News

The UK National Food Crime Unit is asking food businesses to be alert to the fact
that adulterated saffron has been identified on the UK market.

Saffron sampling results have revealed adulteration through the addition of
synthetic colours in contravention of Regulation 1333/2008.

Issues have also been discovered around saffron not meeting the grade stated on packaging (Grades I, II or III) due to lower than required levels of colouring strength (expressed as Crocin).

Failed samples are the subject of ongoing enquiries in the UK and internationally.

ACTION RECOMMENDED
If you are purchasing saffron either to sell or to use within a further processed product, please be aware of the risk of adulteration. Consider the following advice:

1. Ensure that reputable suppliers are being used and are being reviewed in line with supplier approval policies.
2. Be conscious of product being supplied at a lower price than would be expected for the quality and grade of saffron being purchased. If you have any suspicions, ask your supplier for traceability documents including any analysis reports.

Read full alert.

In this study (purchase required) a machine vision system was used to capture the images of saffron samples at different safflower mixture proportions. Then three feature extraction algorithms - gray level co-occurrence matrix, gray-level run-length matrix, and Local Binary Pattern -  were applied to extract the textural features of data. Discriminant Analysis, Support Vector Machine, and Artificial Neural Network algorithms as supervised classification models were applied to classify datasets.

The models were applied for 3 class and 6 class datasets to explore classification ability. The best outcome for the 6-class dataset was with the Support Vector Machine model and with all features with an accuracy of 80 %. For 3 class datasets, Discriminant Analysis model had the best result with all features and with the accuracy of 97.78 %.

To explore the statistical importance of different features, two Minimum Redundancy Maximum Relevance and Chi-Square Test algorithms were applied. For the gray level co-occurrence matrix extracted features, Chi-Square Test algorithm with 10 features had the best accuracy with a test accuracy of 76.94 %.

The authors conclude that the proposed approach could be utilized in designing a system for checking saffron authenticity at a business-to-business point of sale..

The Authenticity Methodology Working Group (AMWG) evaluates scientific research developed within Defra’s Food Authenticity Research Programme, ensuring methods developed are robust and fit for purpose. AMWG also advises on the wider application of methods.

This paper provides a summary of the group’s work in 2024. Activities include:

• providing technical direction on research to develop tools and methods for authenticity testing throughout the lifecycle of projects
• peer-review of final reports
• the provision of technical advice supporting specific issues to inform the development of policy on food labelling, composition and standards.

Read AMWG 2024 Annual Summary.

Unauthorized GM (UGM) refers to those GM crops or products that have not received approval in a particular country..

DNA-based methods are preferred for testing of raw or partially processed food products.  More heavily processed food can be more of an analytical challenge; DNA methods are still applicable provided the quality of DNA enables amplification despite being hampered by the processing procedures or presence of inhibitors or due to complexity in composition in terms of ingredients.

This review (purchase required) focused on possible approaches for adhering to the regulatory requirements while verifying UGM in processed food products. Technical challenges and approaches for extracting purified DNA and necessary quality checks are described. The article covers amplification as well as sequencing-based methods which can be applied to check for presence of UGM ingredients amongst a complex mix of other ingredients. The authors discuss the comparative assessment of these methods in the context of regulatory testing requirements and availability of information of transgenic DNA.

Photo by Kseniya Nekrasova on Unsplash

This new book from CRC Press is available to purchase either as an e-book or as hard copy.

After a general historical introduction to the adulteration of spices, the book then includes a chapter on global standards and regulations regarding spice purity (both adulteration/authenticity, and contaminants such as mycotoxins and pesticide residues).  It then has separate chapters giving an overview of adulteration and authenticity testing for specific spices; black pepper, chilli, nutmeg, saffron, ginger and turmeric.  After sections on mycotoxins and pesticides, it ends by discussing supply-and-demand pressures and the sustainable and ethical sourcing of spices.

Photo by Calum Lewis on Unsplash

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 April and May 2025 reports have been added and can be found here.

FAN produces rolling 3-month graphical analysis. 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), 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 subjective, intended only to give a high-level overview. 

• Unlicenced trade in high-risk foods (which includes attempts at illegal import, along with production from unlicenced operator sites) continues to feature frequently
• Fraudulent or missing traceability paperwork (including Heath Certificates and analytical test certificates) continues to be prevalent
• The increase in suspected false DoP/PGI claims in May was largely attributed to fraudulent Proscecco
• Ingredient quantities and quality feature persistently, particularly meat and fish in manufactured food
• Of incidents of traditional "adulteration", edible oils (particularly olive oil) and honey feature regularly.  Vanilla, coffee, spices, jams and spirits make more sporadic appearance.

As with all incident collation reports, interpretation must be drawn with care.  This 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.  There are important differences in the data sources, and thus the interpretation that can be drawn, of these data compared to other incident collations.  For example:

• JRC Monthly Food Fraud Summaries (which underpin the infographics produced monthly by FAN member Bruno Sechet) - these are unverified media reports, rather than official reports, but hugely valuable in giving an idea of which way the fraud winds are blowing
• Official reports (as collated from commercial databases such as Fera Horizonscan or Merieux Safety Hud, which underpin FAN's annual Most Adulterated Foods aggregation) - these are fewer in number and give a much more conservative estimate of fraud incidence, and may miss some aspects which have not been officially reported
• The Food Industry Intelligence Network (Fiin) free SME Hub.  This excellent new resource collates anonymised UK industry test results for the benefit of Small and Medium Enterprises in the food sector (registration and approval required to obtain login).
• Verified reports (where the root cause has been scrutinised and interpreted by a human analyst, for example the FoodChainID commercial database) - these are also few in number, less suitable for drawing overall trends, but give specific insight and information.

If looking at trends over time, you must also be wary of step-changes due to the introduction of new data sources.  For example, Turkey's public "name-and-shame" database of foods subject to local authority sanctions went online in January 2025 and has resulted in an apparant increase in incident reports from Turkey.

Are you an English-speaking FAN member who also speaks either Arabic or Mandarin Chinese?

We have had FAN's Annual Summary translated into various languages using a professional agency.  We would like to sense-check these translations before pubication.  Would you be able to help?  We are not looking for editorial or grammatical corrections - merely a quick check that there is no inadvertent offensive language or any obvious scientific misunderstandings.  It should only take 5 minutes.

If you are able to help then please e-mail secretary@foodauthenticity.global.  Thank you

There are a large number of both commercial and in-house-written digital tools that attempt to classify and predict food safety risks based upon historic records in the EU Rapid Alert Service in Food and Feeds (RASFF) database.  With all such tools, it is important to remember that RASFF records are not a representative sample of either tests or results, and were never intended as a source of trends; the purpose of RASFF is rather to share specific individual alerts which may require regulatory action  on a cross-border basis.  Official tests are highly targeted, and often informed by previous RASFF alerts, so more alerts about a specific issue drives more official tests which drives more alerts (i.e. a feedback mechanism).  Also, RASFF only records the “positive” results, so there is no denominator; no indication of the number of “negative” results or the % incidence of an issue.  And finally, RASFF only records issues with a food safety concern so most food authenticity test results are excluded.

Despite these caveats, RASFF is still one of the most extensive and systematic public databases of food safety incidents and is likely to form the basis of many AI risk-prediction systems for years to come.

This paper (purchase required) evaluated the effectiveness of the Machine Learning models that sit behind such systems. The authors report that transformer-based models significantly outperform traditional machine learning methods, with RoBERTa achieving the highest classification accuracy. SHAP analysis highlights key hazards salmonella, aflatoxins, listeria and sulphites as primary factors in serious risk classification, while procedural attributes like certification status and temperature control are less impactful.

They conclude that despite improvements in accuracy, computational efficiency and scalability remain challenges for real-world deployment of AI risk-scoring and prediction systems.

Photo by Clarisse Croset on Unsplash

Nontargeted analysis for food authenticity by liquid chromatography–mass spectrometry (LC-MS) can provide data on thousands of chemical features. However, most studies that train machine learning models for food authentication use sample sizes in the tens or hundreds.  Such training sets are typically considered too small to be optimal, as it introduces the problem of overfitting when working with such a large feature-to-sample ratio.

This study (open access) aimed to mitigate this issue with a machine learning protocol designed for sub-optimal training sets, using honey as an example.   A recursive feature elimination (RFE) pipeline was developed specifically to address the challenges of optimizing the honey chemical fingerprint for multiclass machine learning classifiers on a limited number of samples with imperfect labels. A support vector machine was used for both RFE and classification to reduce the 2028 nontargeted features down to just 54 features (a 97.3% reduction) without any loss of classification performance.

The authors report that the resulting model was a 6-class classifier, capable of identifying monofloral blueberry, buckwheat, clover, goldenrod, linden, or other honey with a nested cross-validation Matthews correlation coefficient (MCC) of 0.803 ± 0.046. The development of a k-nearest neighbours filter and the decision to continue the RFE process beyond the iteration with the highest classification score were instrumental in achieving this outcome.

They conclude that this work shows a complete pipeline that automates feature selection from nontargeted LC-MS spectra when working with a limited number of samples and imperfect labels. This process can also be expanded to other food groups and spectral data.

Photo by Andrea De Santis on Unsplash

This paper (open access) reports the development of a label-free electrochemical immunosensor for the detection of low quantities of porcine gelatin.  The sensor is based on a boron-doped diamond electrode modified with aryl diazonium salt. The diazonium electrografting enabled stable covalent immobilization of anti-porcine gelatin antibodies via protein A, preserving anti­body orientation and activity.

The optimised conditions were a 500× antibody concentration, 60 minute antibody incubation, and 15 minute gelatin incubation. Detection was performed using differential pulse voltammetry with [Fe(CN)₆]3-/4- as a redox probe, allowing label-free monitoring of anti­body-antigen interactions based on changes in current.

The authors report that the immunosensor demonstrated excellent analytical performance, with a detection limit of 142.15 pg mL-1. Specificity testing showed no cross-reactivity with bovine gelatin.

Although suitable validation would be required, the authors conclude that this immunosensor has potential to form the basis of a rapid, highly sensitive, and specific platform for porcine gelatin detection, offering great potential for food authentication and halal verification.

Researchers at Henley Business School (UK) are conducting a global research study on how organizations screen and evaluate new suppliers in food and agri-food supply chains.

Effective supplier screening is critical to managing risks such as food fraud, regulatory non-compliance, and unethical sourcing. Your insights will contribute to a broader understanding of current practices and help shape future standards.

If you have been involved in screening new suppliers in the past 5 years, please take part and share your valuable experience.

Survey Details:
Duration: 6–8 minutes
Anonymous and compliant with UK GDPR
Optional iPad draw
Option to receive a summary of findings
Take the survey now.

Your participation will help identify what’s working, what’s not, and what’s next in global supplier screening—ensuring that diverse industry voices are represented.

A new e-seminar pictorial guide for verification of previously frozen poultry has been published.

This e-seminar provides a guide for the implementation of a method for the verification of the labelling of previously frozen poultry by measurement of hydroxyacyl-coenzyme A dehydrogenase (HADH) activity.

When meat is frozen and then thawed, the muscle mitochondria (a type of intramuscular organelle) are damaged during the process and the enzyme HADH is released into the intracellular fluid. The relative increase in the amount of HADH found in the intracellular fluid before and after analytical method freezing procedure may be indicative as to whether the meat has previously undergone freezing. The measurement of HADH activity in the intracellular fluid, taken by pressing the meat and analysing the fluid using a spectrophotometer, is a simple, rapid and reliable procedure for a laboratory to undertake when evaluating the reported cryological history of raw chicken or turkey samples.

This e-seminar provides information and guidance relevant to understanding how to apply an HADH-based spectrophotometric method to differentiate between chilled and previously frozen poultry samples.

This e-seminar was produced by the Joint Knowledge Transfer Framework for Food Standards and Food Safety Analysis, funded by the Food Standards Agency, the Department for Environment, Food and Rural Affairs, Food Standards Scotland and the Department for Science Innovation and Technology via the Government Chemist.

The e-seminar has also been added to FAN's Training section.

Chlorogenic acids (CGAs) are phenolic compounds found in plant-based foods including coffee. This study (open access) aimed to evaluate the profile of three CGAs (5-CGA, 4-CGA, and 3-CGA )in medium-roasted Coffea arabica L. and Coffea canephora Pierre ex A.Froehner beans originating from diverse geographical regions.

The researchers reported that 5-CGA was the predominant compound across all samples analyzed.  C. canephora samples contained significantly higher and more variable levels of CGAs compared to C. arabica samples.

Statistical analysis using ANOVA, combined with Duncan, Tukey, and Dunn post hoc tests, confirmed species-related differences in CQAs content. Additionally, violin plots provided a clear visualization of these distinctions. Principal Component Analysis (PCA) further indicated that the geographical origin of the samples may influence the accumulation of chlorogenic acids.

The authors conclude that both botanical species and environmental factors influence the CGA composition of coffee. Understanding such variability could both give a useful authenticity marker and could guide the development of value-added coffee-based products tailored to consumer preferences and health-related expectations.

Photo by Clay Banks on Unsplash

Energy-Dispersive X-Ray Fluorescence (EDXRF) is a cheap non-destructive technique to measure metal and mineral content, typically operated as a laboratory benchtop method.

In this study (open access), researchers at the European Commission Joint Research Centre used market samples of oregano that had been previously tested under the EU co-ordinated official control plan to investigate whether EDXRF could be used as a screening technique.  This was a serendipitous extension of the use of EDXRF for checking compliance with EU limits for copper contamination.  After a relatively simple sample preparation, they measured a panel of 36 metals and minerals.

They found that, at it simplest level, the ratio of copper-to-zinc was a good indicator of adulteration with olive leaves without any need for modelling statistics.  Once multivariate statistics were used, samples could also be classified by geographic origin.  This classification required 2-stage modelling (SIMCA then PLS-DA) to achieve full potential, and then was limited because the reference dataset was not sufficiently comprehensive in terms of countries of origin.

The researchers concluded that their work demonstrates that EDXRF is a suitable screening method to detect oregano adulteration with other species, and to authenticate the geographical origin of the product. The method is clean, cheap and has a high sample throughput because it does not require sample digestion. For those reasons, the approach is ideal to be used by control laboratories.

SIMCA allowed the authentication of the geographical origin of oregano. The performance of the authentication could be improved with a combination of SIMCA with PLS-DA that provides sensitivities and specificities higher than 90 %. However, a database well populated with results obtained with samples coming from all the main producing countries, would be needed.

Issue 19 of the Food Authenticity Network Newsletter is now available.

This issue includes the following updates from FAN:

1. FAN Strategy 2025 - 2027
2. Global Food Fraud report 2024
3. Fundamentals of Food Fraud Prevention
4. New CEN Standards
5. Precision Breeding
6. Cultivated Meat
7. FAN Partnerships

As well as updates from the European Food Fraud Community of Practice project and our Food Authenticity Centre of Expertise.

Plus two interesting Guest Articles from the UK National Food Crime Unit and the Food Standards Agency on honey authenticity.

Our Food 2024: An annual review of food standards across the UK

The Food Standards Agency (FSA) and Food Standards Scotland (FSS) have published their annual ‘Our Food’ report, which reviews food safety and standards across the UK for 2024, which highlights ongoing food safety and standards challenges.

Overall, food safety and authenticity standards were stable in 2024, but several aspects of the food system remain under considerable pressure:

1. Local authorities still do not have enough resources to address the substantial backlog of inspections, nor deal with the growing number of new food businesses that should be inspected. 
2. There has been progress in implementing documentary and physical checks at our borders, however more comprehensive and accurate data would allow consumers to be better protected.
3. It is also still the case that too many households are struggling to afford food, and that more action is required to improve the healthiness of the food we eat.

The FSA and FSS are calling on government, industry, and regulators to work together to respond to these risks in our food system, to uphold high food standards, and to achieve a food system that works for everyone.  

Most test methods and research into the authenticity of edible oils are focussed on differentiating different plant species or on different grades of olive oil.  There has been relatively little focus on different grades of sunflower oil.  Commercial sunflower oil is sold as three different grades with increasing price premium; standard Sunflower Oil (SFO), Medium Oleic Acid (MOSFO) and High Oleic Acid (HOFSO).  HOFSO is more stable to repeated heating/cooling cycles and so is the grade typically required for fast food restaurants.  It is also available as a premium product sold direct to consumers.

In this paper (open access) the researchers used Spatially Offset Raman Spectrocopy (SORS, a portable non-invasive sensor) to build statistical models that could differentiate HOFSO from those that were not HOFSO (i.e. either MOSFO or SFO).  Although the reference samples used to build the model were purchased from commercial outlets rather than being of verified authenticity, the fact that two different unsupervised mathematical plus a number of supervised approaches all led to similar classification models, and that the models were validated with samples independent of the training sets, gave increased confidence in the model.

The authors conclude that the use of  SORS in combination with the developed chemometric models is an effective tool for the HOSFO authentication. The approach is simple and rapid, with instrumental fingerprints from portable analyser in less than 2 min and without requiring sample preparation.  This approach would class as Green Analytical Chemistry.

Image from the paper

The European Union has 13 flagship projects designed to tackle critical challenges in food safety, traceability and combatting fraud.  They are all under the funding umbrella of the Horizon R&D programme. FAN members will be familiar with the European Food Fraud Community of Practice (EFF-CoP) but this is just one project within the cluster.

All of the projects in the cluster are now indexed on the website of THEROS, one of the first of the projects to be launched.  The index includes project summaries and links.

• THEROS - An integrated toolbox for improved verification and prevention of adulterations and non-compliances in organic and geographical indications food supply chain
• EFF-CoP – the European Food Fraud Community of Practice
• Alliance – digital solutions for data veracity and transparency in food supply chaines
• CUES – consumers’ understanding of eating sustainably
• FishEUTrust - Increasing consumer trust and engagement in seafood products
• Sea2See - Blockchain traceability technology and stakeholders’ engagement strategy for boosting sustainable seafood consumption
• TealHelix – Inclusive and personalised food labelling
• Watson – digital and technical track-and-trace solutions, predominantly aimed to defend against counterfeiting
• Titan - Enabling transparency in food supply chains by implementing innovative solutions to boost health, sustainability, and food safety
• FOODGUARD - Microbiome applications and technological hubs as solutions to minimize food loss and waste
• ROSETTA - Reducing food waste due to marketing standards through alternative market access
• INfoodMATION - Optimising food information and communication towards healthier and more sustainable dietary patterns
• DRG4FOOD - Developing a European roadmap and a practical toolbox to guide policymakers, businesses, and civil society in designing food systems

The Food Industry Intelligence Network (fiin) was established in 2015 by industry technical leaders, in response to the recommendation of the ‘Elliott Review’ for the industry to create a ‘safe haven’ to collect, collate, analyse and disseminate information and intelligence relating to fraud risks. Fiin now has over 70 industry members, UK-based national and multinational food companies, who contribute their own testing data and insight which can then be anonymised, aggregated, and shared amongst the group.

Fiin has now launched a service to make some of this insight freely available to the wider food industry community, particularly to Small and Medium Enterprises (SMEs) which may have limited in-house technical expertise to mitigate food fraud risks.  The new SME hub requires users to register their details but there are no subsequent fees or restrictions.  Information and resources are listed in a clear and easy-to-navigate manner.

The hub offers

• Fiin's quarterly commodity watch-list - this is based on over 50,000 authenticity tests conducted by fiin's industry members, and so gives a different angle than data in the public domain (such as FAN's "most adulterated foods" collation) which are typically constructed from Official Reports.
•  Food fraud definitions and examples -  clear explanations and real-world illustrations
•  Food fraud prevention guides and training materials -  practical, SME-friendly tools
•  News & events -  updates and events relevant to food fraud
•  Food fraud reporting channels - know where and how to report concerns

All resources are free and tailored specifically to meet the needs of SMEs across the food sector.

We are proud, within FAN, that fiin has been a longstanding and supportive Partner.

Low Field (LF) Nuclear Magnetic Resonance (NMR) spectrometers have been used for routine inline quality control in the processed meat industry for many years.   Recent advancements have made the technology more accessible for other applications.

This proof-of-concept study (open access) demonstrates the potential of LF NMR for rapid oil authentication in an industrial setting. Their approach was based on solvent-free oil analysis using a single scan ¹H NMR measurement on a LF 80 MHz NMR instrument. The analysis identified the allylic signals at δ 2.0 ppm as a potential diagnostic region, effective in detecting adulteration in the oil samples. They limited the integration to this one spectral region in order to make data analysis rapid and easy to use in a food factory.

The authors demonstrated successful detection of adulteration in two types of vegetable oils rich in polyunsaturated fatty acids (PUFA), rapeseed and sunflower oil, at levels ranging from 5 % to 25 %. Specifically, the study found that adulteration in rapeseed oil could be detected at levels as low as 5 % when adulterated with soybean oil, 10 % when adulterated with sesame and cottonseed oils, and 25 % for corn oil and safflower oil. In the case of sunflower oil, cottonseed oil can be identified at 5 % adulteration, while corn, sesame, and safflower oils can be detected at 25 % adulteration.

The authors consider that the approach is fast, user-friendly, and ecological.  LF NMR could be a valuable tool for identifying adulteration in edible oils, with applications in various industries. This method would benefit from further research to validate the allylic region as a diagnostic region of oil adulteration.

Photo by Fulvio Ciccolo on Unsplash