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This paper (open access) introduces the workflow MEATiCode, a comprehensive proteomic liquid chromatography tandem mass spectrometry (LC-MS/MS) method for the simultaneous identification of species in meat authentication.

This novel database search approach enabled the differentiation of meat species (as demonstrated for beef, pork, chicken and lamb) in raw and cooked food products following a simple sample preparation procedure and LC-MS/MS analysis of extracted meat peptides.  Peptides and proteins were characterised from reference samples using an untargeted protocol.  The MEATiCode database was then constructed in the Mascot Server search engine, with the objective of creating artificial proteins comprising the concatenated amino acid sequences of the peptides identified as specific for each species.

The authors report that the efficacy of the MEATiCode method was demonstrated through its application to a range of meat products, achieving high sensitivity (0.5 % Limit of Detection (LoD)) and reliability in the detection of adulteration, even in highly processed or cooked meats.

📥🧾🪧🔖 EFF-CoP promotional materials are ready for distribution. To help spread the word and engage new members, EFF-CoP has developed a range of promotional materials designed to inform, inspire, and connect!

Here’s what you can find:

• Leaflet - A quick and clear introduction to the EFF-CoP mission, objectives, and activities. Perfect for events, classrooms, and partners.
• Stickers - Fun, visual reminders of the EFF-CoP identity - great for students, notebooks, or toolkits.
• Bookmarks - A small but meaningful way to stay connected with EFF-CoP while promoting learning and awareness.

These materials reflect the heart of the project: to build a robust, collaborative European network focused on eradicating food fraud through collaboration, innovation, and shared knowledge.

🌐 EFF-CoP’s website and HUB are currently under construction and will be available soon.

By becoming a member, you will be able to read and write articles focused on food fraud, and engage in conversations with people, experts, and stakeholders who are passionate about this topic. You will also be able to download all promotional materials.

In the meantime, stay tuned to EFF-CoP’s LinkedIn profile, where we will keep you informed about all upcoming updates.

The Instutute of Food Science and Technology (IFST) has published a Technical Brief on the difference between Food Risks vs. Hazards.

John Points (FAN's very own Technical Director) and Peter Wareing dissect this critical distinction with clarity and real-world relevance. From unpacking ISO terminology to examining practical case scenarios, such as allergen mislabelling or aflatoxins in confectionery, this is essential reading for anyone involved in food production, safety, or regulation.

The brief explores:
– How to assess risk magnitude using likelihood and consequence
– When to withdraw, recall, or trade through
– Why pre-emptive planning beats reactive chaos
– What ‘tolerable risk’ really means under UK law.

Access this free Technical Brief here.

This brief has also been added to the 'Report' part of the 'Tools /Guides /Reports' tab of FAN's Food Fraud Prevention section.

There is a premium market, particularly in the US, for beef labelled as “grass-fed”.  From 2007 to 2016, the United States Department of Agriculture (USDA) carried voluntary marketing claim standards to help regulate grass-fed beef (GFB). These standards were discontinued, but producers can still seek approval from the USDA to market GFB.  This can only come from meat derived from cattle fed 100% forage, but the USDA also allows for partial claims (e.g., 50% grass-fed). Participating producers can define their own claim and need to comply with written protocols and sign an affidavit, but no audits are conducted.

In this study, (open access) three reference populations were used; 100% grass-fed, grain-fed, and grape-supplemented.  Red Angus steers (n = 54) were randomly allocated to one of the three feed regimes. Beef samples were collected in September 2019 and October 2020 in a USDA-regulated slaughter facility. All animals were slaughtered on the same day at 16–18 months old for GRAIN and GRAPE and 24–26 months old for GRASS. Ribeye samples were collected from the left side of the carcass between the 11th and 13th rib.

A multi-omics approach (gene expression quantification, metabolomics, and fatty acid [FA] profiling) was used to classify the three groups.  FAs were measured by gas chromatography-mass spectrometry (GC–MS), secondary metabolites were identified using ultra-high-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS), and gene expression analysis was performed using quantitative reverse transcription polymerase chain reaction (RT–qPCR).

The authors report that all target genes were upregulated in beef from GRASS compared to the other two groups. Multivariate analyses showed that long-chain n-3 polyunsaturated FAs, the n-6:n-3 ratio, vitamin E, organic acids, amino acid derivatives, and the nephronectin isoform X1 (NPNT-1) gene were the most important compounds for group separation. These compounds showed higher concentrations in beef from GRASS.

The success of beef separation by dietary treatment was highlighted by the 90.4% prediction accuracy of the random forest model, with beef from GRASS being 100% accurately predicted and beef from GRAPE being 94.4% accurately predicted. Beef from GRAIN was 76.5% accurately predicted.

The authors conclude that coupling gene expression analysis to metabolomics and FA profiling allowed for the separation of beef samples from varying dietary backgrounds with a high degree of confidence.

Thank you to FAN member Lucas Krusinski for flagging this article

This is a first in a series of invited blogs from FAN's laboratory Centres of Expertise.  In this article, Christophe Noel from SGS's food analysis molecular biology team, discusses the likely challenges in authenticating lab-grown meat.

The Infinite Animal: Mutation, Identity, and Authentication in Lab-Grown Meat

Lab-grown meat, or cultured meat, presents a new frontier in food production.  It introduces complex authentication challenges involving traceability, product integrity, and regulatory compliance.

With novelty and uncharted territory comes the need for new and emerging technological and regulatory strategies, as well as a combination of different methodological approaches to address these issues.

For example, the use of molecular and isotopic fingerprinting to distinguish lab-grown meat from conventional meat products—by analysing specific metabolic markers or isotopic ratios unique to cell culture processes—appears to be a promising option. DNA-based methods, including barcoding and genetic tracing, are also being proposed to verify cell line origins and production authenticity. Additionally, blockchain technology could offer transparent supply chain management, providing immutable records of production steps from cell culture to final product labelling, which would be extremely valuable.

Regulatory bodies are contributing by drafting frameworks that require rigorous documentation and verification at each stage of production, helping to establish standards for what legally constitutes "cultured meat."

Despite these advances, the field remains in its infancy, and ongoing research is crucial for validating these methods across different production platforms and global regulatory systems.

When considering the potential role of DNA/RNA analysis, its scope can include confirming the animal species present, detecting potential adulteration, identifying the nature of heterogeneous scaffolding, or even verifying the brand of the product if a unique genetic signature—either naturally occurring or engineered as a molecular label—is used.

An important aspect to consider in terms of authentication is the genetic stability of the product. A fundamental characteristic of cultured meat is that once cells are collected from the animal, this step is not repeated. The cell line must become immortal, ideally multiplying indefinitely and remaining genetically identical. However, cell culturing and propagation are, by nature, driving forces for potential mutations, raising the question of how we monitor these effects and their impact on the integrity of the final product.

Ultimately, an important question for authentication arises: are we destined to eat a definitive, unchanging version of one animal forever?

SGS Analytics United Kingdom Ltd is a UKAS (ISO17025) accredited analytical laboratory specialising in molecular and immunological detection of contaminants and adulterants in foodstuffs. With specific reference to authenticity testing our core area of expertise is the detection of meat, plant, fish and crustaceans using endpoint PCR; real time-PCR, PCR-sequencing and Next Generation Sequencing based technologies.  FAN has more information here.

In this study (purchase required) the researchers used bioinformatics methods to identify specific sequences of cattle, pig, chicken, and duck, and designed primers and probes accordingly.

They developed a method based on recombinase polymerase amplification (RPA) combined with lateral flow dipstick (LFD) for rapid visual authentication of beef and beef products. The RPA reaction was conducted at 37℃ for 20 min. The amplification products were then diluted and applied to the sample pad of the LFD. Results were visible to the naked eye within 5 minutes.

They report that the results demonstrated the method could specifically differentiate components of bovine, porcine, chicken, and duck origin, with a limit of detection (LOD) of approximately 20 copies for each species.

They applied the method to 10 commercially available beef products. Of which, five samples were detected with porcine-derived components. The results of the RPA–LFD method were verified using PCR and observed to be consistent between the methods.

The researchers conclude that this method is easy to use, requires no specialized equipment, and delivers results in about 30 min from amplification to detection, making it suitable for rapid visual detection on-site.

This study (open access) proposes a strategy to verify the authenticity of Mozzarella di Bufala Campana (MdBC).  MdBC is, a Protected Designation of Origin (PDO) cheese, Buffalo breeds are highly similar genetically, so detecting foreign buffalo milk in commercial cheese is more complicated than identifying cow, goat, or sheep milk. Fraud involving cow milk is particularly concerning because it is cheaper and more widely available, especially during peak MdBC production seasons

The researchers used a reference set of sixty-four anonymized PDO MdBC and foreign mozzarella samples provided by the Italian Central Inspectorate for Fraud Repression and Quality Protection of the Agrifood Products and Food, Ministry of Agricultural and Forestry Policies (Rome, Italy).  They used a sequential approach to verifying foreign milk species in buffalo mozzarella.  As a first screen, the casein was separated on a polyacrylamide gel.  This was generally sufficient to identify extraneous cows’ milk proteins.  In a second stage, the isolate casein was then digested with trypsin and the peptides analysed by MALDI-ToF-MS.

In cases requiring confirmation, nano-liquid chromatography coupled to electrospray tandem mass spectrometry (nano-LC-ESI-MS/MS) is used in central state laboratories for the highly sensitive detection of extraneous milk proteins in PDO buffalo MdBC cheese. The researchers report that analysis of the pH 4.6 soluble fraction from buffalo blue cheese identified 2828 buffalo-derived peptides and several bovine specific peptides, confirming milk adulteration.

They conclude that, despite a lower detection extent in the pH 4.6 insoluble fraction following tryptic hydrolysis, the presence of bovine peptides was still sufficient to verify fraud. This integrated proteomic approach, which combines electrophoresis and mass spectrometry technologies, significantly improves milk adulteration detection.

Photo by Audric Wonkam on Unsplash

In common with most jurisdictions, India has regulatory analytical criteria for authentic honey.  This includes various stable isotope ratios.

In this study (open access) the researchers set out to construct an analytical database of fully traceable authentic honeys in order to verify the criteria set by the Food Safety and Standards Authority of India.

They collected 98 authentic samples (covering 19 botanical sources, 42% multifloral and 58% monofloral).  They covered 17 states and provinces.  Sample were from collection centres of the All-India Coordinated Research Project on Honey Bees and Pollinators (AICRP, HB&P), under the auspices of the Indian Council of Agricultural Research (ICAR).   In addition, beekeepers registered with the National Bee Board (NBB) were also identified for sample collection. All samples were fully traceable.

The researchers generated a database of stable carbon isotope ratios (¹³C/¹²C) by Elemental Analyzer/Liquid Chromatography–Isotopic Ratio Mass Spectrometry (EA/LC-IRMS). The samples were analyzed for the parameters δ¹³C_Honey(δ¹³C_H), δ¹³C_Protein(δ¹³C_P), δ¹³C individual sugars, ∆δ¹³C_{Protein-Honey}(δ¹³C_P-H), C4 sugar, ∆δ¹³C_{Fructose-Glucose}(δ¹³C_Fru-Glu), ∆δ¹³C_max, and foreign oligosaccharides as per the official methods of analysis of the Association of Official Analytical Chemists (AOAC 998.12) and the FSSAI.

The results were evaluated against the published literature and Indian regulatory criteria for authentic honey. The δ¹³C value for honey (δ¹³C_H) ranged from −22.07 to −29.02‰. It was found that 94% of samples met the criteria for Δδ¹³C_P-H (≥−1.0‰), Δδ¹³C_Fru-Glu (±1.0‰), and C4 sugar content (7% maximum), with negative C4 sugar values treated as 0% as prescribed by the AOAC method.  86% of samples met the accepted foreign oligosaccharide criteria (maximum 0.7% peak area).

They conclude that the data of this study provide scientific backing for these four parameters as per the FSSAI regulation. However, the non-compliance of a high number (47%) of authentic honey samples for Δδ¹³C_max (±2.1‰) compels further systematic investigation with a special focus on bee feeding practices. Further, they found that honey samples with a Δδ¹³C_P-H greater than +1‰ and a C4 sugar content more negative than −7% also did not comply with the Δδ¹³C_max criteria. They suggest that Δδ¹³C_P-H values (>+1‰ equivalent to C4 sugar < −7%) could be an indicator of C3 adulteration to some extent.

One of the limiting factors in developing any untargeted analysis is sourcing samples for the reference database.  The samples labelled as “authentic” in the model developed must be of absolute trustworthiness (fully traceable back to authentic production) and also fully representative of every variable within the “authentic” scope (e.g. different permitted agricultural inputs, harvest seasons, species, variety and geographic origin).

This pilot study (open access) uses a statistical approach to circumvent this need.  The authors still attempted to source reference samples that were representative of an “authentic” scope but they made no attempt to verify the samples; all reference samples were purchased from online direct-to-consumer vendors.  They then built a results dataset of 38 different fatty acid methyl esters, tocopherols and phytosterols measured by GC-MS and LC-MS.  They selected from within this dataset using Monte Carlo sampling to choose different “reference databases” and build a large number of One Class Classification models.  They then used statistical analysis to see if each of these models appeared internally consistent (i.e. suggestive that all of the reference samples had been “authentic”) or not (i.e. suggestive that some of the reference samples had been “inauthentic”).  They chose the model with best internal consistency.

They piloted this approach on 40 avodado oils, identifying 6 of them a potentially adulterated.  Subsequent targeted chemical analysis showed that these 6 adulterated samples had been correctly identified.

This publication (open access) describes the launch of FISH-FIT.  FISH-FIT is a biobank of seafood species samples which are linked to an authentic database of morphology, genetic information, and other physical characteristics. It also contains a library of PCR analytical methods.   It was developed under an EU-funded project and free access is currently only available to EU regulatory bodies, although wider access is planned.  The databank is hosted by the Max Ruber Institute.

FISH-FIT has been added to FAN’s index of authenticity reference databases, a useful search tool for existing databanks or commercial testing services..

(image from the paper)

The UK National Meaurement Laboratory (NML) at LGC has shared a case study on a groundbreaking method for DNA meat species quantitation. This method, funded by the Food Standards Agency (FSA) and Department for Environment, Food and Rural Affairs (Defra), has now been officially published as a European standard (EN 18033:2024) by the European Committee for Standardisation (CEN).

Released in January 2025, this is the first harmonised standard for the relative quantitation of horse DNA in food samples and was initiated following the 2013 horse meat incident which challenged consumer confidence in the quality and authenticity of the food they were buying. The standard provides business operators, regulators, and compliance authorities with a robust and repeatable approach for determining the level of meat adulteration in raw beef products.

This milestone represents a significant advancement in food authenticity and food fraud prevention testing, ensuring greater transparency and trust in the food industry.

Read more about this case study.

This study (open access) used the fingerprint of trace elements (measured by inductively-coupled plasma – mass spectrometry, ICP-MS) as a marker for the use of mineral vs organic fertilisers, and hence as a marker for the mislabelling of Organic apple juice.  The concept was proven on juices made from apples grown in two regions of Northern Germany.

Reference data sets were generated from juices made by the researchers from apples of known provenance.  59 apple juice samples (31 organic and 28 conventional) from four crop years (2020–2023) were analyzed regarding their element profiles and used for model creation. All samples were from Schleswig-Holstein, Germany. Afterwards, the model was expanded using 24 apple juice samples (11 organic and 13 conventional) from Hamburg, Germany (crop year 2020–2023). Prior to analysis, the whole apple samples were washed with deionized water and then dried. Afterwards, the samples were processed to apple juice using a commercial juice extractor.

The authors report that, using an environment-friendly sample preparation strategy and a ratio-based evaluation approach in combination with a random forest classification model, it was possible to distinguish between the cultivation methods of processed apples.  The results were verified by analyzing samples from local supermarkets. Furthermore, the detection of adulterated mixtures of conventional juice to organic juice was studied using a regression analysis (5–50 % adulteration). Adulteration could reliably be detected from a proportion of 20 % Thus, falsification of the cultivation method can be detected even in mixtures.

The authors conclude that the study shows great potential towards sustainability, reducing sample preparation time, hazardous chemicals and energy consumption.  The identified molybdenum as a potential routine marker for organic apple juice.

Authentication of mushroom commodities often relies on visual identification, including microscopy. The methods usually involve physical observation with high subjectivity, which may lead to mushroom-product fraud and mislabelling.

This review (purchase required) covers molecular methods and “chemical” methods coupled with chemometrics and/or artificial intelligence. These include DNA barcoding, which is an identification strategy based on the DNA sequence of the mushroom sample, specifically the internal transcribed spacer (ITS) region. The review discusses the advancements in the usage of both DNA barcoding and chemometrics-coupled methods in the authentication of mushrooms and their derivative products; and how these can solve some major hurdles relating to mushroom products.

Photo by Damir Omerović on Unsplash

This study (purchase required) reports a direct method to verify the purity and authenticity of commercial sweetener raw materials; erythritol, xylitol, and stevia.  Analysis is by near- and mid-infrared spectroscopy combined with a DD-SIMCA classification model. The model was enhanced with virtual samples created by adding PCA residuals and noise.  The authors report that this improved the model's robustness and accuracy. Validation was performed using independent sample sets, including commercial natural sweeteners and in-house samples adulterated with saccharin, sucrose, acesulfame, and silicon dioxide.

The authors conclude that the approach was efficient for xylitol and erythritol authentication.  Efficiency rates were 90 % or higher for xylitol, erythritol, and stevia, but stevia sampling is challenging due to stevia's variable composition and needs improvement before the model could be applied with confidence.

Photo by rama purnama on Unsplash

In recent years the responsibility for enforcing food sustainability claims in the Netherlands has been unclear.  It has now been agreed that the Authority for Consumers and Markets (ACM) will take the lead, but will consult the Netherlands Food and Consumer Product Safety Authority (NVWA) before taking action.  Both are designated as Competent Authorities.

The ACM has announced a focus on sustainability claims in the food sector in 2025.  It has recently run successful similar campaigns in the energy and clothing sectors.  The ACM does not have the power to directly impose fines, but previous warnings to companies making unsubstantiated claims (including major brands such as H&M and Greenchoice) have resulted in changes to packaging, advertising, and substantial corporate donations to sustainability charities in lieu of a fine.

The ACM’s sustainability claims guidelines can be found here.

In this study (open access) the authors made a reference dataset of comminuted meat mixtures by dicing and mixing 140 commercially-purchased steaks of beef, duck and chicken.  They built a classification model to discriminate between the three species in the mixtures.

They used a hand-held Hyperspectral Imaging (HSI) (with a Raspberry Pi controller, which has real-time image acquisition and processing covering  a spectral range from 400 nm to 800 nm) to develop a discrimination model for chicken/duck adulteration in diced beef. The portable push broom HSI was designed with the spectral resolution of 5 nm and spatial resolution of 0.1 mm. To improve generalization, a model transfer method was also developed to achieve model sharing across instruments

The authors report that their model transfer method can effectively correct the spectral differences due to instrument variation and improve the robustness of the model. The support vector machine (SVM) classifier combined with spectral space transformation (SST) achieved a best accuracy of 94.91%. Additionally, a visualization map was proposed to provide the distribution of meat adulteration.

They conclude that the portable HSI enables on-site analysis, making it an invaluable tool for various industries, including food safety and quality control.

There is an increasing market of mildly processed chilled Not-From-Concentrate (NFC) orange juices, preserved by methods such as high pressure processing (HPP) and pulsed electric fields (PEF).

To protect consumers from food fraud, analytical methods to differentiate such juices from thermally pasteurized juices are required.

This paper (open access) sought to identify volatile chemical markers specific to the preservation process.  To screen for appropriate candidate markers, the authors applied a complementary non-targeted volatilomics and sensomics approach.  This identified 58 candidate markers, among which 20 were quantitated and nine were statistically confirmed.

Extension of the quantitations to stored and doubly-treated juices finally resulted in the identification of (S)-carvone and vanillin as promising candidate markers. In combination, the two compounds could distinguish the HPP-treated juice from thermally treated juices and could even identify an HPP-treated juice that had received an additional thermal pasteurization.

Photo by ABHISHEK HAJARE on Unsplash

The authors of this study (purchase required) report that they systematically separated and authenticated the triacylglycerols composition of milks from holstein cattle, goats, mongolian horses, bactrian camels, yaks and buffaloes,  using supercritical fluid chromatography coupled to high-resolution mass spectrometry (SFC-Q-TOF-MS). Subsequently, the fingerprinting of triacylglycerols from different livestock milks was modelled using chemometric methods. The results showed that the statistical grouping of different livestock milks was consistent with the species taxonomy, and the accuracy of internal as well as external validation was satisfactory.

They conclude that this work not only provides an innovative strategy for authentic traceability of livestock milk, but also offers potential for the establishment of nutritional databases.

Photo by Polina Kuzovkova on Unsplash

This paper (open access) reports the construction of a classification model to detect the adulteration of white pepper with mung bean flour utilizing Fourier Transform Infrared (FTIR) spectroscopy combined with chemometric techniques.

The authors prepared their own reference samples in-house by grinding locally sourced white pepper (Malaysian origin) with bean flour ranging from 3 – 50%.

They report that adulterants can be detected even at the lowest concentration prepared using the Partial Least Squares (PLS) method and chemometrics.. The second derivative FTIR spectrum in the range of 3712-650 cm⁻¹ was identified as the optimal calibration model.  The PLS Discriminant Analysis (PLS-DA) method also successfully classified pure white pepper samples from those adulterated with various concentrations of mung bean flour.

This study (purchase required) reports the development of a novel recombinase aided amplification (RAA) assisted Cas12a assay to authenticate the commercially important Pacific oyster.

The COI gene was selected as a genetic marker for primer design. The authors report that the developed species-specific RAA assay was optimal at 40 °C for 25 min. The Cas12a assay successfully detected the target Pacific oyster DNA sequence in RAA products using 0.05 μM gRNA and 0.05 μM Cas12a enzyme within 40 min at 37 °C. The developed RAA primers and gRNA for CRISPR-Cas12a assay showed no cross-amplification and high specificity for C. gigas compared with C. belcheri and C. iridalei. The sensitivity test showed the ability of the assay to detect DNA concentrations as low as 10 fg/reaction. In addition, the developed assay successfully authenticated oyster samples in all processed forms, including boiled, steamed, fried, and canned samples.

A small follow-up survey found that 1 of the 15 commercial samples tested was mislabeled.

The authors conclude that the developed assay was a valuable technique with high potential for food safety authorities and stakeholders in ensuring authenticity, in which substitution and adulteration of seafood products can be detected.