<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 25th 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>presentation by <strong>MDCW / University of Washington</strong> and poster by <strong>Waters Corporation!</strong></p><h3><strong>1. MDCW / University of Washington: Implementing Tile-Based Fisher Ratio Analysis of GC×GC-TOFMS Data to Obtain a Master Peak Table of All Detected Analyte Compounds in Many Petroleum-Based Samples</strong></h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_9_Halvorsen_136808e314.pdf"><strong>Full PDF for download</strong></a></li></ul><section><div><div><div><div><div><div><p>This presentation describes the development of a new chemometric workflow for processing <strong>GC×GC-TOFMS (comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry)</strong> data from petroleum-derived fuels. The work was conducted by researchers from the University of Washington in collaboration with Chevron Technical Center and focuses on creating a comprehensive <strong>Master Peak Table (MPT)</strong> containing all detectable analytes across multiple fuel sample classes. The motivation was that moving from conventional GC to GC×GC dramatically increased chromatographic peak capacity and resolution, but also created a need for more sophisticated data analysis methods capable of handling the much larger number of detected compounds.</p><p>The analytical platform employed <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/37"><strong>GC×GC-TOFMS</strong></a> with a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1289"><strong>thermal-flow modulator</strong></a> and a TOF mass spectrometer detector. Fuel samples were analyzed using an <strong>Rxi-1 column (30 m × 0.25 mm × 0.25 μm)</strong> in the first dimension and an <strong>Rxi-17MS column (2.0 m × 0.18 mm × 0.20 μm)</strong> in the second dimension. The sample set included multiple petroleum products such as hydrobates, reformates, naphthas, FCC gasoline, and blank samples. Log-scale chromatograms revealed numerous late-eluting trace compounds and significant compositional differences between fuel batches that were not readily visible in conventional chromatographic representations.</p><p>To process these highly complex datasets, the researchers implemented a <strong>tile-based Fisher Ratio (F-ratio) chemometric approach</strong> using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/348"><strong>ChromaTOF Tile software</strong></a>. The method divides GC×GC chromatograms into small tiles and identifies regions where analyte signals differ significantly between sample classes by comparing between-class and within-class variance. Instead of relying solely on F-ratio ranking, the workflow incorporates statistical testing against blank samples. Three blank chromatograms were used to establish tile-specific confidence intervals and p-test thresholds, allowing the system to determine whether detected signals represent genuine compounds rather than background noise. This strategy enabled the creation of a unified master peak table while minimizing problems caused by retention-time shifts and reducing the need for manual user intervention.</p><p>Application of the method generated <strong>719 statistically significant analyte hits</strong> across the fuel classes and demonstrated excellent reproducibility, with only <strong>6.3% of classifications showing inconsistent behavior among replicates</strong>. Detected compounds included hydrocarbons and aromatic species such as <strong>1-methylnaphthalene, 1,2,4-trimethylbenzene, β-vatirenene, 2-octene, and tetramethylheptadecane</strong>, whose occurrence patterns aligned well with expected fuel chemistry. The resulting master peak table contained both shared and sample-specific compounds and provided a much more comprehensive representation of petroleum composition than conventional workflows. The authors conclude that the approach should be broadly applicable to other complex sample types and could be extended using alternative chemometric metrics beyond Fisher Ratio analysis to address different experimental objectives.</p><h3><strong>2. Shimadzu: A Simple Method for Determination of β-Sitosterol as a Marker of Vegetable Oil Adulteration in Ghee by GC</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_06_sip_gc_042_en_f4c307005b.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li>ShimadzuNexis GC-2030 can be effectivelyused for determination of adulteration of Ghee with vegetable oil.</li><li>The NexisGC-2030 easily meets the acceptancecriteria as per the proposed FSSAI method no.01.097:2022.</li><li>This method is suitable for routine food industry analysis and academic research.</li></ul><p>The most common milk product in Indian cuisine is ghee or clarified butter. Due to its high cost, ghee is adulterated with vegetable oil, to gain commercial benefits. β-sitosterol, a common phytosterol, found in a variety of natural sources, including vegetable fat or oil from plants (Figure 1). It is used as a marker of vegetable oil adulteration in ghee<sup>1</sup>.</p><p>The primary goal of this study is to assess the vegetable oil adulteration in ghee by analyzing β-Sitosterol as a marker using gas chromatography (GC). This will establish a foundation for the rapid detection of ghee adulteration. In this study, a simple, sensitive, and inexpensive method is developed for extraction of β-Sitosterol from ghee which is then subjected to gas chromatography with flame ionization detection for quantitation. The effects of several important parameters influencing the extraction efficiencies of β-Sitosterol including water-immiscible organic solvent (type and volume), volume of water, sonication time, surfactant (type and concentration), and organic modifier (type and volume) were investigated. This method is ideal for routine analysis in the food industry during the manufacturing, processing, and commercial testing of milk fat samples, or for academic research purposes.</p><h4><strong>&nbsp;Sample Preparation and Analysis Conditions</strong></h4><p>Standard and sample preparations were conducted following the guidelines outlined in the proposed FSSAI method No. 01.097:20222. The analysis was performed using a Shimadzu <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=Nexis+GC-2030"><strong>Nexis GC-2030</strong></a> equipped with a Flame Ionization Detector (FID), as shown in Figure 2. The specific configuration and analytical conditions used during the study are provided in Table 1.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>A gas chromatography–based method using GC-2030 was developed for the estimation of β-Sitosterol in ghee. The method exhibits a detection limit of 2 %, enabling the identification of vegetable oil adulteration in ghee at levels as low as 2 %. At this adulteration level, the signal of β-Sitosterol achieved a signal-to-noise (S/N) greater than 3, confirming the reliability of detection at the established limit. This method provides a reliable and effective approach for the quantification of β-Sitosterol, which serves as a marker for vegetable oil adulteration in ghee.</p><h3><strong>3. Waters Corporation: Multidimensional Characterization of Short Chain Chlorinated Paraffins (SCCPs) with GC-APCI and Cyclic Ion Mobility</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720009386_3232568115.pdf"><strong>Full PDF for download</strong></a></li></ul><p>This poster presents a multidimensional analytical approach for the characterization of <strong>short-chain chlorinated paraffins (SCCPs)</strong>, a class of persistent environmental pollutants included in the Stockholm Convention due to their environmental persistence and bioaccumulation potential. SCCPs are highly complex mixtures of chlorinated hydrocarbons containing thousands of possible congeners, making their analysis particularly challenging. The study evaluates the use of <strong>gas chromatography coupled with atmospheric pressure chemical ionization, cyclic ion mobility spectrometry, and high-resolution mass spectrometry (GC-APCI-IMS-HRMS)</strong> as a powerful platform for improved SCCP characterization.</p><p>The analytical instrumentation consisted of a modified <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890"><strong>Agilent 8890 gas chromatograph</strong></a> equipped with an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=7&amp;search=Rxi-5SilMS"><strong>Rxi-5SilMS capillary column</strong></a><strong> (30 m × 0.25 mm × 0.25 μm)</strong> and connected to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/202"><strong>Waters SELECT SERIES Cyclic IMS</strong></a><strong> high-resolution mass spectrometer</strong> using an <strong>Atmospheric Pressure GC (APGC) ion source</strong>. Unlike conventional electron ionization methods, APCI was operated in negative-ion mode with a chloroform modifier, promoting the formation of characteristic <strong>[M+Cl]⁻ molecular ions</strong>. This significantly reduced fragmentation and provided strong molecular-ion signals, improving both qualitative identification and quantitative analysis of SCCP homologues.</p><p>Using three commercial SCCP technical mixtures (51.5%, 55%, and 63% chlorine content), the researchers demonstrated that GC-APCI-HRMS could selectively detect SCCP homologues according to carbon-chain length and degree of chlorination. Although substantial chromatographic overlap was observed among many homologues, the addition of <strong>ion mobility spectrometry (IMS)</strong> introduced a valuable extra separation dimension. Distinct drift-time versus m/z trendlines were observed for compounds sharing the same number of chlorine atoms, allowing homologues to be grouped and characterized more effectively than by chromatographic retention and mass spectrometry alone. The IMS data also enabled estimation of the relative abundance of individual homologue groups within each technical mixture.</p><p>The authors further explored the resolving power of the <strong>cyclic IMS device</strong> by employing multiple ion-mobility passes (up to eight cycles) to investigate individual SCCP congeners. While complete separation of individual congeners within a homologue group was not achieved, higher ion-mobility resolution revealed measurable differences between early- and late-eluting species and provided additional insight into compositional differences among SCCP mixtures. The study concludes that combining <strong>GC-APCI, cyclic ion mobility spectrometry, and high-resolution mass spectrometry</strong> offers a powerful multidimensional workflow for the characterization of complex chlorinated paraffin mixtures and may improve environmental monitoring and future regulatory analysis of these challenging contaminants.</p></div></div></div></div></div></div></section>

News from LabRulezGCMS Library - Week 22, 2026

<p><strong>Low analyte recovery is one of the most common challenges encountered during Solid Phase Extraction (SPE) sample preparation. In this Phenomenex Video Tip, Genevie Hodson, Senior Manager of Technical Support and Applications at Phenomenex, explains a practical troubleshooting approach for situations where analyte recovery after an </strong><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products?category=6&amp;manufacturerItems=50"><strong>SPE </strong></a><strong>procedure is lower than expected.</strong></p><p>The key objective of troubleshooting is not simply to identify that recovery is low, but to determine exactly where the analyte was lost during the extraction process. Once the point of loss has been identified, corrective actions can be implemented systematically.</p><h3><strong>Fundamental Principle of SPE Recovery Troubleshooting</strong></h3><h4><strong>Determine Where the Analyte Is Going</strong></h4><p>The most important step when troubleshooting low recovery is to identify the location of the missing analyte.</p><p>The process begins with the assumption that the sample originally contained 100% of the target analyte. If the final recovery is lower than expected, some portion of the analyte has been lost during SPE processing.</p><p>To identify the loss point, each fraction generated during the SPE procedure must be collected and analyzed separately.</p><h5><strong>Fractions to Collect</strong></h5><p>During the SPE workflow, the following fractions should be collected individually:</p><ul><li><strong>Load fraction (flow-through)</strong></li><li><strong>Wash fraction(s)</strong></li><li><strong>Elution fraction</strong></li></ul><p>Each collected fraction is transferred into a vial and analyzed using the laboratory’s analytical method.</p><p><strong>The analytical results reveal whether the analyte is:</strong></p><ul><li>Present in the load fraction</li><li>Present in one of the wash fractions</li><li>Present in the elution fraction</li><li>Missing from all collected fractions</li></ul><p>The location of the analyte provides direct information about the root cause of the recovery problem and guides the next troubleshooting steps.</p><h4><strong>Scenario 1: Analyte Detected in the Load Fraction</strong></h4><h5><strong>What It Means</strong></h5><ul><li>If the analyte appears in the load fraction, it was not retained by the SPE sorbent during sample loading.</li><li>Instead of binding to the stationary phase, the analyte passed directly through the cartridge.</li></ul><h5><strong>Possible Causes</strong></h5><ol><li><strong>Incorrect Phase Selection</strong></li></ol><ul><li>The selected SPE sorbent may not be appropriate for the analyte being extracted.</li><li>If the analyte cannot interact effectively with the sorbent chemistry, retention will not occur.</li></ul><ol start="2"><li><strong>Excessively Strong Sample Solvent</strong></li></ol><ul><li>The sample may be dissolved in a solvent that is too strong.</li><li>A strong sample solvent can prevent proper interaction between the analyte and the sorbent, causing breakthrough during loading.</li></ul><ol start="3"><li><strong>Incorrect pH Conditions</strong></li></ol><ul><li>Retention often depends on maintaining the analyte in the correct ionization state.</li><li>If the sample pH is not appropriate, the analyte may not interact correctly with the sorbent and therefore will not be retained.</li></ul><ol start="4"><li><strong>Sorbent Overload</strong></li></ol><ul><li>The SPE cartridge may have received:<ul><li>Too much analyte</li><li>Too much sample matrix</li></ul></li><li>When sorbent capacity is exceeded, analytes can break through the cartridge and appear in the load fraction.</li></ul><h5><strong>Corrective Action</strong></h5><p>When overload is suspected, increasing the amount of sorbent can improve analyte retention and prevent breakthrough.</p><h4><strong>Scenario 2: Analyte Detected in the Wash Fraction</strong></h4><h5><strong>What It Means</strong></h5><p>If the analyte appears in the wash fraction, it was initially retained but subsequently removed during the washing step.</p><h5><strong>Possible Causes</strong></h5><ol><li><strong>Wash Solvent Too Strong</strong></li></ol><ul><li>The most common cause is an overly aggressive wash solvent.</li><li>A wash solution with excessive elution strength can remove the analyte before the intended elution step.</li></ul><ol start="2"><li><strong>Incorrect pH During Washing</strong></li></ol><ul><li>When pH control is required for retention, the same conditions must be maintained throughout the process.</li><li>A pH change during washing may alter analyte retention and cause premature elution.</li></ul><h5><strong>Corrective Action</strong></h5><p><strong>Review:</strong></p><ul><li>Wash solvent composition</li><li>Wash solvent strength</li><li>pH conditions during washing</li></ul><p>Adjustments should ensure that the analyte remains retained until the elution step.</p><h4><strong>Scenario 3: Analyte Missing from All Fractions</strong></h4><h5><strong>What It Means</strong></h5><ul><li>If the analyte is not found in the load fraction, wash fractions, or elution fraction, it is likely still retained on the SPE sorbent.</li><li>In this situation, the analyte has not been effectively eluted from the cartridge.</li></ul><h5><strong>Possible Cause</strong></h5><p><strong>Elution Solvent Not Strong Enough</strong></p><ul><li>The final elution solvent may lack sufficient strength to remove the analyte from the sorbent.</li><li>As a result, the analyte remains bound to the stationary phase and never reaches the collection vial.</li></ul><h4><strong>Corrective Action</strong></h4><ul><li>Increase the strength of the elution solvent during the final SPE elution step.</li><li>A stronger solvent can improve analyte desorption and recovery.</li></ul><h3><strong>Summary of Low Recovery Troubleshooting Strategy</strong></h3><p>The recommended troubleshooting workflow consists of four steps:</p><h4><strong>1. Collect All SPE Fractions</strong></h4><p><strong>Gather:</strong></p><ul><li>Load fraction</li><li>Wash fraction(s)</li><li>Elution fraction</li></ul><h4><strong>2. Analyze Each Fraction Separately</strong></h4><p>Use the analytical method to determine where the analyte is located.</p><h4><strong>3. Identify the Root Cause</strong></h4><div><div><h5><strong>Analyte Location&nbsp;&nbsp;/&nbsp;&nbsp;Likely Cause</strong></h5><ul><li><strong>Load fraction:</strong> Incorrect phase, strong sample solvent, incorrect pH, sorbent overload</li><li><strong>Wash fraction: </strong>Wash solvent too strong, incorrect pH</li><li><strong>Not found in any fraction:</strong> Incomplete elution from sorbent</li></ul></div></div><h4><strong>4. Apply Corrective Measures</strong></h4><p><strong>Modify:</strong></p><ul><li>Sorbent selection</li><li>Sample solvent composition</li><li>pH conditions</li><li>Sorbent amount</li><li>Wash solvent strength</li><li>Elution solvent strength</li></ul><h3><strong>Other Common SPE Issues</strong></h3><p>In addition to low recovery, several other SPE-related problems are commonly encountered.</p><p><strong>These include:</strong></p><ul><li>Contamination</li><li>Reproducibility Problems</li><li>Flow-Related Issues</li></ul><h3><strong>Conclusion</strong></h3><p>Successful troubleshooting of low SPE recovery depends on identifying where the analyte is lost during the extraction process. Collecting and analyzing individual SPE fractions provides a systematic method for locating the analyte and determining the underlying cause of poor recovery.</p><p>Whether the analyte passes through during loading, elutes during washing, or remains bound to the sorbent after extraction, fraction-based investigation enables targeted corrective actions. This structured approach allows SPE methods to be optimized efficiently and helps restore acceptable analyte recovery.</p>

SPE Troubleshooting: Understanding and Resolving Low Recovery in Solid Phase Extraction

<p><strong>New edition of the Wiley Registry of Mass Spectral Data expands compound coverage to over 915,500 reference spectra, strengthening a foundational layer of Wiley’s scientific data and research intelligence portfolio.</strong></p><p>Wiley, a global leader in authoritative content and research intelligence for the advancement of scientific discovery, innovation and learning, today released the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/887"><strong>Wiley Registry of Mass Spectral Data 2026</strong></a>.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Wiley_Registry_of_Mass_Spectral_Data_2026_bc408d8245.jpg" alt="Wiley: Registry of Mass Spectral Data 2026" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Wiley_Registry_of_Mass_Spectral_Data_2026_bc408d8245.jpg 110w," sizes="100vw" width="1527" height="2162"></p><p>As AI-driven research pipelines become standard practice across pharmaceuticals, materials science and forensic investigation, the quality of reference data underpinning those pipelines determines whether results are trusted—or stall at the verification stage.</p><p>The Wiley Registry of Mass Spectral Data 2026 addresses this issue directly: <strong>One of the world’s largest and most trusted mass spectral reference databases</strong> for identifying unknown chemical compounds, it is now expanded and validated to meet the analytical demands of modern, AI-assisted laboratory workflows.</p><p>Laboratories face increasingly complex analytical challenges—from detecting emerging environmental contaminants to identifying novel substances in forensic investigations. As a result, having access to a comprehensive, rigorously validated spectral database has become foundational to getting results right. It is also a critical validation layer for AI-driven workflows.</p><p>The Wiley Registry database helps scientists and researchers around the world with <strong>environmental testing, forensic investigation, food safety research, drug analysis, defense research, homeland security</strong>, and more, providing critical reference data for identifying and analyzing chemical compounds and materials.</p><p>The new edition represents a significant expansion of the collection, which now contains <strong>more than 915,500 reference spectra</strong>—the unique chemical “signatures” used to identify substances—making it among the most comprehensive databases of its kind. <strong>More than 42,000 new GC-MS spectra</strong> representing <strong>34,100 compounds </strong>have been added, with data from laboratories, world patents and peer-reviewed scientific literature. Each spectrum is validated to the same rigorous standard, ensuring that the data perform reliably whether a researcher is running a manual comparison or feeding it into an automated identification pipeline.</p><p>The Wiley Registry is part of Wiley’s growing portfolio of scientific data and research intelligence solutions, which includes spectral databases, chemistry and materials literature, and analytical tools designed to support researchers and organizations from discovery through to applied outcomes. Wiley spectral database solutions are trusted by the world’s top pharmaceutical companies and many Fortune 500 organizations—reflecting Wiley’s mission to transform knowledge into actionable intelligence, equipping researchers with the rigorous reference data that drives real-world scientific discovery.</p><p><strong>Updated every three years,</strong> the Wiley Registry is among the company’s longest-standing data products, with a history spanning decades of continuous development.</p><p>“<i>The Wiley Registry is a foundational layer of intelligence for any laboratory conducting GC-MS analysis—and increasingly, for the AI-assisted workflows being built around that analysis</i>,” said Armughan Rafat, Wiley senior vice president and chief AI &amp; data analytics officer. “<i>By expanding compound coverage and applying consistent quality measures across the collection, we help analysts provide a deterministic layer to move from unknown to identified faster.</i>”</p><p>Available in the most common instrumentation manufacturer formats, the Wiley Registry of Mass Spectral Data 2026 also offers bundled options to access even more data with its <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/885"><strong>Wiley Registry/NIST</strong></a> and the <strong>KnowItAll GC-MS Library collection</strong> for labs seeking the most comprehensive coverage and workflow integration. The Wiley Registry is also available as a KnowItAll subscription for continued access to new data as they are added to the collection.</p><p>Spectral databases contain detailed “<strong>fingerprints</strong>” of substances captured through analytical techniques such as <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/934"><strong>mass spectrometry (MS)</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1271"><strong>Raman spectroscopy</strong></a>, enabling researchers to identify unknown materials by comparing their spectral signatures against validated reference spectra. This data is essential for quality control, research verification and discovery across industries from pharmaceuticals to materials science.</p><blockquote><p>For more information about the Wiley Registry of Mass Spectral Data 2026, please <a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/solutions/technique/gc-ms/wiley-registry-of-mass-spectral-data/"><strong>visit this website</strong></a>.</p></blockquote><h4><strong>About Wiley</strong></h4><p>Wiley (NYSE: WLY) is a global leader in authoritative content and research intelligence for the advancement of scientific discovery, innovation, and learning. With more than 200 years at the center of the scholarly ecosystem, Wiley combines trusted publishing heritage with AI-powered platforms to transform how knowledge is discovered, accessed, and applied. From individual researchers and students to Fortune 500 R&amp;D teams, Wiley enables the transformation of scientific breakthroughs into real-world impact. From knowledge to impact—Wiley is redefining what's possible in science and learning. Visit us at Wiley.com.</p>

Wiley releases the 2026 edition of the Wiley Registry of Mass Spectral Data to expand GC-MS identification intelligence

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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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