News

Is food fraud on the decrease or the increase?

Our latest article, in New Food magazine, discusses the evidence and points to watch-out commodity classes.  It covers and summarises much of the insight which is freely available to all FAN members on our incidents trends page (note that you must be logged onto the site as a FAN member to view all of the content on this page)

The answer to the question, unfortunately, is definitely not "decrease" despite the industry effort and focus. The evidence is increasingly of a small but sustained increase.

It is always difficult to gather hard evidence rather than allegorical suspicions for this type of analysis. Thanks to Fera Horizonscan, Meriuex SafetyHud and FoodChainID for making their data available pro-bono.  Links to all these platforms can be found on our incidents trends page. Thanks also to FAN's partners for funding this work.  One of the benefits of Gold or Platinum partnership is early sight (quarterly reporting dashboards) of some of this insight.  Please get in touch if your organisation would like to talk about partnership opportunities.

The Chinese government is consulting on proposed changes to national food safety law.  This would not only affect food manufacturers within China - it also impacts any food companies operating in China or involved in cross-border commerce, including e-commerce.  The public consultation closes on 10 November.  The draft legislation can be found here (Chinese language). 

Some of the proposed changes would bring in clauses relating to food authenticity and enforcement that are amongst the most stringent in the world.  These include

• Mandatory CCTV and internet-of-things sensors at vulnerable points in the manufacturing chain, with live feeds directly to the regulator
• Existing point-of-sale requirements for approval and veracity of label claims to be expanded to include any "advertising and promotion", encompassing 3rd-party promotion on Tik-tok, Instagram etc.
• Scope of food law and enforcement to be expanded to include e-commerce, including 3rd party platforms and consumer-to-consumer sales
• For cross-border e-commerce supply to Chinese consumers, the overseas originator (including distribution platforms) has responsibility for food safety including handling recalls.  Online information to consumers must be in Chinese.
• Personal liability for company officials found at fault.

Although many of the proposed requirements build on existing regulatory practices in China, incorporating them into the Food Safety Law may provide a stronger legal basis for enforcement.

There are multiple research groups who have built species-classification models for fish based upon infra-red (IR) spectra.  Most models, ultimately, are driven by differences in the fingerprints of IR absorbing compounds such as lipids and amides.

This study (purchase required) highlights the importance of standardising the part(s) of the fish (tail, belly etc) selected to train the model, and the different outcomes that can be obtained by using models that are trained on different parts of the fish.

The researchers examined whether intra-fillet anatomical heterogeneity influenced the ability of PLS-DA models based on ATR-FTIR spectra to discriminate three pooled high-value salmon species (Salmo salar, Oncorhynchus kisutch, and Oncorhynchus tshawytscha) from rainbow trout (Oncorhynchus mykiss). A total of 122 spectra from the back, belly, and tail regions of 14 fish were evaluated using outer Leave-One-Fish-Out validation with inner fish-grouped latent-variable selection. The final full-data models used 3, 3, and 4 latent variables for back, belly, and tail, respectively; model size was selected separately within each outer validation fold. Fish-level balanced error rates were 0, 0.063, and 0.250, and AUC-ROC values (based on relatively small cohorts) were 1.000, 0.979, and 0.917, respectively.

They found that the tail model showed lower specificity and less stable latent-variable selection under fish-level internal validation. VIP analysis identified 366 wavenumbers shared across all three regional sets, representing 12.99% of their union, while most selected variables remained region-dependent. Predictive performance differed across anatomical sampling locations.

They conclude that, for their application, dorsal and ventral muscle are less suited as sampling sites for routine deployment.

Photo by Creab ThePolymath on Unsplash

The Government Chemist Review 2025, published on 23 October 2026, highlights the Government Chemist's work in strengthening food and feed measurement science, supporting regulatory decision-making and addressing emerging challenges in food safety and authenticity.

The Review showcases developments in several areas relevant to food authenticity, including analytical approaches to detecting adulteration in olive oil and meat, strengthening honey authenticity testing through improved reference databases and sampling protocols, and addressing analytical challenges associated with novel and alternative proteins.

It also highlights the importance of collaboration, knowledge sharing and scientific capability building, including the work of the Food Authenticity Centres of Expertise.

The Review recognises the Food Authenticity Network's (FAN) contribution to strengthening global collaboration and knowledge exchange in food authenticity and food fraud prevention. A dedicated section highlights FAN's tenth anniversary in 2025, its continued international growth, the publication of its 2025–2027 Strategy, and key activities including its inaugural Analysis 4 Authenticity online conference and annual Global Food Fraud Report.

The Review also features progress made by the European Food Fraud Community of Practice (EFF-CoP) in supporting collaboration and knowledge sharing to help prevent food fraud.

Together, these activities demonstrate the importance of robust analytical science, trusted information and international cooperation in protecting the integrity of global food supply chains.

Read the full Government Chemist Review 2025.

This monthly highlights report has been produced for FAN members by one of our Partner organisations, iComplai.

The report is an AI-generated prioritised digest.  It is blended from online regulatory and media reports of food fraud incidents and suspicions, giving a summary of each selected issue and suggested mitigation steps.

iComplai are one of a number of commercial providers of digital horizon scanning tools, many of whom are signposted by FAN on our Incident Trends hub.  We do not endorse any specific company.  We are grateful to iComplai for providing this free digest which we hope you find useful.

It is noteable that iComplai's analysis includes common themes to many current reports of suspicion and watch-out trends, irrespective of their data sources or purpose:

• Unregistered food production sites, linked to faked documentation with falsified registration details
• Repackaging or over-labelling of date-expired foods with false expiry dates
• The continued use of dyes - particularly Sudan reds - to disguise adulteration of spices and oils

If you know of any other trending or insight tools which are not on our Trends hub and would be beneficial to signpost to our membership, or if you are interested in becoming a FAN Partner, then please get in touch.

The UK government is consulting on changes to the legal protection of whistleblowers.  These were established through the Public Interest Disclosure Act 1998 and the Employment Rights Act 1996.

Some of the proposed changes would affect the legal protection of people using food fraud reporting lines.

For example:

• At present, in order to be protected, someone must have "reasonable suspicion that the allegation is true" in if they report an allegation to any regulator.  The government is consulting on whether this wording discourages people with less founded suspicions to report, when it would be adventageous for earlier suspicions to be captured.
• The government is considering expanding the list of protected individuals to include non-executive directors, volunteers, and some self-employed contractors.

The consultation closes on 8 January.  It can be found here.

As part of the European Food Fraud Communit of Practice project (EFF-CoP), University College Dublin (UCD) is mapping communication and collaboration ties across the food fraud prevention and detection network.

By understanding these connections, we can identify strengths in the existing network, potential communication gaps and opportunities for stronger collaboration across the food fraud fighting community.

Anyone working in food fraud prevention or detection can participate; your input is essential to help identify how information flows across the sector.

The time required for completing the survey is only 3-5 minutes.

You can access the survey through: https://eu.surveymonkey.com/r/effcop

The survey will close on 6 November 2026.

Thank you for contributing to this research!

Infrared thermography is a promising technology for food authenticity testing.  It is non-contact, high speed, and can detect structural and compositional heterogeneities through thermal patterns.

This review (purchase required, USD $31.50) explains the principles of the technique and potential for food fraud detection.  It compares and contrasts it with other techniques, including spectroscopy, electronic sensing systems (nose, tongue, and eye), as well as hyperspectral imaging,

The authors conclude that infrared thermography provides a distinct methodological paradigm, offering a novel and non-destructive avenue for food authenticity monitoring. Findings point to the promising achievements of thermography in detecting adulterants, while, its systematic advantages and methodological limitations remain insufficiently codified. Transitioning from laboratory-scale success to industrial implementation faces critical hurdles, including the dynamic nature of food fraud, the lack of standardized thermal stimulation frameworks, and the necessity of integrating thermography with sophisticated AI algorithms for latent pattern analysis. Future advancements must focus on developing integrated, scalable, and robust approaches to bridge the gap between laboratory precision and industrial efficiency requirements.

In this study (open access) the researchers developed and validated  HPLC and  LC-MS method for curcumin in turmeric and coumarin in cinnamon .  They used the methods to test approximately 150 samples of each; a mix of market samples taken at Rotterdam port and samples sourced from the country of origin.  They tested for statistical outliers as a flag for potential fraud.

 For turmeric, they report that just under 5% were flagged as significant outliers. These were characterized by curcumin concentrations drastically lower than the population mean. Specifically, their values fell below 15% of the mean value and below 20% of the standard deviation of the entire dataset. All outliers were samples collected at Rotterdam port.  The authors conclude that this low curcumin content provides strong evidence of adulteration with non-curcuminoid fillers (such as starch, chalk, or other cheaper turmeic varieties) or, less likely, spice material of exceptionally low quality.

 For cinnamon (47 declared C. verum samples) three samples were found to be significant outliers. These outliers had coumarin concentrations which were orders of magnitude higher than the group's mean and are far more consistent with the coumarin profile of cassia varieties. This strongly indicates that these three samples, despite being labeled as C. verum, were adulterated with cheaper, high-coumarin cassia species. This finding translates to a non-compliance rate of 6.4% within the subset of samples declared as authentic Sri Lankan cinnamon.

Photo by iKshana Productions on Unsplash

This critical review of detection methods and global surveillance data ((GBP £48 purchase required)  reveals persistent contamination of chilli-based products and edible oils, with marked geographic heterogeneity in prevalence rates and concentration ranges spanning seven orders of magnitude – from trace-level cross-contamination to gross adulteration at g/kg levels. Notably, the detection of Sudan dye variants beyond the classical Sudan I–IV series, including Sudan Red B, Sudan Yellow, and Sudan Black B, indicates evolving adulterant selection by fraudsters responding to enhanced regulatory surveillance.

The review identifies critical knowledge gaps and proposes future directions encompassing expanded analytical target panels, enhanced non-targeted screening capabilities, and field-deployable technologies to strengthen food supply chain integrity.

Photo by IKRAM ULLAH on Unsplash

This study (purchase required, $USD 31.50) evaluated Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric analysis as a rapid screening tool for the discrimination of sugar products differing in botanical origin and technological background.

A model was trained using white brown and white cane sugars, sourced direct from manufacturers.  Spectra were collected from sugar crystals in reflectance mode and from sugar solutions in transmission mode, and the obtained data were processed using distance match and discriminant analysis models.  The authors report successful discrimination between beet and cane.

A further model was trained with brown-type cane sugars classified as cane gold sugars (CGS), cane brown sugars (CBS), and cane raw sugars (CRS)..  The authors report that these brown-type cane sugars showed markedly higher heterogeneity, with CBS representing the least distinct class and displaying variable similarity to CRS or CGS. Transmission spectra improved CBS class definition, whereas reflectance spectra better reflected relationships associated with the production route.

The authors conclude that  FT-NIR spectroscopy supported by chemometrics is a promising rapid approach to assess sugar authenticity and to assess the quality of the brown-type cane sugar classification.

Photo by Kairi Kaljo on Unsplash

The main purpose of this study (open access) was to map the chemical aroma profiles of different species of oregano, plus different adulterated blends, using gas chromatography with ion mobility mass spectrometry (GC-IM-MS).  The study does, however, report two distinct adulteration markers which could be detected by more routine chromatographic techniques.

In general, the authors report that aroma profiles of adulterated blends are highly complex with an absence of distinct markers for individual species.  The exception, however, was isobutyl isobutyrate (as a marker for myrtle) and piperityl acetate (as a marker for marjoram)

Photo by Anne Nygård on Unsplash

The Food Standards Agency (FSA) and Food Standards Scotland (FSS) have published new guidance from the FSA’s Cell-Cultivated Products Sandbox, covering regulatory authorisation, growth media and toxicology assessment.

As cell-cultivated foods develop, questions around product identity, composition, traceability and verification will be important considerations.

FAN has previously highlighted FSA research examining analytical methods for cell-cultivated products. The latest guidance forms part of the wider programme of work to support businesses developing these products and preparing applications for market authorisation.

Read the new guidance and access further resources about novel foods and emerging technologies that require market authorisation through the FSA’s Innovative Foods Guidance Hub.

Algae are growing in popularity as an alternative protein source. Because algae-based foods undergo drying, grinding, and extraction during processing, morphological identification of raw materials becomes almost impossible, increasing the risk of substitution with lower priced species and the unintended incorporation of allergenic species.

In this study (purchase required, $USD 31.50) the authors developed single polymerase chain reaction (PCR) and multiplex PCR assays for the authentication of six edible algal species (Pyropia spp., Saccharina japonica, Undaria pinnatifida, Ecklonia stolonifera, Chlorella spp., and Arthrospira platensis). Species-specific primers were designed based on multiple sequence alignment, targeting the rbcL, nad5, and cox1 genes.

They report that specificity and sensitivity were confirmed by capillary electrophoresis, achieving detection limits of 0.1 pg for single PCR and 1 pg for multiplex PCR. Application to commercial algae-based processed food products further confirmed the feasibility of the developed assays in real food matrices.

Photo by Supliful - Supplements On Demand on Unsplash

In this study (open access), a UHPLC-QToF-MS method was used to quantify 15 desulfoglucosinolates (GSLs) in Brassica vegetables and pungent species.  The authors modelled a GSL profile for each species.  They report that authenticated plants displayed distinct group–specific profiles. Kale, broccoli, cauliflower, and Brussels sprouts contained higher levels of indole GSLs (glucobrassicin, neo-glucobrassicin) along with glucoraphanin and glucoiberin, whereas turnip, horseradish, wasabi, and watercress were characterised by elevated sinigrin, gluconasturtiin, and total aromatic GSLs

They then used the same method to test 69 dietary supplements, purporting to be brassica-based sources of GSLs, which were purchased on the US retail market.. They do not discuss how LC-MS matrix effects might impact the transferability of the model from fresh vegetables to supplements.  Irrespective of this, they report that nominally-similar commercial supplements exhibited substantial compositional variability, with some differing by many orders of magnitude in their GSL content and patterns.

They conclude that this study has revealed group-associated patterns in authenticated materials and highlighted the compositional heterogeneity of commercial products. Many current supplements do not reliably reflect authentic GSL profiles, underscoring challenges for product standardisation, authenticity evaluation, and dietary intake assessment.

Photo by Anna Evans on Unsplash

The adulteration of ground cinnamon (particularly the substitution or dilution of “true” cinnamon with cheaper species such as cassia bark) has been high on many vulnerability risk registers for the past few years.  This has driven the development of classification models (“chemometrics”) built from Machine Learning trained on spectral signatures from reference samples of pure and adulterated cinnamons.  Most of these models are built on Infra Red (IR) or Near Infra Red (NIR) scanners.

This study (purchase required, $USD 31.50) reports the development of a classification model using UV-visible spectrometry.  Whilst generating more signal noise than IR or NIR, UV-vis is cheaper and does not require the same control of environmental temperature and humidity to avoid spectral fluctuations and baseline drift.

The model was trained on two predominant species: C. verum (n = 51) and C. cassia (n = 64). All C. verum samples were harvested from Sri Lanka, whereas C. cassia samples were collected from Guangdong Province (n = 34) and Guangxi Zhuang Autonomous Region ( n = 30) of China.

The authors report that their PLS-DA species discriminant model correctly classified all the samples in the training, internal and external prediction sets..

Photo by Angelo Pantazis on Unsplash

This study (open access) aimed to develop a non-invasive computer-vision method based on deep learning to detect water adulteration in orange juice.  The model was trained on three juices (freshly squeezed juice, juice from concentrate, and orange nectar ) with water-adulteration levels ranging from 1% to 15%

The authors repeated the study with different shutter-speed-based exposure conditions.  Training data were analyzed using ResNet50 convolutional neural networks. The best classification accuracy (88%) was found with images acquired at 1/250 s

The model was then evaluated using 240 independently prepared and blindly coded samples obtained from subsequent purchases of the same three commercial products. This independent blind validation achieved a 24-class accuracy of 86.7%. Misclassifications occurred predominantly between adjacent or closely related water-adulteration levels within the same juice product. The authors report that when the independent-validation predictions were collapsed into a binary pure-versus-adulterated screening task, the model achieved 100.0% sensitivity, 93.3% specificity, 99.2% accuracy, and 96.7% balanced accuracy.

They conclude that the method is feasible as a rapid pre-screening tool for detecting visible-image patterns associated with controlled water dilution when there is no need to discriminate between different levels of adulteration. The independent validation provides evidence of transferability to newly prepared samples from subsequent purchases of the same products. However, further validation across additional brands, production batches, orange origins, seasons, and acquisition environments is required before broader applicability can be established.  

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The Food Standards Agency have published their incidents prevention report for September here.  You can sign up to receive these free reports direct to your e-mail by using the same link.

These short monthly bulletins cover all food safety and food authenticity issues.  They include basic mitigation steps that all food businesses should take.

Issues highlighted in September include undeclared food colours and compliance failures at import and border control.  These are themes picked up in all incident collation tools signposted on FAN's "Incident Trends" page, which signposts a range of different reports that give a variety of different insight from different data sources.

This monthly highlights report has been produced for FAN members by one of our Partner organisations, iComplai.

The report is an AI-generated prioritised digest.  It is blended from online regulatory and media reports of food fraud incidents and suspicions, giving a summary of each selected issue and suggested mitigation steps.

iComplai are one of a number of commercial providers of digital horizon scanning tools, many of whom are signposted by FAN on our Incident Trends hub.  We do not endorse any specific company.  We are grateful to iComplai for providing this free digest which we hope you find useful.

If you know of a tool which would be beneficial to signpost to our membership, or if you are interested in becoming a FAN Partner, then please get in touch.

It is rare for scientific publications to focus objectives that were unachieved, or results that failed.  This is unfortunate, as such studies can offer valuable insight to other researchers.

In this paper (USD $27 purchase required) the authors report a near-infrared (NIR) spectroscopy-based partial least squares regression (PLS) method for distinguishing and quantifying soluble coffee and soluble chicory percentages in a solid mixture. They also attempted a NIR-based PLS method to distinguish soluble Arabica coffee, soluble Robusta coffee and soluble chicory in one shot; however, they found that NIR with PLS is unable to differentiate soluble Robusta and soluble Arabica when these three components are in a mixture.  They contrasted this with ¹H NMR and ¹³C NMR spectroscopy, which could differentiate.

They conclude that their results show the limitations of NIR spectroscopy in combination with PLS to distinguish the type of soluble coffee (soluble Arabica and soluble Robusta) when mixed with soluble chicory.

Photo by Archer Allstars on Unsplash