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This study (open access) used Nuclear Magnetic Resonance (NMR) ratios of key signals to differentiate the origin of Peppermint Essential Oil (PEO) as well as for the identification of adulterants in commercial PEO samples. Comprehensive analyses of 1D and 2D NMR spectra allowed for the identification of characteristic ¹H NMR signals associated with the key components of PEO.  Signals were assigned for 12 key components.  Significant compositional variations between PEOs from different geographical origins were revealed.

The US and India are the two primary production regions for PEO.  The model was built from authentic PEO samples of US origin (18),  India origin (15), twenty-seven blended PEO (US/India) samples and five de-mentholized cornmint (Mentha arvensis) oils.  All reference samples were collected by the National Center for Natural Products Research (NCNPR), University of Mississippi.

To facilitate differentiation, a straightforward indicator ratio method was developed to distinguish between PEOs from the United States and India.

A total of 50 commercial PEO samples were evaluated using the indicator model.  These included forty-three samples claiming to be pure PEO and seven claiming to be premium or therapeutic grade PEO.  They were purchased from various domestic and international suppliers of the US market

Results indicated a high adulteration rate (42 %). Adulterants, including synthetic chemicals, de-mentholized cornmint oil, and lower-cost oils, were identified.

The authors conclude that NMR is a useful tool for quality assessment and authenticity testing of essential oils. The methodology presented may also be extended to other essential oils to ensure product integrity.

For an explanation of the principles of NMR see FAN's introductory guide.

Photo by Anna Hliamshyna 💙💛 on Unsplash

This study (open access) aimed to identify the volatile compound components in chicken, beef, pork, and mixed (2-to-1 proportions) pork-containing satay, as well as determined the biomarker compounds for each type of satay meat.  The satay products were as commonly eaten in Indonesia, with cubes of meat barbequed on a skewer before adding the sauce.  Cooking in this manner gives the meat a distinct flavour, and the aim was to differentiate this by analysis of volatiles.

The volatile components in satay were extracted using the solid-phase microextraction (SPME) and analysed by gas chromatography-mass spectrometry (GC-MS). The data were processed using multivariate data analysis. 15 key volatile chemicals were measured.

Each type of satay meat exhibited good separation with the multivariate model. Beef and chicken satay were distinctly separated, whereas samples of pork and mixed pork-containing satay were positioned closely together.

The volatile compounds with the highest intensity in beef satay samples were nonanal, carbon disulfide, hexadecanal, and benzaldehyde. Chicken satay samples showed the highest levels of benzaldehyde, nonanal, hexadecanal, and hexadecane among the volatile compounds. In pork satay, the highest volatile compounds were cyclohexanol, 5-methyl-2-(1-methylethyl)-(1.alpha.,2.beta.,5.alpha.), hexanal, nonanal, benzaldehyde, and hexadecanal. Each type of satay meat was effectively separated, and mixed meat satay was positioned close to the pork satay group. The compounds identified as markers in beef satay were hexadecanal, nonanoic acid, ethylbenzene, pentadecanal, and heptadecanal. Chicken satay marker components included benzaldehyde; 2,3,5-trimethyl-6-ethylpyrazine; 2-nonenal, (E)-; linalool; 2-methylbutanal; and 3-methylbutanal. The marker components for pork satay and its mixtures were hexanal; thiophene, 2-methyl-; cyclodecene, (E)-; 2-methyl-2-butenal; and cyclodecene, (Z)-. These marker compounds present in each meat were highly correlated in the separation of satay samples.

The authors conclude that SPME-GC-MS successfully differentiated the satay meats and determined the compounds contributing most strongly to the separation.

Photo by Keriliwi on Unsplash

This blog, from consultancy group Forensic Risk Alliance, outlines the principles that a company must follow to demonstrate due diligence under the UK Failure to Prevent Fraud Act.  It is not specific to the food industry but the principles are generic,.  These same principles are good practice even for companies in countries that do not have similar legislation (i.e. a legal onus to take due diligence to prevent fraud) and for smaller UK companies not within the legal scope of the act.  The blog discusses how the principles can be implemented in practice:

• Implement a risk-based approach
• Incorporate fraud into other risk assessments
• Use existing data and technology
• Employee involvement and training
• Cross-industry collaboration

The blog contains links to other open-access blogs and articles on the same topic.

The authors of this study developed a targeted proteomics approach using LC–MS/MS and cross-species marker peptides with the potential to quantify meat in vegan and vegetarian foods. The method is designed to achieve the threshold of 0.1% w/w that is commonly applied for unintended cross-contamination.

 Protein extraction and digestion were optimized for rapid, simplified, and highly efficient sample preparation. Three matrix calibrations (0.1–5.0% w/w meat, each) were applied to vegan sausages and burger patties spiked with pork, chicken, or beef meat. The four markers DFNMPLTISR, DLEEATLQHEATAAALR, IQLVEEELDR, and LDEAEQLALK showed the highest accuracies for the determination of meat contents (recovery rates of 80–120%).

Although purchase is required for the full paper (here) the work builds upon previous publications and this supporting information is available free of charge (following the same link).  This includes detailed description of the statistical analysis; meat marker peptides before and after their re-evaluation; pea marker peptides; details of the LC runs; base materials and further ingredients for the vegan sausages and vegan burger patties; defatting/dehydration efficiencies of PLE and in-tube defatting/dehydration; comparison of extraction buffers and trypsin concentrations (matrix: vegan sausage with chicken meat); properties and comparison of different trypsins; chromatograms of the meat marker peptides from different matrixes; linear regressions derived from the quantifiers of the meat marker peptides in different matrixes; trueness and precision; mean signal-to-noise ratios at given meat contents.

Photo by LikeMeat on Unsplash

In this study (open access) researchers developed and piloted a single in-line sensor to classify yoghurt as either sheep, goat or milk origin and simultaneously check viscosity and pH Quality Attribute Specifications.  Their goal is a rapid in-line sensor that incorporates automated decision making, for routine use in the dairy industry.

Their reference dataset was sourced directly from two reputable Spanish companies and included both pasteurised and UHT yoghurts.

They found that the animal origin of milk could be predicted by building models based on the spectral data between 400 and 600 nm whilst viscosity and pH could be predicted by building models based on the spectral data between 800 and 1800 nm. To identify the animal origin of milk, they used Partial Least Squares-Discriminant Analysis (PLS-DA), achieving 100 % accuracy (95 % confidence interval). The model used to predict pH and viscosity was built with Partial Least Squares Regression (PLSR). The predictive power was generally very good (MSE=0.04–0.06; R²=0.94–0.96; MAE=0.16–0.17).

They conclude that their study demonstrates that the proposed spectroscopic method offers a more efficient approach for the simultaneous prediction of pH, viscosity, and milk origin in yogurt compared to existing methods, that require separate and slower analyses. Further work still needs to be carried out to optimize the model and achieve real-time monitoring that enables automated decision-making.

[picture – from the publication]

Are you interested in pursuing a PhD?

Would you like to tackle real-world challenges in food and nutraceutical analysis, using state-of-the-art instrumentation and advanced data handling, to uncover hidden adulteration and ensure consumer safety?

If you're passionate about science with impact, join this cutting-edge PhD project at the intersection of analytical chemistry and food integrity. The project is led by Prof Kate Kemsley and co-supervised by Dr Maria Marin (University of East Anglia) and Dr Lionel Hill (John Innes Centre), with joint funding from UEA and the UK Community for Analytical Measurement Science.

For further information and to apply visit: PhD Fast chemical profiling for detecting fraud in foods and nutraceuticals (KEMSLEYEK_U25SCICAMS) 2025/26 | UEA

The Food Fraud Prevention Think Tank is run out of Michegan State University by FAN Advisory Board member Professor John Spink.

It offers a series of regular blogs, plus training and other resources both online and in-person.

Professor Spink's latest blog is on the topic of the "Grief Cycle" - the progression of corporate emotions and responses when a fraud is uncovered.  This moves the thinking on from prevention to incident response and continual improvement.

We are grateful that Professor Spink has also collaborated with FAN to write a "back to basics" guide to the principles of fraud risk assessment and mitigation.  This valuable resource can be found on our website here.

The Food Authenticity Network works on a 3-year governance cycle of strategies and targets.  We have been working on our strategy for 2025-2027, and have published a summary here.  Please take a look.  The document summarises our achievements over the past 3-year period and lays our path for the next.

The strategy restates our commitment to being an open-access network, free to all global stakeholders, advancing and sharing best practice in food fraud prevention and detection.  We will leverage our impact by collaborating with other likeminded organisations.  We will seek to accelerate our successful membership growth (currently over 5,700 members), reducing our current bias towards UK members (currently 50%) by targeting membership growth particullarly within Europe and the "5-eyes" intelligence alliance countries.  We will continue to review the resources we offer and signpost, in order to provide maximum benefit and insight to our members, from multinational food companies and testing laboratories to local small businesses and enforcement officials.  The strategy relies upon a sustainable funding model, and includes targets to grow and diversify our valued funding partner organisations and to strengthen the resilience of our IT infrastructure.

As we approach our 10-year anniversary, our vision remains as a world where collaboration and shared best practices in food fraud detection and prevention creates a safer, more transparent, and trusted global food supply for all consumers.  It is as relevant now as it was at FAN's inception.

The Joint Research Centre of the European Commission have published their monthly collation of food fraud reports for March 2025 here Thanks again to FAN member Bruno Sechet who has turned these into an infographic.  The original infographic, along with his commentary, is on Bruno's LinkedIn feed where you can also access his other food safety infographics and services.

These collations from the JRC are based on global media reports, and so give a different picture to EU agri-food "suspicions" (as analysed in our most recent blog), which is different again to annual collations of official reports as aggregated in our annual summaries.  It is important, when conducting your own risk assessments, to appreciate what a specific data source includes and what it does not.  It is helpful to look at multiple, complementary, data sources and aggregates.

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 March 2025 report was added earlier this week and can be found here.

FAN has produced this 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), 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 but intended to give a high-level overview.  One consistent stand-out is unlicenced production, trade or import in high risk foods, often backed up by forged documentation.  Although the details behind the reports are not public, allegorical evidence is that these cases are not just "grey market" trade to small shops and market stalls.  Much of the suspicious trade enters mainstream markets.

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
• 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.

EFF-CoP has launched its website: 🌐 https://www.eff-cop.eu or scan the QR code to explore:

• Everything you need to know about the EFF-CoP project
• Profiles of EFF-CoP partners.
• EFF-CoP promotional materials.
• The evolving EFF-CoP landscape (including the Food Authenticity Network).
• The latest updates: news, events, newsletters, and more.

📣 Coming soon: EFF-HUB!
The EFF-HUB, your one-stop digital space to connect, collaborate, and stay informed on food fraud prevention will be vailable very soon.

🎧 And if you speak Greek, don’t miss the EFF-CoP feature on CNN Greece where EFF-CoP partner Smart Agro Hub (Dissemination- Exploitation- Communication Work Package Leader), represented by Evangelia Zavitsanou, joined the journalist Kostas Pliakos in a podcast about food fraud, food safety, and how consumers can protect themselves and make informed choices.

Listen to the podcast here:
▶️ CNN Greece
🎧Spotify
🍏 Apple Podcasts

The FAN EFF-CoP page has also been updated.

In this study (purchase required) the authors propose and develop a strategy for a field-based screening test for crude honey adulteration (adulteration with inverted sugars) using Near InfraRed Spectroscopy (NIR) hand-held scanners.  They developed a single class classification model that was sufficient to either give an “unadulterated” verdict or to refer the sample for confirmatory (IRMS) analysis.

The authors developed their SIMCA model using “genuine” adulterated honeys that had been previously seized in a Brazilian police operation that had cracked down on industrial-scale addition of invert sugar to honeys over a three year period, along with unadulterated honeys collected by police during the same operation.  The traditional SIMCA was improved by optimizing the class boundaries based on receiver operating characteristic (ROC) curves and the estimate of an uncertainty region, thus optimising the model for a screening application.

Photo by Roberta Sorge on Unsplash

Dehydrated bee pollen is a premium product with a growing market as a food supplement, functional food ingredient, and is also an ingredient in biomedical and healthcare formulations.  Pollen from certain species of stingless bees is higher priced than the equivalent from the much more common honey bee, as the former is considered to have enhanced nutritional and health benefits.  Pollens from different bee species are visually identical, giving an incentive for potential fraud.

In this paper (purchase required) the authors built a classification model to discriminate pollens from different bee species.  The researchers integrated digital image processing and machine learning to classify pollen loads produced by honey bees (Apis mellifera) and pot-pollen from stingless bee species based on their colour patterns. A total of 246 pollen loads and pot-pollen samples from five bee species (Apis mellifera, Melipona marginata, Melipona quadrifasciata quadrifasciata, Scaptotrigona bipunctata, and Tetragona clavipes) were collected, and high-resolution images were captured using a smartphone. Colour parameters extracted from images such as R, B, H, and V were analyzed, and classification models employing CatBoost, XGBoost, Random Forest, and k-Nearest Neighbors (kNN) algorithms were tested.

They reported that CatBoost achieved the highest performance, with accuracies of 100 % in the training phase and 98.9 % in the testing phase. Linear Discriminant Analysis (LDA) further validated the classification by grouping the pollen samples into five distinct clusters corresponding to the bee species studied.

They conclude that combining digital image processing from a smartphone with machine learning offers an effective approach to classifying pollen from different bee species, with promising applications in apiculture to ensure product quality and authenticity.

Photo by Aaron Burden on Unsplash

In this study (open access) a non-targeted method of headspace-solid phase microextraction with gas chromatography coupled to mass spectrometry (HS-SPME-GC–MS) was developed to achieve the characterization, classification, and authentication of different coffee samples according to geographical production region, and variety (arabica/robusta). Moreover, decaffeinated and non-decaffeinated instant coffee samples were analyzed. Some samples of chicory, a potential coffee adulterant, were also been included. The GC–MS fingerprints were used to classify and characterise the analyzed coffees using principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and partial least squares (PLS) regression.

185 reference samples were used.  42 were chicory (a typical coffee substitute or adulterant), 96 were coffee from three geographical production regions (Vietnam, Cambodia, and Costa Rica) and species (Arabica, Robusta, and Arabica-Robusta mixture), and 47 samples were soluble coffee (decaffeinated and non-decaffeinated). The chicory samples were purchased from Barcelona supermarkets and the coffee samples were from Vietnam, Cambodia and Costa Rica local supermarkets. Each paired PLS-DA model was built using 70 % of samples randomly selected from each group as the calibration set while the remaining 30 % of the samples were employed as the prediction set.  The authors compared models generated using two different GC columns and operating conditions.

The authors tested their model on mixtures prepared in-house from the same reference set:: Vietnamese Arabica Coffee adulterated with Vietnamese Robusta Coffee and Vietnamese Robusta Coffee adulterated with chicory.  They reported that the model could classify adulteration levels down to 15%.

(image from the paper)

A recent review by FranZ Ulberth and Robert Koeber describes the role of RMs in food authenticity testing, including their applications in method validation, calibration, quality control, and the definition of conventional measurement scales.

It also reviews the availability of RMs that can be used in measurement procedures to authenticate food. Furthermore, the applications of RMs in targeted adulterant detection methods, for compositional parameters used to authenticate foods and food supplements, isotopic measurements, untargeted food authenticity testing methods, and detection and quantification of genetically modified organisms (GMOs), are explored.

The review concludes by recommending the development of research grade test materials or representative test materials to harmonise untargeted testing methods and improve comparability of results across laboratories and over time.

Access the full review: DOI https://doi.org/10.1007/s00216-025-05743-0

The review has also been added to the Quality section of the FAN website.

Mechanically Separated Meat (MSM – sometimes called Mechanically Recovered Meat, MRM) must be declared on-pack if used in meat products.  Testing for undeclared MSM can be a significant analytical challenge.  Traditional official control methods rely on microscopy, which requires experienced interpretation and can be highly subjective. 

This paper (open access) builds upon previous published work from the same researchers to develop targeted LC-MSMS methods that are suitable for official control applications..

In contrast to a comparable study on MSM from poultry, the authors report that the use of cartilage/intervertebral disc material was not useful for porcine MSM. They therefore report a new marker protein from porcine MSM, protegrin-4, which allows the detection of 5/3/1 mm MSM. The validity of the developed assay was ensured by the investigation of 182 blinded samples. After unblinding, all samples containing 5/3/1 mm MSM and all negative control samples were correctly classified. Additional new results related to the investigation of the species specification of chicken, turkey, and pork also are presented.

They concluded that LC-MS/MS-based detection of of undeclared MSM has been successfully extended from poultry to porcine MSM. The assay was successfully transferred to a triple quadrupole LC-MS system to facilitate routine use.

Photo by Branimir Petakov on Unsplash

This paper (open access) reviews the history and exportation of turmeric in Africa and the safety issues of some toxic adulterants.

Priority adulterants were determined from global food safety alerts. A systematic bibliographic search was performed to identify appropriate methods and techniques for authentication and safety testing. The quality of each study was assessed according to PRISMA guidelines/protocol.

The authors report that African turmeric exportation is on the rise due to recent insights into the suitability of local cultivars, soil and climate for growing high-quality turmeric. There are limited data on turmeric adulteration for domestic consumption and export markets..

Global alert databases revealed lead chromate as the top hazard identified of all adulterants. Current techniques to detect adulterants are laboratory-based, and while efficient, there is a need for more rapid, field-friendly, non-destructive analytical tools.  The authors consider that – if lead chromate is considered to be the main tisk - then pXRF would be ideally suited as a field-based test in Africa. In the hope that it could be further developed and calibrated to detect below the regulatory level of 1.5 mg/kg lead in turmeric powder. There would be a need to cross-check pXRF screening results against a validated and accredited ICP-MS method as a reliable confirmatory tool.

Photo by Md Shakil Photography on Unsplash

Authenticity testing of honey is the best-known example of the need for a weight-of-evidence approach.  One analytical test is unlikely to give a definitive answer.  Using a panel of different tests, using different techniques and principles, can give an incremental list of suspicions.

The routine use of machine learning for constructing reference databases has enabled the rapid expansion of techniques that – in principle – can discriminate differences between “authentic” and “inauthentic” reference samples, and thus could be added to this weight-of-evidence armoury.  Two recent publications are a case in point.

In the first study (purchase required) the authors produced their own reference set by adulterating honey with syrups, then showed that they could be discriminated from authentic honey using Differential Scanning Calorimetry (DSC)  (they used graph-based semi-supervised learning to construct the classification model).  DSC is based on melting curves, and is an indirect measure of water content.  It is a cheap, routine, test widely used in many industries and – as such – would be ideal as the first step in an analytical workflow in order to screen out the most crudely adulterated samples.

This second study (purchase also required) is an example of building much more focussed and granular reference databases to address a highly specific authenticity question.  The authors measured carbon-13 ratios in honeys (and in their constituent protein) from 196 authentic honeys sourced from 56 cities in Turkey.  This analytical technique is usually used as a marker for exogenous sugar addition, but in this study the authors used the more subtle variations (driven by changes in flora, temperatures and humidities) to build a classification model for regional origin.  They were able to cluster the honeys into one of 7 distinct geographical regions of Turkey based upon their carbon-13 ratios.

[thanks to FAN-member Peter Farnell for spotting that the original version of this blog referenced "Differential Scanning Colourimetry" - which would, indeed, have been a novel technique worthy of comment]

A recent New Zealand case is a reminder that any food – or related  product - where the price is based on an indirect marker of quality can be a target of adulteration.  This is an increasing watch-out with the industry focus on waste valorisation and the circular economy.

In this case, the perpetrators (commercial meat producers) manipulated the Free Fatty Acid profile of tallow by adding extraneous fats and oils.  A lower FFA profile increases tallow’s value in the biofuels market.  The adulterated tallow was destined for export into biofuels feedstock.

The fraud was brought to light by a whistleblower.  The perpetrators have been fined 1.6 million NZ dollars.

A full media report is here

Photo by Wilman Aro on Unsplash

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The UK Serious Fraud Office (SFO) have issued guidance that clarifies that if a corporate self-reports suspected wrongdoing and co-operates fully with investigators, it can expect to be invited to negotiate a Deferred Prosecution Agreement (DPA) rather than face prosecution.

Whether a DPA applies is a judicial decision, but a key consideration is how promptly, after the fraud was uncovered, the company then self-reports.  The guidance makes clear that “waiting for an internal investigation to conclude” is not an excuse to delay reporting.

SFO Director Nick Ephgrave put it in these terms at the Guidance launch event on 24 April. “As soon as you have reasonable suspicion – it doesn’t have to be ‘beyond reasonable doubt’, doesn’t have to be ‘on the balance of probabilities’, just reasonable suspicion you’ve got some offending going on – that’s the point at which you stop, that’s the point at which you talk to us.”

The guidance also includes details on how to report, including a secure online self-reporting form.