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In regions where clarified dairy fat (ghee) is a staple food, the potential for adulteration with palm oil (solid vegetable fat) is a continual concern.  Analytical differentiation can be difficult.

In this study (open access) the authors experimentally compared and contrasted a range of analytical techniques that have been proposed for identifying palm oil mixed into ghee at levels down to 5 – 10% (generally considered the lower limit for economically motivated adulteration). 

They concluded that  both Butyro refractometer readings and iodine value analysis were not as efficient in detecting adulteration at lower level. Reichert-Meissl value analysis alone was not able to draw a conclusion regarding the purity of ghee. However, the Kirshner value analysis could be an effective parameter to detect adulteration of palm oil in ghee down to 5%. 2,2-Diphenyl-1-picrylhydrazy and ferric chloride-based chromogenic tests were very effective to detect the presence of palm oil in milk fat or ghee rapidly; thus, these tests could be used in field conditions. The use of triglyceride analysis (S-value) and plant sterol detection offered a comprehensive laboratory-based confirmation to detect palm oil adulteration in ghee at 5% levels.

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Incidence data – Seafood mislabelling in Asia

12264338893?profile=RESIZE_180x180This review (open access, pre-publication) collates published surveys of seafood mislabelling in Asia (the time period reviewed is not stated).  The authors list results along with the testing methodologies used, listing separate results for China, India, South Korea, Indonesia, Malaysia, Philippines, Singapore and Taiwan.  Some surveys, particularly of specialist fish powders or premium smoked fish products, reported mislabelling rates of 70 or 80%.  More typical mislabelling rates for fillets sold as a single species were around 7 or 8%.

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JRC Fraud Media Reports Collation - January 2025

The Joint Research Centre of the European Commission have published their monthly collation of food fraud reports for January 2025 here, following on quickly from the final 2024 collations which were highlighted in our blog on 5th February..  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.

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The source for the JRC collation are global media reports, and these always gives a slightly different picture than collating official reports.  Best practice in horizonscanning is to look at multiple collations of fraud incidents/suspicions along with their commentaries, both official and media, and make an intelligent assessment of their complementary scopes and limitations when drawing insight.  FAN's annual aggregated report gives a high-level annual overview for 2024 from official reports.

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13458705693?profile=RESIZE_400xIn this paper (open access) the authors trained a Machine Learning model to differentiate between Top, Bottom and Spontaneous fermented bottled beers.  Data were collected using a non-invasive hand held NIR scanner pointed directly through the unopened bottle using a customised foam attachment.  The model was trained on 25 samples of major brands purchased online, rather than reference samples of verified traceability, but the training samples covered a wide range of beer types from stouts to light ales, and a wide range of bottle types and colours.

The authors report good classification based on fermentation method.  They consider that evidence of a wrong fermentation method could be one quick and easy check that could flag counterfeits.  They also correlated the NIR data with sensory panel assessments and SPME-GC-MS data and concluded that non-invasive NIR has the potential to classify beers based on their aroma profiles.

Image from the paper

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13458684075?profile=RESIZE_400xThere has been a lot posted recently about honey authenticity and test methods.  This blog from the FSA pulls it all together in one concise and systematic page.  It includes

  • Honey sampling guidelines
  • The weight of evidence approach to interpreting test results
  • The UK AMWG review of the EU “From the Hives” report
  • New testing methods developed under FSA-funded research
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13456741690?profile=RESIZE_400xThe results of the EC 2021-2022 honey sampling and analysis co-ordinated action, following the  From the Hives report, were concerning.  This 2023 report concluded that all 10 of the sampled honeys imported from the UK were “suspicious”. 

This finding prompted further investigation by the UK Department of Environment, Food and Rural Affairs (Defra).

Defra have now published an independent expert review into the analytical methods used in the survey.  There is a lot of technical content in the review.  It re-emphasises that no single honey authenticity test is likely to be definitive, and that a weight of evidence approach should be used with some tests being weighted higher than others.  When the total weight of evidence is not strong then phrasing such as “warrants further investigation” would be a fairer conclusion than “suspicious”.

One specific learning from the review is that laboratories must take care with the selection of authenticity markers, depending on the analytical question being asked.  The example given is oligosaccharides.  Some of these markers are known to vary between honey that has had moisture mechanically removed compared to honey that has not.  Moisture removal may be a production necessity (in humid climates where honey will not evaporate naturally) or a commercial choice to speed the harvest cycle (as is commonly used in China).  Moisture-removed honey is common within UK blends of Chinese origin honeys  but is not permitted in some EU countries.  Thus a test based on oligosaccharide markers could differentiate UK honey from EU for reasons that are already understood.  It might not provide any new insight, for example, on sugar or syrup adulteration.

Photo by Art Rachen on Unsplash

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FAN 2024 Global Food Fraud Report

13454783085?profile=RESIZE_584xThis report provides a summary of global food fraud reports in 2024 from three of the world’s leading commercial food fraud incident collation tools: FoodChain ID Food Fraud Database, HorizonScan and Safety HUD.

Food fraud reports published by global regulatory agencies during 2024 do not provide evidence of a consistent, significant trend during 2024, and in fact, are like those seen in 2023. The activity associated with official food fraud and food safety reports remained fairly consistent across the four quarters of 2024.

The top three commodities with the most food fraud reports varies depending on the source of reports and the tool used:

  • Using official reports only, Beverages’, ‘Processed foods’ and ‘Milk & diary products’.
  • Using official, media & peer reviewed publication reports, ‘Seafood’, ‘Honey’ and ‘Dairy’.

Although ‘milk & dairy products’ is the only common commodity in the top three foods with the greatest number of reports above, seven commodities are common in the top ten foods from both the average of official reports only and data from Food Chain ID. In fact, many of these commodities are also common in Food Chain ID’s data over a ten-year period, demonstrating that these foods are most reported as being fraudulent, year on year.

It should be noted that the featuring of commodities in this report does not necessarily mean that these are the world’s most fraudulent foods, as many of these commodities are often the subject of targeted sampling and analysis by regulators and inter-agency operations conducted by Europol, Interpol etc… Other factors can also have an influence, for example, the number of peer reviewed publications on commodity-specific authenticity issues.

 The number of official food fraud reports published in 2024, by an average of forty-seven sources, is very low at only ~8% of food safety reports. There were no new sources of food fraud data reported by regulatory agencies in 2024. If analysis of official food fraud reports is to be meaningful, more regulatory agencies should publish their data in an open access format.

Botanical and animal origin fraud were the most reported type of food fraud in 2024, followed by use of non-food substance and dilution. Of these frauds, using non-food substances in food has the potential to do the most harm as seen in the Sudan dyes in chilli powder and melamine in infant formula incidents.

This report is the second annual report to be produced for this Partner project.

Platinum and Gold FAN Partners receive quarterly dashboard reports at the end of each quarter. Please contact FAN, if you are interested in receiving these reports.

Commercial food fraud incident collation tools are not all the same; there are differences in purpose, how data are classified, collected and curated. Before choosing a tool, it is important to understand what it does so that the most appropriate tool for the intended purpose is selected.

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12212937491?profile=RESIZE_400xThis peer-reviewed pre-print (open access) reports a classification model for different Greek olive oil cultivars using combined data from two analytical techniques: volatile component analysis (6 marker compounds) by solid phase microextraction – gas chromatography (SPME-GC-MS) and spectral analysis by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR)

The model was built to differentiate Greek oils from 3 cultivars: Koroneiki, Megaritiki and Amfissis.  The reference database was constructed from samples collected over 3 harvest seasons.  The authors report that application of the supervised methods of linear and quadratic discriminant cross-validation analysis, based on volatile component data, provided a correct classification score of 97.4 and 100.0%, respectively. The corresponding statistical analyses were used in the mid-infrared spectra where the 96.1% of samples were discriminated correctly.

The authors conclude that ATR-FTIR and SPME-GC-MS, in conjunction with the appropriate feature selection algorithm and classification methods, are powerful tools for the authentication of Greek olive oil. They consider that the proposed methodology could be used in industrial settings for the determination of Greek olive oil botanical origin.

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13451272699?profile=RESIZE_400xMembers of the Global Alliance on Food Crime (GAFC) had a very busy week when they met up in Edinburgh during December last year (2024).

Representatives from the Australian Department of Agriculture, Fisheries and Forestry (DAFF), the Canadian Food Inspection Agency(CFIA) and USA FDA made the long journey to Scotland to join up with UK representatives, Food Standards Scotland’s Scottish Food Crime and Incidents Unit and FSA’s National Food Crime Unit, to discuss all things food crime.

As a bonus, all those attending were able to take part in Opson/EMPACT event, which was also taking place in Edinburgh at the same time. Opson is an annual operation, led by Europol, which targets fake and unsafe food and beverages.  This event was attended by law enforcement and regulators from across Europe and was a fantastic opportunity for GAFC members to interact with their European counterparts and discuss common issues. There was also an opportunity to formally showcase GAFC work and future plans in the form of a presentation given by GAFC members Murray from DAFF and Jodi from the CFIA.

In terms of the GAFC meeting, a significant number of matters were discussed and an action plan, designed to deliver the GAFC strategic objectives and developing processes that will allow joint activity to be undertaken by member countries, was agreed.  These discussions were very positive and will see the GFAC’s work progress over the next 12 months. 

 In addition, The Food Authenticity Network’s (FAN) very own Executive Director, Selvarani Elahi, addressed the group on the work of FAN and gave an overview of the new the European Food Fraud Community of Practice (EFF-CoP) project that FAN is a partner of. This three-year Horizon Europe project aims to revolutionise the combat against food fraud and enhance transparency in food supply chains. Further information can be found on the FAN EFF-CoP page or its LinkedIn page.  All GFAC members agreed to consider partnering with FAN and see how they could assist FAN in extending its reach in each member country.

The group also took some time to meet with representatives from the United Nations Industrial Development Organisation (UNIDO) to find out more about the work they are doing globally and how the GAFC could assist some of this work from a food crime perspective moving forward. Further meetings will take place early in 2025 to map out exactly how the GAFC can support UNIDO’s work in this regard.

GAFC members will take the time to meet regularly during 2025, working collaboratively to deliver the GAFC action plan and consider options for additional joint activity on common issues. 

Regular updates will be provided over the coming months, however, further information on the work of the GAFC can be obtained by emailing the Chair of the GAFC, Ron McNaughton at ron.mcnaughton@fss.scot.

The December update has also been added to the 'Global Alliance on Food Crime' page on this website. Further information on the members of the GAFC will be added soon so watch that page...... 

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13450152482?profile=RESIZE_400xShrimp surimi-based products (SSPs) are composed of minced shrimp meat and are highly susceptible to fraudulent substitution by cheaper fish surimi.

This study (open access) employed a double-gene metabarcoding approach to authenticate SSPs sold in bulk (business-to-business) on Chinese e-commerce platforms. 16S rRNA and 12S rRNA genes were amplified and sequenced from 24 SSPs. Mislabeling was evaluated based on the correspondence between the ingredients (only those of animal origin) reported on the products’ labels and the molecular results.

The authors found that 21 of the 24 products were mislabeled. The replacement of Penaeus vannamei with other shrimp species was particularly noteworthy. In some samples the primary species detected in terms of sequence abundance were not shrimp but fish, pork, chicken, and cephalopods. The 12S rRNA sequencing results revealed that fish species like Gadus chalcogrammus, Evynnis tumifrons, and Priacanthus arenatus were added to some SSPs in significant proportions, with certain products relying on fish priced from “Low” to “High” levels to substitute higher-cost shrimp. Notably, many fish species in SSPs were highly vulnerable to fishing, raising sustainability concerns.

The authors conclude that the high mislabeling rate, as well as the detection of endangered fish species (Pangasianodon hypophthalmus), underscores significant quality control and supply chain integrity issues.

Photo by Fernando Andrade on Unsplash

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The Joint Research Centre of the European Commission have published their monthly collation of food fraud reports, combined for November and December 2024, here.  Thanks again to FAN member Bruno Sechet who has turned these into an infographic.  The original infographic (in much better resolution!), along with his commentary, is on Bruno's LinkedIn feed.

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The source for the JRC collation are global media reports, and these always gives a slightly different picture than collating official reports.  Best practice in horizonscanning is to look at multiple collations of fraud incidents/suspicions along with their commentaries, both official and media, and make an intelligent assessment of their complementary scopes and limitations when drawing insight.  FAN's annual aggregated report gives a high-level overview of food fraud incidents in 2023 as recorded in official reports. The 2024 version is in preparation and will be published on our website soon.   

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13444689466?profile=RESIZE_584xThe  aim of this proof-of-principle study (open access) was to design a universal DNA microarray (“DNA Chip”) to distinguish all edible fish species by comparing hybridization signal patterns from samples with patterns obtained from reference specimens.

The researchers designed a universal set of 96 DNA probes that cover all fish species of food interest.  These were narrowed down by virtual modelling experiments from a long-list of 28,000 candidates which they had generated experimentally. They also included 4 control probes (sequences not present in edible fish).  All probes were based on sequences from either 16S ribosomal RNA or cytochrome b.

DNA was isolated with either a CTAB method or with commercial DNA extraction kits. The gene markers cytb (approx. 464 bp) and 16S rDNA (approx. 600 bp) and an additional pUC57 vector DNA region (542 bp) were amplified in triplex PCRs. The DNA probes were spotted contactless using piezoelectric dispensing technology as 19 × 19 arrays. For hybridization of the generated PCR amplicons on the prepared microarrays the INTER-ARRAY Hybridization Kit was used according to manufacturer's specifications. The arrays were measured directly after staining and then processed using the INTER-VISION GENOTYPING 1.2.0 software.

The authors tested 86 fish fillets sourced from verified suppliers and were able to correctly identify all species by hierarchical clustering analysis of the results.  The entire process takes a few hours.  They conclude that the method is ready for further validation and ruggedness testing. More replicates and species should be analyzed to confirm current results. Likewise, the robustness of the DNA array should be determined, e. g. by using different thermocycler or users and laboratories.

Graphical abstract from the paper

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13443907282?profile=RESIZE_400xIn this paper (open access) two optical spectroscopic techniques,  Laser-Induced Breakdown Spectroscopy (LIBS) and UV-Vis-NIR absorption spectroscopy, are assessed for EVOO adulteration detection, using the same reference database of olive oil samples. In total, 184 samples were studied, including 40 EVOOs and 144 binary mixtures with pomace, soybean, corn, and sunflower oils, at various concentrations (ranging from 10 to 90% w/w). The reference class of “pure” EVOOs were limited to oils from a specific geographic region (either Crete, Lesvos, Kalamata or Achaia, with a different model built for each case).

The emission data from LIBS, related to the elemental composition of the samples, and the UV-Vis-NIR absorption spectra, related to the organic ingredients content, were analyzed, both separately and combined (i.e., fused), by Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs), and Logistic Regression (LR). In all cases, very highly predictive accuracies were achieved, attaining, in some cases, 100%.

The authors conclude that both techniques have the potential for efficient and accurate olive oil verification test protocols, with the LIBS technique being better suited as it can operate much faster.

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The Food Authenticity Network team, led by our Executive Director, Selvarani Elahi attended the kick-off meeting of the European Food fraud Community of Practice (EFF-CoP) project in Amsterdam.

Led by Professor Saskia van Ruth, the project brings together a community of scientists, regulators, small- and large-sized businesses, laboratories and other stakeholders to create a research and innovation ecosystem to enhance food authenticity and traceability.

The event was highly interactive including meeting charades, a cooking workshop to cook our dinner, lots of creative workshops and a flash mob dance on the streets of Amsterdam.

The Food Authenticity Network is very excited to be a Project Partner, leading Work Package 2 (Establishment of needs & developing a future research agenda that will address these needs).

We have created a page on FAN for EFF-CoP in which we’ll be updating on project progress.

Please visit this page, follow EFF-CoP and join us in the fight against food fraud!

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Introduction to databases for non-targeted, multianalyte analytical methods

This eSeminar was based on the Food Integrity Scientific Opinion on databases, and introduces the viewer to the subject of the design, structure, and limitations of databases for non-targeted multianalyte methods. Examples of relational databases will be provided, and common challenges discussed. Guidance and recommendations for troubleshooting are also provided along links to useful sources of additional information.

This e-seminar is intended for individuals currently working within the non-targeted food testing , the food industry and those involved with the UK official control system. The production of this e-seminar was co-funded by the UK Department for Environment, Food and Rural Affairs, Defra, the Food Standards Agency, Food Standards Scotland and the Department for Science Innovation and Technology via the Government Chemist, under the Joint Knowledge Transfer Framework for Food Standards and Food Safety Analysis.

A copy has also been added to our eSeminar Training Page.

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An Ode to Food Authenticity

Food fraud is a serious global issue that is inextricably linked to food safety and negatively impacts legitimate food business and consumers. By greater cross-sector, multi-disciplinary and multi-country collaboration, we can be better prepared to fight food fraud.

The FAN Team got together with our colleagues and collaborators to produce ‘An Ode to Food Authenticity’. We hope you enjoy it.

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12176971656?profile=RESIZE_400xThis paper (purchase required) reports the use of a portable optical sensor (Multi-Spectral Imaging) to build a classification model for detecting milk adulteration. This encompassed mixtures of milk from different species (cow, goat, and sheep), as well as dilution of cow’s milk with water. The study's scope also included milk with diverse heat treatments, fat content, and commercial brands.

The authors report that discriminant analyses provided reliable predictive models, with Accuracy and Cohen's Kappa values ranging between 0.80 and 1. In quantitative studies, the quantification of milk mixtures at a minimum percentage interval of 10% was detected with Mean Absolute Error (MAE) values between 0.14 and 0.05, and 0.03 for cow's milk adulterated with water at adulteration levels of 5%.

The authors conclude that the portability of these instruments adds a significant advantage by enabling on-site and real-time determination and quantification of milk adulteration.

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13431797661?profile=RESIZE_400xCocoa is high on many companies’ current risk radar for authenticity threats, due to recent supply pressures and price increases. Carob has legitimate uses as a cocoa replacement, and carob flour has been cited as a potential cocoa adulterant.

This paper (purchase required) reports the use of direct analysis in real time mass spectrometry (DART-MS) as a rapid laboratory-based authentication test with the potential for a portable device. Reference samples of cocoa powders, carob flours, and mixtures of the two were extracted with buffer and interrogated by DART-MS. The mass spectra were used to develop classification models. A principal component-linear discriminant analysis (PCA-LDA) model was used to discriminate between cocoa powder and cocoa powder amended with 15% carob flour. The accuracy using internal validation was 100%. Using an external validation dataset, the accuracy, precision, and recall were 96.0%, 94.8%, and 97.3%, respectively.

For a descriptor of DART-MS see FAN’s analytical method explainers.

 

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13420552478?profile=RESIZE_710xThe National Food Crime Unit (NFCU) of the UK Food Standards Agency has issued an AMBER Food Crime Alert on the Risk of Document Fraud in Laboratory Results.

WHY NFCU IS ISSUING THIS ALERT?
There has been an increase in the use of fraudulent laboratory results being seen in issues that have been investigated by the National Food Crime Unit.

As such, this alert is being issued so that any business within the food supply chain who use testing results as a means to assess food safety, compliance with legislative requirements or to approve the use of a product or supplier can be aware of the recommendations raised in this alert.

ACTION RECOMMENDED
If you have reason to believe that a test result you are being shown may be false, we recommend taking the following actions:

  1. Review the document carefully. Look for any errors in wording or layout, including differences between dates that appear on the certificates.
  2. Layout issues could include shadowing or misalignment around key data including dates, signatures or data values. 
  3. If reviewing the documentation on site, ask to see original emails or review the results directly through result portals (if available). Do not rely on second hand references to results such as excel spreadsheets - these may be used legitimately for companies to consolidate and present trends, but should not be accepted as an alternative to sample certificates.
  4. Consult the laboratory name on the certificate if in doubt, or report to the National Food Crime Unit at: Food Crime Confidential or by freephone on 0800 028 1180. For non UK mobiles or calls from overseas please use 0207 276 8787. 
  5. Where there are concerns that testing results are false or not authentic, consideration should be given whether this introduces a food safety concern, or food safety non-compliance, in particular when the test results are a legal requirement. 
  6. Be aware of the risks of document fraud for other certificates such as third party assurance certificates, Protected Designation certificates or product specifications and report any other concerns around document fraud using the information above.

CONTACT NFCU - If you become aware of information relevant to this Food Crime Alert, please share with NFCU via:

  • WEBSITE – visit food.gov.uk and click 'Report' at the top of the page.
  • TELEPHONE –08000 28 11 80.
  • EMAIL – foodcrime@food.gov.uk.

Please quote the alert number A003 in correspondence. Our processes enable us to handle information discreetly.

Read full alert.

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12633554080?profile=RESIZE_400xAn electronic nose (“e-nose”) is a sensor used to selectively measure volatile organic compounds.  Although e-noses have advantages in terms of cost and ease of use, they also have inherent limitations in terms of sensitivity to detect subtle variations in compound concentrations, leading to inconsistent results if not properly managed. The data generated by e noses generally require advanced processing techniques for interpretation of complex signal patterns. This is why e-nose food classification applications tend to use Deep Learning techniques such as Recurrent Neural Networks.

In this publication (open access) the authors used an array of 7 sensors to build a model to differentiate pork, bovine and fish gelatin.  The model was based on a commercial sample of each, dissolved in water as a 1% solution and warmed.  The model was then applied to different in-house mixtures of the gelatins at different time-points after preparation.  The authors do no report if it was validated with orthogonal samples of verified origin.  The sensors had selective sensitivity to a range of volatiles including ethanol, methane, propane, butane, ammonia and hydrogen sulfide.

The authors report that classification efficiency, as measured by the AUC (Area Under the ROC Curve), was variable when considering one sensor in isolation but was good when all 7 sensors were multiplexed.  The AUC increased with time from sample preparation, rising to over 98% at 2-hours from the samples being prepared.  The authors conclude that this makes the technique a promising candidate for constructing a routine instrument to check the species of commercial gelatin.

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