<p><span><strong>The countdown is on for one of the world’s leading conferences dedicated to food analysis. The 12th International Symposium on Recent Advances in Food Analysis (RAFA 2026), taking place in Prague from 3–6 November 2026, is approaching two important milestones: the Early Bird registration deadline and the deadline for oral abstract submissions, both ending on 30 June 2026. Researchers, analysts, industry professionals, regulators, and students are encouraged to secure their participation at a reduced registration rate and take advantage of the opportunity to present their latest scientific results to the international food analysis community.</strong></span></p><p><span>RAFA 2026 will once again bring together leading experts from academia, industry, and regulatory organizations to discuss the latest developments in food safety, authenticity, contaminants, non-target screening, PFAS, microplastics, food contact materials, artificial intelligence, and advanced analytical technologies. The preliminary program already features an outstanding lineup of internationally recognized speakers addressing some of the most pressing challenges in modern food analysis and food safety.</span></p><h3><strong>Registration</strong></h3><blockquote><p class="cke-user_style-important"><strong>👉</strong><a target="_blank" rel="noopener noreferrer" href="https://rafa.easyevent.eu/tickets/"><strong>Register to attend RAFA 2026!</strong></a></p><p><strong>Early-bird registration until 30 June, 2026</strong></p></blockquote><p>After successful submission of your registration, you will receive by e-mail (i) an automatic confirmation of receipt indicating the total amount of payment due (either for bank transfer or for on-line payment by card), (ii) login to Member section where you can administrate your profile, submit your abstracts, and from <strong>September 2026</strong> register for satellite events, workshops and seminars. If it will not be delivered within a few minutes after submission of your registration please contact us at <strong>RAFA2026@vscht.cz</strong>.</p><p>As soon as payment of registration fee is processed, you will also receive by e-mail (iii) confirmation of payment.</p><h4><strong>Registration Fees</strong></h4><ul><li><strong>Smart rate (Until 30 June, 2026): </strong>€ 655</li><li><strong>Regular registration (Until 30 September, 2026):</strong> € 855</li><li><strong>Late registration (From 1 October, 2026):</strong> € 955</li><li><strong>Student rate*&nbsp;</strong><ul><li><strong>Until 30 June, 2026: </strong>€ 465</li><li><strong>From 1 July, 2026:</strong> € 605</li></ul></li></ul><p><i>* The proof of student status (e.g. copy of a student identify card) is requested as an attachment of the registration form.</i></p><h5><strong>The regular registration fee will include:</strong></h5><ul><li>Attendance at all scientific sessions, workshops, seminars, vendor seminars and other satellite events</li><li>Access to the poster and exhibition area</li><li>Conference kit (name badge, conference bag, conference documents, certificate of participation, certificate of presentation on request)</li><li>Final programme</li><li>E-Book of Abstracts</li><li>Coffee breaks</li><li>Buffet luncheons</li><li>Welcome drink</li></ul><p>In the registration form you can r<strong>egister free of charge for the welcome drink</strong> and you can also order <strong>ticket(s) for the conference dinner (€ 85).</strong></p><p><i>Please note that your registration is firm when your payment has been received.</i></p><h3><strong>Abstract submission</strong></h3><blockquote><p class="cke-user_style-important"><strong>👉</strong><a target="_blank" rel="noopener noreferrer" href="https://rafa.easyevent.eu/tickets/"><strong>Submit your ABSTRACT HERE!</strong></a></p><p>In order to submit an abstract, you must <strong>first register to attend RAFA 2026</strong>. <strong>After registration</strong> you will receive an <strong>access to the Member section </strong>where you can <strong>submit your abstract(s).</strong></p></blockquote><p>&nbsp;After successful submission of your abstract you will receive an automatic confirmation of receipt of submitted abstract via e-mail. If it will not be delivered within a few minutes after submission of your abstract please check at first your spam box-mail and then contact us at <strong>RAFA2026@vscht.cz</strong>.</p><p>Please ensure that your abstract does not contain spelling, grammatical or scientific errors. Even though modifications of your submitted abstract will be allowed in the Abstract section of the Member section until the deadline for the abstract submission, please be confident on the lack of errors in your final version prior to starting the submission process.</p><p>The abstract will be reproduced exactly as submitted. No proofreading will be done, although some corrections may be requested by the reviewers.</p><p>If you need to withdraw your abstract, a written statement reflecting the reasons for this decision must be sent to <strong>RAFA2026@vscht.cz</strong> no later than <strong>September 30, 2026</strong>.</p><h4><strong>Important notice:</strong></h4><blockquote><p><strong>1 registered participant can submit no more than 2 abstracts.</strong></p></blockquote><p>To apply for a young scientist’s oral presentation, as an indication please respect the rule that you should be born in 1991 or later.</p><p>The <strong>text of the abstract </strong>should be<strong> limited to 3000 characters including spaces</strong>. Tables and pictures are not allowed.</p><p><strong>Abstracts of oral presentations and posters</strong> will be included into the "<strong>e-Book of Abstracts</strong>" only after receipt of the conference registration fee. Any payment of the registration fee has to be received until <strong>September 30, 2026</strong>, otherwise contribution(s) will not be included into the e-Book of Abstracts and the Conference programme.</p><h3><strong>Program</strong></h3><h4><strong>Invited Speakers</strong></h4><h5><strong>Food allergen risk assessment: Past, present, future</strong></h5><ul><li>Ben Remington from Remington Consulting Group B.V., Utrecht, the Netherlands &amp; University of Nebraska, USA</li></ul><p>Food allergy and allergen management are global public health issues, and consumer protection depends on transparent and trustworthy labelling. While 30 years of scientific advances show that low level exposures, or reference doses, are unlikely to trigger reactions in most allergic individuals, harmonized and globally aligned regulations are still needed to promote consistent allergen risk assessment and labelling decisions. This presentation will discuss the landscape of allergen risk assessment, global policy developments, current research and future needs in the area.</p><h5><strong>Unmasking hidden adulteration in honey: Strategies for detecting and characterizing honeys produced from sugar-fed colonies</strong></h5><ul><li>Oscar Nuñez Burcio from University of Barcelona, Barcelona, Spain</li></ul><p>From hive to jar, not all honey tells the same story. This presentation addresses hidden adulteration associated with sugar-fed bee colonies, highlighting implications for authenticity, production practices, and the need for a more precise characterization of honey to ensure consumer trust.</p><h5><strong>Analytical and toxicological methods for safety of food contact materials</strong></h5><ul><li>Erich Leitner from Graz University of Technology, Institute of Analytical Chemistry and Food Chemistry, Graz, Austria</li></ul><p>Food packaging plays a critical role in protecting and preserving food throughout the supply chain. All packaging materials intended to come into contact with food must comply with strict EU safety rules. Due to increased complexity of packaging solutions, lowering legal limits and increasing numbers of target compounds, we also have to adapt the strategies used to verify and guarantee the safety of these materials.</p><h5><strong>Data-driven strategies to reduce complexity in non-target HRMS analysis of food samples</strong></h5><ul><li>Anneli Kruve from Stockholm University, Stockholm, Sweden</li></ul><p>LC/HRMS based non-target screening can detect hundreds to thousands of chemical features in complex food and environmental samples. Investigating all of these in detail is practically impossible, so which ones to focus on? Novel computational tools developed in computational mass spectrometry allow predicting the toxicity and concentration of the detected chemical features even if these have not yet been fully structurally elucidated. Combining these predictions enables us to focus on the chemical features possessing high risk.</p><h5><strong>Role of AI in the identification of emerging food systems risks</strong></h5><ul><li>Ákos Bernard Jóźwiak from Syreon Research Institute, Budapest, Hungary</li></ul><p>AI-assisted evidence synthesis – spanning curated scientific evidence, exposome data, and network analysis – is the backbone of next-generation emerging chemical food safety risk identification, enabling earlier signals and more resilient surveillance systems.</p><h5><strong>Challenges of microplastics analysis in the context of food safety</strong></h5><ul><li>Dorte Herzke from Norwegian Institute of Public Health, Oslo, Norway</li></ul><p>Food and food packaging are major sources of micro- and nanoplastic exposure. Plastics used in processing, storage, and packaging can release particles through heat, abrasion, and degradation. Small particles below 2 µm may cross gut barriers and carry chemicals, highlighting the need for robust and sensitive measurement methods.</p><h5><strong>Never-ending analytical challenges in PFAS analysis of food</strong></h5><ul><li>Darina Dvořáková from University of Chemistry and Technology Prague, Prague, Czechia</li></ul><p>Exploring and monitoring PFAS in food requires advanced strategies. Combining targeted, suspect, and non-target HRMS analysis, determination of total fluorine and sum parameters, and novel sensors provides new insights into PFAS occurrence and supports food safety monitoring.</p><h5><strong>An overview of the latest scientific knowledge on process contaminants in food for appropriate risk management measures</strong></h5><ul><li>Thierry Delatour from Nestlé Research, Lausanne, Switzerland.</li></ul><p>Recent scientific findings related to acrylamide and furans formation, occurrence and analytical methods in food provide important insights for risk management.</p>

Deadline for RAFA 2026 Early Registration and Oral Abstract Submission is approaching

<p><strong>Traditional EI library searching works exceptionally well when the unknown compound is already present in the spectral library. However, analytical chemists frequently encounter compounds that are structurally related to library entries but are not exact matches. NIST's EI Hybrid Search addresses this challenge by combining conventional spectral matching with mass-shifted fragment alignment, allowing the identification of analogs, derivatives, and novel compounds that may not exist in the database.</strong></p><p>This guide summarizes the workflow presented by James Little and demonstrates how to use NIST 26 Hybrid Search in conjunction with the Chromatogram Window and MS Interpreter to obtain more meaningful structural information from unknown EI GC-MS spectra.</p><blockquote><p><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/paper/33370"><strong>You can download PDF Handout for this guide HERE</strong></a></p></blockquote><h4><strong>Why Hybrid Search Matters</strong></h4><p>Conventional EI library searching relies on direct matching between an unknown spectrum and spectra stored in a library. Hybrid Search expands this concept by allowing fragment ions to be matched even when they are shifted by a consistent mass difference (Δm). This enables the software to recognize compounds that differ by substituents or structural modifications.</p><p><strong>Key advantages include:</strong></p><ul><li>Identification of structurally related compounds not present in the library</li><li>Detection of analogs, derivatives, and emerging compounds</li><li>Integration of exact fragment matches and mass-shifted matches into a single score</li><li>Improved interpretation of unknowns through proposed structural modifications</li><li>Enhanced confidence when molecular weight information is available</li></ul><p>Although Hybrid Search does not require the molecular ion to be present, performance improves significantly when the molecular weight can be estimated or constrained. In these cases, the observed mass difference between the unknown and library compounds can often be translated directly into a plausible chemical modification.</p><h4><strong>Configuring Hybrid Search</strong></h4><p>Before performing a search, the Hybrid Search settings should be configured within the NIST Library Search window.</p><p>Recommended settings:</p><ul><li>Select the Similarity search mode</li><li>Enable Hybrid Search</li><li>Keep the In Spectrum option checked under normal circumstances</li><li>Allow the software to estimate the molecular weight automatically</li><li>If the estimated molecular weight appears incorrect, manually enter a nominal molecular weight</li><li>Select all relevant libraries (NIST, Wiley, user libraries, etc.)</li><li>Save the configuration for future use</li></ul><p>The selected molecular weight used to constrain the search is displayed in the lower-right corner after the search is completed.</p><h4><strong>Optimizing the Results Display</strong></h4><p>Hybrid Search produces several unique parameters that are not shown in the default results table.</p><p>Additional columns should be added by right-clicking within the results table and selecting Properties. Recommended columns include:</p><ul><li>DeltaMass</li><li>dForm</li><li>pctRelForm</li><li>o.Match (equivalent to the traditional EI Normal Match score)</li></ul><p>Without these parameters, much of the structural interpretation power of Hybrid Search is lost.</p><h4><strong>Working with Spectra from the Chromatogram Window</strong></h4><p>Currently, the Chromatogram Window performs standard EI library searching. When a potentially interesting unknown is encountered:</p><ul><li>Perform the normal EI search in the Chromatogram Window</li><li>Send the spectrum to the standalone Library Search module</li><li>Perform a Hybrid Search using the enhanced Hybrid Search functionality</li></ul><p>This workflow combines rapid screening with advanced structural interpretation.</p><h4><strong>Understanding Key Hybrid Search Parameters</strong></h4><h5><strong>DeltaMass (Δm)</strong></h5><p>DeltaMass represents the mass difference between the unknown compound and the matched library compound.</p><p>A consistent Δm across multiple high-scoring hits often indicates a specific structural modification.</p><h5><strong>dForm</strong></h5><p>dForm provides a proposed elemental composition that explains the observed mass difference.</p><p>Examples include:</p><ul><li>H–1F</li><li>CH₂</li><li>O–1</li><li>C₁H₃F</li></ul><p>These transformations can suggest likely chemical modifications between the library compound and the unknown.</p><h5><strong>pctRelForm</strong></h5><p>New in NIST 26, pctRelForm estimates how frequently a given elemental transformation occurs relative to the most common transformation (CH₂ = 100%).</p><p>Interpretation:</p><ul><li>High values indicate chemically plausible and commonly observed transformations</li><li>Very low values suggest the proposed transformation may be accidental or unlikely</li></ul><h5><strong>o.Match</strong></h5><p>Represents the traditional EI match score and allows comparison between standard searching and Hybrid Search results.</p><h4><strong>Case Study: Identifying a Fluorinated Unknown</strong></h4><p>The presentation demonstrates the identification of an unknown compound with a proposed molecular weight of 152 Da.</p><p>Hybrid Search returned several high-scoring hits with consistent evidence suggesting fluorine substitution:</p><p><strong>Hit #1</strong></p><ul><li>Library MW: 134</li><li>DeltaMass: +18</li><li>dForm: H–1F</li><li>Interpretation: Addition of fluorine</li></ul><p><strong>Hit #5</strong></p><ul><li>Library MW: 138</li><li>DeltaMass: +14</li><li>dForm: CH₂</li><li>Interpretation: Related fluorinated analog</li></ul><p><strong>Hit #6</strong></p><ul><li>Library MW: 168</li><li>DeltaMass: –16</li><li>dForm: O–1</li><li>Interpretation: Structurally consistent fluorinated compound</li></ul><p>Multiple independent hits converged on the same conclusion: the unknown was likely a fluorinated analog of known aromatic compounds.</p><p>This illustrates the major strength of Hybrid Search: identifying structural relationships even when the exact compound is absent from the library.</p><h4><strong>Moving from Hybrid Search to Structural Interpretation</strong></h4><p>Once a plausible structure is identified:</p><h5><strong>Step 1 – Export the Structure</strong></h5><p>From the Hybrid Search results:</p><ul><li>Right-click the best hit</li><li>Use Send To → Default Structure Editor</li><li>Open the structure in a drawing program such as ChemSketch</li><li>Copy the proposed structure to the clipboard</li></ul><h5><strong>Step 2 – Open MS Interpreter</strong></h5><p>Within MS Interpreter:</p><ul><li>Paste the proposed structure</li><li>Load the unknown spectrum</li><li>Compare predicted fragmentation with observed ions</li><li>Evaluate isotope patterns and fragment assignments</li></ul><h5><strong>Step 3 – Validate the Proposal</strong></h5><p>Indicators of a good structural proposal include:</p><ul><li>Consistent isotope ratios</li><li>Logical fragmentation pathways</li><li>Agreement between observed and predicted fragment ions</li><li>Multiple Hybrid Search hits supporting the same structural modification</li></ul><p>In the example presented, MS Interpreter showed excellent agreement between the proposed fluorinated structure and the observed EI spectrum, although the exact positions of the fluorine and methyl substituents on the aromatic ring could not be definitively assigned.</p><h4><strong>Best Practices</strong></h4><ul><li>Always review DeltaMass, dForm, and pctRelForm together</li><li>Do not rely solely on the highest match score</li><li>Give greater weight to transformations supported by multiple independent hits</li><li>Use molecular weight information whenever possible</li><li>Confirm structural hypotheses using MS Interpreter</li><li>Consider Hybrid Search as a structure-elucidation tool rather than simply a library search tool</li></ul><h4><strong>Conclusion</strong></h4><p>NIST 26 Hybrid Search significantly extends the capabilities of conventional EI library searching by identifying structural relationships between unknown compounds and library entries. Through the use of DeltaMass calculations, elemental transformation analysis (dForm), and statistical plausibility metrics (pctRelForm), Hybrid Search provides valuable clues for elucidating compounds that are absent from spectral libraries.</p><p>When combined with MS Interpreter, it becomes a powerful workflow for the characterization of unknowns, analogs, degradation products, metabolites, and emerging compounds in EI GC-MS analysis.</p>

Lib Search: Similarity EI Hybrid Search for Spectra Sent from Chromatogram Window

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezGCMS Library in the week of 22nd 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<strong> </strong>by<strong> Shimadzu </strong>and posters by <strong>MDCW / William &amp; Mary </strong>and <strong>Waters Corporation / ASMS!</strong></p><h3><strong>1. Agilent Technologies: Operational efficiency for your GC and GC/MS labs with the </strong><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890B"><strong>Agilent 8890B gas chromatography system</strong></a></h3><ul><li>Brochure</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/br_8890b_gc_5994_9205en_agilent_fb5db29527.pdf"><strong>Full PDF for download</strong></a></li></ul><section><div><div><div><div><div><div><h4><strong>Analyze more samples every day with faster cycle times&nbsp;</strong></h4><p>The <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890B"><strong>8890B GC </strong></a>provides faster maximum oven temperature ramps and cooldown rates for conventional dual-channel GC. If you want even faster cycle times, an optional rapid cooling fan is available.&nbsp;</p><h4><strong>Achieve excellent repeatability across multiple injections&nbsp;</strong></h4><p>Consistent retention time and area count performance are the cornerstone of what you can expect from the 8890B GC. The system also meets all specifications required for regulatory methods such as USP Method &lt;467&gt; (residual solvents).</p><h4><strong>Shine a new light on your connections and components&nbsp;</strong></h4><p>Make maintenance easier and faster with a built-in oven light that illuminates the internal space on the 8890B GC. That means you can perform tasks like troubleshooting and column installation in less time—and with fewer errors.</p><h4><strong>Boost GC performance with capillary flow technology&nbsp;</strong></h4><ul><li><strong>Backflush</strong> significantly reduces timing between injections, increasing productivity.&nbsp;</li><li><strong>Purged Ultimate unions</strong> provide leak-free connections, reducing downtime.&nbsp;</li><li><strong>Flow splitters</strong> enable data collection from up to three detectors.&nbsp;</li><li><strong>New CFT calculators</strong> let you download calculated setpoints to your method—avoiding time-consuming, error-prone manual input.&nbsp;</li><li><strong>Multidimensional chromatography:&nbsp;</strong><ul><li><strong>Deans Switch</strong> facilitates heart cutting, enabling resolution of trace compound in complex matrices.&nbsp;</li><li><strong>GC x GC flow modulation</strong> enables multidimensional chromatography without the need for cryogen</li></ul></li></ul><h4><strong>Helium conservation module&nbsp;</strong></h4><p>Extend the life of your helium tanks by up to 30 times. The Agilent helium conservation module allows you to use helium for your GC runs and switch to a different gas (typically nitrogen) when your GC is idle. That means you can achieve better control of laboratory operating expenses and avoid workflow disruptions.</p><h4><strong>Alternative carrier gas support&nbsp;</strong></h4><p>Alternative carrier gases like hydrogen and nitrogen are popular low-cost options.&nbsp;</p><p>The <strong>Agilent Hydrogen Sensor Module Series 2 </strong>option adds built-in peace of mind when operating with H2 gas.&nbsp;</p><p>The <strong>Agilent HydroInert source </strong>minimizes sensitivity loss and spectral anomalies associated with H2. It also:&nbsp;</p><ul><li>Maintains spectral fidelity, even for compounds highly susceptible to hydrogenation.&nbsp;</li><li>Improves peak shape for high-boilers, especially polycyclic aromatic hydrocarbons (PAHs)</li></ul><h3><strong>2. MDCW / William &amp; Mary: Decomposition Analysis Using Differing Data Processing Methods to Identify Volatile Organic Compounds</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/P_25_Weina_2ae94f0998.pdf"><strong>Full PDF for download</strong></a></li></ul><section><div><div><div><div><div><div><p>When a body decomposes it emits volatile organic compounds (VOCs). VOCs from decomposing remains have been studied before,1 however there is a gap that remains in the knowledge on VOCs that evolve from submerged decomposing remains. 2 In this study, submerged animal tissue was tested to see how the VOCs can be utilized in forensic settings.&nbsp;</p><p>This study used data obtained last summer using comprehensive two-dimensional gas chromatography – time-of-flight mass spectrometry (GC×GC-TOFMS). Pigs (Sus scrofa domesticus) are often used as analogs in decomposition studies due to similarities with humans. 3 In this study, pork belly samples were submerged in mason jars filled with water and stored at different temperatures. The GC×GC-TOFMS instrument was used to collect odor from above water samples for a period of twelve days.</p><p>GC×GC is often used to analyze complex odor profiles. When analyzing samples with GC×GC, there is a lack of standardization to process the data acquired from time trials. There is a need to discover effective ways to track VOCs for chemical identification and longitudinal analysis.</p><p>The goal of this research was to compare different software approaches to analyze GC×GC longitudinal data to achieve accurate analyte identification, representation of data over time, and effective class characterization.</p><h4><strong>Methods</strong></h4><p>For the first four days, 5 mL of water was taken and run on the <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/37"><strong>GC×GC-TOFMS</strong></a>. Thereafter, the samples were sampled and run every three days until day twelve. Data were analyzed initially using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/41"><strong>ChromaTOF</strong></a> (LECO Corporation). The samples were then analyzed using <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/348"><strong>ChromaTOF Tile </strong></a>and <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1873"><strong>ChromaTOF Sync 2D</strong></a> to compare how they can be used in a forensic setting.</p><p>Peak tables were curated in Tile and Sync 2D. Then, ChromaTOF was used as a guide to confirm which peaks were identified correctly. If the compound was correctly identified, the peak would be accepted or kept on the table. If not correctly identified, the peak would either be rejected or deleted. Once the tables were complete from the different software approaches, they were exported to Excel for further comparison.</p><p>In excel, the relevant hits that were identified as decomposition VOCs were compared across the software approaches. The compounds that were most commonly found were compared to existing literature on decomposition VOCs. From there, two tables were built; where one table was compounds confirmed with literature and the other table was compounds not confirmed with literature.</p><p>Comparison between software approaches included looking at different criteria to see which method was the most effective and efficient. The criteria included how long the software approach took, the number of compounds found in each software approach, the accuracy of peak area in the software approach compared to ChromaTOF data, and principal component analysis (PCA) comparison to see which software approach was best used for forensic practice or research.</p><h4><strong>Conclusions</strong></h4><p>Sync 2D showed the capability of showing trends in decomposition compounds</p><ul><li>This software approach can be used in research environment where longitudinal data is needed to determine the trends of decomposition data&nbsp;</li><li>Best purpose: research applications requiring longitudinal tracking&nbsp;</li></ul><p>Coefficient of Variance’s and Fold Change’s PCA plots showed their ability for clear class differentiation&nbsp;</p><ul><li>The two software approaches can be used to distinguish water containing decomposition in a crime scene&nbsp;</li><li>Best purpose: comparison at a single point and time [detailed analysis]&nbsp;</li></ul><p>Fisher Ratio showed a proficiency in identifying top differentiating compounds and showed aptitude for class differentiation&nbsp;</p><ul><li>The software approach would be helpful for cursory analysis for crime scene evidence&nbsp;</li><li>Best purpose: quick comparison of biggest difference for swift identification</li></ul><h3><strong>3. Shimadzu: Determination of Ester and Linolenic Acid Methyl Ester Contents in Biodiesel According to EN 14103:2020</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_05_sca_180_054_en_93d1029b72.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=2060"><strong>NexisTM GC-2060</strong></a> provides reliable analysis of biodiesel samples regarding their ester and linolenic acid methyl ester contents using hydrogen as a cost-effective carrier gas&nbsp;</li><li>It delivers excellent repeatability and provides appropriate safety measures for the use of hydrogen as carrier gas&nbsp;</li><li>The optional multi-mode injection unit (MMI) offers performance equivalent to a conventional split-splitlessinjection unit (SPL), significantly reducing maintenance downtime</li></ul><p>Biofuels play an important role in enabling countriesto meet their greenhouse gas reduction targets. They can be produced from a range of raw materials, including plant oils, animal fats, and used cooking oils. Biodiesel is mainly being produced from rapeseed, soybean, and palm oils. Due to this diversity of feedstocks, continuous quality control is necessary as the quality of the raw material has an enormousimpact on the quality of the biodiesel.&nbsp;</p><p>The EN 14103:2020 describes the determination of total ester content and linolenic acid methyl ester content in biodiesel by means of gas chromatography with flame ionization detection (GC-FID). The total ester content covers a range of fatty acid methyl esters (FAME) from C6 to C24, and linolenic acid methyl ester is investigated separately as interfering reactive compound that might influence long-term stability of the biodiesel. According to the standard, the total ester content must be greater than 90 % (m/m) and the linolenic acid methyl ester content shall be between 1 and 15 % (m/m)1.</p><p>This study shows the analysis of biodiesel originating from rapeseed,soybean, and palm oil using the Nexis GC-2060.</p><h4><strong>Multi-Mode Injection Unit as Alternative&nbsp;</strong></h4><p>Shimadzu’s multi-mode injection unit (MMI) offers reliable splitsplitless mode functionality over a broad range of carbon numbers. It allows analysis of biodiesel samples with high repeatability and provides chromatographic results comparable to those measured with SPL as dedicated split-splitless injection unit. The use of MMI significantly shortens maintenance downtime from tens of minutes for a classical split-splitless injection unit to just a few minutes. Figure 3 gives a direct comparison of an exemplary rapeseed-based biodiesel sample measured with SPL (top) and MMI (bottom).</p><h4><strong>Conclusion</strong></h4><p><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=1&amp;search=2060"><strong>Nexis GC-2060</strong></a> provides reliable analysis of biodiesel samples regarding their ester and linolenic acid methyl ester content according to EN 14103:2020, covering the various ester types commonly in use. It allows repeatable analysis using hydrogen as a cost-effective carrier gas. The optional multi-mode injection unit (MMI) can be selected as alternative to a classical splitsplitlessinjection unit (SPL).</p><h3><strong>4. Waters Corporation / ASMS: Putting the (Atmospheric) Pressure on Forever Chemicals: A Targeted Analysis of GC Amenable PFAS using APGC–MS/MS</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720009413_dfa4082b45.pdf"><strong>Full PDF for download</strong></a></li></ul><p>This poster presents a targeted analytical method for the determination of gas chromatography (GC)-amenable PFAS compounds using atmospheric pressure gas chromatography coupled with tandem mass spectrometry (APGC-MS/MS). The study addresses a key limitation of conventional LC-MS/MS methods, which are highly effective for many PFAS but less suitable for more volatile species such as fluorotelomer alcohols (FTOHs), fluorotelomer acrylates (FTAcr), and fluorotelomer acetates (FTOAc). By incorporating GC-MS/MS analysis, laboratories can obtain a more complete characterization of PFAS contamination across environmental, biological, and food-contact materials.</p><p>The analytical workflow utilized an <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 autosampler and coupled to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/554"><strong>Waters Xevo TQ Absolute tandem mass spectrometer</strong></a> operated with APGC ionization in positive-ion mode. Separation was performed on an Rtx-200 capillary column, while data acquisition and quantification were managed using <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1544"><strong>waters_connect software</strong></a>. Unlike traditional electron ionization (EI), APGC generated intact protonated molecular ions, significantly improving sensitivity and selectivity for GC-amenable PFAS. A method covering 35 PFAS compounds was developed, achieving lower limits of quantification (LLOQs) of 0.025–0.1 ng/mL for most analytes.</p><p>Several sample types were evaluated, including wastewater, surface water, soil, fish tissue, shrimp, and food-packaging materials. Environmental and biological samples were prepared according to EPA Method 1633A and had previously been analyzed by LC-MS/MS, allowing the GC-MS/MS method to serve as a complementary technique. Food-packaging samples were extracted with methanol containing ammonium hydroxide and analyzed for targeted PFAS. While no target PFAS were detected in the tested packaging materials, multiple GC-amenable PFAS were found in environmental samples. Fluorotelomer alcohols were the most frequently detected compounds, with wastewater and ski-wax-impacted soils showing the highest concentrations.</p><p>The results demonstrate that APGC-MS/MS provides a sensitive and robust solution for the analysis of volatile PFAS classes that may be missed by LC-MS/MS alone. The method delivered accurate quantification, effective chromatographic separation, and reliable recovery in spiked samples. The authors conclude that combining APGC-MS/MS with established LC-MS/MS workflows enables a more comprehensive assessment of PFAS contamination, particularly for emerging fluorotelomer compounds and other GC-amenable PFAS that are increasingly relevant in environmental monitoring and food-packaging investigations.</p></div></div></div></div></div></div></section></div></div></div></div></div></div></section>

News from LabRulezGCMS Library - Week 26, 2026

Article

Events

Application

Academy

Video

Software

Poster

RAFA provides an overview of contemporary trends in ANALYTICAL & BIOANALYTICAL STRATEGIES IN FOOD QUALITY AND SAFETY CONTROL and discusses CHALLENGES / NOVEL APPROACHES IN FOOD AND NATURAL PRODUCTS ANALYSIS.

Recent Advances in Food Analysis (RAFA)

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My main interest is the identification of organic compounds by mass spectrometry in organic mixtures.

James Little/Mass Spec Interpretation Services

LECO ChromaTOF Identification Grading System

ChromaTOF Identification Grading System assigns confidence scores to GC×GC-HRTOFMS identifications using accurate mass, spectra, fragments, and retention indices for faster, defensible results.

<h3><strong>Bring the power of accurate mass and confidence to compound identification</strong></h3><p><strong>The LECO Identification Grading System™ (IGS™) is designed to simplify and accelerate data review by clearly indicating how confident you can be in each compound identification. By consolidating all available chemical evidence into a single, intuitive grading metric, IGS enables users to quickly justify identifications, report results with confidence, and make informed decisions on next analytical steps.</strong></p><p>IGS is tightly integrated with data generated by the Pegasus GC-HRT 4D platform and is especially powerful for non-targeted workflows, where large numbers of unknown or partially known compounds must be evaluated efficiently and objectively.</p><h3><strong>Key Features</strong></h3><p>The Identification Grading System leverages the full chemical information content produced by GC×GC-HRTOFMS to strengthen confidence in “unknown unknown” identification.</p><p><strong>Each compound identification is assigned a grade based on four independent criteria:</strong></p><ul><li>Presence and accurate mass of the molecular ion</li><li>Presence and strong spectral similarity score from a curated reference library</li><li>Accurate mass agreement of chemically reasonable fragment ion formulae associated with the library hit</li><li>Retention index agreement with the corresponding library entry</li></ul><p>The resulting numerical grade provides an immediate, transparent indicator of identification confidence—the higher the grade, the stronger the supporting evidence.</p><h3><strong>Theory of Operation</strong></h3><p>The full potential of confident identification is realized through the combined capabilities of comprehensive two-dimensional gas chromatography (GC×GC) and high-resolution time-of-flight mass spectrometry (TOFMS).</p><p>GC×GC separates co-eluting species that would otherwise interfere with spectral interpretation, producing significantly cleaner mass spectra. High-resolution TOFMS delivers accurate, unskewed mass spectra across the entire chromatographic peak, including extremely narrow second-dimension peaks. When combined with advanced deconvolution, this results in exceptionally high-quality spectral information.</p><p>Developed in response to the challenges highlighted by the U.S. EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT), IGS directly addresses the time-consuming and subjective nature of traditional data review. It provides a systematic, evidence-based approach to filtering large result sets and focusing attention on the most reliable identifications.</p><h4><strong>Practical Impact in Non-Target Analysis</strong></h4><p>In GC×GC-TOFMS datasets from the EPA ENTACT challenge, unfiltered deconvolution can yield thousands of tentative identifications. Applying the IGS filter dramatically reduces this number, immediately highlighting the most confident matches and allowing analysts to focus on true unknowns with high priority.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_Identification_Grading_System_3_9b33a4d091.jpg" alt="LECO: Identification Grading System" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_Identification_Grading_System_3_9b33a4d091.jpg 245w," sizes="100vw" width="513" height="120"></p><p>In blind ENTACT samples, IGS correctly identified approximately 80% of known compounds introduced into the sample, demonstrating that the combination of GC×GC separation and high-resolution TOFMS is essential for high-confidence non-target identification.</p>

LECO ChromaTOF® Tile analytical software

ChromaTOF Tile enables rapid, statistically robust GC×GC-TOFMS data comparison using tiled heatmaps, reducing false positives and cutting data mining from days to minutes.

<p><strong>Turn Your Data into Chemistry</strong></p><p><strong>The volume of GC×GC-TOFMS data can quickly become overwhelming. Thousands of analytes across multiple tables may require months of pixel-level data mining, often complicated by false positives caused by normal chromatographic variation.</strong></p><p><strong>ChromaTOF Tile</strong> fundamentally changes how GC×GC data are compared and interpreted. Instead of evaluating individual pixels, the software statistically compares tiled regions of chromatograms. Truly significant differences rise immediately to the surface—allowing you to focus on chemical meaning rather than data hunting.</p><blockquote><p><i>“ChromaTOF Tile is an enabling technology that has reduced our peak picking time from 10 days to 10 minutes.”</i></p></blockquote><h3><strong>Designed for Sample Group Differentiation</strong></h3><p>Integrated heatmap tables provide instant visual feedback on relative intensities and variance. ChromaTOF Tile highlights differences between two or more sample classes, including:</p><ul><li>Biomarkers distinguishing healthy vs. diseased samples</li><li>Off-odor detection in food quality control</li><li>Environmental samples upstream/downstream of contamination sources</li><li>Petroleum samples from different geographic origins</li><li>Any complex GC×GC comparison where class differentiation matters</li></ul><h3><strong>Key Features</strong></h3><ul><li><strong>Stand-alone software</strong> designed to work in conjunction with <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/41"><span><strong>ChromaTOF</strong></span></a></li><li><strong>Rapid statistical comparison</strong> of GC×GC data sets</li><li><strong>Exportable tables and plots </strong>for efficient reporting</li><li><strong>Clean, library-searchable spectra generated</strong> from tiling</li><li><strong>Approximate trend quantification using</strong> tile areas</li><li><strong>Reduced false positives </strong>from chromatographic misalignment</li></ul><h3><strong>Theory of Operation</strong></h3><p><strong>The challenge:</strong> Comparing large sets of GC×GC chromatograms is slow and alignment-sensitive, often taking weeks or months even with automated tools.</p><p><strong>The solution:</strong> In collaboration with <span>Robert Synovec</span>, ChromaTOF Tile applies Fisher ratios to partition chromatograms into regions (“tiles”). Instead of pixel-by-pixel comparison, corresponding tiles are averaged and statistically evaluated.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_Chroma_TOF_Tile_analytical_software_816a8a7705.jpg" alt="LECO ChromaTOF® Tile analytical software" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_Chroma_TOF_Tile_analytical_software_816a8a7705.jpg 245w," sizes="100vw" width="854" height="462"></p><p>The result is a set of intuitive heatmaps that immediately direct attention to regions of high variance and high analytical relevance—without altering the underlying data.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/LECO_Chroma_TOF_Tile_analytical_software_2_c3c8abd476.jpg" alt="LECO ChromaTOF® Tile analytical software" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_LECO_Chroma_TOF_Tile_analytical_software_2_c3c8abd476.jpg 245w," sizes="100vw" width="1045" height="566"></p>

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