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

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

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

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

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

Photo by Syed F Hashemi on Unsplash

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

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

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

Image – from paper 

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

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

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

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

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

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

The recent proliferation of tariffs on internationally-traded food has led to an increased focus on origin claims.  False origin claims can enable tariff avoidance.  On-pack claims also resonate strongly with consumers in some countries as consumers push back against the politics of tariffs and they increasingly champion home-produced food.  One of the clearest examples of this trend is in Canada.

There has been a renewed enforcement focus on “home-produced” claims in Canada.  A blog by legal firm Blake Cassels & Graydon gives a good summary of the rules and links to the primary legislation

• “Product of Canada” / “Canadian”: Vvirtually all of the ingredients, processing and labour must be Canadian. In practice, less than 2% of the ingredients can be sourced from outside of the country.  Products labelled as “100% Canadian” must be entirely made in Canada, from ingredients to processing and labour. Certain types of food, such as meat, fish and dairy, are subject to specific requirements to be labelled as a “Product of Canada.”
• “Made in Canada”: Products must have undergone their last substantial transformation in Canada. Claims must always be accompanied by a qualifying statement clarifying the origin of the ingredients, such as “Made in Canada with domestic and imported ingredients.” Even products made in Canada that contain no domestically produced ingredients can be labelled as “Made in Canada from imported ingredients” where the last substantial transformation occurs in Canada.
• “Local”: For food products to be considered “local”, the CFIA guidelines state that these must either be sold in the same province where they are produced, or within 50 kilometres if sold across provincial borders.
• Use of the Maple Leaf: Use of the stylized maple leaf from the National Flag of Canada (the 11-point maple leaf) may only be done with the permission of the Department of Canadian Heritage. The use of maple leaves other than the 11-point maple leaf is permitted on food packaging; however, depending on the context, this may be construed as a “Product of Canada” claim.
• Other Claims: More specific claims that describe the Canadian value added may be used, such as “packaged in Canada” or “brewed in Canada,” where the above claims cannot be made.

Photo by Nong on Unsplash

Classification models for food authenticity tests can - in principle -  be based on any analytical technique that collects multi-variate data.  In the case of spectrometric data (such as NIR or multi-spectral imaging) the equipment can be relatively cheap.  For collecting chemical data, researchers often use high-end equipment such as advanced LC-MS or GC-MS

This proof-of-concept study (purchase required) is a rare example of building a classification model using a cheaper test (HPLC with fluorescence detection) to measure a chemical parameter.  The authors prepared cold-pressed walnut and pumpkin seed oils adulterated with 0 – 50% of sunflower oil.  They developed a classification model based on the concentrations of the four tocopherols (α-, β-, γ-, and δ-).  They report that the model was capable of discriminating sunflower oil adulteration down to 2-3%.

This short communication describes a simple chemical-based colour test for detecting fake saffron.  No details are provided in the abstract about the basis of the test, or whether it discriminates botanically-related adulterants such as safflower.  We have not purchased the full article in order to review it.

The abstract describes it as a panel of 4 simple chemical tests which take around 30 minutes to perform and can be read by eye from a colour card.  The attached image is from a correction (open access) to the original publication (which remains behind a paywall).

N-glycans are a class of biological compound that are chemically bound to proteins.  They are generally stable to food processing and heating.

In this paper (open access) the authors investigated how N-glycans varied amongst three different fish species; red snapper, barramundi (Asian Sea Bass) and the potentially cheaper adulterant, tilapia.  They measured N-glycan profiles using liquid chromatography with ion mobility and mass spectrometry (LC-IM-MS).  They identified four N-glycan structures containing different degrees of O-acetylated sialic acids (O-Ac-Sias) as species-specific markers and found clear clustering based on their percentage abundance.  This enabled a multi-class species classification model.  They found that this clustering and classification model remained valid even after the fish had been cooked and processed.

They conclude that this approach could complement DNA testing when looking at heavily processed or manufactured food.

One approach to verifying that undeclared GMOs in raw materials are compliant with legal thresholds is to test for GMOs in the manufactured product and then extrapolate the GMO content in the ingredient using the pro-rata recipe proportions.  This is an attractive approach for enforcement testing which does not always have access to the raw materials.

The authors of this paper (purchase required) investigated the inherent bias in this approach.  They prepared in-house model processed foods (heat-treated soybeans) containing GM events and then tested them using a GMO quantification method incorporating taxon-specific real-time PCR with longer amplicons They observed that the extrapolated GMO content increased with the length of the amplicon used in the taxon-specific PCR assay. When a longer amplicon was deliberately employed, the GMO content calculated for the processed food always exceeded that expected by pro-rata calculation from the raw material.

They conclude that this finding can be used to advantage. The use of longer amplicons in taxon-specific PCR can lead to an overestimation of GMO content at the raw material stage based on the measurements from processed foods. If the overestimated value remains below the labelling threshold, the appropriateness of GMO labelling can still be confirmed. The proposed method offers a simplified and practical screening approach for use in routine inspections.

The EU Joint Research Centre (JRC) have now published their monthly collation of fraud media reports for July 2025 and September 2025 (these collations are published retrospectively, and August’s report was published in advance of July’s).  The full index of reports can be found here

These new reports have also been added to the JRC database that underpins a searchable front-end for media reports of food fraud incidents.  It allows filtering by commodity, country, fraud type and other key criteria.

The JRC collation is 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.  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, which gives very high level smoothed data.

This project, funded by the UK Food Standards Agency and conducted by Fera Science Limited, aimed to identify and review current and emerging methods to detect adulteration in edible oils, focusing on issues relevant to UK consumers and the economy. The study involved a comprehensive literature review, stakeholder engagement, and consultation of proficiency testing and Fera Science’s HorizonScan™ data to assess future risks.

The review covered rapid screening methods, mainly spectroscopic, and confirmatory techniques such as fatty acid and triacylglycerol profiling.

The authors report that many approaches are still under development and lack thorough validation. A key challenge is the increasing sophistication of fraud within the supply chain, with businesses often relying on proprietary protocols, which hampers standardisation.

The report recommends addressing the lack of standardisation and regulation in edible oil testing, investment in widespread testing and point-of-use methods, and developing confirmatory techniques. Spectroscopy methods like Fourier Transform Infrared and Raman show promise for rapid, low-cost testing, while triacylglycerol analysis could serve as a confirmatory method for laboratories. Authentic certified reference materials are also essential to support quality control and encourage proficiency testing uptake.

A link and signpost to this report has been added to FAN’s Research Reports index.

This paper (open access) reports the development of a hand-held device that can detect methanol addition in alcoholic spirits by scanning directly through the unopened glass bottle.  Such a device has obvious benefit to enforcement inspectors at ports and retail outlets. The paper also describes the operating principle of the device, including all the modifications made by the authors and why they were needed, in clear language understandable to non-specialists.

For an overview of Raman spectroscopy see FAN’s method explainers. 

The authors of this paper describe the three main challenges to overcome in order to make a practical Raman Spectroscopy scanner which can read through glass bottles; 1) the spectroscopic signal from the container masking the sample signal; 2) the intrinsic fluorescence signal of the sample that can overwhelm the weaker Raman peaks; and 3) the opacity and colour of the glass attenuating the signal both entering and exiting the container.

They use of a combination of approaches to circumvent these challenges.  They use an axicon lens to generate a conical excitation beam, which effectively circumvents the bottle signal.  They also use a relatively long-wavelength excitation combined with wavelength modulation (Wavelength Modulated Raman Sprectroscopy, WMRS) to minimise and then offset any natural fluorescence from components in the drink. 

To quantify, they compared the signals attributable to methanol with those from ethanol as an internal standard.  They used the nominal %ABV of ethanol for this calculation, on the assumption that adulterated spirits would have a lower than declared ethanol content and therefore they would over-estimate the methanol content (i.e. erring on the side of caution, for a screening test).

They report the successful detection of methanol adulteration at well below the 2% level that causes acute serious health concerns.  The method has been validated on one real spirit sample but has yet to be tested for robustness over a range of samples.

Image from the publication

Here is our regular monthly graphic from the EC Reports of Agri-Food Fraud Suspicions, showing a rolling 3-month trend.  These EU reports are a useful tool for estimating fraud incidents, signposted on FAN’s Reports page.  They can be found here.

Our graphical analysis contains some subjectivity in the interpretation of the report data. In order to show consistent trends 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 intended only to give a high-level overview. 

The absolute count of incidents are creeping up a little but are generally fairly steady.  The split of incidents between different categorisations is also fairly consistent over time, with a significant number relating 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.

Another consistent theme is underweight premium ingredient content in processed food, generally (but not always) the meat or seafood content.  Often this is associated with excess glaze or water in frozen food. 

Although we do not count unapproved (but declared on-pack) additives in these graphs, it is noteworthy that there has been a consistent rise in recent months in the number of regulatory siezures of food (often confectionary) imported into the EU that contains titanium dioxide (an additive permitted in many regions of the world, but now banned in the EU).  This increased enforcement activity may account for some of the general insight reports, based on analysis ot EU Agri-Food Suspicions, that "food fraud incidents are increasing". 

The EC Monthly reports are only one source of information.  A comparison of the many different information sources now available, and the complementary insight that can be gained from using a variety of information sources, is given in an earlier blog this year.

Species identification in canned tuna is much more challenging than for processed fish in most cooked foods.  This is because the DNA is substantively degraded during the canning process.

In this paper (purchase required) the authors present a protocol to increase concentration and purity of DNA extracted from canned samples. The experiment mainly consists of: (1) drying the canned tissue in paper filter, (2) washing it with a PBS solution, (3) store in ethanol 96 % at −20°C, and (4) perform DNA extraction.

They report that the pre-treated samples showed an increase of both DNA concentration and purity indicating that some of the inhibiting molecules were successfully removed. These differences between the two treatments were statistically significant (p < 0.01). At the amplification level, the pre-treatment allowed the recovery of complete fragments of the barcode region COX1 with approximately 650 base pairs.

The authors recommend their approach should be used in combination with other methodologie such as mini-barcoding.

Photo by Grooveland Designs on Unsplash

This research (purchase required) set out to design a rapid point-of-use test to detect cassava starch as an adulterant in higher-value starches.  The test method used Proofreading enzyme-mediated probe cleavage (Proofman) coupled with ladder-shape melting temperature isothermal amplification (LMTIA). The optimal detection temperature of this Proofman-LMTIA method was 62℃ and the reaction could be finished within 20 minutes with a detection sensitivity of 100 pg/μL of genomic cassava DNA. Nine different species were collected and verified for the specificity of cassava ITS primers and probes. The detection limit of cassava DNA derived from artificially premixed starch powders was 1 % (w/w).

As a proof-of-concept, the researchers used their Proofman-LMTIA assay to test 33 commercial products from the food and medicine sectors.  They report that – based on their assay -  16 samples contained undeclared cassava components.

Photo by Daniel Dan on Unsplash

A Belgian local newspaper has conducted a survey of 32 samples of branded Extra Virgin Olive Oil sold through major retail outlets including supermarkets such as Carrefour.  The brands include internationally-recognised household names.  The newspaper commissioned testing at expert and Olive Oil Council approved laboratories.

Results are summarised in this press article.  20 of the 32 “Extra Virgin” samples failed to meet the specification standard of Extra Virgin Olive Oil (EVOO).  Although oil degradation over time could be a hypothesis in some cases, in other cases the analytical weight of evidence was that the oil was Lampeter (a lower grade of olive oil) or – in one case – adulterated with sunflower oil.

Investigations are continuing into suspected certification fraud in the upstream supply chain.

n this paper (open access) the authors used of solid-phase microextraction (SPME)-gas chromatography-time-of-flight mass spectrometry (GC/Q-ToF-MS) combined with chemometrics to detect key differences between adulterated and non-adulterated ground roast coffee. They drilled into these differences and found two potential chemical markers for common adulterants.

They compared the aroma profiles of ground roasted coffee with some commonly used adulterants (ground roasted barley, corn and soybean). The SPME fibre collected and concentrated the headspace volatiles. Non-adulterated and adulterated samples were distinguished after applying some chemometric tools (principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA)) on the obtained chromatographic data. Two volatile compounds (1H-imidazole-4-methanol and benzene-2-(1,3-butadienyl)-1,3,5-trimethyl) were identified as potential markers for the determination of adulterants (ground roasted barley, corn or soybean) in ground roasted coffee (p-value cut-off<0.001 and fold change (FC) cut-off>10). Also, 2-furanmethanol and 2-formyl-1-methylprrrole were found as marker candidates for roasted coffee powder.

The authors tested this approach and were able to detect selected herbal adulterants (5% w/w) found in ground coffee.

A Belgian local newspaper has conducted a survey of 32 samples of branded Extra Virgin Olive Oil sold through major retail outlets iand supermarkets.  The brands include internationally-recognised household names.  The newspaper commissioned testing at expert laboratories.

Results are summarised in this press article.  20 of the 32 “Extra Virgin” samples failed to meet the specification standard of Extra Virgin Olive Oil (EVOO).  Although oil degradation over time could be a hypothesis in some cases, in other cases the analytical weight of evidence was that the oil was Lampeter (a lower grade of olive oil) or – in one case – adulterated with sunflower oil.

Investigations are continuing into suspected certification fraud in the upstream supply chain.

In this paper (open access) the authors  propose two novel metrics—the Geographical Differentiation Index (GDI) and Environmental Heritability Index (EHI)—to quantify spatial variation in fatty acids and their environmental drivers. These methodologies are derived from classical genetic theory - traditional heritability quantifies the contribution of genes to traits by calculating the ratio of additive genetic variance to phenotypic variance.  The authors applied this same methodology to the fatty acid profile of oils, in order to diagnose their geographic origin.

They systematically investigated the fatty acid profiles of four main oil-rich crops (olive, camellia, walnut, and peony seed) and revealed that fatty acid distributions follow elevation- and latitude-dependent patterns, with peony seed oils showing the strongest latitudinal sensitivity. Key fatty acids like stearic acid (C18:0) and linoleic acid (C18:2) correlated significantly with geographic factors globally, while the biomass of certain specific fatty acids varies significantly in high-altitude/low-latitude regions. They conclude that their findings establish specific fatty acid signatures as a robust tool for geographic authentication. They provide a chemical rationale for classification models, based on Machine Learning, that measure differences in fatty acid profiles.

Photo by Reinis Bruzitis on Unsplash

In this paper (open access) the authors built a classification model to discriminate premium from non-premium grades of vacuum packed sliced Iberian ham.  They used a Near Infrared Scanner reading directly through the packaging.

The model was constructed from a database of 312 purchased from retail on a weekly basis over a two-year period (2023–2024). The samples were obtained as vacuum-packed slices from a range of commercial brands and industrial producers, in a manner analogous to typical consumer purchasing behaviour in supermarkets, encompassing the four official commercial categories: black seal, red seal, green seal, and white seal. These samples were preliminarily grouped into premium (201 samples) and non-premium (111 samples) categories based on their commercial labelling.  The researchers further verified the label categorisation by free fatty acid analysis.

The classification was based on the quality and sensory differences that appear in products derived from animals fed with natural resources (acorn and grass) in extensive systems (premium category), as opposed to those from animals fed with compound feeds (non-premium category}

The authors report 100 % sensitivity, specificity, and non-error rate (NER) for both of two different NIR sensors tested during external validation.  They report that a lower cost miniaturised model performed less well, with 100 % sensitivity but 85.71 % specificity and 94.74 % NER, limiting its applicability for samples near the classification threshold.

They conclude that their results confirm the suitability of NIRS technology for rapid and non-destructive in situ classification of high-value foods, including pre-sliced Iberian ham.