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🌍🔍 EFF-CoP Editorial Board📝💡

The European Food Fraud Community of Practice (EFF-CoP) Editorial Board has been created to ensure knowledge flows freely to help experts unite, and members to shape the conversation!

The EFF-CoP  Editorial Board is comprised of a team of passionate professionals 🏆 dedicated to delivering insightful, high-quality content tailored to our members' interests. Each article will be written by specialists in their fields, ensuring accuracy, depth, and practical insights. All EFF-CoP members are invited to actively participate in shaping our content by engaging with our LinkedIn page.

This Editorial Board isn’t just about writing - it’s about listening and collaborating! Using LinkedIn polls 📊, EFF-CoP will ask YOU, our community, to vote on the topics that matter most. This way, every article will reflect real needs, real concerns, and real expertise!

All these articles will be uploaded to the upcoming EFF-HUB, a dynamic digital platform designed to connect minds, foster innovation, and provide invaluable resources. Only registered members will have access to Good Practice Recommendations, factsheets, case studies, educational content and a forum - all aimed at strengthening the global fight against food fraud.

EFF-CoP invites YOU to be part of this journey! Engage with our polls, suggest topics, and help shape the future of EFF-CoP!

Together, we can build a thriving, knowledge-rich community and strengthen our global fight against food fraud!

🎉 Stay tuned for the launch of the EFF-CoP website and HUB, upcoming articles, and let’s shape the future of food fraud detection and prevention together! 🙌

Please visit the the FAN for EFF-CoP for all EFF-CoP updates.

Jaggery is one of the most popular foods in India.

 This research (purchase required) presents a classical, novel colour-based method for detecting  adulteration in jaggery. A colour sensor is used to detect the colour of melted jaggery samples, and an Arduino Uno (opensource microcontroller board) is used to further analyse the colour. This research exploits the direct relationship between the captured pixel intensities of the jaggery and its purity in order to develop a linear regression model. The developed product is validated using samples having varying percentages of adulterations (10% to 70%) caused due to single and multiple adulterants (sugar and food colour) in jaggery. The abstract does not describe how these reference samples were sourced or prepared. 

The authors report that their machine learning approach gave promising results with accuracy of 94.67% and precision as 92.6%. The developed method for identifying tampered jaggery is user friendly, affordable, portable and non-destructive.

Photo by Prchi Palwe on Unsplash

Cocoa is high on many companies’ current risk radar for authenticity threats, due to recent supply pressures and price increases. Carob has legitimate uses as a cocoa replacement, and carob flour has been cited as a potential cocoa adulterant.

 A number of chemometric classification methods to differentiate cocoa from carob recently have been proposed, including one featured in our blogs in January based on DART-MS.  In a more recent publication (purchase required) the authors use the alternate method of near and mid-infrared spectroscopy before applying various chemometric approaches.

Spectral data were collected using four different infrared spectrometers: a benchtop FT-NIR system, two portable NIR instruments, and a benchtop FT-MIR-ATR. Reference samples included pure cocoa, pure carob, and their mixtures with carob concentrations ranging from 0 % to 60 %. Both classification and regression models were developed to detect and quantify the presence of carobs in cocoa powder. Classification models, including Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Soft Voting Classifiers, demonstrated superior performance for discriminating between cocoa powder, carob powder, and cocoa-carob mixtures, particularly using the benchtop FT-NIR. Similarly, regression models - RF, SVM, MLP, kNN, Partial Least Squares Regression, and Voting Regressor- exhibited robust predictive capabilities, particularly, FT-MIR and portable NIR.

Overall, these findings highlight and prove the potential of NIR and MIR spectroscopy as rapid, robust, and non-destructive tools for screening and quality control in food authentication.

Photo by lindsay Cotter on Unsplash

A2 cows’ milk is from selective breeds that produce a higher ratio of the A2/A1 form of β-casein in the milk.  It is sold mostly in Australia, New Zealand, China, and the United States and commands a price premium over conventional milk.  Authenticity testing has been difficult, typically requiring genetic techniques.

In this study (purchase required) the researchers piloted portable NIR to differntiate A2 milk versusnon-A2 milk and their mixtures using a portable NIR spectrometer.  They built a 1-class classification model.  63 samples of whole A2 milk were selected (authentic set), and 40 samples (fraudulent set) composed of non-A2 milk and mixtures in 3 different proportions (10, 25, and 50% v/v) of non-A2 milk in A2 milk. The abstract gives no further details of the reference samples in terms of production systems or seasonality.  For spectra collection, a MicroNIR was used.

Full data were pre-processed using different methods, but they found the most effective approach was the combination of the first derivative with Savitzky-Golay smoothing and Standard Normal Variate (SNV). A Data-driven Soft Independent Modeling of Class Analogy (DD-SIMCA) was applied. Using the Kennard-Stone algorithm, the authentic samples were split into two sets (45 for calibration and 20 for external validation). The non-A2 and fraudulent samples were added to the external validation set, and the model’s performance was evaluated using the metrics of sensitivity, specificity, accuracy, and precision.

They report that the DD-SIMCA model, utilizing 2 PCs, showed 100% results in all metrics, indicating no errors in the recognition of authentic samples.

They conclude that the model is suitable for use with portable equipment. Additionally, this fast and non-invasive technique can be optimized for applications in industrial management, food control, and A2 product authentication.

This study (open access) examined how variations in δ²H and δ¹⁸O values of cooking water affect the isotopic fingerprint of noodles with different gluten-to-starch formulations.

Eight differently formulated noodles were boiled using waters with six distinct isotopic compositions ranging from of −160‰ to +50‰ for δ²H and from −22.9‰ to +99.9‰ for δ¹⁸O, respectively.

It was found that formulation and water isotopic composition significantly affected the δ²H in cooked noodles. Additionally, the δ²H values of noodles changed with the isotopic signatures of the cooking water. Conversely, δ¹⁸O in the noodles remained stable despite boiling processing and was also not changed by the water's isotopic signature.

The authors derived an equation for determining the exchange factor (f(H)ex) between noodles and cooking water. The fraction of hydrogen atoms in different noodles for exchange was highest at 19.3% in noodles with the formulation of 45:55(gluten-to-starch) and the lowest at 11.1% in noodles with 100% gluten.

The authors conclude that cooking water systematically alters the isotopic signatures of noodles, underscoring the necessity of considering this type of effect in food authentication and traceability practices.

Photo by M. W on Unsplash

In this paper (open access) the authors demonstrate an optical, contactless method to discriminate different types of commercial milk (whole, partially skimmed and skimmed) and identify its adulteration with water and 12.5% water-glucose solution.  This adulterant was selected since it exhibits a refractive index comparable to that of whole milk, rendering such adulteration unnnoticed when performing a routine quality test based on refractive index measurements.

The prototype sensor employs a CMOS digital camera to acquire speckle pattern images generated by shining the beam of a red semiconductor laser onto milk samples placed in a plastic cuvette. The collected data are then analyzed to extract informative parameters, such as the average intensity and the speckle grain size.

The authors report that the system can distinguish between different types of milk and detect diluted samples with both water and glucose.

There is a growing market for the use of insect protein in feed.  The cricket species Gryllus assimilis. is approved in the EU for feeding farmed animals whilst the closely-related G. locorojo is only permitted for pets. The two are difficult to distinguish analytically in a highly processed product.

This paper (purchase required) reports a method developed on the basis of the cytochrome oxidase I gene, (COI), which was sequenced with thoroughly characterised G. locorojo and G. assimilis samples. The method is highly sensitive, detecting 0.8 pg G. locorojo-DNA or 0.1% G. locorojo incurred in feed, respectively. Authentic G. assimilis specimens were used to ensure that the G. locorojo method (Gloco-PCR) discriminates this closely related sister taxon, with a comfortable Ct-difference of 10-15. For cross analysis of true G. assimilis, similar primers with another probe were employed (Gassim-PCR) and the annealing temperature was increased from 60 °C to 62 °C.

Under these conditions, authentic G. assimilis crickets were detectable with Ct-values around 20, while G. locorojo samples showed a low detection at cycles around Ct 35. An investigation of ten ‘G. assimilis’ samples collected from Germany and four other European countries revealed that all of them were of the G. locorojo type.

The authors conclude that this small preliminary survey proves the usefulness of the method and supports the assumption that many G. assimilis crickets marketed in the EU indeed belong to the species G. locorojo. Consequently, European legislation, currently based on a white list of allowed insect species, is critically questioned.

Photo by Ivan Ivanovič on Unsplash

Laser Induced Breakdown Spectroscopy (LIBS) is a technique that can provide fast multi-elemental analysis , and hence food authenticity “fingerprints”, without requiring any sample preparation. It is an under-utilized technology in the food sector because of its high setup cost and lack of LIBS expertise in this sector.

This review (subscription required) gives an outline of LIBS fundamentals and instrumentation alongside published scope and capabilities of LIBS for detecting adulteration in various food samples.  The authors consider that LIBS is a pre-existing tool within the authenticity testing armoury which could be better utilised in the food sector..

The Food Authenticity Online Conference – Analysis for Authenticity, was held online on February 11-12 2025.

Over the two days, the >800 attendees, had the opportunity to listen to the keynote speeches of the Chief Scientific Advisers for Defra (Professor Gideon Henderson) and FSA (Professor Robin May) who both emphasised the importance of science and evidence in underpinning government policy and decision making. They also highlighted government’s critical role in supporting the development of new technology and innovation, allowing us to address future challenges in the food system.

Our speakers from day one explained untargeted testing and machine learning in engaging & understandable terms, showing that AI applications have been in use for decades. We also learned just how much authenticity research Defra and FSA is doing to try and stay ahead of fraudsters to protect legitimate businesses and consumers.

On day two, speakers highlighted the importance of authenticity reference databases/ datasets, their reliability and the accessibility of such data. We also heard how we need to have greater and continued cross sector collaboration to be more effective in detecting and preventing food fraud in the global food supply chain.

The conference recordings are now available here.

Presentations from both days are also available at the links below.

For anyone who attended and hasn't yet completed the very short feedback survey, we'd love to hear your views please.

Thank you

Selvarani

FAN Executive Director

FAN has been delighted over the past year to have collaborated with Professor John Spink to produce a stepwise guide through the fundamentals and terminology of food fraud prevention - Spink's Food (Fraud) for Thought.

We have now published the final two sections (here - within our Food Fraud Prevention menu) - "ISO31000 and COSO-Based Enterprise Risk Management (ERM)" and "Diagnosis treatment prognosis and conclusion ". 

The guide takes you through the whole risk assessment and risk management thought process.

We hope you find it useful.  Watch for the star of the next “Spink’s Food (Fraud) for Thought” blog post series on the Food Authenticity Network (FAN) website in 2025.

The EC Monthly Reports of Agri-Food Fraud Suspicions reports are a useful tool for estimating fraud incidents, signposted on FAN’s Reports page.  The January 2025 report has been published here.

As with all incident collation reports, interpretation must be drawn with care.  The EC collation is drawn from the iRASSF system – these are not confirmed as fraud, and the root cause of each issue is usually not public.

In FAN’s graphical analysis, shown here, we have excluded cases which appear to be unauthorised sale but no intent to mislead consumers of the content/ingredients of a food pack (e.g. unapproved food additives, novel foods), excluded unauthorised health claims on supplements, and we have excluded residues and contaminants above legal limits.

We have grouped the remaining cases into crude categories.  It can be seen that the majority are either unregistered trade (e.g. illegal import, or unlicenced premises) or substandard meat quality/content in processed foods (what used to be termed “QUID”).  Olive oil is the most adulterated specific product, including both substitution with other vegetable oils or mislabelling of Lampeter oil as EVOO.  Document forgery is a current watch-out in the UK, and the EU cases include a forged Health Certificate.

On February 12th, a virtual gathering of experts, researchers, and policymakers took place at the Analysis 4 Authenticity Online Food Authenticity Conference, hosted by the Food Authenticity Network.

Among the key speakers was Professor Saskia van Ruth, coordinator of EFF-CoP, who took the stage and introduced the project, a collaborative initiative designed to strengthen cooperation in the fight against fraudulent food practices. She emphasized how EFF-CoP is working to bridge the gaps - bringing experts together and reinforcing food integrity across global supply chains.

Updates will also be added to the FAN EFF-CoP page.

In the heart of Amsterdam, on January 15-16 2025, a group of passionate partners[1] came together to officially embark on an important journey - the fight against food fraud. This moment marked the beginning of EFF-CoP, a collective effort dedicated to making food systems more transparent, authentic, and resilient. With a shared vision, the partners made a powerful commitment to:

🌍 Enhance collaboration and knowledge sharing
📖 Develop and disseminate best practices
🚀 Promote innovative learning and capacity building
🔍 Strengthen food authenticity, traceability, and transparency
🤝 Expand and sustain a vibrant community through EFF-HUB
🌱 Ensure long-term viability and impact

Over the course of two inspiring days, Work Package Leaders and partners, took the stage, presenting their ideas, brainstorming strategies, and setting ambitious goals - not just for the project’s success, but for its lasting legacy beyond its official duration. But it wasn’t all work; they also cooked together, danced together, and built the kind of trust and camaraderie that turns a group of professionals into a true team.

In the coming months, EFF-CoP will launch its official website and EFF-HUB, a dynamic digital platform designed to connect minds, foster innovation, and provide invaluable resources. Members will have access to Good Practice Recommendations, factsheets, case studies, educational content and a forum - all aimed at strengthening the global fight against food fraud.

But learning won’t be confined to static resources. EFF-CoP is set to revolutionize engagement with interactive activities like food fraud festivals, gamified training sessions, living labs, webinars, podcasts, and hands-on workshops.

Whether you’re an expert, a researcher, a policymaker, or simply someone who cares about the integrity of the food on your plate - you are welcome to join. The fight against food fraud needs a community, and that community starts here.

[1] UNIVERSITY COLLEGE DUBLIN, AGROKNOW IKE, SMART AGRO HUB, NOFIMA AS, WAGENINGEN UNIVERSITY, REZOS BRANDS, LGC LIMITED, 8D GAMES BV, DELOITTE LIMITED, RETE INTERNAZIONALE PER LE PICCOLEE MEDIE IMPRESE, EUROFINS ANALYTICS FRANCE SAS, WENGER-TRAYNER UNIPESSOAL LDA, AUTORIDADE SEGURANCA ALIMENTAR E ECONOMICA, SSAFE, International Federation of Organic Agriculture Movements - EU Group, FIIN LTD, UDRUGA DIH AGRIHRANA HRVATSKA, Food Fraud Prevention Think Tank LLC, Stichting Fraude Film Festival, Stichting FSSC.

Updates will also be added to the FAN EFF-CoP page.

This study (open access) advances the field of “natural” vanilla flavour authentication, and of geographic origin of vanilla pods, by investigating minor volatile organic compounds and their isotopic ratios. Vanilla pods from the two main vanilla species, V. planifolia and V. tahitensis, were investigated using GC-MS/MS to analyze their aromatic profile and GC-C/Py-IRMS to determine compound-specific isotope ratios, providing, for the first time, detailed and authentic isotopic and aromatic profiles.

The researchers quantified more than 50 volatile compounds in different vanilla pods.

A key finding was the confirmation—through UHPLC-HRMS analysis—that ethyl vanillin and its glucoside precursors were absent in genuine vanilla extracts, reinforcing that the detection of ethyl vanillin remains a reliable marker of fraud.

The authors conclude that their study provides new insights into the natural pathways of biosynthesis in vanilla. For the first time, compound-specific isotope analysis has been applied to minor aromatic compounds in vanilla pods, opening new avenues for their use in authentication and botanical and geographical traceability of vanilla flavours. The study pioneers the application of isotope analysis to authentic vanilla extracts spiked with synthetic ethyl vanillin, enabling a more precise assessment of the impact on the isotopic composition of foods flavoured with natural vanillin following fraudulent augmentation with small amounts of ethyl vanillin.

Photo by Jocelyn Morales on Unsplash

In this episode from legal firm Brodies LLP the panellists discuss the growing popularity of buying and selling Scotch whisky casks, risks of fraud and some cautionary advice for buyers.

Grant Strachan is a partner at Brodies LLP, with a specialist focus on the food and drink sector, and Vikki Bruce is the founding director of CaskNet, a tech start-up that is building a register for Scotch whisky casks. It is hosted by David Lee, an experienced journalist, writer and broadcaster based in Scotland.

Together, they cover a range of issues, including the reasons for consumer interest in Scotch whisky, the process involved when buying a Scotch whisky cask and the legal/regulatory factors to consider, the challenges with unscrupulous brokers, the approach by regulatory bodies, and what buyers can do to minimise the risk of fraud. 

Photo by Thomas Park on Unsplash

Further to the review of methods used in the EU co-ordinated survey of honey collected 2021-22 (“From the Hives” survey) – see previous blog here:

The Joint Research Centre of the European Commission have now published (open access) more details of some of the test methods used and results interpretation.  This publication relates particularly to the two qualitative Liquid Chromatography–High-Resolution Mass Spectrometry (LC-HRMS) methods that were developed to detect mannose (Man), difructose anhydride III (DFA III), 2-acetylfuran-3-glucopyranoside (AFGP), and oligo-/polysaccharides with degrees of polymerization (DPs) of 6 to 11.

The presence of mannose and unusual oligo-/polysaccharides was the main reason that many of the samples were flagged as “suspicious” in the previously published report.

Forbes has reported a 27% increase in US cargo thefts—over 3,600 in 2024—with foods like nuts, avocados, and eggs among the top targets.  A recent example was the theft of over 100,000 organic eggs in Pennsylvania. 

This trend chimes with reported incidents worldwide, such as the deceptive theft of 22 tonnes of cheese from Neal’s Yard in the UK last year.  As food prices increase, theft and grey-market diversion on an industrial scale becomes and increasingly attractive criminal target.

Photo by Oscar Nilsson on Unsplash

Applications are invited for a temporary post of a Food Fraud Network Researcher within UCD School of Agriculture and Food Science.

In regions where clarified dairy fat (ghee) is a staple food, the potential for adulteration with palm oil (solid vegetable fat) is a continual concern.  Analytical differentiation can be difficult.

In this study (open access) the authors experimentally compared and contrasted a range of analytical techniques that have been proposed for identifying palm oil mixed into ghee at levels down to 5 – 10% (generally considered the lower limit for economically motivated adulteration). 

They concluded that  both Butyro refractometer readings and iodine value analysis were not as efficient in detecting adulteration at lower level. Reichert-Meissl value analysis alone was not able to draw a conclusion regarding the purity of ghee. However, the Kirshner value analysis could be an effective parameter to detect adulteration of palm oil in ghee down to 5%. 2,2-Diphenyl-1-picrylhydrazy and ferric chloride-based chromogenic tests were very effective to detect the presence of palm oil in milk fat or ghee rapidly; thus, these tests could be used in field conditions. The use of triglyceride analysis (S-value) and plant sterol detection offered a comprehensive laboratory-based confirmation to detect palm oil adulteration in ghee at 5% levels.