Available applications include structure verification, purity determination, mixture analysis, chemical reaction optimization, and affinity selection mass spectrometry analysis. Explore the <a target="_blank" rel="noopener noreferrer" href="http://www2.mestrelab.com/l/271522/2024-04-10/3p2gsd"><strong>full list of applications</strong></a> for comprehensive coverage.</li></ul><h3><strong>Markets</strong></h3><h5><strong>Academic, Government &amp; Industrial</strong></h5><h4><strong>PRACTICAL APPLICATIONS</strong></h4><ul><li>Quality Control (NMR, Mass Spectrometry, etc)</li><li>Natural Product Dereplication by NMR</li><li>Reaction Monitoring by NMR</li><li>Quantitative NMR</li><li>Targeted mixtures analysis by NMR</li><li>Multi-technique reporting</li><li>Protein-ligand Interaction</li><li>Product ID confirmation</li><li>Structure verification</li><li>DNA Profiling by LCMS</li><li>Ligand-based drug screenings</li><li>Reaction Optimization (HTE)</li><li>Determination of solubility, lipophilicity</li><li>System Suitability testing (NMR, LCMS)</li><li>Compound separation and purification workflows</li></ul>

Mestrelab Mnova Verify software

Designed for synthetic and medicinal chemists, which automatically checks a proposed structure against a set of analytical data, LC-MS or GC-MS and/or 1D NMR and/or 2D NMR.

<h4><strong>Automatic confirmation of structure identity based on NMR and/or LC/GC-MS data</strong></h4><p>Designed for synthetic and medicinal chemists, which automatically checks a proposed structure against a set of analytical data, LC-MS or GC-MS and/or 1D NMR and/or 2D NMR.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li><strong>Solution coupling NMR </strong>(1H, 13C and HSQC) and <strong>MS techniques</strong> in a commercial off-the-shelf software.</li><li><strong>Helps non-expert users </strong>to take decisions while reducing the workload of the experts.</li><li><strong>Fully automatic analysis</strong> for singletons or batch data.</li><li><strong>Mnova Verify checks</strong> whether the proposed compound is correct!</li><li><strong>Flexibly accepts a variety of inputs</strong> and generate automatically fully customized reports.</li><li><strong>Straightforward results</strong> check that warns about inconsistencies.</li></ul><h4><strong>Features:</strong></h4><h5><strong>Powerful algorithm running behind the scenes</strong></h5><p><strong>This powerful algorithm integrates multiple modules to deliver:</strong></p><ul><li>Automated processing and analysis of 1D &amp; 2D NMR (¹H NMR, HSQC) and LC/GC-MS data.</li><li>Generation of theoretical data for proposed structures.</li><li>Prediction of NMR spectra (¹H, ¹³C, HSQC) and MS data (mass ion, isotopic cluster, fragmentation, MS/MS).</li><li>Comparison of experimental and theoretical data.</li><li>Results presentation using an innovative scoring system.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Verify_Powerful_algorithm_running_behind_the_scenes_be1dfb5e2f.webp" alt="Mestrelab Research: Mnova Verify - Powerful algorithm running behind the scenes" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Verify_Powerful_algorithm_running_behind_the_scenes_be1dfb5e2f.webp 245w," sizes="100vw" width="936" height="427"></p><h5><strong>An easy and flexible scoring system</strong></h5><p>Evaluates analytical data to judge the compatibility of proposed structures:</p><ul><li>Provides a quality score (-1.0 to +1.0) for each structure, combining score and significance.</li><li>Displays detailed results for each test applied.</li><li>Ranks structures to quickly identify the best fit with experimental data.</li><li>Allows easy re-working of unclear results in Mnova.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Verify_An_easy_and_flexible_scoring_system_da1c07eb98.webp" alt="Mestrelab Research: Mnova Verify - An easy and flexible scoring system" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Verify_An_easy_and_flexible_scoring_system_da1c07eb98.webp 245w," sizes="100vw" width="936" height="427"></p><h5><strong>Automated batch &amp; real-time operations</strong></h5><p>Provides QA for large data volumes like libraries or registration QC samples:</p><ul><li><strong>Batch Mode: </strong>Verifies libraries and identifies unsuitable samples for assay submission, generating an HTML report.</li><li><strong>Real-Time Analysis Mode: </strong>Performs unassisted verification of individual samples, providing immediate feedback on structure-data compatibility.</li><li>Requires Mnova v10 or higher (NMR, NMRPredict, and/or MS modules). Additional modules like Mnova DB or qNMR can also be used.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Verify_Automated_batch_and_real_time_operations_6fa9241b15.webp" alt="Mestrelab Research: Mnova Verify - Automated batch &amp; real-time operations" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Verify_Automated_batch_and_real_time_operations_6fa9241b15.webp 245w," sizes="100vw" width="936" height="427"></p>

Mestrelab Mnova DB software

A professional database integrated within Mnova for efficiently shared storage of molecular structures, NMR, LC-MS, GC-MS, and other Mnova objects.

<h4><strong>Store, share and search your chemical and analytical data efficiently</strong></h4><p>A professional database integrated within Mnova for efficiently shared storage of molecular structures, NMR, LC-MS, GC-MS, and other Mnova objects.</p><p>Create new databases, save records with a couple of clicks, and run searches by structure, substructure, specific peak positions, multiplets, or mass peaks.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li><strong>Database enviroment for storing analytical data</strong> (NMR, LC/GC-MS, UV-Vis, IR, etc.) and chemical structures.</li><li><strong>Ranked queries by chemical struture and/or analytical data,</strong> enabling faster and more efficient data retrieval.</li><li><strong>Tools for managing and controlling your databases</strong>, with password protection for security.</li><li><strong>Quickly create new databases</strong> and save your analytical records in seconds, enhancing efficiency and productivity.</li><li><strong>Access and view reports in a variety of formats</strong>, including tables, records, well plates, and HTML views, for versatile data presentation.</li><li><strong>Manage user accounts with different privilege levels-administrator</strong>, power user, and standard user.</li></ul><h4><strong>Features:</strong></h4><h5><strong>Create a database and save your legacy data</strong></h5><ul><li>Companies and research institutes often have large volumes of analytical data spread across various sources, needing better management to make it shareable and searchable.</li><li>Once stored in the DB, chemists can easily access this data using Sample ID, Compound ID, peak, or Retention Time searches, especially for unknowns.</li><li>Mnova provides scripts to upload multiple NMR and/or LC/GC/MS datasets into a database.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_DB_Create_a_database_and_save_your_legacy_data_7e9ee2e6f7.webp" alt="Mestrelab Research: Mnova DB - Create a database and save your legacy data" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_DB_Create_a_database_and_save_your_legacy_data_7e9ee2e6f7.webp 245w," sizes="100vw" width="1380" height="801"></p><h5><strong>Save your data during the workflow</strong></h5><ul><li>Chemists can save their work to the Database, making it easily accessible to others later.</li><li>Mnova DB integrates with Mnova Suite, allowing you to save or search data seamlessly during your workflow.</li><li>When working with NMR or LC/GC/MS datasets in Mnova, you can save raw data, parameters, analysis results, annotations, and molecular structures to the database with a couple of clicks.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_DB_Save_your_data_during_the_workflow_169e99f805.webp" alt="Mestrelab Research: Mnova DB - Save your data during the workflow." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_DB_Save_your_data_during_the_workflow_169e99f805.webp 245w," sizes="100vw" width="1472" height="854"></p><h5><strong>Efficient sharing and searching of your analytical data</strong></h5><p>The basic search query can be the following:</p><ul><li>Structure or substructure</li><li>1D or 2D peak locations</li><li>1D multiplets</li><li>m/z values</li><li>Texts or numerics</li><li>Mnova DB allows you to combine any of the queries listed above, using logic and or operators, this is useful when you need to refine your search results iteratively. Mnova DB also save the results into a temporary database and continue to search to refine it.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_DB_Efficient_sharing_and_searching_of_your_analytical_data_8bde34ce47.webp" alt="Mestrelab Research: Mnova DB - Efficient sharing and searching of your analytical data" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_DB_Efficient_sharing_and_searching_of_your_analytical_data_8bde34ce47.webp 245w," sizes="100vw" width="1472" height="854"></p><h5><strong>Seamlessly deliver data to your organization</strong></h5><ul><li>We can automate the DB setup so data is automatically imported when acquired by your instruments.</li><li>Chemists will no longer need to browse through file folders to review or analyze data. They can retrieve it directly in Mnova using Sample ID, Sample Number, Compound ID, or structure searches.</li><li>This will save your team time and significantly improve productivity.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_DB_Seamlessly_deliver_data_to_your_organization_b4c354eeb3.webp" alt="Mestrelab Research: Mnova DB - Seamlessly deliver data to your organization" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_DB_Seamlessly_deliver_data_to_your_organization_b4c354eeb3.webp 245w," sizes="100vw" width="1472" height="854"></p>

Products from category NMR software focused on Software distributed by SciY/Mestrelab Research - page 1

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