<p>🎤 <strong>Presenter</strong>: Christina Kelly (LECO Corporation, Saint Joseph, USA)</p><p>💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/paper/28903"><strong>PDF presentation</strong></a></p><blockquote><p>💡 <strong>Book in your calendar: </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/events/787"><strong>17th Multidimensional Chromatography Workshop (MDCW)</strong></a><strong> 13 - 15. January 2026</strong></p></blockquote><h3><strong>Abstract</strong></h3><p><strong>As an increasing number of pathways for producing alternative aviation fuels are accepted, with some synthetic fuels being described as “more like what we want from jet fuel than what we pull from the ground,” more robust and easier-to-interpret </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=2"><strong>GC×GC </strong></a><strong>data processing comparing batches of samples becomes of greater importance for workflows that monitor fuel quality. Hydrocarbon group-type composition is often the main focus for routine GC×GC analysis of aviation fuels.</strong></p><p>However, the distribution of aromatics and other heteroatomically-substituted compounds that are usually present in much smaller concentrations than the predominant paraffins and naphthenes can supply additional information. These sulfur-, nitrogen-, and oxygen-containing or substituted multi-ringed aromatic analytes can provide further insight into the processes used to generate these fuels and their potential impacts on fuel performance.</p><p>This presentation explores the different types of results that are possible when approaching diverse samples using several statistical methods to highlight and identify significant variation between aviation fuel samples. Multiple aviation fuels from production processes including Fischer-Tropsch reactions and hydroprocessed esters and fatty acids (HEFA) are compared to traditional petroleum-based fuels.<br>&nbsp;</p>

Statistical data processing for GC×GC differentiation of aviation fuels (Christina Kelly, MDCW 2025)

<p><strong>🎤 Presenter:</strong> Wenjing Ma (University of Washington)</p><p>💾 <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/paper/32776"><strong>PDF presentation</strong></a></p><h3><strong>Abstract</strong></h3><p><strong>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 </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?instrumentationItems=2-3-13-7"><strong>SPME-GC×GC-TOFMS</strong></a><strong> 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;</strong></p><p>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.</p><h3><strong>Video Transcription</strong></h3><p>In this study, <span>Wenjing Ma</span> from the <span>University of Washington</span> 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 <i>Pseudomonas aeruginosa</i> and <i>Aspergillus fumigatus</i>, 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.</p><p>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.</p><h4><strong>Experimental Approach</strong></h4><p>The experimental design includes four distinct sample classes: a media control, monocultures of <i>Pseudomonas aeruginosa</i> and <i>Aspergillus fumigatus</i>, 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.</p><p>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"><strong>GC×GC–TOF-MS</strong></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.</p><h4><strong>Data Processing Strategy</strong></h4><p>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.</p><p>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.</p><p>The workflow is implemented using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/348"><strong>Chromatof Tile software</strong></a>, which produces ranked lists of statistically significant features and supports visualization tools such as principal component analysis (PCA) plots for confirming class separation.</p><h4><strong>Results and Interpretation</strong></h4><p>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.</p><p>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.</p><p>The reproducibility of the experimental setup is supported by consistent signal patterns across replicates, indicating reliable sample preparation and data acquisition.</p><h4><strong>Statistical Evaluation</strong></h4><p>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.</p><p>The results suggest that certain metabolites are dominant in <i>Pseudomonas aeruginosa</i> monocultures but are altered in co-culture conditions, indicating potential biological competition or suppression mechanisms.</p><h4><strong>Alternative Approach for Limited Data</strong></h4><p>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.</p><p>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.</p><h4><strong>Discussion</strong></h4><p>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.</p><p>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.</p><h4><strong>Conclusion</strong></h4><p>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.</p><h4><strong>Future Perspectives</strong></h4><p>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.</p><blockquote><p><strong>This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.</strong></p></blockquote>

Tile-based Analysis of GC×GC-TOFMS Data of SPME Sampled VOCs Produced from Pseudomonas aeruginosa and Aspergillus fumigatus (Wenjing Ma, MDCW 2026)

<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>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 1st June 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 brochure by <strong>Agilent Technologies</strong>, application note by<strong> Shimadzu, </strong>and<strong> </strong>presentation by <strong>MDCW / University of Washington!</strong></p><h3><strong>1. Agilent Technologies: </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890B"><strong>Agilent 8890B Gas Chromatograph</strong></a></h3><ul><li>Brochure</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/ds_8890_gas_chromatograph_5994_9064en_agilent_94b0175782.pdf"><strong>Full PDF for download</strong></a></li></ul><section><div><div><div><div><div><div><p>The Agilent 8890B GC delivers high‑performance gas chromatography through advanced electronic pneumatic control, precise oven temperature management, and Agilent capillary flow technology, which provides reliable, leak‑free, in‑oven connections and supports flexible system configurations with minimal method development time.&nbsp;</p><p>An upgrade, more responsive 7-inch capacitive touchscreen and a full Agilent GC Assist interface, offer real-time status, configuration, signal monitoring, and remote access for setup, troubleshooting, leak checking, maintenance, and method management across devices.&nbsp;</p><p>Sustainability features include programmable sleep and wake modes to reduce resource consumption, along with gas and power usage calculators that track method‑level efficiency. Known for reliability and long life, Agilent GC systems are supported by a 10‑year use guarantee to ensure a low cost of ownership.</p><h4><strong>GC system capabilities&nbsp;</strong></h4><ul><li><strong>Retention time repeatability: </strong>&lt; 0.008% or &lt; 0.0008 minutes&nbsp;</li><li><strong>Area repeatability:</strong> &lt; 0.5% RSD&nbsp;</li></ul><p>Built-in intelligence autonomously monitors the health of the system, alerts the user of potential issues before they affect chromatographic performance, and offers helpful step-by-step guides to resolve issues.&nbsp;</p><p>The 7-inch touch screen gives a visual report of the system configuration, letting you update the active method, perform maintenance routines, and check GC instrument status.&nbsp;</p><p>The GC Assist interface provides remote connectivity enabling you to monitor your smart GC system, check system logs, and perform diagnostics tests, from inside or outside the&nbsp;laboratory.&nbsp;</p><p>Maintenance and service modes, including autonomous leak checks, are easily accessed from the touchscreen and browser interface.&nbsp;</p><p>Atmospheric pressure and temperature compensation are built into every system, so results do not change, even when the laboratory environment does.</p><p>Automatic liquid sampling is fully integrated into mainframe control and includes up to 3-layer sandwich injections and sample preparation capabilities such as heating, mixing, standard addition, and derivatizations.&nbsp;</p><h5><strong>Simultaneously supports&nbsp;</strong></h5><ul><li>Two inlets&nbsp;</li><li>Five detectors&nbsp;</li><li>Four detectors (with additional large valve oven&nbsp;option)&nbsp;</li><li>Support for up to 10 valves&nbsp;</li><li>Six GC column Smart Key ports&nbsp;</li></ul><h5><strong>Instrument dimensions&nbsp;</strong></h5><ul><li><strong>Height: </strong>49 cm (19.2 in)&nbsp;</li><li><strong>Width: </strong>58 cm (22.9 in) with EPC inlet and detectors; 68&nbsp;cm (26.8 in) with detector as TCD or with certain valving options mounted on the left side of the GC&nbsp;</li><li><strong>Depth: </strong>51 cm (20.2 in)&nbsp;</li><li><strong>Typical weight:</strong> 49 kg (108 lb)&nbsp;</li></ul><h5><strong>Maintenance and support services&nbsp;</strong></h5><ul><li>Integrated early maintenance counters that allow planned maintenance and help eliminate unnecessary downtime&nbsp;</li><li>Instrument events or shutdowns displayed on keyboard display or data system</li><li>Remote diagnostics&nbsp;</li><li>Performance verification services&nbsp;</li><li>Easy parts identification and part number finder software (standalone software, does not require Agilent CDS)</li></ul></div></div></div></div></div></div></section><h3><strong>2. MDCW / University of Washington: Tile-based Analysis of GC×GC-TOFMS Data of SPME Sampled VOCs Produced from Pseudomonas aeruginosa and Aspergillus fumigatus</strong></h3><ul><li>Presentation</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/O_8_Wenjing_34786a3e4a.pdf"><strong>Full PDF for download</strong></a></li></ul><section><div><div><div><div><div><div><p>This presentation describes a novel chemometric approach for analyzing complex GC×GC-TOFMS datasets generated from volatile organic compounds (VOCs) released by the pathogens <i>Pseudomonas aeruginosa</i> and <i>Aspergillus fumigatus</i>. These microorganisms frequently coexist in the lungs of cystic fibrosis patients, where their interactions are associated with worsening respiratory function and poorer clinical outcomes. The study aims to identify VOC biomarkers that distinguish microbial monocultures, cocultures, and growth media controls, providing insights into pathogen interactions and potential diagnostic markers.</p><p>To achieve this, the researchers combined solid-phase microextraction (SPME) sampling with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (<a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/37"><strong>GC×GC-TOFMS</strong></a>). This analytical platform offers high chromatographic resolution, rapid spectral acquisition rates of up to 500 spectra per second, and rich three-dimensional datasets containing chromatographic and mass spectral information. However, each chromatogram can contain more than one million data points, creating significant challenges for data processing and interpretation.</p><p>The core of the work focuses on tile-based feature selection methods implemented through the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/348"><strong>ChromaTOF Tile software</strong></a>. The primary approach, tile-based Fisher Ratio (F-ratio) analysis, identifies analytes that differ statistically between sample classes by comparing between-class and within-class variance. Using this strategy, the authors discovered 576 VOC features capable of differentiating growth media, <i>P. aeruginosa</i> monocultures, <i>A. fumigatus</i> monocultures, and cocultures. Additional pairwise statistical testing was then used to determine which specific classes were responsible for the observed differences. Representative VOCs characteristic of each class were identified, including heptanal for <i>P. aeruginosa</i>, 1-octen-3-ol for <i>A. fumigatus</i>, and several unique compounds associated with coculture conditions.</p><p>The presentation also addresses practical challenges associated with high-throughput experimentation, where replicate measurements may be limited by time, cost, or sample availability. To overcome these constraints, the team developed complementary tile-based coefficient of variation (CV) and fold-change (FC) approaches that can identify important analytes using as little as a single replicate per sample class. The authors conclude that selecting the appropriate feature-selection strategy—F-ratio, CV, or fold-change—depends on the experimental design and available data. Future work will focus on advanced analyte identification using chemometric tools such as PARAFAC, enabling deeper biological interpretation of microbial VOC signatures and their relevance to cystic fibrosis research.</p><h3><strong>3. Shimadzu: Alcohol Determination of Sanitizer Gel Using </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=Nexis+GC-2060"><strong>Nexis GC-2060</strong></a></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_01120_en_ed83896803.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li>The newly developed FID on the Nexis GC-2060 provides excellent linearity for high-concentration samples.</li><li>The Nexis GC-2060 enables rapid oven heating and cooling, allowing results to be obtained quickly.</li><li>A special liner for aqueous solutions and an Xtra Life Microsyringe enable highly repeatable data acquisition</li></ul><p>Methods for alcohol determination are specified in USP (United States Pharmacopeia) General Chapters &lt;611&gt; ALCOHOL DETERMINATION, which describes both a distillation and a gas chromatography method. In USP &lt;611&gt;, Method II (gas chromatography) includes a capillary column method (Method IIb).&nbsp;</p><p>For GC analysis of samples containing a large amount of water (e.g., sanitizing ethanol), sample vaporization may become unstable, resulting in abnormal peak shapes and poor repeatability. In addition, with a standard micro-syringe for AOC, the plunger may become stiff during aspiration and dispensing of aqueoussolutions, which can degrade repeatability.&nbsp;</p><p>This article presents an example of alcohol determination in sanitizing ethanol using the Nexis GC-2060 equipped with a newly developed FID. The Nexis GC-2060 can be configured with a newly developed FID that has high sensitivity and excellent linearity. In addition, a special liner for aqueous solutions and an Xtra Life Microsyringe were used to achieve repeatable analysis.</p><h5><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=Nexis+GC-2060"><strong>Nexis GC-2060</strong></a><strong> and the Newly Developed FID</strong></h5><p>The Nexis GC-2060 is a gas chromatograph that combines world-class analytical performance with workflow optimization based on the latest technologies. Rapid oven heating and cooling shorten analysis cycle time and improve throughput. The GC-2060 can also be equipped with a newly developed FID (Fig. 1). This FID offers world-class sensitivity and improved linearity.</p><h4><strong>Conclusion&nbsp;</strong></h4><p>The alcohol concentration in a commercially available sanitizing gel was determined using the Nexis GC-2060 equipped with the newly developed FID. The results met the system suitability requirements of USP &lt;611&gt; Method IIb, confirming the effectiveness of the Nexis GC2060 equipped with the new FID, along with the special liner for aqueous solutions, and the Xtra Life Microsyringe for repeatable analysis of aqueoussamples</p></div></div></div></div></div></div></section>

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