<p><strong>Concerns about injecting water into a GC system largely come from older column designs, where stationary phases were simply coated and could be affected by water exposure. Modern capillary columns are typically chemically bonded, which improves resistance to water injections and even supports water rinsing in many cases.</strong></p><p>One practical exception involves <strong>very polar polyethylene glycol </strong>(PEG / “wax”) phases. Because these phases strongly interact with water, repeated aqueous injections can allow water to accumulate and gradually shift selectivity. For many PEG-based columns, minimizing frequent water injections helps maintain consistent performance.</p><p>A separate—and often more important—issue is water’s<strong> very large vapor expansion in a hot inlet</strong>. If the vapor volume exceeds the inlet liner capacity, backflash can occur, sending sample vapor into cooler, unheated zones and increasing the risk of carryover. Using smaller injection volumes helps reduce this risk.</p><p>In workflows where water is the<strong> sample matrix </strong>(for example, volatile analysis), analytes may elute at relatively low oven temperatures while water and higher-boiling residues can linger. Periodic bake-off (high-temperature cleanup steps) is commonly used to restore column performance when water remains on the phase at low temperatures.</p><p>In summary, the biggest hesitation about water injections is rooted in legacy, coated column stories. With chemically bonded phases, the key considerations tend to be inlet expansion/backflash behavior, water retention on very polar phases, and routine maintenance practices like bake-out.</p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1571"><strong>Zebron ZB-WAX</strong><i><strong>PLUS</strong></i><strong> GC Columns</strong></a></h4><ul><li>100% aqueous stable, excellent for aqueous samples; bonded and solvent rinseable</li><li>Extremely inert for acidic compounds</li><li>Enhanced selectivity for low boiling solvents; high retention of alcohols and chlorinated solvents</li><li>Increased efficiency at 20&nbsp;°C</li></ul></blockquote>

Water in GC: Safe or Risky? Here’s What You Need to Know

<p><strong>🎤 Presenter:</strong> <strong>Paul-Albert Schneide </strong>(University of Copenhagen)</p><p>💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/paper/32780"><strong>PDF presentation</strong></a></p><h3><strong>Abstract</strong></h3><p><strong>Tensor decomposition methods such as PARAFAC and PARAFAC2 have been proposed for resolving co-elutions, and extracting purified spectra in two-dimensional chromatography datasets. However, the application of these methods is usually restricted to local regions-of-interest (ROIs). Therefore, additional pre-processing steps, are required and processing multiple samples at once can be difficult due to retention time shifts (e.g., it is difficult to define a ROI that captures the same analyse signal in multiple samples).</strong></p><p>This work presents an application of fast, shift-invariant tensor decomposition, which was used for the decomposition of full-scale GCxGC-VUV datasets. The results show that chemical groups can be resolved, which are non-separable with conventional, template-based approaches, such as aliphates and olefines, Furthermore, the extracted peak areas are in good agreement with the quantitative results of the reference methods (e.g., bromine number for olefines).</p><h2><strong>Video Transcription</strong></h2><h3><strong>Background and Motivation</strong></h3><p>The project originated from a collaboration initiated by Alexandra Alvich from IFPEN, who approached Prof. Rasmus Bro’s research group at the University of Copenhagen for help analyzing a GC×GC-VUV dataset acquired during her PhD research. The work was then continued by Paul-Albert Schneider and Jesper Henri from the Technical University of Denmark.</p><p>The primary objective was to develop a simpler and less manually intensive approach for petrochemical group quantification while maintaining strong quantitative performance and spectral interpretability.</p><p>According to Schneider, the proposed method is based on two key principles:</p><ul><li>Simple implementation with a minimal number of tuning parameters</li><li>Accurate estimation of peak patterns, VUV spectra, and peak areas</li></ul><p>The workflow specifically targets group-level petrochemical analysis rather than individual compound identification.</p><h3><strong>Data Analysis Workflow</strong></h3><p>The presented workflow begins with a collection of GC×GC-VUV measurements. In this study, the researchers analyzed:</p><ul><li>14 gas oil samples</li><li>24 total measurements, including replicates</li><li>Samples processed using different treatment methods</li></ul><p>Initial preprocessing was performed using a dedicated plug-in module developed during Alexandra Alvich’s PhD work. The preprocessing included:</p><ul><li>Signal smoothing</li><li>Downsampling</li><li>Basic data conditioning</li></ul><p>The processed data were then submitted to the decomposition engine developed by the Copenhagen research group. The decomposition produced:</p><ul><li>Resolved VUV spectra</li><li>GC×GC chromatographic landscapes</li><li>Peak areas or peak volumes</li></ul><p>An in-house spectral library from IFPEN was subsequently used to verify that the extracted spectra corresponded to known petrochemical groups, enabling group-level quantitative analysis.</p><p>Importantly, the workflow currently requires only a single major tuning parameter: the number of components used in the decomposition model. The researchers also noted ongoing efforts to automate this selection step.</p><h3><strong>Comparison with Existing Template-Based Methods</strong></h3><p>The study benchmarked the new approach against Alexandra Alvich’s previously developed template-based quantification method.</p><p>In the template-based workflow:</p><ul><li>Elution regions are predefined using a grid structure</li><li>Peak intensities within those regions are summed</li><li>Quantitative estimates are obtained for specific petrochemical classes</li></ul><p>Although the template-based strategy demonstrated strong performance compared with other analytical techniques, Schneider highlighted several limitations:</p><ul><li>Sensitivity to retention time shifts</li><li>Manual adjustment of integration regions</li><li>Dependence on prior knowledge of coeluting compounds</li></ul><p>The new decomposition-based method was designed to reduce manual intervention and improve robustness against chromatographic variability.</p><h3><strong>Shift-Invariant Trilinear Decomposition</strong></h3><p>The proposed workflow is conceptually related to PARAFAC and multivariate curve resolution methods.</p><p>One important advantage of GC×GC-VUV data is the applicability of the Beer–Lambert law, which enables mathematically structured decomposition approaches.</p><p>The researchers evaluated two possible strategies:</p><ol><li>Decomposing each sample individually</li><li>Decomposing all samples simultaneously after concatenation into one large dataset</li></ol><p>The second strategy became the central focus of the work. Multiple samples were concatenated into a large matrix and factorized to extract shared chemical information across all measurements simultaneously.</p><p>Unlike many conventional GC×GC deconvolution pipelines, the workflow does not rely on a prior region-of-interest detection step. Instead, the entire dataset is decomposed directly.</p><h3><strong>Addressing Retention Time Shifts</strong></h3><p>A critical challenge for PARAFAC-type models is the assumption of strict trilinearity. In GC×GC datasets, however, second-dimension retention time shifts caused by temperature programming violate this assumption.</p><p>Schneider demonstrated how standard one-component PARAFAC models fail to properly model shifted peaks, leaving characteristic residual patterns. When fitting additional components, conventional models often allocate extra components to describing peak shifts instead of resolving true coeluting compounds.</p><p>The shift-invariant model solves this issue by explicitly accounting for chromatographic shifts. As a result:</p><ul><li>Large shifted peaks are modeled correctly</li><li>Residuals are minimized</li><li>Small coeluting peaks become resolvable</li></ul><p>The algorithm uses fast Fourier transform (FFT)-based operations internally to correct for chromatographic shifts efficiently.</p><p>According to the presentation, the approach is also significantly faster than PARAFAC2, which is commonly used for shifted datasets.</p><p>The researchers additionally described extensions of the model that can accommodate:</p><ul><li>Retention time shifts</li><li>Peak shape variations</li><li>Additional flexibility without losing uniqueness properties</li></ul><h3><strong>Quantitative Results</strong></h3><p>The team applied a seven-component model to the complete dataset containing 24 GC×GC-VUV measurements.</p><p>The decomposition extracted seven characteristic VUV spectra, which were compared against the IFPEN in-house spectral library. The extracted spectra matched known petrochemical group signatures very well.</p><p>The resolved components included:</p><ul><li>Two monoaromatic components</li><li>Two diaromatic components</li><li>Additional petrochemical group patterns</li></ul><p>For quantitative evaluation, chemically related components were grouped together.</p><h4><strong>Reproducibility</strong></h4><p>Replicate measurements demonstrated strong quantitative reproducibility:</p><ul><li>Relative standard deviations below 10%</li><li>Consistent peak area estimation across petrochemical groups</li></ul><h4><strong>Comparison with the Template-Based Approach</strong></h4><p>The decomposition workflow showed good agreement with the existing template-based quantification strategy, with monoaromatics representing the main discrepancy requiring further investigation.</p><p>Schneider explained that the differences were likely related to relative response factor scaling used in the template-based method but not yet implemented in the decomposition workflow at the time of the presentation.</p><h4><strong>Future Development</strong></h4><p>The researchers outlined several next steps for the project:</p><ul><li>Running models on individual samples to achieve higher resolution</li><li>Resolving sulfur-containing compounds</li><li>Extracting more sample-specific differences</li><li>Automating component number selection</li><li>Investigating deviations in monoaromatic quantification</li><li>Integrating the workflow into an open-source software project</li></ul><p>The team also expressed interest in future collaboration with other researchers working on related GC×GC data analysis workflows.</p><h3><strong>Conclusion</strong></h3><p>The presented shift-invariant trilinear decomposition workflow demonstrated promising performance for automated petrochemical group analysis in GC×GC-VUV datasets. By combining robust mathematical modeling with minimal manual intervention, the method offers a simplified alternative to traditional template-based approaches while maintaining strong quantitative agreement and spectral interpretability.</p><p>The work highlights the growing role of advanced multivariate data analysis strategies in comprehensive chromatography and demonstrates how shift-aware decomposition methods can significantly improve the analysis of highly complex petrochemical datasets.</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>

One-shot tensor decomposition of full-scale GC×GC-VUV datasets for resolving petrochemical groups (Paul-Albert Schneide, MDCW 2026)

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 11th May 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 note by<strong> Shimadzu, </strong>poster by <strong>MDCW / William &amp; Mary</strong> and brochure by <strong>Thermo Fisher Scientific!</strong></p><h3><strong>1. MDCW / William &amp; Mary: Optimizing Solvent Selection and Data Reduction Workflows for GC×GC Fire Debris Analysis</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/P_14_O_Brien_63304669ee.pdf"><strong>Full PDF for download</strong></a></li></ul><section><div><div><div><div><div><div><h5><strong>What are Ignitable Liquid Residues (ILRs)?&nbsp;</strong></h5><ul><li>Chemical remains of ignitable liquids (IL) (e.g., gasoline or diesel) that did not fully burn during a fire&nbsp;</li><li>ILRs are usually petroleum-based products and contain complex mixtures of volatile chemicals, like hydrocarbons&nbsp;</li></ul><h5><strong>Forensic Analysis of ILRs&nbsp;</strong></h5><ul><li>Collection of porous materials like wood from the fire’s point of origin&nbsp;</li><li>Must be stored in airtight containers to prevent volatile ILR from evaporation&nbsp;</li><li>Passive headspace extraction onto activate charcoal strips (ACS) is used to extract ILRs from debris, and dichloromethane (DCM) is used to extracts ILRs from charcoal strips&nbsp;</li><li>Emerging is the use of two-dimensional gas chromatography – time of fight - mass spectrometry (GC×GC-TOFMS), with higher separation and resolution</li><li>Resulting chromatograms presents a pattern of peaks which can be used to identified specific type of accelerant used&nbsp;</li></ul><h5><strong>Chemometrics&nbsp;</strong></h5><ul><li>The discipline that focuses on the application of statistical and mathematical methods to analyze chemical data and extract meaningful information (Bovens, 2019)&nbsp;</li><li>Helps make sense of large datasets from analytical instruments, find pattens, and classify samples</li></ul><h4><strong>Methods</strong></h4><h5><strong>Analytical Instrumentation&nbsp;</strong></h5><ul><li>LECO corporation <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1097"><strong>BTX4D GC×GC-TOFMS</strong></a> with <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1289"><strong>Paradigm Shift reverse fill-flush (RFF) flow modulator</strong></a></li></ul><h5><strong>Software&nbsp;</strong></h5><ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1873"><strong>ChromaTOF Sync 2D</strong></a> (LECO corporation)&nbsp;</li><li>R Statistical Software</li></ul><h4><strong>Conclusions</strong></h4><p>This study demonstrated that the use of chemometrics can be integrated into a data processing workflow to better understand data sets and create a streamlined process. Using UMAP, patterns identified that were not originally seen in PCA lead to the preferred solvents of ILR extraction being hexane and methanol. Since UMAP allowed visualization of local and global structures, groupings of gasoline samples allowed for better analysis of solvent effect.</p><h3><strong>2. Shimadzu: Multiresidue Pesticides Analysis in Solvent Extract of Annatto (Natural Color Additive) Using </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-TQ8050+NX"><strong>GCMSTQ8050 NX</strong></a></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_06_sip_gc_043_en_95576a9ac4.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li>&nbsp;Accurate and reproducible trace level quantitation of pesticides in highly pigmented complex matrix using Ultra-fast GCMSTQ8050 NX and QuEChERS extraction</li><li>Single-laboratory partial validated method for LOQ determination meeting regulatory guidelines</li><li>Ease of instrumental method optimization using GCMS Smart Pesticides Database Ver.2 from Shimadzu Corporation</li></ul><p>Annatto is an orange-red condiment and food colorant derived from the seeds of the achiote tree (Bixa Orellana) (Fig. 1). It is often used to impart a yellow to red-orange color to foods, but sometimes also for its flavor and aroma. Similar effects can be obtained by extracting some of the color and flavor principles from the seeds, which are then added to the food. Annatto and its extracts are now widely used in an artisanal or industrial scale as a coloring agent in many processed food products, such as cheeses, butter, baked goods, potatoes, snack foods, breakfast cereals, smoked fish etc. In these uses, Annatto is a natural alternative to synthetic food coloring compounds.&nbsp;</p><p>Owing to its large culinary uses and other diverse applications, use of chemical pesticides for its production in large quantities is imperative. Dye extraction process may result in concentration of pesticides and in turn contribute to adverse impact on human health when incorporated in various preparations. Hence quantitation of residual pesticides in Annatto extract becomes very important. As the oleoresin is a complex matrix, it is required to develop a rugged, sensitive and efficient method for extraction of residual pesticide and its analysis.&nbsp;</p><p>This study reports a highly sensitive method for simultaneous quantification of multiple pesticides in highly pigmented complex matrix of Annatto using modified QuEChERS[1] with triple quadrupole gas chromatography (GC-MS/MS) system.</p><h4><strong>Materials and Methods</strong></h4><p>For this study, customized reference standard mixture of most commonly observed pesticides in Annatto seeds was procured from Restek Corporation.</p><p>The calibration standards were analyzed from 0.5 to 1000 μg/L. Linearity was plotted by external standard method and using weighted regression of 1/C2. Sample was spiked at 3 levels i.e., 10, 25 and 100 μg/kg. Recovery samples were prepared in 6 replicates at each level. <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=GCMS-TQ8050+NX"><strong>GCMS-TQ8050 NX</strong></a> (Fig. 2), manufactured by Shimadzu Corporation Japan, was used for quantitation. LabSolutions InsightTM was used for data processing, which helped in evaluating validation parameters with ease.</p><h4><strong>Conclusion</strong></h4><p>This study shows that the modified QuEChERS method combined with GC-MS/MS system is a reliable and efficient tool to quantify residual pesticides in Annatto extract sample. Although Annatto extract is a highly pigmented complex matrix, the modified QuEChERS method significantly reduces interference. Also, extremely sensitive Shimadzu GC-MS/MS allows trace level detection even after multifold dilution of sample. This helps in reducing contamination and enhancing ruggednessresulting in reproducible detection of analytes.</p><h3><strong>3. Thermo Fisher Scientific: Sentinel PRO 710 environmental mass spectrometer Precise real time gas analyzer for toxic volatile organic compounds</strong></h3><ul><li>Product specification</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/sentinel_pro_710_spec_sheet_en_886523bfc2.pdf"><strong>Full PDF for download</strong></a></li></ul><p>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=3&amp;search=Sentinel+PRO"><strong>Thermo Scientific™ Sentinel PRO Environmental Mass Spectrometer </strong></a>is a highly reliable and versatile gas analyzer ideal for reporting the presence of toxic VOCs from fugitive emissions in industrial settings to ensure worker safety.</p><h4><strong>Fugitive emissions of toxic VOCs&nbsp;</strong></h4><p>Wherever there is potential danger for fugitive emissions of toxic organic vapors from a chemical production unit, regulating authorities often require plants to keep records of ambient vapor concentrations to protect workers from exposure. Various forms of capture include evacuated vessels, organic vapor monitors, or purge and trap devices, all of which require samples to be sent for analysis. Alternatively, electrochemical sensors or open path FT-IR spectroscopy can provide more immediate results. However, none of these technologies are capable of providing the spatial and temporal resolution required to qualify as actionable information.&nbsp;</p><h4><strong>Analytical platform&nbsp;</strong></h4><p>The primary feature of the Sentinel PRO mass spectrometer is the magnetic sector analyzer. This field-proven technology has demonstrated the strongest performance for industrial on-line gas analysis. Magnetic sector technology offers precision, accuracy, long intervals between calibrations. The new touchscreen interface further enhances usability, providing intuitive in-person operation, simplified maintenance, and real-time data viewing. Multi-role user permissions ensure security and control allowing operators to customize settings based on their specific needs.&nbsp;</p><h4><strong>Comprehensive data collection&nbsp;</strong></h4><p>With speed and precision, the Sentinel PRO analyzer monitors all the critical areas for short-term exposure levels of toxic VOCs, and also provides accurate and time-weighted average exposure data. With a high number of sample points available, many can be located close to potential leak points such as valve stems, etc., enabling leak detection and correction before any toxic hazard is created.&nbsp;</p><h4><strong>Membrane inlet mass spectrometry&nbsp;</strong></h4><p>The analyzer is fitted with a membrane inlet to reduce the pressure of the sample air from atmospheric to the working pressure of the enclosed ion source. This allows a method of sample introduction that greatly enhances the system’s sensitivity to VOCs. Since the membrane is more permeable to VOCs than to major air gases, it provides enrichment by several orders of magnitude, enabling detection as low as 5 ppb (species dependent) permits easy membrane replacement during annual routine maintenance to minimize downtime and increase productivity.</p></div></div></div></div></div></div></section>

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Mestrelab Mnova ElViS software

By integrating Mnova ElViS into your analytical workflows, you can enhance the efficiency and accuracy of your spectroscopic data analysis across a wide range of techniques.

<h4><strong>Visualize, process, analyze and report various electronic and vibrational spectroscopic techniques</strong></h4><p>Carry out the analysis of various optical spectroscopy data including ultraviolet and visible (UV/Vis), near and mid infrared (NIR/MIR), Raman, fluorescence, and other spectroscopic methods operating in the whole region of wavelength from 100 nm to 100 µm.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li>Spectroscopic processing and analysis of ultraviolet and visible (UV/Vis), near and mid infrared (NIR/MIR), Raman, fluorescence methods.</li><li>Vendor and Operating System agnostic, supports most data file formats.</li><li>Wide range of processing functions, including a dynamic processing template.</li><li>Manual and automatic peak picking analysis.</li><li>Data Analysis of stacked/arrayed spectra.&nbsp;</li></ul><h4><strong>Features:</strong></h4><h5><strong data-start="1039" data-end="1108">PLS-Based Baseline Correction Tool (IarPLS)</strong></h5><p>To improve the processing of electronic and vibrational spectroscopy data, Mnova 15 introduces an enhanced PLS-based baseline correction tool, IarPLS (penalized-least-squares based). It now features an automatic button to compute the near-optimal asymmetry parameter.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_PLS_Based_Baseline_Correction_Tool_Iar_PLS_f3180f6927.webp" alt="Mestrelab Research: Mnova EIVis - PLS-Based Baseline Correction Tool (IarPLS)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_PLS_Based_Baseline_Correction_Tool_Iar_PLS_f3180f6927.webp 245w," sizes="100vw" width="1500" height="845"></p><h5><strong>Normalization Processing Modes</strong></h5><p>Three Normalization Processing modes, namely Largest Peak, Integral and Vector Length are available in Mnova 15. These three features aim to simplify the baseline correction process, streamline your workflow, and provide a rapid method for normalizing data to address quantitative issues.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Normalization_Processing_Modes_675cb7c407.webp" alt="Mestrelab Research: Mnova EIVis - Normalization Processing Modes" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Normalization_Processing_Modes_675cb7c407.webp 245w," sizes="100vw" width="1500" height="844"></p><h5><strong>Electronic and Vibrational Spectroscopies supported file formats</strong></h5><p>Mnova ElViS supports the most frequently used data file formats for these analytical techniques, including import and export of single and multi-column matrix-like spectra (i.e., ASCII XY and XYY, as .txt or .csv files). Supported formats include SPC (Galactic), JCAMP-DX, OPUS (Bruker), SPA and SPG (Omnic), Perkin Elmer's .sp files, and ASCII (XY and XYY).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Electronic_and_Vibrational_Spectroscopies_supported_file_formats_bb6afc3eab.webp" alt="Mestrelab Research: Mnova EIVis - Electronic and Vibrational Spectroscopies supported file formats" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Electronic_and_Vibrational_Spectroscopies_supported_file_formats_bb6afc3eab.webp 245w," sizes="100vw" width="1500" height="910"></p><h5><strong>Manual &amp; automatic spectral analysis</strong></h5><p>Mnova ElViS provides both manual and automatic peak picking, offering flexibility and convenience in your spectral analysis. It also features advanced tools for the analysis of stacked or arrayed spectra, ensuring thorough data evaluation and interpretation for all your spectral needs.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Manual_and_automatic_spectral_analysis_e24a19a151.webp" alt="Mestrelab Research: Mnova EIVis - Manual &amp; automatic spectral analysis" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Manual_and_automatic_spectral_analysis_e24a19a151.webp 240w," sizes="100vw" width="940" height="611"></p><h5><strong>Main processing features</strong></h5><p>Mnova ElViS comes with a range of useful processing features, including Multiplicative Scatter Correction, Probabilistic Quotient Normalization, Baseline correction (both automatic IarPLS and tailored Multipoint Baseline Correction), several smoothing techniques (such as Savitzky-Golay, Nonlocal Means, Exponential), normalization and Standard Normal Variate (SNV), derivatives, and units conversion between absorbance and transmittance.<br><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Main_processing_features_bc6eb64b99.webp" alt="Mestrelab Research: Mnova EIVis - Main processing features" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Main_processing_features_bc6eb64b99.webp 245w," sizes="100vw" width="1074" height="581"></p>

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Water in GC: Safe or Risky? Here’s What You Need to Know

News from LabRulezGCMS Library - Week 20, 2026

One-shot tensor decomposition of full-scale GC×GC-VUV datasets for resolving petrochemical groups (Paul-Albert Schneide, MDCW 2026)