News

This study (purchase required) shows the potential for stable isotope ratio analysis (SIRA) to be used to verify whether the cocoa in bean-to-bar chocolate originated from the Amazon region.  Higher-sugar chocolate is too complex a product to test in this way.  Such tests will be important for checking compliance with future EU Deforestation Regulations. The proposed method can also be used to estimate the cocoa content of chocolate.

Chocolate is a complex product in terms of its carbon isotope distribution.  Cocoa, from a C₃-photosynthetic plant, is its main raw material, while sugar from sugarcane (C₄-metabolism) is also commonly included. The authors analyzed the δ¹³C and δ¹⁵N composition of Brazilian chocolates, including Conventional, Bean-to-bar, and Imported brands across White, Milk, Semisweet, Dark <70 %, and Dark ≥70 % versions. They report that conventional White, Milk, and Semisweet chocolates showed no significant isotopic differences, with average δ¹³C values around −22 ‰, indicating high C₄-derived ingredient content. Bean-to-bar chocolates presented δ¹³C values 2–3 ‰ lower, and those made with Amazon cocoa were ∼1.5 ‰ lighter than those from Atlantic Forest, enabling accurate prediction of cocoa origin. Imported chocolates showed even lighter δ¹³C values, suggesting greater use of C₃-based ingredients. δ¹³C and δ¹⁵N values also enabled reliable estimation of cocoa content.

Photo by Boudhayan Bardhan on Unsplash

PAS 96 Protecting and defending food and drink from deliberate attack - Guide

Scope 

This PAS provides guidance on preventing and mitigating deliberate (intentional) threats to food and drink and their supply chains, using the Threat Assessment Critical Control Points (TACCP) risk management approach.

It applies to organizations of all sizes and at all point in the food and drink supply chain, from primary production through manufacturing, distribution, retailing and foodservice. The guidance is particularly valuable for small and medium sized enterprises (SMEs) who might not have access to specialist risk management expertise.

The scope is limited to intentional acts, (e.g. sabotage, adulteration, extortion, espionage, or cyberattack carried out for ideological, financial gain or malicious reasons). Unintentional incidents, (e.g. accidental contamination or naturally occurring hazards) and routine food safety or quality issues fall outside the scope of this PAS and are addressed by other standards such as HACCP-based food safety management systems.

Read and comment on the draft by 17 January 2026.

Wishing you a wonderful holiday period and a Happy New Year!

Your FAN Team

In this survey a total of 107 tuna cans were collected between 2020 and 2022 from 7 different Portuguese commercial brands.  Samples were purchased from main supermarket chains, large-scale distributors and local stores.  Samples were categorised by the quarter of the year when the tuna was canned (inferred from the expiry date) with each sample was tested in triplicate.  Extracted DNA was purified and iteratively tested using molecular metabarcoding methods.

The researchers report (open access) that the occurrence of different species was observed only in products canned in brine or water (i.e. all products canned in oil were Skipjack Tuna). Skipjack tuna was predominant across all canning liquids and brands analysed. Nonetheless, other species like Thunnus obesus and T. albacares, or Auxis spp. (not considered true tuna) were also detected. The use of different species was limited to cans produced during the second quarter of the year, which could reflect differences in seasonal availability of different tuna species or in sourcing strategies/market preferences of each company. For four brands, multiple species were detected inside the same can.  This violates current European legislation.

The researchers conclude that these results provide the first broad assessment of species used in the Portuguese tuna canning industry and showed the inclusion of vulnerable species is limited.

Photo by Grooveland Designs on Unsplash

The EU Joint Research Centre (JRC) have published today their monthly collation of fraud media reports for October and November 2025.  The full index of reports can be found here

The JRC collation underpins a searchable front-end for media reports of food fraud incidents.  It allows filtering by commodity, country, fraud type and other key criteria.

This is just one of the incident databases available from different organisations.  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.  For the past few years, FAN member Bruno Sechet has produced a useful infographic based on each month's data
• EU Agri-Food Suspicions – These are solely EU Official Reports, and only suspicions.  The root cause of each incident is unknown.  The data include cases less likely to be deliberate fraud such as pesticide residues above their MRLs or unpermitted (but labelled) additives.  FAN produce our own infographic on a rolling 3-month basis.
• 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.

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 largest commercial providers (Fera Horizonscan, Meriux SafetyHud, FoodChainID), which gives very high level smoothed data.

The Canadian Food Inspection Agency (CFIA) is Canada's leading authority on food fraud oversight. This report shares how they prevented a significant amount of misrepresented food from being sold in Canada.

Between April 1, 2023, and March 31, 2024, the CFIA conducted a number of activities to prevent, detect and deter food fraud. Activities included:

• monitoring and analyzing risks and planning mitigation activities
• promoting awareness
• working with international counterparts
• advancing research and method development
• targeting surveillance and taking enforcement action where appropriate.

CFIA conducted 2 types of sampling during this timeframe: marketplace monitoring sampling (also referred to as targeted surveys) and targeted inspectorate sampling.

• Marketplace monitoring sampling involves samples collected by an independent third party contracted by the CFIA and occurs only at retail stores to gauge overall compliance of certain food products in the Canadian marketplace. 323 marketplace monitoring samples (coconut water, fresh meat, spices, sunflower oil, tea) were assessed to detect specific types of misrepresentation through laboratory analysis. Results demonstrated high compliance, except for coconut water which was lower.
• Targeted inspectorate sampling involves inspection and sampling by CFIA inspectors at different types of food businesses such as importers, domestic processors and retailers. The likelihood of finding non-compliance is higher because it targets food businesses associated with risk factors such as a history of non-compliance, gaps in preventive controls or unusual trading patterns. 712 targeted samples (fish, honey, meat, olive oil, organic fresh or frozen fruits and vegetables, other expensive oils, grated hard cheese, fruit juice, other foods) were assessed to detect specific types of misrepresentation through laboratory analysis. CFIA also conducted 345 label verifications, including basic label verifications and net quantity verifications. Overall, compliance rates were similar to previous years. Highlights include:
  • grated hard cheese, olive oil and other expensive oils had the lowest satisfactory rates for authenticity testing, whereas fish, fruit juice, meat and honey had the highest.
  • fish, olive oil and other expensive oils had the lowest compliance rates for label verifications, while organic fresh fruit and vegetables, grated hard cheese and other foods had the highest.

Read full report here.

Read more on CFIA's role in combatting food fraud.

This research (purchase required) presents a nanozyme-based colorimetric sensor array for effective coffee variety discrimination by analyzing characteristic components.  Nanozymes are nano-scale molecules (for example, some metal oxides) that can catalyse biological reactions in an analogous way to enzymes.  

The authors report the development of an optimized sensor array containing 13 nanozymes.  No details of the reference samples or Machine Learning training are reported in the public abstract.  They report that their sensor enabled the discrimination of coffee key compounds (no details given in the abstract) within the concentration range of 0.8 μmol/L to 100 μmol/L. The sensing mechanism involves coffee components modulating nanozyme peroxidase-like activity through electron transfer and hydrophobic interactions.

They report that this technology successfully distinguished two main coffee categories and their subtypes with high accuracy.

They also report the development of a mobile phone app based on their sensor.  It achieved identification of coffee varieties and the detection of semi-quantitative adulteration levels. The conclude that this portable platform demonstrates significant potential for commercial coffee quality monitoring, providing a reliable tool for authenticity verification in the coffee industry.

Photo by Art Rachen on Unsplash

Combining data from multiple analytical tests into a single classification model can give greater discrimination than a model based on a single analytical technique.  There are three levels of data fusion: low-, mid- and high-level. Low-level fusion is the concatenation of analytical data obtained from several different sources.  Mid-level strategies imply that only the most significant features are fused after conducting a feature extraction step. High-level fusion combines the classification or regression results after they have been extracted separately from each type of data source.

This study (open access) aimed to build a model, testing different low- and mid- level fusion approaches of Raman and ATR FT-IR spectroscopy data, that can discriminate honey from different botanical origins (acacia, linden, colza, and raspberry) and different harvest years (2020 and 2021).  The models were trained using honeys exclusively collected from Romania but was then validated using honeys (of both the classes sought and of unrelated classes) from other countries of origin.

The authors tried different data fusion approaches.  They concluded that data fusion provided more accurate classification results than those resulting from a single input data type (i.e. Raman or IR) in most of the cases. Nevertheless, the simple increase of the number of input variables through the concatenation of the experimental data will not automatically generate an improvement in the models' prediction rate.  In order to obtain the best results using the data fusion approach, it is essential to find the best way of reducing the input space to those variables that have the highest discrimination capacity.

They found that the best performances were obtained when the low-level data fusion approach was used for both botanical and harvesting year recognition models. The differentiation potential of the classifiers was proven using an external validation set, leading to test accuracies between 84% and 100% for the two investigated classification criteria. The single difference was that for the botanical differentiation, the use of the fingerprint spectral regions proved to be more effective, while for the harvesting year classification, the involvement of the entire spectral ranges led to a better performance.

Mid-level data fusion provided similar differentiation accuracies in cross-validation, either for the use of the fingerprint regions or of the entire measured spectral ranges.

Photo by Roberta Sorge on Unsplash

Analytical tests that are underpinned by statistical classification models are often based on reference sets of relatively few (in statistical terms) “authentic samples”.  There is a risk that these may not reflect the entire scope of variability within an authentic food population.  Laboratories building these models therefore need to take great care in how they process the reference data (e.g. dimension reduction, feature selection) to avoid the problem of over-fitting.  Over-fitting results in a statistical model too tightly tailored to the reference set which then fails when applied to a sample that differs in some way.  There is best-practice guidance available for laboratories – see signposts on FAN.

This latest research (open access) develops specific recommendations and a workflow for laboratories to deal with dimension reduction.  It comes from a statistical, rather than analytical, scientific journal. The authors evaluated different statistical approaches, using a model dataset of ICP-MS data from 28 apples of 4 origin classes.  They compared Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) algorithms. Their workflow integrated Principal Component Analysis (PCA) for feature extraction, followed by supervised classification using LDA and PLS-DA. Model performance and stability were systematically assessed. The dataset was processed with normalization, scaling, and transformation prior to modeling. Each model was validated via leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, balanced accuracy, detection prevalence, p-value, and Cohen’s Kappa.

They report that, as a linear projection-based classifier, LDA provided higher robustness and interpretability in small and unbalanced datasets. In contrast, PLS-DA, which is optimized for covariance maximization, exhibits higher apparent sensitivity but lower reproducibility under similar conditions. They also emphasise the importance of dimensionality reduction strategies, such as PCA-based variable selection versus latent space extraction in PLS-DA, in controlling overfitting and improving model generalisability.

They conclude that their proposed algorithmic workflow provides a reproducible and statistically sound approach for evaluating discriminant methods in chemometric classification.

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.