News

One of the limiting factors in DNA analyses, in terms of both the time taken and the need to send samples to a laboratory for testing.  There are a number of modern point-of-use technologies that circumvent the need for amplification (see FANs methods explainers).  Currently these cannot compete on price-per-test with “traditional” laboratory-based Polymerase Chain Reaction amplification methods.

In this paper (purchase required) the authors have developed a novel point-of-use biosensor that can detect trace levels of different species' DNA in parallel (“multiplex”).  They conducted proof of concept for low-level meat species contamination in complex food matrices.  The sensor is based on Surface Enhanced Raman Spectroscopy (SERS – a technique that has been used for sensors to detect clinical markers in biological samples).  The authors have enhanced the technique by using argonaute endonuclease coupled with guide DNA to specifically cleave the target nucleic acids and maximise the signal.  The system is programmable, and the authors report that controllable polystyrene nanoparticles encapsulating SERS probes significantly improved detection sensitivity.

In this study (purchase required), Fourier transform near-infrared spectroscopy (FT-NIRS) was combined with two distinct machine learning algorithms to detect and quantify the peanut adulteration rate (%) in ground hazelnut.

Ground hazelnut samples were mixed with various levels of peanut content (0–50%). The spectral data were collected in the wavelength (λ) range of 4000–10000 cm⁻¹. Feature selection was carried out using the Lasso and Elastic Net algorithms to determine and eliminate unnecessary spectral variables and improve the accuracy of prediction. The Lasso model was found to be more accurate compared to the Elastic Net model for the same λ value (0.001). The authors report that the prediction accuracy indicator values improved as λ values decreased. Cross-validation confirmed the robustness of the Lasso model, indicating it is highly generalisable.

The authors conclude that FT-NIRS, supported by ML-based feature selection and modelling, provides an efficient, fast and non-destructive approach for the detection and quantification of hazelnut adulteration with ground peanut. This approach offers a rapid, waste-free, and eco-friendly solution to food adulteration detection, aligning with sustainable production principles by minimising sample preparation and resource consumption in the frame of greener analytical workflows.

Photo by David Gabrielyan on Unsplash

Authenticity tests for coffee tend to focus on the variety (Arabica vs Rustica) or adulteration of roasted ground coffee (e.g. with chicory).  There has been relatively little focus on authenticating the origin of green beans, for example to underpin Fair Trade traceability.

Proteomics has previously shown differences among cultivars.  This paper (subscription required) built on previous studies that had showed that long-term adaptation to a distinct climate (associated with the geographical location), are likely to significantly affect various metabolic processes and thus protein profiles.  Most proteins in beans are likely to be enzymes, such as oxidases and peroxidases. Previous researchers had identified 531 proteins in C. arabica cultivars in high-altitude African and low-altitude South American samples. Further analysis pointed out that only a few proteins were significantly different between them, plausibly corresponding to the concentration of certain compounds (e.g., flavonoids) alongside the adaptation to the environmental niches (e.g., colder climate or predominant pathogens). Post-harvest processing modifies proteomic profile.

This study used a combination of proteomic profiling with linear discriminant analysis for the classification of the geographical origin of green specialty coffee beans from well-known harvesting regions in Central America, South America, Africa, and Asia. Out of 1596 identified proteins, the authors selected the top 30 target markers ranked by ANOVA. They report that the model's prediction performance using leave-one-out cross-validation reached 85.3 %, with the lowest accuracy in the prediction rate for Asian samples. Model performance and prediction sensitivity to random states were tested using 5-fold cross-validation. After 20 iterations, the model performance slightly decreased to 84.0 %. Specificity and sensitivity confirmed that the model appears to be reliable at distinguishing Asian and African samples.

Photo by wisnu dwi wibowo on Unsplash

One of the simplest frauds to perpetrate for frozen seafood, particularly small items such as prawns, is to bulk up the declared weight by including the weight of some, or all, of the ice glaze.  A glaze is essential for product quality.  In most jurisdictions, including the US, EU and UK, the declared net weight on the pack must exclude the weight of any added glaze. 

The US FDA have just released results from 28 imports of frozen seafood tested between 2022 and 2024.  They found that 10 of the 28 were violative for short weighing.  The % of short weighing ranged from 2.4 – 9.9% of the declared pack weight.  These samples were not randomised – they includes some samples taken as the result of complaints, as well as the FDA’s surveillance samples which are targeted on a risk basis.  Samples were in retail packs, and were collected at port of entry.  Further details, including countries of origin, can be found here.

Photo by AM FL on Unsplash

The report for the Food Authenticity Network (FAN) 2023 Partner Projects is now available.

This report describes two projects delivered in 2023 by LGC, via FAN, which were jointly funded by Defra, Food Standards Agency and Food Standards Scotland with the aim of supporting UK analytical lab capability for food authenticity testing, ensuring industry and law enforcers have access to information on emerging/ topical analytical testing issues.

 Project 1: Open Data Project

The report describes the development and production of a searchable online 'Open-Data' tool, signposting to organisations that have food databases that contain information can be used to help verify food authenticity.

Currently the Food Authenticity Database Tool signposts to 220 authenticity databases.

If you owner of an authenticity database and would like FAN to signpost to it then please contact us at Secretary@foodauthenticity.global. 

Project 2: Compendium of Food Authenticity Testing Techniques Project

This project involved the development of a compendium of food authenticity testing techniques, designed for food industry stakeholders who do not have an analytical science background but may be required to interpret and apply the results of food authenticity analysis. The compendium is comprised of 10 sections, each covering an overview and explanation of a different technique, including Mass Spectrometry and Stable Isotope Ratio Analysis. The compendium is written at a technical level appropriate to food industry professionals with a strong scientific background, but no analytical expertise.

The Compendium of Analytical Techniques is available in the Research and Methods section of the FAN website.

This report has been added to FAN's Research Reports section.

This 2019 publication, by Food Authenticity Network Advisory Board Member, Dr John Spink, is now free to download. The food fraud prevention update includes a practical recommendation for ‘How to Start?’ and ‘How Much is Enough?’

A practical approach to food fraud prevention was laid out in the Food Fraud Implementation Method (FFIM). This method has been refined over the years and was finally formalized and published in 2019 and is applicable today.

After conducting an incident review and hazard identification, the method includes 10 questions, 2 concepts, 7 steps and 1 decision. (To note, the article had seven questions but over time this was later expanded to ten.)

Photo by Irham Setyaki on Unsplash

The Food Fraud Implementation Method (FFIM): “How to Start”

“10 Questions”: For this first pass, the response is just “yes” or “no.”

1. Have you conducted at least one Food Fraud Vulnerability Assessment (Y/N)
2. Is it written (and can you show it to me now) (Y/N)
3. Have you created a Food Fraud Prevention Strategy (Y/N)
4. Is it written (and can you show it to me now) (Y/N)
5. Can you demonstrate Implementation (Y/N)
6. Do you have Executive Level Sign-off (Y/N)
7. Have you minimally conducted an annual Food Fraud Incident Review (Y/N)
8. Do you have a method to review your incidents and general market incidents (Y/N)
9. Note: Do you address all types of Food Fraud (e.g., adulterant-substances, stolen goods, diversion, intellectual property rights counterfeiting, etc.) (Y/N)
10. Note: Do you address all products from both incoming goods (e.g., ingredients) and outgoing goods (e.g., finished goods) through to the consumer.” (Y/N)

“2 Concepts”:

1. Concept One—Formally and specifically, mention food fraud as a ‘food’ issue (e.g., in a formally approved and published corporate policy handbook)
2. Concept Two—Create an enterprise-wide food fraud prevention plan (e.g., this is the Food Fraud Prevention Strategy, and it is the only link between the food fraud incident assessments and calibration with the risk tolerance assessment to the enterprise-wide system)

“7 Steps”:

1. Convene a Food Fraud Task Force
2. Create an Enterprise-wide Food Fraud Policy/Mission Statement and begin drafting a Food Fraud Prevention Strategy/Plan
3. Conduct the pre-filter Food Fraud Initial Screening (FFIS) (e.g., this is a very high-level vulnerability assessment that covers all products across the entire enterprise. One risk matrix or assessment could meet the objective.)
4. Review additional needs, including additional information or a more detailed Food Fraud Vulnerability Assessment (FFVA) (e.g., in ERM/ COSO terms, this is a “detailed assessment.”)
5. Review-specific Food Fraud vulnerabilities in an enterprise risk map (Enterprise Risk Management)
6. Consider countermeasures and control systems to address the ‘very high’ and ‘high’ vulnerabilities (e.g., it is helpful to provide examples of possible countermeasures or control systems. These examples will help calibrate if there is enough information to make a confident resource-allocation decision.)
7. Propose a Food Fraud Prevention Strategy, including the calibration of the Food Fraud risks on the enterprise risk map (E.g., this should be in a corporate human resources template to facilitate actual resource-allocation decision discussions.)

“1 Decision”:

• Finally, after the FFPS proposal is submitted, the last step is for management to decide on the optimal plan. It is essential to consider that no decision on the new proposal is a decision – no decision is a decision that accepts the status quo. In some situations, the total resources applied to the problem may be reduced.

Enterprise Risk Management: How Much is Enough?

The connection of the Food Fraud Vulnerability Assessment to the enterprise-wide risk assessment leads to a calibration of the problems. The enterprise-wide risk map defines the issues that are above the risk tolerance. The most valuable part of the process is that the same map illustrates when there is “enough” of a risk treatment. Zero risk is not practical and often not even possible.

The FFIM has been added to the 'Guides' tab of FAN's Food Fraud Prevention section.

A draft CEN standard titled 1 'Food authenticity - Non-targeted testing methods - Part 1: General considerations and definitions' (WI 00460015) is available for comment via national standards body:

Please contact your national standards body and submit any comments to them.

For example, in the UK visit British Standards Institution - Project input comments by 23/09/2025.

During March, EFF-CoP launched the EFF-CoP Editorial Board Team! This newly formed board not only produced its first article but also collaborated with New Food Magazine.

By registering on EFF-HUB and becoming a member, you can access the article “The Rising Tide of Food Fraud”, written by EFF-CoP Coordinator, Prof. Saskia van Ruth. As an EFF-HUB member/ ambassador, you will also be the first to hear about all events organised or attended by EFF-CoP.

The first EFF-CoP community-wide event, titled “Igniting Conversation in the European Food Fraud Community,” gathered more than 100 participants from various professions, all united by a common goal: to detect food fraud and deepen their understanding of the topic. You can find more details about the event here: Our First Spark – The EFF-CoP Community in Action.

And the story continues! EFF-CoP is now preparing its first virtual workshop, “Food Fraud Synergies”. In this workshop, representatives from EC sister projects will share their initiatives and actions, bringing fresh perspectives to the fight against food fraud.

👉 Don’t miss it - visit Workshop: Food Fraud Synergies to register today!

This update has also been added to the FAN EFF-CoP page.

This study (purchase required) is unusual in that it sought to investigate the seasonality of fraud over a 12-month period.

Samples were collected from three Peruvian coastal cities—Lima, Chiclayo, and Piura. A total of 1189 samples were collected from 76 retail points, including restaurants, supermarkets, and municipal markets. DNA barcode sequencing was used for species identification, revealing a 67.5 % substitution rate. Restaurants exhibited the highest substitution rate (73.8 %), followed by municipal markets (71.1 %) and supermarkets (27.9 %). Fraud was identified in 89.7 % of substitution cases, often involving high-demand or threatened species, such as hammerhead sharks Sphyrna zygaena and Atlantic eel Anguilla anguilla.

The authors report that seasonal patterns were observed, with certain species like dolphinfish Coryphaena hippurus and searobin Prionotus stephanophrys used more frequently at certain times of year.

Photo by Patrick Browne on Unsplash - for illustration, there is no suggestion that this dish is fraudulent

This paper (open access) looked at classifying quinoa, amaranth and wheat flours.

Reference mixtures were prepared in-house:

i) Pure flours, including wheat, quinoa, and amaranth, with two varieties analysed for both quinoa and amaranth;

ii) Double mixtures, which comprised binary combinations of quinoa:wheat flours at 50:50 and 25:75 ratios, and amaranth:wheat flours at 20:80 and 10:90 ratios; and

iii) Triple mixtures, involving combinations of quinoa, amaranth, and wheat flours at 25:10:65 and 12.5:5:82.5 ratios

Volatile profiles of all reference mixtures were measured by both SPME-GC-MS and using a previously-published “electronic nose” sensor (a multiplex of 8 electrochemical sensors).

Twenty-four volatile compounds were identified, including limonene, 1R-α-pinene, and L-β-pinene, which were exclusive to pseudocereal flours, and hexanal, abundant in wheat flour as an oxidation indicator. The authors report that the E-nose achieved 89.7 % accuracy in discriminating between quinoa, amaranth, and wheat flours and effectively separated double and triple mixtures. A PLS model revealed a strong correlation between E-nose data and concentrations of limonene, α-pinene, and β-pinene (R²CV = 0.94–0.95). The integration of GC-MS and E-nose proved highly efficient for flour authentication, with canonical discriminant analysis successfully identifying pseudocereal flours in mixtures with wheat flour,

Photo by Vlad Kutepov on Unsplash

On September 1, 2025, Spain will launch the country’s new independent whistleblower protection authority - Autoridad Independiente de Protección del Informante (AIPI).  This is part of the implemention of Law 2/2023  This law generally applies to companies of 50 employees or more although there are nuances and caveats in the scope of application.  Companies do not necessarily have to be domiciled in Spain to fall within scope - it also applies to foreign-registered companies operating in Spain.  If you are unsure if you are within scope of the law then the advice is to check.

The AIPI brings in new new obligations regarding internal reporting systems and the designation of responsible officers.

From 1 September, companies in scope will have two months to notify the AIPI of the appointment or removal of their designated internal reporting system officers. Although the law does not yet specify the format or platform for these notifications, it is expected that the AIPI will issue operational guidance shortly after its launch.

The AIPI is designed to be a central enforcement and support body with broad powers, including:

• Managing the external reporting channel for whistleblowers.
• Providing protection and support to individuals who report misconduct.
• Initiating and resolving sanctioning procedures for violations of Law 2/2023.
• Issuing circulars and recommendations to guide best practices in whistleblower protection and compliance.
• Developing public sector crime prevention models, which may influence private sector compliance standards

Source - Baker McKenzie blog on Lexology site

This paper (open access) reports the results of an authenticity testing survey of insect-containing food and feed products, purchased both within and outside the EU.

119 commercial products were tested for the declared insect species, using two DNA-based methods, real-time PCR and metabarcoding,. All samples (test portions of 100 mg) were extracted following the method recommended by the European Union Reference Laboratory for Animal Proteins in Feedingstuffs

The authors report that 50% of the products contained insect species not listed on the label, or lacked the species that were declared. In particular, cross-contamination was observed when manufacturers worked with more than one type of insect.  Some products contained insects that are not currently allowed for use in the European Union. Some insect meals also contained traces of animal DNA, which may come from the substrates the insects were raised on. The authors point out that this could cause legal problems if these meals are used in certain types of animal feed.

The authors conclude that their study highlights the need for better quality control in the insect production chain. It also shows that DNA tests are useful tools for authenticating the declared insect species in food and feed products.

Photo by Yeyo Salas on Unsplash

This paper (purchase required) describes the development and validation of a highly sensitive lateral flow immunoassay (LFIA) test kit for detecting trace pork in meat products. A rapid bead-based sample extraction was developed (1–5 min) for the  biomarker (porcine IgG).  The authors report that total test time to result was 20 minutes. The reported detection limit was 0.001 % (w/w), which is 5–500 × more sensitive compared to current commercial lateral flow kits. The LFIA was validated with a range of meat and processed food products, confirming its high specificity to pork without cross-reactivity to other animal species or non-meat ingredients. Moreover, a long-term stability study confirmed that the LFIA maintained its performance at room temperature storage for 2 years.

This study (purchase required) explores the ratio between the values of the saponification and iodine indices in edible oils.  The relationship is used as the basis of a classification model. The approach is termed a Quantitative Structure-Property Relationship (QSPR) study..

The authors report that QSPRs were formulated in 144 vegetable oils, composed of 1–8 fatty acid components. Details of the varieties and sources used for the training and validation sets are not available in the online abstract.  A set of 25,118 mixture descriptors was calculated as linear combinations of the non-conformational descriptors of the fatty acid components and their weight percent compositions. This approach was found useful for discerning natural oils.  The Replacement Method variable subset selection technique was applied afterwards to select the best mixture descriptors in the predictive model.

To test the model, different vegetable oils with known composition, but unknown experimental saponification and iodine indices data, were successfully classified using the established QSPR.

The authors conclude that two-variable QSPR analysis can be extended to fats and other types of oils, such as fish oils. It also serves as a background and database for other methodologies.

New front-of-pack labelling requirements are being introduced in the US.  This will introduce a motivation for fraud which already exists in many other countries with similar compulsory traffic light systems: deliberately omitting or under-declaring a “bad” ingredient or additive in order to make the front-of-pack summary look “healthier”.

The US “Transparency, Readability, Understandability, Truth, and Helpfulness” (TRUTH) in Labelling Act was introduced last month and would require FDA’s proposed rule regarding front of package nutrition labelling (90 FR 5426 (Jan. 16, 2025)) to be finalized within 180 days of the bill’s enactment.

A principal display panel must identify foods with high amounts of added sugars, sodium, and saturated fat.  High amounts will be based on Daily Reference Values (DRVs). The phrase “High in” and a conspicuous exclamation point icon would be required.

The front of pack panel must also declare the presence of non-nutritive sweeteners and a “factual” statement that such sweeteners are not recommended for children. The wording of this statement has still to be defined, is contentious, and may be dropped from the final version.

Source: Keller and Heckman blog on the Lexology platform.

Photo by Tsvetoslav Hristov on Unsplash

This study (purchase required) evaluated two portable NIR spectrometers (900–1700 nm and 1450–2450 nm) and a benchtop FTIR device (4000–550 cm⁻¹) for authenticating edible insect flours. The reference data were constructed from flours produced in-house from insects or larvae purchased online: mealworm (Tenebrio molitor) larvae (23 samples), buffalo worm (Alphitobius diaperinus) larvae (28 samples) and crickets (Acheta domesticus) (28 samples).  Data-Driven Soft Independent Modelling Class Analogy (DD-SIMCA) and soft Partial Least Squares Discriminant Analysis (sPLS-DA), were used on the spectral data.

Principal Component Analysis (PCA) showed that spectral data of pure insect flours were clustered in the scores plot. DD-SIMCA achieved 100 % sensitivity (SNS) in the test set using FTIR for all insects. NIR Spectrometer in the range of 1450–2450 nm reached 100 % SNS and 100 % specificity (SPS) for buffalo worm and mealworm flour. sPLS-DA showed class sensitivity (CSNS) between 75 % and 100 %, for all three devices tested, with spectrometer in the range of 1450–2450 nm reaching class efficiency rate (CEFF) and total efficiency (TEFF) values ranging from 93 % to 100 %. Also, PLSR achieved RMSEP values as low as 0.44 %, demonstrating its robustness as a tool.

The authors conclude that IR spectroscopy with soft modelling is a non-destructive solution for authenticating insect flours, filling the current gap in rapid and reliable analytical tools for this emerging industry.

Photo by Olga Kudriavtseva on Unsplash

This work (open access) provides genetic marker information and proposes a standard method to support the regulatory classification of premium rice varieties including Basmati, Jasmine, Sushi and Risotto.  It builds on previous reference data sets provided by the same research group (including the University of Bangor, one of FAN’s Centre of Expertise laboratories).

Updated DNA fingerprinting was done for reference samples of 158 commercial rice varieties from 14 countries, collected since 2004. Most samples were obtained directly from the appropriate regulatory body in each jurisdiction, with provenance further substantiated by genetic cluster analysis.

DNA fingerprinting based on 10 SSR (Simple Sequence Repeat) markers was introduced in the early 2000s for authenticity testing of Basmati rice. Subsequently the addition of 5 SSRs and the fragrance gene fgr have refined the method for routine use.

This new study evaluated the applicability of the 15-SSR method for authenticity testing of more diverse types of commercially relevant rice that are traded on an international scale. The extensive range of reference samples covered this commercial scope. Most varieties were found to have distinct marker profiles except for eight near isogenic lines and eight closely related traditional varieties. The fgr marker detected several non-fragrant varieties that were incorrectly labelled as Jasmine fragrant rice, one of which was listed as fragrant and tariff-exempt in the EU Viet Nam Free Trade Agreement.

To assess the authenticity of samples obtained from unofficial sources in the trade, UPGMA algorithm and Principal Coordinate Analysis (PCoA) were used for marker-based clustering of samples. Most of the unofficially sourced samples clustered according to their expected geographical and genetic origin, supporting their authenticity. The study supports the broader utility of this 15-SSR test, supplemented by the fgr marker, for global rice variety authentication.

The authors conclude that their proposed markers are ideal to underpin ond enforce industrial, legal and free trade agreement standards.

Photo by Rens D on Unsplash

The latest version of this regular free round-up of US and Canadian regulation in the food industry, from legal firm DLA Piper, has been published on the Lexology blog platform.

August's edition includes commentary on:

• Plans to reorganise the USDA
• New senior appointments at USDA and FDA
• CFIA inspection frequency of Safe Food for Canadians (SFC) licensed premises
• FDA releases new food toxicity screening tool
• FDA food traceability compliance deadline extended to 2028
• FDA-commissioned report: "Roadmap to Produce Safety: Summary Report of the Produce Safety Dialogue"
• Saskatoon Farm foodborne illness outbreak linked to contaminated water.
• FDA moves to reclassify a synthetic opioid derived from kratom as a controlled substance
• FDA announces 2026 user fees for VQIP and TPP.
• FDA proposes amending Standard of Identity for pasteurized orange juice
• Canada-Australia beef trade reopens after 20-year ban.
• Brazilian coffee companies redirect coffee sales to China in response to US tariffs
• US tariffs may hurt US chocolate producers
• Misleading “Made in Canada” branding prompts scrutiny of grocer compliance
• Federal lawsuit targets Oregon’s Plastic Pollution and Recycling Modernization Act
• CDC: Americans get more than half of their calories from ultra-processed foods.
• IFIC report: consumer confidence in safety of the food supply is at a 13-year low
• Avian flu update.

The EC Monthly Reports of Agri-Food Fraud Suspicions reports are a useful tool for estimating fraud incidents, signposted on FAN’s Reports page.  They can be found here.

FAN produces rolling 3-month graphical analysis, a little later than usual this month due to holiday. 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 subjective, intended only to give a high-level overview. 

Our main takeaway message is that industry risk-assessment too often focusses on specific ingredients as "high risk".  In actual fact, it is the TYPE of fraud that is consistent; falsification of traceability or health documents/certification, illegal import, bulking out more expensive ingredients with cheaper ones.  The affected ingredients or products vary.  This suggests that risk assessment should focus more on motivation and opportunity in the supply chain, and less on "counting RASFFs".

As with all incident collation reports, interpretation must be drawn with care.  This EC collation is drawn from the iRASSF system – these are not confirmed as fraud, and the root cause of each issue is usually not public.  There are important differences in the data sources, and thus the interpretation that can be drawn, of these data compared to other incident collations.  For example:

• JRC Monthly Food Fraud Summaries (which underpin the infographics produced monthly by FAN member Bruno Sechet) - these are unverified media reports, rather than official reports, but hugely valuable in giving an idea of which way the fraud winds are blowing
• Official reports (as collated from commercial databases such as Fera Horizonscan or Merieux Safety Hud, which underpin FAN's annual Most Adulterated Foods aggregation) - these are fewer in number and give a much more conservative estimate of fraud incidence, and may miss some aspects which have not been officially reported
• The Food Industry Intelligence Network (Fiin) free SME Hub.  This excellent resource collates anonymised UK industry test results for the benefit of Small and Medium Enterprises in the food sector (registration and approval required to obtain login).
• Verified reports (where the root cause has been scrutinised and interpreted by a human analyst, for example the FoodChainID commercial database) - these are also few in number, less suitable for drawing overall trends, but give specific insight and information.

This study (purchase required) reports development of a Loop-Mediated Isothermal Amplification (LAMP) assay to detect several common avian meat species as adulterants in raw and heat- and pressure-treated meat products. This is an on-site test, taking about 1 hour, with the results visualised by colour changes in the SYTO 24 nucleic acid marker dye.

Conserved regions of the glyceraldehyde-3-phosphate dehydrogenase (gapdh) gene were targeted to design a LAMP primer set specific to avian species. To assess the assay’s performance, six common avian species (chicken, turkey, goose, duck, ostrich, quail) and four non-avian species (sheep, cattle, goat, camel) were tested. DNA was extracted using a salt-based method, and the assay’s specificity and sensitivity were evaluated on raw, cooked, and autoclaved samples.

The authors report that the LAMP assay successfully detected chicken, turkey, goose, and duck DNA. They report detection limits of 110 femtograms chicken DNA In chicken–beef mixtures, 0.1 % chicken in raw and cooked samples and 1 % in autoclaved samples.

For the principle of LAMP, see FAN’s method explainer pages.

Photo by FitNish Media on Unsplash