News

The National Food Crime Unit (NFCU)'s latest industry update:

• Highlights the key risks and issues that may be impacting the food industry
• Shares best practice to strengthen the industry’s response to food crime
• Tells you about NFCU's ongoing work.

In this edition:

• man sentenced following NFCU joint investigation
• industry alert
• preventing dairy fraud
• horizon scanning
• NFCU auditor training launch
• using director identity verification to strengthen fraud prevention

You can contact the NFCU Prevention team to feedback, raise a concern or possibly contribute to a future update.

Read NFCU December Industry Update.

This study (open access) builds on a previously-published proof of concept.  The authors are working towards producing a hand-held multi-mode scanner (combining fluorescence, visible, NIR, and short-wave IR spectroscopy) to support species verification of white fish fillets in business-to-business supply (currently reliant, largely, on visual recognition by experienced traders).

The explain that one of the key challenges in using machine learning for fish species identification is managing the large number of classes, as the variety of fish species is extensive. In their previous research, they introduced a novel multi-mode, highly multi-class machine learning framework based on a hierarchy of dispute models. This approach involved training a global model, and then recognizing groups of classes that have feature subspaces too similar for effective single-stage classification. By partitioning the overall space into smaller, distinct subspaces, they trained specialized models that are more tailored to these specific subsets of the dataset. In practice, the global model initially classified a sample to determine the appropriate subspace, while the dispute model then identified the precise species within that subspace.

The objective of this latest study was to apply this approach data acquired with the multi-mode handheld spectroscopy device. Tissue spectra were acquired at 25 positions on 68 fillets from 11 species, in both frozen and thawed states.

They report that feature-level fusion across the four spectroscopy modes enabled higher classification accuracy than any single mode alone. A global machine-learning model classified all species with 85 ± 2.8 %, while specialized dispute models for commonly misclassified species improved performance to 90 % ± 6.1 %. Individual models for thawed and frozen fillets achieved 90 ± 6.0 % and 90 ± 5.4 %, respectively, with dispute models in the thawed dataset increasing accuracy to 93 ± 4.3 %.

They conclude that their results demonstrate that portable multi-mode spectroscopy, combined with machine learning, can provide a fast, non-destructive and reliable tool for on-site fish species identification.

The European Council and Parliament have reached a provisional agreement on the new regulatory framework for New Genomic Techniques (NGTs), supporting food security, innovation, and reduced dependence on external inputs.

Key Points of the Agreement:

1. Confirms that NGT-1 plants (those equivalent to conventional plants) will follow a simplified procedure, with:

• Verification only at first generation
• No mandatory labelling for food/feed products
• Labelling only for seeds and reproductive material

2. Defines an exclusion list of traits not allowed in NGT-1 (e.g., herbicide tolerance).

Image courtesy of our Member Cesare Varallo.

3. NGT-2 plants (with more complex changes) remain under full GMO legislation, including:

• Authorisation
• Labelling
• Traceability
• Monitoring
• Member State opt-out options.

4. Includes provisions to improve transparency on patents and licensing, including a public database and an expert group on patenting.

5. The European Commission will publish a study on patent impacts one year after entry into force.

Next steps

The provisional agreement will now have to be endorsed by the Council and the Parliament before it can be formally adopted.

Read full European Council press release. 

The Food Standards Agency (FSA), in partnership with Food Standards Scotland (FSS), has published the UK's first safety guidance for cell-cultivated products (CCPs).

Cell-cultivated products are new foods that don’t involve traditional farming such as rearing livestock or growing plants and grains. They are made by taking cells from plants or animals, which are then grown into food. The FSA and FSS’s CCP Sandbox Programme focusses on animal cells only.

These are the first of several pieces of guidance being produced by the programme. The first confirms that cell-cultivated products produced using animal cells, sometimes called ‘lab-grown meat’, are defined as products of animal origin. This means that businesses must apply existing food safety regulations during the production process.

Image: gov.uk

The second provides guidance on allergenicity assessments and how nutritional quality will be assessed as part of the approval process for all cell-cultivated products.  

More information and guidance for businesses on cell-cultivated products can be found at Innovative Food Guidance Hub.

Authentication of Extra Virgin Olive Oil (EVOO) sometimes requires a panel of different tests and – with more sophisticated adulteration – a weight of evidence interpretation.  For more crude adulterations a single test is often enough.

One of the available tests is for fatty acids ethyl esters (FAEE).  These are more concentrated in lower quality oils (e.g.improperly stored or overripe), formed from ethanol which is a result of fermentation. EU legislation specifies a maximum 35 mg per kg FAEE concentration in EVOO.

FAEE concentration is officially measured using gas chromatography (GC) after recovery by silica gel column chromatography. While highly accurate, this method is complex, time-consuming, and relatively expensive.

This paper (purchase required) reports an alternative approach to FAEE measurement by using infra-red spectroscopy (FT-IR) with machine learning. A dataset of 170 olive oil samples with FAEE concentrations ranging from 1.81 mg/kg to 109.00 mg/kg were analysed using FTIR. Spectral data were preprocessed and used to train various regression models.

The authors report that the best performance was obtained with an XGBoost model. Explainable AI techniques (SHAP) enabled interpretation of the model and identification of spectral regions mostly associated with FAEE content.

They conclude that combining FT-IR spectroscopy with advanced ML models—particularly XGBoost—can effectively predict the concentration of FAEE.

Photo by Massimo Adami on Unsplash

Here is our latest monthly graphic from the EC Reports of Agri-Food Fraud Suspicions, showing a rolling 3-month trend. 

Our interpretation of the reports is subjective. In order to show consistent trends we have excluded cases which appear to be unauthorised sale but with no intent to mislead consumers (e.g. unapproved food additives, novel foods which are declared on pack), we have excluded unauthorised health claims on supplements, and we have excluded residues and contaminants above legal limits.  We have grouped the remaining incidents into crude categories.  Our analysis is intended only to give a high-level overview. 

It is notable how consistent is the relative frequency of different types of fraud.  The highest proportion always relate to falsified or unlicenced trade in high risk food (illegal operators, missing or falsified health certificates, attempts at illegal import) and relating to falsified or missing traceability documentation.  Particularly prominent over the past 3 months were:

• Documentation forgeries (eg invoices)  to falsify traceability
• Smuggling (deliberate avoidance of import checks)
• Excess water or low net weight of frozen seafood
• Non-prosecco “Prosecco”
• Adulterants in Dubai chocolate

Some incidents relate to absence of expected “premium” ingredients in manufactured food.  This is a reminder that compliance is judged not only against the ingredient declaration but also against the artwork (including any pictures alongside online sales) which give the impression that the product contains a particular ingredient.

These Agri-Food suspicions are just one of the incident databases available.  Different databases collect different information, in different ways, and therefore show a different angle on the true picture.  All of these sources are signposted on FAN.  Best practice is to use a combination of all sources, but the final critical question is “how vulnerable is my own supplier”.

• JRC – These are solely media reports.  They exclude cases not in the public domain, and can be biased by shocking but highly localised incidents in local food supply within poorly regulated countries.  They now incorporate a search and trending tool to produce graphs and charts
• EU Agri-Food Suspicions – These are solely EU Official Reports, and only suspicions.  The root cause of each incident is unknown.  The data include pesticide residues above their MRLs. unapproved supplements and novel foods, and unapproved health claims.
• Food Industry Intelligence Network Fiin SME Hub – These are aggregated anonymised results from the testing programmes of large (mainly UK) food companies.  The testing programmes are targeted and risk-based, not randomised, and the fraud risks within the suppliers of large BRC-certified retailers and manufacturers may be different than the companies supplying small manufacturing businesses or hospitality firms.  The Fiin dataset has just (November 2025) been updated.

Many testing laboratories also supply their own customers with incident collations, and there are many commercial software systems that scrape reports from the internet.  All collect and treat the data slightly differently.  FAN produce a free annual aggregate of "most adulterated foods" from three of the commercial providers, which gives very high level smoothed data.

Rating – AMBER RAG Rating –

Goat meat substitution - Speciation failures

The Food Standards Agency National Food Crime Unit is asking businesses to be alert to goat meat being substituted with other species.

The FSA's Retail Surveillance Survey sampling has identified goat meat on sale that has been substituted with other species, most commonly sheep. It has affected a mixture of frozen and chilled product bought in small food businesses but also on online marketplaces and online shops selling directly to customers.
Unsatisfactory samples are the subject of ongoing enquiries.

ACTION RECOMMENDED

• If you are purchasing goat meat or products containing goat meat to sell, please be aware of the risk of substitution and consider the following advice:
• Ensure that reputable suppliers are being used, who have traceability systems in place for goat meat you are purchasing. Review suppliers in line with yoursupplier approval policies and procedures.

Read full alert.

The use of toxic testile dyes, such as the Sudan Red group, to adulterate food has been a high risk alert since the early 2000’s.  Over the past few years there have been persistent reported incidents with no apparent decline.  Original watch-outs were red spices and sauces, but more recently the focus has been on the adulteration of cheaper vegetable oils with red dye to pass them off as palm oil.  Palm oil from West Africa has been particularly implicated.

A recent media report from Ghana suggests that – far from improving – the problem is increasing in the case of palm oil on the local market.

Sudan Red dyes are classified as a Group 3 human carcinogen by the IARC and their widespread use in food is an obvious health concern for the local population.  For companies importing palm oil from countries where adulteration is endemic within the local market then traceability becomes key; being sure that your own stock comes from plantations and refineries with good and trusted oversight and has not been substituted for cheaper (adulterated) oil.  It is relatively easy to test for Sudan dyes, and periodic analysis is always a good way to check that assumptions about strong traceability are correct.

Photo by IKRAM ULLAH on Unsplash

A project, funded by the UK Government Chemist and Defra's Food Authenticity Programme, has delivered a practical framework that will enable independent scrutiny of proprietary honey authenticity databases, which are often unpublished and opaque, yet underpin significant commercial testing decisions. Lack of transparency in these databases has led to legal disputes and undermines confidence in non-targeted analytical methods used for verifying honey authenticity. 

The Government Chemist convened an independent expert group led by Professor Michael Walker and Dr David Hoyland. This group developed a framework, which offers detailed guidance on evaluating database scope, composition, metadata, representativity, and method validation. It also includes safeguards for database owners and describes international standards and UK/EU regulations.

This framework will enable the assessment of the fitness for purpose of authenticity databases used to interpret authenticity test results, enabling reliable enforcement decisions and reducing legal ambiguity. It empowers both regulators and industry, supporting transparent, science-based scrutiny and advancing the integrity of the global food system.

Access the documents:

• Framework for interrogation of honey authenticity databases
• Annex 1 - Terms of reference: Members and modus operandi of the working group
• Annex 2 - Appendices 1-3
• Annex 3 - Guidance notes on appendices 1-3
• Annex 4 - Review exercise summary report.

Issue 20 of the FAN newsletter has been published!.

This edition includes a summary of our 10th anniversary celebration activiities and an updates on FAN initiatives, the EFF-CoP and Watson Horizon Europe projects and on our Food Authenticity Centres of Expertise (remember you
can find direct contact details for each of them on our website).

We also have a fascinating case study from Cesare Varallo that gives an account of a very complex food fraud investigation he was involved in, an article describing the Food Law Group and an article from FAN Technical Director, John Points, describing how we select articles each month.

Plus, we have lots to update you on in our ‘Partnerships’ section from our SSAFE Partner Profile, fiin’s new SME Hub and a new Partnerships page on our website that shows the benefits of partnering with FAN.

Please share with colleagues and encourage them to join the FAN community.

Government Chemist Review 2024 

The Government Chemist Annual Review provides a summary of the work undertaken by the Government Chemist team, including highlights from the resolution of referee cases, advisory work and capability building activities. The review also details the impact of the work obtained though active engagement with a wide range of stakeholders.

The main topics described in this review are:

• Referee cases: acephate in frozen okra, bubble tea, formaldehyde migration, aflatoxins in rice, and propiconazole in rice.

• Capability building: the review highlights particular projects the Government Chemist team worked on to be ready for future challenges, extending the analytical capabilities non-dairy substitutes (such as soya, oat, coconut and almond) and microplastics in food.

• Knowledge sharing activities to further the impact of the referee and advisory functions: the review highlights some of the publications, webinars and other engagement activities, including the Food Authenticity Network, undertaken by the team to ensure that the breadth of knowledge generated through the Government Chemist’s programme reaches its target audiences.

Read full review.

The NFCU's industry updates highlight the key risks and issues that may be impacting the food industry, share best practice to strengthen the industry’s response to food crime and inform on the ongoing work of the NFCU.

In this edition:

• Refrozen poultry labelling
• Operation OPSON
• Horizon scanning
• Food Fraud Resilience Self-Assessment Tool

Read the November Update here.

You can contact the NFCU Prevention team to feedback, raise a concern or possibly contribute to a future update.

This paper (open access) reports the development of a classification model for the geographic origin on black tea based upon measuring a panel of 15 trace elements by X-ray fluorescence (XRF).  XRF is a non-destructive technique.  The only sample preparation required is grinding the tea leaves into a fine powder.

The model could discriminate between 10 major tea-producing regions.  It was built using reference samples obtained, via tea industry contacts, directly from plantations or primary processing facilities.   791 black tea samples were collected in total: Assam (272 samples), Burundi (40 samples), Darjeeling (145 samples), Ethiopia (40 samples), Keemun (115 samples), Kenya region 1 (41 samples), Kenya region 2 (40 samples), Malawi (40 samples), Rwanda (10 samples), and Sri Lanka (48 samples).

Two unsupervised analysis techniques were used to visualize high-dimensional data, and six supervised models were employed to discriminate the ten GI regions.

The authors report that machine learning models, including random forest, support vector machine, k-nearest neighbours, linear discriminate analysis, and the deep learning multilayer perceptron (MLP) model, demonstrated superior predictive capabilities compared to the traditional partial least squares discriminant analysis model. The MLP model achieved the highest performance, with a 97.7 % overall F1 score in predicting the geographical origins of 532 authentic samples across ten GI regions.

The authors also Identified Rb, Sr, Mn, Si, and Cl as geographical markers for African region discrimination.

The conclude that their work could form the basis and foundation for an international database of tea Geographic Origin, enabling cheap and quick authenticity verification testing.

Photo by Oleg Guijinsky on Unsplash

 Artificial Intelligence (AI) is increasingly applied in food safety management, offering new capabilities in data analysis, predictive modelling, and risk-based decision-making.

A review of the literature identifies three primary areas of application: scientific advice, inspection and border control, and operational activities of food safety competent authorities.

Five country examples with the real-world use cases illustrate diverse uses of AI tools, including pathogen detection, import sampling prioritization, and language models for regulatory data processing.

Regulatory frameworks, as well as voluntary governance, addressing AI in the public sector are emerging worldwide. National and international initiatives often highlight the importance of data governance, transparency, ethical considerations, and human oversight. Challenges such as biased data, explainability, and data governance gaps appear across different contexts, along with potential risks from deploying AI systems prematurely. Access to high-quality, interoperable data and collaboration among stakeholders can support effective integration of AI technologies.

AI readiness often depends on understanding specific problems to be addressed, current capacities, and the quality of available data. Human oversight and continuous evaluation contribute to maintaining trust in AI systems.

Collaborative efforts involving academia, the private sector, and international organizations help build shared knowledge and resources for AI development in food safety. Overall, AI presents opportunities to enhance resilience, efficiency, and responsiveness in food safety systems. Careful consideration of governance, data management, and multi-stakeholder cooperation can shape AI’s contribution to achieving sustainable and equitable outcomes in agrifood systems.

Read full report here: https://openknowledge.fao.org/handle/20.500.14283/cd7242en

Injera is a dietary staple in Ethiopia, eaten with most meals.  It is a flatbread made with teff flour.  Injera is vulnerable to adulteration with cheaper gesso or cassava flours.

This paper (purchase required) reports a simple, affordable, portable, and easy-to-use method based on a paper analytical device to indicate adulteration qualitatively.

The authors report that the developed test card generated a red-orange colour on lane B (ferric detection), red on lane D (ferrous detection), Prussian blue on lane F (ferric detection), and Turnbull’s blue colour on lane H (ferrous detection) for pure teff injera. The colour barcodes generated by pure teff injera differ from those produced by teff injera that contain gesso or cassava.

In a survey of local market produce, the test card colour result was less intense or inactive in most cases. It indicates that inexpensive cereals might be used in place of authentic teff flour or flours have been blended before baking.

The authors cross-validated their method by analysing the elemental composition of samples using microwave plasma atomic emission spectrometers.

Photo by Syed F Hashemi on Unsplash

Job opportunity description

The vacancy is within the Unit JRC.4 “Food Integrity”, whose mission is to produce and validate the knowledge for ensuring authenticity, quality and sustainability of foods and to contribute to the fight against adulterated and illicit consumer products.

For complete information, please download the Vacancy Notice.

From Boardroom to Berlin: EFF-CoP in Action

For more than half a year, the EFF-Editorial Board has been steadily shaping the voice of a growing community. Through frequent meetings, the Editorial Board reviews ideas, discusses themes, and schedules each upcoming EFF-publication in the New Food Magazine.

Their collaborative rhythm has already produced three published articles, all available to registered users on the EFF-Hub, forming a reliable stream of insights for everyone across the food-fraud field. Read HERE the latest EFF-article.

That same spirit of shared expertise came to life at the International Food Fraud Conference 2025 in Berlin. EFF-CoP partners from across Europe gathered to exchange knowledge and spark new conversations. On the first day (5/11/2025), EFF-CoP led a workshop exploring the “state of the art and main challenges,” inviting participants to identify gaps and build a shared vision for safer, more transparent food systems.

The second day (6/11/2026) featured a joint session with the Watson Project, examining how climate change, geopolitical shifts, and economic pressures may reshape food fraud in the decade ahead. The day concluded with an uplifting presentation by EFF-CoP’s coordinator, Prof. Saskia van Routh, titled “The Power of We: The European Food Fraud Community of Practice Story,” in which she also introduced the Food Fraud Festival in Dublin (27–28 May 2026)—a future gathering where collaboration, innovation, and community will continue to grow, offering many insights into food fraud.

For more details, photos and videos, click HERE.

Finally, don’t forget to register on the EFF-HUB - the central meeting point for our food fraud community. There, members can chat, exchange knowledge, and access EFF-CoP’s materials, webinars, and workshop updates. Together, we’re not just learning about food fraud - we’re building the future of prevention, one workshop at a time.

This update has also been added to the FAN EFF-CoP page.

Food fraud prevention and detection priorities can be different in different countries.  In Iran, as in many countries, pork meat is an unlikely adulterant in beef or chicken sausages as there is virtually no pork production; it is legally and culturally proscribed.  Donkey and horse, however, is not food grade but is cheap and readily available as an adulterant.  Laboratories with PCR are scarce and the need is for rapid, portable verification tests.

The researchers in this study (open access) sought to address this need by developing a classification model using non-destructive FTIR.  They deliberately omitted any extraction or defatting step so that the test could be applied directly to a 3mm slice of the intact sample.  They trained the model using sausages prepared in-house that mimicked – as far as possible – the typical recipes used in Iran (40 – 60% meat content, along with soy flour, egg, herbs and spices).  They prepared 20-each of beef, chicken, horse and donkey sausages using meat sourced directly from veterinary schools.  For the training set, triplicate sub-samples were measured from each sausage and then the triplicates averaged.  Some pre-processing was applied to the data before dimension reductions using supervised machine learning.  30% of the samples were reserved as a validation set and kept independent of the training set.  Additionally, within the training set, a 5-fold cross-validation procedure was used to iterate an internal check against over-fitting.

The researchers were able to separate the four species into distinct clusters using Principle Component Analysis.  They also postulate a chemical rationale as to why these identified signals should differ between species.  They conclude that their approach could form the basis of a rapid non-destructive test with practical application.

Image – from paper 

Spain has a legal limit of 3% for undeclared vegetable proteins in meat patties.  The aim of this open-access study was to evaluate the feasibility of point-based near infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) to verify compliance.

The model was trained on patties prepared in-house.  They were all prepared from the same cut of beef, so the robustness of the model has not been verified.  A total of 240 patties were fabricated, of which 60 contained pea (PP), 60 contained soybean (SP), and 60 chickpea protein (CP) at levels from 1 up to 6 % (w/w). 60 pure beef patties were included.

The authors report that they could clearly discriminate the type of protein added, using either partial least squares-discriminant analysis (PLS-DA) or linear discriminant analysis (LDA), with >90 % of the samples in the test set correctly classified. Based on protein inclusion, LDA discriminated 100 % of the PP, SP and CP samples with both NIR and HSI. PLS-DA classified 100 % of the PP and CP burgers using the NIR instrument. To manage double classification tasks, a hierarchical model classifier (HMC) was proposed for both NIR and HSI spectra, achieving classification rates of at least 83% by combining LDA and PLS-DA models at the nodes.

The authors conclude that NIR spectroscopy is suitable for detecting low levels (1 %) of vegetable protein flours added to beef burgers.

This article (open access) discusses some of the current challenges in testing animal feed for compliance with European legislation using microscopy (one of the official methods mandated by the legislation).

Although the article is pre-publication and not peer-reviewed, it is generously illustrated with colour photographs of microscope slides - such as that shown here - which could be a valuable training aide to Official Control analysts who are relatively new to microscopy.  The author discusses key areas where expert interpretation is needed, describing the examples of bovine spray-dried plasma protein (legal, within restrictions, in the US but banned in the EU in ruminant feed), differentiating milk powder from blood powder, and differentiating hydrolysed proteins from vegetable vs animal sources.