Interlaboratory comparisons (ILCs) have been used for more than a century and many important concepts are linked to them. This leaflet addresses the basic terminology of ILCs, why some colloquial terms can be misleading or misunderstood, and the importance of harmonisation.&nbsp;An ‘ILC’ is the organisation, performance and evaluation of measurements or tests on the same or similar items by two or more laboratories in accordance with predetermined conditions [1]. ILCs have a variety of overall – and detailed – objectives, the three most common [2] being:<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Let_s_call_a_PT_scheme_a_PT_scheme_3_9993b9fb0f.jpg" alt="Eurachem: Let’s call a PT scheme a PT scheme" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Let_s_call_a_PT_scheme_a_PT_scheme_3_9993b9fb0f.jpg 245w," sizes="100vw" width="1516" height="472"><h4>Some comparisons have special names&nbsp;</h4>The evaluation of participant (laboratory, organisation or individual) performance is typically referred to as ‘proficiency testing’ (PT) or ‘external quality assessment’ [1]. When the main objective is evaluating the performance of, for example, a candidate standard method, the ILC is often called a ‘method performance study’ or a ‘collaborative study’ [2, 3]. The former is quite clear whereas wording such as “collaborative” and “cooperative” just stresses joint effort. A project aimed at producing a new certified reference material may involve several laboratories conducting analyses on agreed properties [4]. Not surprisingly, this work is sometimes labelled as a ‘material certification study’. Special ILCs used to demonstrate measurement capabilities of National Metrology Institutes are called ‘key comparisons’, ‘supplementary comparisons’, and ‘pilot studies’ [5].&nbsp;<h4>Some terms can be confusing&nbsp;</h4>Other terms for ILCs, including ‘ring test’, ‘round robin’ and ‘circle analysis’, are common in the literature. They have, however, been used for different objectives and different designs and may be ambiguous or understood differently by laboratory staff. The use of these terms may, for example, imply a specific way of distributing the ILC sample. ISO/IEC 17043 uses ‘sequential scheme’, for a PT where a unique item is transferred between the participants and ‘simultaneous scheme’, in which similar items from a batch are distributed simultaneously to the participants [1].&nbsp;Terms like ‘measurement audit’, ‘interlaboratory testing scheme’, ‘measurement comparison scheme’, ‘rapid performance evaluation scheme’ and ‘intercalibration’ may not correctly reflect the category of laboratory involved, or the type of work participants do during the ILC.&nbsp;When in 1994 the IUPAC defined and described three major types of ‘interlaboratory studies’, the word ‘study’ was preferred to synonyms such as ‘trial’, ‘exercise’, ‘test’, ‘evaluation’ or ‘check’ [2].<h4>Let’s try to harmonise&nbsp;</h4>Many laboratory staff are unaware of official terms related to ILCs but recognise the acronym PT and words like ‘ring test’. Ideally, we would use a common scientific language, but harmonisation is time consuming. Terms appear and disappear and definitions change. The same term can be defined differently, even in different international standards. Lack of standards and guidelines in local languages can cause confusion and contribute to spreading of less appropriate terms.&nbsp;<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Eurachem_Let_s_call_a_PT_scheme_a_PT_scheme_2_820043ce8b.jpg" alt="Eurachem: Let’s call a PT scheme a PT scheme" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Eurachem_Let_s_call_a_PT_scheme_a_PT_scheme_2_820043ce8b.jpg 234w," sizes="100vw" width="1006" height="671">Much confusion can be avoided by clear reference to relevant standards and sectoral guides. ISO/ IEC 17043 [1] and ISO 13528 [6] contain many important concepts related to PT schemes and other ILCs. Some of these are also incorporated into ISO/IEC 17025 [7]. Additional useful, and free, tools are the Eurachem PT Guide [8] and the ISO online browsing platform [9].&nbsp;In writing guidance and instructions, remember that the term ‘interlaboratory comparison’ has a broad meaning and can be used for all cases described above. Sometimes this is clear enough for the reader. In other cases, use acknowledged terms that reflect the detailed objective of the ILC. Let’s call a PT scheme a PT scheme!

Let’s call a PT scheme a PT scheme!

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.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.<h3>Registration</h3><blockquote>👉<a target="_blank" rel="noopener noreferrer" href="https://rafa.easyevent.eu/tickets/">Register to attend RAFA 2026!</a>Early-bird registration until 30 June, 2026</blockquote>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 September 2026 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 RAFA2026@vscht.cz.As soon as payment of registration fee is processed, you will also receive by e-mail (iii) confirmation of payment.<h4>Registration Fees</h4><ul><li>Smart rate (Until 30 June, 2026): € 655</li><li>Regular registration (Until 30 September, 2026): € 855</li><li>Late registration (From 1 October, 2026): € 955</li><li>Student rate*&nbsp;<ul><li>Until 30 June, 2026: € 465</li><li>From 1 July, 2026: € 605</li></ul></li></ul>* The proof of student status (e.g. copy of a student identify card) is requested as an attachment of the registration form.<h5>The regular registration fee will include:</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>In the registration form you can register free of charge for the welcome drink and you can also order ticket(s) for the conference dinner (€ 85).Please note that your registration is firm when your payment has been received.<h3>Abstract submission</h3><blockquote>👉<a target="_blank" rel="noopener noreferrer" href="https://rafa.easyevent.eu/tickets/">Submit your ABSTRACT HERE!</a>In order to submit an abstract, you must first register to attend RAFA 2026. After registration you will receive an access to the Member section where you can submit your abstract(s).</blockquote>&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 RAFA2026@vscht.cz.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.The abstract will be reproduced exactly as submitted. No proofreading will be done, although some corrections may be requested by the reviewers.If you need to withdraw your abstract, a written statement reflecting the reasons for this decision must be sent to RAFA2026@vscht.cz no later than September 30, 2026.<h4>Important notice:</h4><blockquote>1 registered participant can submit no more than 2 abstracts.</blockquote>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.The text of the abstract should be limited to 3000 characters including spaces. Tables and pictures are not allowed.Abstracts of oral presentations and posters will be included into the "e-Book of Abstracts" only after receipt of the conference registration fee. Any payment of the registration fee has to be received until September 30, 2026, otherwise contribution(s) will not be included into the e-Book of Abstracts and the Conference programme.<h3>Program</h3><h4>Invited Speakers</h4><h5>Food allergen risk assessment: Past, present, future</h5><ul><li>Ben Remington from Remington Consulting Group B.V., Utrecht, the Netherlands &amp; University of Nebraska, USA</li></ul>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.<h5>Unmasking hidden adulteration in honey: Strategies for detecting and characterizing honeys produced from sugar-fed colonies</h5><ul><li>Oscar Nuñez Burcio from University of Barcelona, Barcelona, Spain</li></ul>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.<h5>Analytical and toxicological methods for safety of food contact materials</h5><ul><li>Erich Leitner from Graz University of Technology, Institute of Analytical Chemistry and Food Chemistry, Graz, Austria</li></ul>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.<h5>Data-driven strategies to reduce complexity in non-target HRMS analysis of food samples</h5><ul><li>Anneli Kruve from Stockholm University, Stockholm, Sweden</li></ul>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.<h5>Role of AI in the identification of emerging food systems risks</h5><ul><li>Ákos Bernard Jóźwiak from Syreon Research Institute, Budapest, Hungary</li></ul>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.<h5>Challenges of microplastics analysis in the context of food safety</h5><ul><li>Dorte Herzke from Norwegian Institute of Public Health, Oslo, Norway</li></ul>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.<h5>Never-ending analytical challenges in PFAS analysis of food</h5><ul><li>Darina Dvořáková from University of Chemistry and Technology Prague, Prague, Czechia</li></ul>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.<h5>An overview of the latest scientific knowledge on process contaminants in food for appropriate risk management measures</h5><ul><li>Thierry Delatour from Nestlé Research, Lausanne, Switzerland.</li></ul>Recent scientific findings related to acrylamide and furans formation, occurrence and analytical methods in food provide important insights for risk management.

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

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.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.<blockquote><a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/paper/33370">You can download PDF Handout for this guide HERE</a></blockquote><h4>Why Hybrid Search Matters</h4>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.Key advantages include:<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>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.<h4>Configuring Hybrid Search</h4>Before performing a search, the Hybrid Search settings should be configured within the NIST Library Search window.Recommended settings:<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>The selected molecular weight used to constrain the search is displayed in the lower-right corner after the search is completed.<h4>Optimizing the Results Display</h4>Hybrid Search produces several unique parameters that are not shown in the default results table.Additional columns should be added by right-clicking within the results table and selecting Properties. Recommended columns include:<ul><li>DeltaMass</li><li>dForm</li><li>pctRelForm</li><li>o.Match (equivalent to the traditional EI Normal Match score)</li></ul>Without these parameters, much of the structural interpretation power of Hybrid Search is lost.<h4>Working with Spectra from the Chromatogram Window</h4>Currently, the Chromatogram Window performs standard EI library searching. When a potentially interesting unknown is encountered:<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>This workflow combines rapid screening with advanced structural interpretation.<h4>Understanding Key Hybrid Search Parameters</h4><h5>DeltaMass (Δm)</h5>DeltaMass represents the mass difference between the unknown compound and the matched library compound.A consistent Δm across multiple high-scoring hits often indicates a specific structural modification.<h5>dForm</h5>dForm provides a proposed elemental composition that explains the observed mass difference.Examples include:<ul><li>H–1F</li><li>CH₂</li><li>O–1</li><li>C₁H₃F</li></ul>These transformations can suggest likely chemical modifications between the library compound and the unknown.<h5>pctRelForm</h5>New in NIST 26, pctRelForm estimates how frequently a given elemental transformation occurs relative to the most common transformation (CH₂ = 100%).Interpretation:<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>o.Match</h5>Represents the traditional EI match score and allows comparison between standard searching and Hybrid Search results.<h4>Case Study: Identifying a Fluorinated Unknown</h4>The presentation demonstrates the identification of an unknown compound with a proposed molecular weight of 152 Da.Hybrid Search returned several high-scoring hits with consistent evidence suggesting fluorine substitution:Hit #1<ul><li>Library MW: 134</li><li>DeltaMass: +18</li><li>dForm: H–1F</li><li>Interpretation: Addition of fluorine</li></ul>Hit #5<ul><li>Library MW: 138</li><li>DeltaMass: +14</li><li>dForm: CH₂</li><li>Interpretation: Related fluorinated analog</li></ul>Hit #6<ul><li>Library MW: 168</li><li>DeltaMass: –16</li><li>dForm: O–1</li><li>Interpretation: Structurally consistent fluorinated compound</li></ul>Multiple independent hits converged on the same conclusion: the unknown was likely a fluorinated analog of known aromatic compounds.This illustrates the major strength of Hybrid Search: identifying structural relationships even when the exact compound is absent from the library.<h4>Moving from Hybrid Search to Structural Interpretation</h4>Once a plausible structure is identified:<h5>Step 1 – Export the Structure</h5>From the Hybrid Search results:<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>Step 2 – Open MS Interpreter</h5>Within MS Interpreter:<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>Step 3 – Validate the Proposal</h5>Indicators of a good structural proposal include:<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>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.<h4>Best Practices</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>Conclusion</h4>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.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.

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

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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)

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

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

James Little/Mass Spec Interpretation Services

Wiley KnowItAll GC-MS Drug Classification Model Package

The KnowItAll GC-MS Drug Classification Model Package categorizes spectra by drug class—even without a match. Covers opioids, steroids, fentanyls, with 99% accuracy for reliable forensics.

<h4>Efficient Drug Classification with KnowItAll GC-MS Models</h4>In drug analysis, rapid identification is essential—especially when new substances outpace reference spectra. The KnowItAll GC-MS Drug Classification Model Package helps labs classify unknown spectra, even without exact spectral matches.With 99% accuracy, these models cover key drug classes like opioids, steroids, fentanyls, and more. Ideal for forensics, toxicology, and law enforcement, the package enhances your ability to:<ul><li>Classify spectra not found in libraries</li><li>Validate spectral search results</li><li>Make fast, first-pass identifications</li><li>Run batch or automated classifications using the KnowItAll Automate SDK</li></ul><h5>Included Drug Models:</h5><ul><li>Natural &amp; synthetic opioids</li><li>Fentanyls</li><li>Piperazines</li><li>Benzodiazepines</li><li>Steroids</li><li>Ketamine, cocaine, cathinones &amp; related compounds</li></ul>Requirements: KnowItAll software + AVX-compatible system. Learn more: <a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com">sciencesolutions.wiley.com</a>

Wiley LIPIDS Mass Spectral Database

The Lipids Mass Spectral Database includes 430 GC spectra from pure standards across 11 lipid classes. It supports peak assignment in complex mixtures and is valuable for research in food analysis, clinical, and medical applications.

<h4>Validated Lipid Identification Made Simple</h4>The Lipids Mass Spectral Database features 430 GC-MS spectra of pure lipid standards across 11 lipid classes, supporting accurate peak identification in complex mixtures. It's an essential tool in food, clinical, and medical research.With over 1,400 Linear Retention Index (LRI) values determined on three different GC columns and using multiple reference mixtures, the library ensures reliable retention data. Each entry includes detailed metadata such as CAS numbers, formulas, and suppliers.Highlights:<ul><li>430 spectra, 428 unique compounds</li><li>11 lipid classes including FAMEs, sterols, and wax esters</li><li>Experimentally validated LRI on SLB-5ms, Equity-1, Supelcowax-10</li><li>Rich compound metadata for confident identification</li></ul>Explore compound classes at <a target="_blank" rel="noopener noreferrer" href="https://www.compoundsearch.com">www.compoundsearch.com</a>

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

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

Let’s call a PT scheme a PT scheme!