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

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

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!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT GCMS AND RELATED TECHNIQUES</a></blockquote><blockquote>👉 Need info about different analytical techniques? Peek into <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library">LabRulezLCMS</a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library">LabRulezICPMS</a> libraries.</blockquote>This week we bring you technical note by Agilent Technologies, presentation by MDCW / University of Copenhagen and application notes by Shimadzu and Thermo Fisher Scientific!<h3>1. Agilent Technologies: What Causes Spots on <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=19&amp;manufacturerItems=1&amp;search=PLOT">PLOT Columns</a>? Will It Affect the Performance of the Column?</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">Full PDF for download</a></li></ul>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.<h3>2. MDCW / University of Copenhagen: One-shot tensor decomposition of full-scale GC×GC-VUV datasets for resolving petrochemical groups</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">Full PDF for download</a></li></ul>The presentation introduces a novel all-samples-at-once, shift-invariant tri-linear tensor decomposition approach for the analysis of full-scale <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?search=GC%C3%97GC-VUV">GC×GC-VUV</a> datasets, 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.At the core of the method is a shift-invariant tri-linear model, 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 accurate estimates of peak landscapes, VUV spectra, and relative peak areas, while remaining agnostic to retention-time variability. Importantly, the approach requires only a small number of tunable parameters, making it practical for routine use.The method was demonstrated on a case study involving 14 petrochemical gas-oil samples, 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 high repeatability (RSD typically 3–9%) and strong correlation with established template-based reference methods.Overall, the work demonstrates that one-shot, shift-invariant tensor decomposition 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.<h3>3. Shimadzu: Py-Screener Ver. 3: Simultaneously Test for Substances Regulated Under RoHS, TSCA, and POPs</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">Full PDF for download</a></li></ul><h5>User Benefits</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">Py-Screener</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>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.<h4>Analysis Process</h4>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.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.<h4>Conclusion</h4>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.<h3>4. Thermo Fisher Scientific: Improving low carbon steel production in specialty steel processes</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">Full PDF for download</a></li></ul>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.<h4>Advantages of mass spectrometry&nbsp;</h4>Traditional Non-Dispersive Infra-Red (NDIR) analyzers are used in many conventional steelmaking processes to measure CO and CO2 , 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;Paramagnetic analyzers can be used to measure O2 , while thermal conductivity analyzers can be used to measure H2 . 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 N2 is calculated by difference, a result that suffers from the sum of the errors of the three analytical techniques.&nbsp;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.<h4>Precision of analysis&nbsp;</h4>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">Thermo Scientific™ Prima PRO Mass Spectrometer</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;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;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;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.

News from LabRulezGCMS Library - Week 07, 2026

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 <a target="_blank" rel="noopener noreferrer" href="https://www.nature.com/articles/s41467-026-68778-6#Abs1">Nature Communications</a>.“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,” said the article’s first author, David Panáček from CATRIN, who worked on the study during his internship at Imperial College London.<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">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. “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,” said one of the corresponding authors, Michal Otyepka.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"> CEITEC – VUT</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.“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,” added another corresponding author, Nuria De Diego.

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

Article

Application

Video

Presentation

Scientific article

Science and research

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

The Multidimensional Chromatography Workshop

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

CATRIN

Everything from the world of analytical chemistry in one place. We connect people in solving their problems. At Labrulez you will find all the necessary information easily, quickly and clearly. Stop searching and start finding.

LabRulez

Mestrelab Mnova Advanced Chemometrics software

Leverage sophisticated multivariate analysis tools such as PCA, PLS, and SIMCA for in-depth data exploration, regression, and classification.

<h4>Advanced Chemometrics for Enhanced Data Analysis</h4>Leverage sophisticated multivariate analysis tools such as PCA, PLS, and SIMCA for in-depth data exploration, regression, and classification. The user-friendly interface ensures seamless integration of spectral data with analysis results, making it ideal for advanced users seeking powerful chemometric techniques for comprehensive data analysis.<h4>Benefits:</h4><ul><li>All-in-One Solution: Offers seamless spectral processing, binning, and analysis with just a few clicks, simplifying workflows and managing complex data efficiently in a single platform.</li><li>Incorporates Recognized Methods: Includes PCA, PLS, and SIMCA for advanced data analysis and unsupervised chemometrics.</li><li>Powerful and Intuitive Interface: Provides a user-friendly design for a streamlined analysis experience.</li><li>Improved Classification and Prediction: Classifies samples with SIMCA and builds predictive models using PLS.</li><li>Noise and Outlier Reduction: Effectively reduces noise and detects outliers, ensuring accurate and reliable results.</li><li>Expandable Plugin: Features Higher Order Structure (HOS) capabilities, powered by Mnova BioHOS.&nbsp;</li></ul><h4>Features:</h4><h5>New MCR Tool for Peak Purity Assessment in LCMS Data</h5>Introduces an MCR tool based on the MCR-ALS algorithm for peak purity assessment, providing comprehensive tables and plots to streamline LCMS data analysis and enhance decision-making. This tool is available with the Advanced Chemometrics or BioHOS plugins and requires an MSChrom plugin license.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Advanced_Chemometrics_New_MCR_Tool_for_Peak_Purity_Assessment_in_LCMS_Data_94993e71c3.webp" alt="Mestrelab Research: Mnova Advanced Chemometrics - New MCR Tool for Peak Purity Assessment in LCMS Data" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Advanced_Chemometrics_New_MCR_Tool_for_Peak_Purity_Assessment_in_LCMS_Data_94993e71c3.webp 245w," sizes="100vw" width="1920" height="1200"><h5>Enhanced Data Analysis with Partial Least Squares (PLS) Regression</h5>Facilitates comprehensive analysis and accurate prediction of dependent variables from independent variables (such as binned spectra), while also providing intuitive graphical evaluation tools for deeper insights.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Advanced_Chemometrics_Enhanced_Data_Analysis_with_Partial_Least_Squares_PLS_Regression_7363eca35a.webp" alt="Mestrelab Research: Mnova Advanced Chemometrics - Enhanced Data Analysis with Partial Least Squares (PLS) Regression" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Advanced_Chemometrics_Enhanced_Data_Analysis_with_Partial_Least_Squares_PLS_Regression_7363eca35a.webp 211w," sizes="100vw" width="1175" height="870"><h5>Advanced Data Classification with SIMCA Modeling</h5>Offers a powerful statistical method for supervised classification of data, allowing researchers to categorize and analyze complex datasets with precision.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Advanced_Chemometrics_Advanced_Data_Classification_with_SIMCA_Modeling_830f63d8a0.webp" alt="Mestrelab Research: Mnova Advanced Chemometrics - Advanced Data Classification with SIMCA Modeling" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Advanced_Chemometrics_Advanced_Data_Classification_with_SIMCA_Modeling_830f63d8a0.webp 195w," sizes="100vw" width="1026" height="820"><h5>Advanced PCA Analysis for In-Depth Data Insights</h5>Provides features for comprehensive data exploration and visualization, including quantile plots (1D NMR), comparison tools, plot labels display, enhanced selection and interactivity in scores and loadings plots, and additional distance. calculation options in scores plots. These enhancements enable precise, interactive analysis for deeper data insights.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_Advanced_Chemometrics_Advanced_PCA_Analysis_for_In_Depth_Data_Insights_32b2644333.webp" alt="Mestrelab Research: Mnova Advanced Chemometrics - Advanced PCA Analysis for In-Depth Data Insights" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_Advanced_Chemometrics_Advanced_PCA_Analysis_for_In_Depth_Data_Insights_32b2644333.webp 245w," sizes="100vw" width="1026" height="647">

Products from category LIMS, QMS, DMS, Statistics focused on Software distributed by SciY/Mestrelab Research - page 1

Related content

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

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

News from LabRulezGCMS Library - Week 07, 2026