<h4>1 - What is fitness for purpose?&nbsp;</h4>The test meets the necessary performance criteria for a clinical binary examination to ensure it is suitable for making accurate and reliable clinical decisions in the intended patient population. Among others, performance criteria include clinical sensitivity and clinical specificity.&nbsp;<h4>2 - How to assess clinical performance?&nbsp;</h4>Assessing the clinical performance of a binary examination, stating results as 'positive' or 'negative,' involves calculating the examination's Clinical Sensitivity and Clinical Specificity. From the physician's point of view, clinical performance is evaluated by using predictive values, i.e., how likely is it that a positive or negative result corresponds to the presence or absence of the condition, which is also influenced by the prevalence of the condition in the population being tested.&nbsp;<h4>3 - What is Clinical Sensitivity?&nbsp;</h4>Clinical sensitivity (SS), also known as the True Positive Rate, measures the proportion of true positives correctly identified by the test. Data from a group of subjects who have been confirmed to have the disease or condition through diagnosis or a gold standard test (D1 in Table 1) are needed to calculate clinical sensitivity.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Table_1_2x2_Contingency_table_for_clinical_diagnosis_241735a171.jpg" alt="Eurachem: Table 1. 2x2 Contingency table for clinical diagnosis." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Table_1_2x2_Contingency_table_for_clinical_diagnosis_241735a171.jpg 245w," sizes="100vw" width="1401" height="461">The formula for clinical sensitivity is:&nbsp;SS = tp / (tp + fn), with:&nbsp;<ul><li>tp, True Positives are the cases where the test correctly identifies the presence of the disease.&nbsp;</li><li>fn, False Negatives are the cases where the test incorrectly identifies the absence of the disease when it is actually present.&nbsp;</li></ul>Table 1 represents the components of clinical sensitivity and specificity. Figure 1 illustrates an example of the overlapping of the number of components. The symbol "∩" represents the intersection of groups. For example, D1∩n denotes the set of negative results obtained for known positive cases, i.e., false negatives.<h4>4 - What is Clinical Specificity?&nbsp;</h4>Clinical Specificity (SP), also known as the True Negative Rate, measures the proportion of true negatives correctly identified by the test. Data from a group of subjects who have been confirmed not to have the disease or condition through a gold standard test (D0 in Table 1) are needed to calculate clinical specificity.The formula for clinical specificity is:&nbsp;SP = tn / (tn + fp), where:&nbsp;<ul><li>tn, True Negatives are the cases where the test correctly identifies the absence of the disease.&nbsp;</li><li>fp, False Positives are the cases where the test incorrectly identifies the presence of the disease when it is actually absent.&nbsp;</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_1_Graphical_representation_of_an_example_of_85ab82a16b.jpg" alt="Eurachem: Figure 1. Graphical representation of an example of tp (D1∩p), tn (D0∩n), fn (D1∩n), and fp (D0∩p). " srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_1_Graphical_representation_of_an_example_of_85ab82a16b.jpg 245w," sizes="100vw" width="1300" height="736"><h4>5 - How to calculate the uncertainty of clinical sensitivity and specificity?</h4>Calculating clinical sensitivity and specificity uncertainty involves statistical methods that estimate each parameter's confidence interval (CI). These intervals provide a range of values within which the true sensitivity or specificity is expected to lie with a certain level of confidence, typically 95 %. Please refer to the AQA:2021 for information about statistical models to compute the 95 % CI [1].<h4>6 - How to define targets?&nbsp;</h4>Target minimum values for clinical sensitivity and specificity should be identified. These values may originate from regulations or criteria based on the intended use of the binary results. A target or minimum value for the lower limit of the 95 % CI for result rate, i.e., SS or SP, (LLSS.95 or LLSP.95) or the upper limit of the 95 % CI for result rate (HLSS.95 or HLSS.95) should also be defined according to the purpose of the analysis. The target or minimum value (LLSStg or LLSPtg) is particularly significant when the impact of false results is critical. For example, for blood components used in transfusions, the screening for infectious diseases should be performed with tests associated with an LLSS.95 close to 100 %.&nbsp;Figure 2 illustrates five different tests, each assessed for clinical sensitivity and its associated uncertainty. These cases reflect different levels of compliance with a target sensitivity of 0.5. Case 1 is non-compliant, Cases 2 to 4 are partially compliant, and Case 5 is compliant. From a simplified statistical power view, the laboratory should always take into account the minimum number of samples when defining its target. This number can be estimated by simulation. For example, for a clinical sensitivity of 90 % the evaluation will require a minimum of 10 samples. It should be emphasized that samples must be representative of the target population for a clinical power evaluation - for example, epidemiologically representative samples of the types and subtypes of a given virological agent are required.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Figure_2_Compliance_assessment_with_clinical_sensitivity_a21ece043c.jpg" alt="Eurachem: Figure 2. Compliance assessment with clinical sensitivity uncertainty using the lower 95% confidence interval limit for five performance assessment scenarios. " srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Figure_2_Compliance_assessment_with_clinical_sensitivity_a21ece043c.jpg 245w," sizes="100vw" width="1500" height="648">

Performance Assessment of Binary Output Examinations in Medical Laboratories

🎤 Presenter: Wenjing Ma (University of Washington)💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/paper/32777">PDF presentation</a><h3>Abstract</h3>Pseudomonas aeruginosa and Aspergillus fumigatus are major pathogens found in the lungs of Cystic fibrosis patients. Their coexistence worsens lung function and leads to poor clinical outcomes. To investigate their metabolic interactions, we analyzed the volatile space of P. aeruginosa and A. fumigatus using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=2-3-13-7">SPME-GC×GC-TOFMS</a> across four sample classes: Media, P. aeruginosa monoculture, A. fumigatus monoculture, and their co-culture. GC×GC-TOFMS provides high-resolution, high-sensitivity separation of complex metabolomic samples, but it also generates high-dimensional data that can be challenging to analyze.&nbsp;Therefore, feature selection and chemometric methods are essential to extract meaningful information. Additionally, peak table alignment issues due to retention time shifting across samples can in principle hinder the comparative analysis of multiple samples. Hence, we applied tile-based Fisher-ratio analysis using ChromaTOF Tile software to discover analytes that are statistically significant in concentration differences across samples with replicates while minimizing retention-time shifts. This platform generates a comprehensive hit list that links detected analytes across all samples. In summary, our study integrated tile-based analysis to generate a comprehensive peak table that relates all analytes in all samples in terms of up and down regulation of their concentrations. With further analyte identification, we have characterized analytes specific to each sample class to ultimately learn more about Pseudomonas aeruginosa and Aspergillus fumigatus pathogens and their co-culture.<h3>Video Transcription</h3>In this study, Wenjing Ma from the University of Washington presents a data-driven approach for analyzing complex GC×GC–MS datasets, with a focus on volatile metabolites produced by clinically relevant microorganisms. The research centers on the interaction between Pseudomonas aeruginosa and Aspergillus fumigatus, two pathogens commonly found in the lungs of cystic fibrosis patients. Their co-existence is known to correlate with worse clinical outcomes, making the understanding of their metabolic interplay particularly important.The main objective of the work is to identify metabolites that differ significantly between monocultures and co-cultures, thereby revealing biochemical interactions that may influence disease progression.<h4>Experimental Approach</h4>The experimental design includes four distinct sample classes: a media control, monocultures of Pseudomonas aeruginosa and Aspergillus fumigatus, and their co-culture. Samples were prepared using a microfluidic platform and analyzed via headspace sampling, enabling the capture of volatile compounds released during growth.To characterize these samples, comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/37">GC×GC–TOF-MS</a>) was employed. This technique provides high acquisition speed and full mass spectral information, allowing detailed chemical characterization. However, it also generates extremely complex datasets. A single chromatogram may contain up to a million data points, and when multiple samples are considered, the data effectively become four-dimensional. Such complexity makes direct manual interpretation impractical.<h4>Data Processing Strategy</h4>Traditional approaches to data reduction, such as generating peak tables or averaging chromatograms, present significant limitations. Peak alignment can be compromised by retention time shifts, while averaging can reduce resolution through peak broadening. To overcome these issues, the study applies a tile-based feature selection strategy built on statistical analysis.The chromatographic space is divided into small regions, referred to as tiles, and each tile is evaluated using a statistical metric that compares variability between sample classes to variability within classes. This approach allows the identification of features that show the greatest differences across experimental conditions. Importantly, the method is extended to two-dimensional chromatographic data and applied across individual mass channels, enabling a highly detailed and systematic analysis.The workflow is implemented using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/348">Chromatof Tile software</a>, which produces ranked lists of statistically significant features and supports visualization tools such as principal component analysis (PCA) plots for confirming class separation.<h4>Results and Interpretation</h4>Initial inspection of raw chromatograms reveals that while the media control exhibits relatively low signal intensity, the biological samples show highly complex and overlapping patterns. Differences between monocultures and co-cultures are not readily apparent without advanced data processing.Using tile-based analysis, hundreds of statistically significant features were identified. These features provide insight into metabolic behavior. For example, compounds that are abundant in the media but reduced in biological samples are likely being consumed by the organisms. Conversely, compounds that are elevated in specific cultures may represent metabolites produced by one organism or influenced by microbial interaction.The reproducibility of the experimental setup is supported by consistent signal patterns across replicates, indicating reliable sample preparation and data acquisition.<h4>Statistical Evaluation</h4>To further refine the interpretation, pairwise statistical comparisons were performed between all sample classes. This enables identification of specific differences between monocultures and co-cultures, highlighting which metabolites are associated with individual organisms and which are affected by their interaction.The results suggest that certain metabolites are dominant in Pseudomonas aeruginosa monocultures but are altered in co-culture conditions, indicating potential biological competition or suppression mechanisms.<h4>Alternative Approach for Limited Data</h4>Recognizing that biological replicates are not always available in practice, the study also explores an alternative strategy based on the coefficient of variation (CV). This method allows meaningful analysis even when only a single replicate per condition is available, making it particularly valuable for high-throughput or resource-limited experiments.By applying this approach, the researchers were able to identify metabolites showing significant changes across conditions, including compounds uniquely produced or suppressed in specific cultures. One example includes the identification of 1-octen-3-ol, a known fungal metabolite, which serves as validation of the analytical workflow.<h4>Discussion</h4>A key outcome of this work is the demonstration that the choice of statistical method must be aligned with the experimental design. When sufficient replicates are available, variance-based methods provide robust results. In contrast, when data are limited, alternative metrics such as the coefficient of variation offer a practical solution. Additional approaches, such as fold-change analysis, can further enhance interpretation by focusing on relative differences between conditions.The tile-based framework proves particularly effective for handling the complexity of GC×GC–MS data, enabling the extraction of meaningful biological information without compromising resolution.<h4>Conclusion</h4>This study highlights the power of advanced data processing strategies in modern analytical chemistry. Tile-based statistical analysis enables the systematic exploration of highly complex datasets and provides valuable insights into microbial metabolism and interaction. The approach is flexible, scalable, and adaptable to different experimental constraints, making it a strong candidate for broader application in metabolomics and environmental analysis.<h4>Future Perspectives</h4>Future work will focus on integrating chemometric tools to improve compound identification and expanding the analytical framework to support more comprehensive, multi-method data 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>

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 (Wenjing Ma, MDCW 2026)

Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 30th March 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 application notes by Agilent Technologies, Thermo Fisher Scientific, brochure by Shimadzu and presentation by MDCW / LECO!<h3>1. Agilent Technologies: Extraction and Analysis of Pesticides from Water by Solid Phase Extraction and GC/MS</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_extraction_analysis_pesticides_water_gcms_5994_8996en_agilent_28fc35d0e7.pdf">Full PDF for download</a></li></ul>Drinking water is defined as water intended for human consumption obtained from different sources. Several guidelines/regulations exist for ensuring that undesirable substances such as pesticides are not present in excessive amounts that may be harmful to human health. These regulations ensure that water intended for human consumption can be consumed safely. In India, the Bureau of Indian Standards (BIS) has set regulatory limits for residual pesticides in drinking water (IS 10500)1 , packaged drinking water (IS 14543)2 , and natural mineral water (IS 13428).3 A total of 26 pesticides are regulated by BIS standards, of which four compounds (monocrotophos, phorate, 2,4-D, and isoproturon) are more suitable for analysis by liquid chromatography.&nbsp;EPA Method 525.2 is a general-purpose method for determination of organic compounds in finished drinking water or water from any source or treatment stage.4 This method is applicable to a range of organic compounds that are partitioned from the water sample onto a C18 organic phase packed in a disk or cartridge. The organic compounds are then eluted using a suitable eluent, concentrated, and analyzed by GC/MS.&nbsp;This application note describes the use of EPA Method 525.2 for extraction of pesticide residues at trace levels in water using solid-phase extraction (Agilent Bond Elut C18: Straight barrel cartridges, p/n 12102118) and analysis using Agilent 8890-5977 GC/MS.&nbsp;Excellent linearity with R2 &gt; 0.995 was obtained for all pesticides considered for the study. Recoveries were obtained in the range of 80–120% with associated RSDs &lt; 15% at 0.005–0.080 ng/mL level of fortification.<h4>Experimental</h4><h5>Instrument parameters</h5><ul><li>GC: <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=8890">Agilent 8890</a></li><li>Column: Agilent <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=J%26W+HP-5ms">J&amp;W HP-5ms</a> UI 30 m × 0.25 mm, 0.25 μm&nbsp;</li><li>Carrier Gas: Helium, constant flow, 1.2 mL/min&nbsp;</li><li>Inlet: Agilent Multimode Inlet (MMI)</li><li>MS: <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=1&amp;search=5977">5977 GC/MS</a></li></ul><h4>Conclusion&nbsp;</h4>A simple, easy, and efficient method based on EPA 525.2 was implemented for the determination of 22 pesticides in water. The method demonstrated good sensitivity, precision, and accuracy, and allows for rapid analysis. The results demonstrate that pesticide residues can be detected below the current maximum residue levels (MRL) required by the BIS specifications. Acceptable recoveries and precision were obtained at concentrations as low as 5 ng/L for all pesticides. The method is ideally suited for use in a regulatory laboratory for the determination of pesticides in surface, drinking, and packaged drinking water.<h3>2. MDCW / LECO: Exploring PFAS in Consumer Goods Using GCxGC and High-Resolution Mass Spectrometry</h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_3_Alonso_6f61e3ea43.pdf">Full PDF for download</a></li></ul>Per- and polyfluoroalkyl substances (PFAS) are widely used in consumer and industrial products due to their unique physicochemical properties, but their persistence and potential health risks have made their detection increasingly important. This study focuses on developing an untargeted analytical approach for screening PFAS and related compounds in consumer goods using advanced chromatographic and mass spectrometric techniques.The analytical workflow combines gas chromatography with time-of-flight mass spectrometry (GC-TOFMS) and comprehensive two-dimensional gas chromatography coupled with high-resolution TOFMS (GC×GC-HRTOFMS), using systems such as the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1097">LECO Pegasus® BTX </a>and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/38">Pegasus® HRT+</a> platforms. Sample introduction is performed via thermal desorption or pyrolysis, enabling analysis of both volatile and complex matrices. High-resolution MS provides accurate mass measurements and elemental composition, which are essential for identifying unknown compounds and reducing matrix interferences.In the first case study, anti-fog solutions were analyzed and found to contain a wide range of compounds, including fluorotelomer alcohols and related PFAS species. While GC-TOFMS enabled initial screening, high-resolution TOFMS was necessary to characterize unknown compounds through accurate mass determination and complementary ionization techniques (EI and PCI). This approach allowed tentative structural assignment using external databases, demonstrating the importance of HRMS in identifying compounds not present in standard libraries.The second case study focused on chewing gum, a highly complex matrix with diverse chemical constituents and trace-level analytes. The use of GC×GC-HRTOFMS significantly improved chromatographic separation and spectral clarity, enabling detailed profiling of volatile and semi-volatile compounds. A dedicated PFAS screening workflow based on mass defect analysis and spectral filtering was applied, leveraging the characteristic CF₂ patterns of PFAS molecules to simplify data interpretation.Despite the complexity of the sample, no PFAS were detected in the chewing gum, its packaging, or the box. Overall, the study demonstrates that combining GC×GC separation, high-resolution mass spectrometry, and advanced data processing tools provides a powerful and reliable strategy for untargeted PFAS screening in complex consumer products, particularly when unknown or emerging contaminants are of concern.<h3>3. Shimadzu: Gas Chromatograph Nexis GC-2060</h3><ul><li>Brochure</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/c184_e056_06c3456516.pdf">Full PDF for download</a></li></ul>The Shimadzu <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?manufacturerItems=3&amp;search=Nexis+GC-2060">Nexis GC-2060</a> represents a new generation of gas chromatographs, built on over 70 years of GC development and designed to serve as a flexible and future-proof analytical platform. It integrates the concept of Analytical Intelligence, which enables automated system monitoring, troubleshooting, and optimization, helping both experienced and less experienced users achieve reliable and consistent results. The system is highly configurable, supporting up to two injectors, three injection units, and four detectors simultaneously, allowing a single instrument to cover multiple analytical applications.A key advantage of the Nexis GC-2060 is its versatility in sample introduction and detection. The Multi-Mode Injection Unit (MMI) supports a wide range of injection techniques, including split/splitless, PTV, direct injection, large-volume injection (LVI), and thermal desorption, significantly reducing the need for complex sample preparation. The system is also equipped with next-generation detectors such as FID and TCD, offering high sensitivity, stability, and linearity, with additional detector options (ECD, FPD, FTD, BID) enabling broad application coverage.The instrument is designed to maximize laboratory efficiency and minimize downtime. Features such as tool-free column installation (ClickTek), automated startup/shutdown, Easy sTop maintenance mode, and Clean Pilot for automated column conditioning simplify routine operation and reduce manual intervention. In addition, Eco Idling technology automatically optimizes gas and power consumption during standby, significantly lowering operational costs while maintaining readiness for analysis.From an analytical performance perspective, the Nexis GC-2060 delivers excellent reproducibility and sensitivity, supported by advanced autosamplers (e.g., AOC-30) and compatibility with third-party systems. The platform supports hydrogen carrier gas for faster analysis and includes smart gas management features such as automatic gas switching. Its modular design allows seamless integration with various sample preparation systems, autosamplers, and detectors, ensuring adaptability to evolving laboratory needs.Overall, the Nexis GC-2060 combines high performance, operational efficiency, and flexibility in a single platform, making it suitable for a wide range of applications, including pharmaceutical analysis, environmental testing, food analysis, and energy-related measurements. Its scalability and continuous software updates ensure long-term usability and alignment with future analytical requirements.<h3>4. Thermo Fisher Scientific: Accurate multi-component blast furnace gas analysis maximizes iron production and minimizes coke consumption</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/epm_AN_Iron_Production_Prima_PRO_bd5de98b44.pdf">Full PDF for download</a></li></ul>Over a billion tonnes of iron a year are produced in blast furnaces, representing around 94% of global iron production¹. The blast furnace consists of a large steel stack, lined with refractory brick. Iron ore, coke and limestone are dropped into the top of the furnace and preheated air blown into the bottom through nozzles called ‘Tuyeres’. Iron oxides are reduced in the melting zone, or ‘Bosh’, forming liquid iron (called ‘hot metal’) and liquid slag. These liquid products are drained from the furnace at regular intervals, and the blast furnace will run continuously for several years, until the refractory lining needs replacing.Analysis of carbon monoxide (CO) and carbon dioxide (CO₂) give vital information on the efficiency of the reduction processes. Historically, two non-dispersive infrared (NDIR) analyzers were used, one to measure CO, the other CO₂. Additional discrete analyzers were required to monitor oxygen and hydrogen – typically paramagnetic O₂ analyzers and thermal conductivity analyzers for H₂.&nbsp;Thermo Scientific™ process mass spectrometers have been widely used for many years for key gas analysis applications in iron and steel plants, including blast furnace, basic oxygen steelmaking, coke oven gas analysis, secondary steel process control, fuel gas analysis and direct reduction iron processes²<h4>Process control requirements&nbsp;</h4>The advantages of process MS over conventional analysis techniques have been proven over many years in the iron and steel industry. The ability to measure a wide range of components on a single analyzer, coupled with advanced calibration, data transmission, and self-diagnostic software, makes the modern mass spectrometer ideal for integration into the modern plant. On blast furnaces, superior gas analysis is being used to calculate gas efficiency, mass and heat balances, and heat profiles through probe analysis, as well as being an essential tool in the early detection of cooling water leaks and sample system failures.&nbsp;Figure 2 shows 64 hours of data from a <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/2305">Thermo Scientific™ Prima™ PRO Process MS</a> monitoring the six gases in a blast furnace top gas every 10 seconds. The pulse every 12 hours is generated by an automatic nitrogen blowback from the sample conditioning system, to keep the sample probe clear of particulates.<h4>Hydrogen analysis&nbsp;</h4>An increase in the hydrogen level in the furnace can indicate a leak from the furnace cooling system, as water dissociates into hydrogen and oxygen in the furnace. This has implications both for plant efficiency and plant safety. Water leaks will lower the furnace temperature, causing heat loss and a consequent increase in fuel consumption. In extreme cases, damage to plant and personnel can be caused by ignition of these hazardous mixtures of explosive gases.&nbsp;Figure 4 shows a plot of top gas hydrogen concentration measured by Thermo Scientific process MS over a 24-hour period, with the departure of the measured H2 value from that predicted by the mass balance model indicating a water leak. This leak was caused by the failure of a cooling member, and the furnace was taken off blast for repair. The mass spectrometer’s ability to provide fast, accurate hydrogen analysis removes the need for an additional discrete analyzer.&nbsp;Gas composition measurement is particularly important during the end-of-campaign blowdown that takes place prior to rebuilds and relines, because explosive gas mixtures can be generated.&nbsp;<h4>Probe analysis&nbsp;</h4>The fast analysis provided by MS offers another significant advantage over discrete analyzers—a number of process streams can be analyzed using a single instrument. This is achieved using a fully automated multi-inlet system, designed to switch between sample streams at high speeds with minimal sample flushing times. For example, as well as furnace top gas, the MS can also analyze gases from probes placed above and below the burden (the furnace charge of iron ore, coke and limestone). These are known as above-burden and sub-burden probes and provide valuable information on fuel efficiency, iron conversion and furnace charging. Figure 5 shows a schematic of these two probes; they can be moved across the furnace to build a cross-sectional profile of the furnace.<h4>Summary&nbsp;</h4>Thermo Scientific process mass spectrometers have been successfully monitoring blast furnace off gas at many of the world’s iron and steel companies for over 30 years. For a blast furnace producing 5,000 tonnes a day, a reduction in coke consumption of just 10 kg per tonne of iron produced provides a payback of over $10,000 a day. Users report that the combination of fast, accurate MS gas analysis for gas efficiency, heat and mass balances and hydrogen analysis, with advanced process control and mathematical modeling, has reduced coke consumption by up to 100 kg per tonne and increased iron production. Upgrading to Prima PRO MS can therefore result in complete installation payback within a few weeks.&nbsp;<ul><li>Reduces coke consumption&nbsp;</li><li>Produces consistent quality iron&nbsp;</li><li>Reduces downstream processing costs&nbsp;</li><li>Detects water leaks fast&nbsp;</li><li>Improves thermal control of furnace&nbsp;</li><li>Enables safer operation during blowdown</li></ul>

News from LabRulezGCMS Library - Week 14, 2026

Article

Application

Academy

Video

Presentation

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

The Multidimensional Chromatography Workshop

Eurachem is a European network promoting international traceability of chemical measurements and good analytical practice. It provides expert guidance, supports method validation, uncertainty evaluation, proficiency testing, and facilitates discussion on technical and policy issues in analytical quality.

Eurachem

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">

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