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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 review (open access) analyses trends in reported food fraud incidents over the past 5 years and trends in detection technologies, particularly the integration of AI and digital traceability and detection systems with analytical testing.  The authors base their analysis on the EC Joint Research Centre monthly collation of food fraud media reports.

The authors highlight that food fraud is a worldwide issue, but its incidence is unevenly distributed across countries. A few countries account for a disproportionate share of reported cases. Notably, Italy has the highest number of food fraud incidents, with over 300 cases. India, and Pakistan also rank in the highest quintile, each reporting well over 150 cases. These three countries alone represent the upper 20 % bracket of fraud occurrence globally. A second tier of countries, including Spain, Brazil, Bolivia, Malaysia, Colombia, and Argentina, report a few dozen cases each.  This skewed distribution suggests that detections of food fraud are concentrated where high-risk products, and active enforcement intersect.

The authors conclude that igrating AI-based predictive analytics with traditional and emerging lab methods significantly improves fraud detection, while blockchain and Internet of Things (IoT) innovations enable secure, real-time tracking of food authenticity. These technologies collectively strengthen the ability to uncover fraud

The paper emphasizes the need for interdisciplinary collaboration, harmonization, and updated regulatory frameworks to support the adoption of these multi-disciplinary approaches.

Spectroscopic techniques are non-destructive, rapid, and often cost-effective tools for detecting cheese adulteration. Cheese is one of the foods most frequently reported as adulterated or misrepresented, particularly when including misrepresentation of PGO or PDI production methods or origin.

This review (open access) references 104 studies and describes the range of vibrational, nuclear magnetic, and mass spectrometric techniques which have been applied for cheese authentication, including Near-Infrared (NIR), Mid-Infrared (MIR), Fourier-Transform Infrared (FTIR), Raman, and Nuclear Magnetic Resonance (NMR) spectroscopy MS-based methods . Emerging non-invasive sensor-based technologies such as electronic nose (E-nose) systems have also been explored in dairy product monitoring and are covered in the review.

The authors consider that each technique offers distinct advantages based on its operational principle and application context. NIR spectroscopy, for example, has demonstrated utility in detecting water addition, milk source substitution, and fat adulteration in a variety of cheese matrices with minimal sample preparation FTIR and ATR-FTIR are valuable for functional group detection and surface compositional analysis, offering rapid screening capabilities . Raman and its variants, such as Surface-Enhanced Raman Spectroscopy (SERS) and Spatially Offset Raman Spectroscopy (SORS), provide molecular vibrational fingerprints useful for identifying foreign substances and analyzing samples through packaging.  ¹H NMR spectroscopy has gained prominence due to its high-resolution metabolomic profiling capabilities and its ability to differentiate PDO cheeses from non-authentic counterparts based on lipid and aqueous phase biomarkers .

Advanced mass spectrometry-based techniques, including LC-MS/MS and MALDI-TOF-MS, have also been effectively utilized for the detection of protein-based adulterants and species-specific peptides in complex cheese matrices, enabling quantification at trace levels.  Isotope Ratio Mass Spectrometry (IRMS) and other isotope-based techniques have proven crucial in verifying geographical and botanical origin by assessing stable isotope compositions such as δ¹³C, δ¹⁵N, and δ³⁴S

The authors aim to provide stakeholders—including researchers, quality control laboratories, and regulatory agencies—with an informed perspective on the strengths and limitations of each technique, thereby supporting the development of more robust authentication frameworks within the dairy industry.

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Unauthorized GM (UGM) refers to those GM crops or products that have not received approval in a particular country..

DNA-based methods are preferred for testing of raw or partially processed food products.  More heavily processed food can be more of an analytical challenge; DNA methods are still applicable provided the quality of DNA enables amplification despite being hampered by the processing procedures or presence of inhibitors or due to complexity in composition in terms of ingredients.

This review (purchase required) focused on possible approaches for adhering to the regulatory requirements while verifying UGM in processed food products. Technical challenges and approaches for extracting purified DNA and necessary quality checks are described. The article covers amplification as well as sequencing-based methods which can be applied to check for presence of UGM ingredients amongst a complex mix of other ingredients. The authors discuss the comparative assessment of these methods in the context of regulatory testing requirements and availability of information of transgenic DNA.

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This paper (open access) reviews the history and exportation of turmeric in Africa and the safety issues of some toxic adulterants.

Priority adulterants were determined from global food safety alerts. A systematic bibliographic search was performed to identify appropriate methods and techniques for authentication and safety testing. The quality of each study was assessed according to PRISMA guidelines/protocol.

The authors report that African turmeric exportation is on the rise due to recent insights into the suitability of local cultivars, soil and climate for growing high-quality turmeric. There are limited data on turmeric adulteration for domestic consumption and export markets..

Global alert databases revealed lead chromate as the top hazard identified of all adulterants. Current techniques to detect adulterants are laboratory-based, and while efficient, there is a need for more rapid, field-friendly, non-destructive analytical tools.  The authors consider that – if lead chromate is considered to be the main tisk - then pXRF would be ideally suited as a field-based test in Africa. In the hope that it could be further developed and calibrated to detect below the regulatory level of 1.5 mg/kg lead in turmeric powder. There would be a need to cross-check pXRF screening results against a validated and accredited ICP-MS method as a reliable confirmatory tool.

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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.

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This review (open access) presents a comprehensive summary of the principles and recent advancements in the application of stable isotope techniques for authenticity assessment. It examines their use in detecting fraud (e.g., identifying edible alcohol, exogenous water, carbonylation, and trace compounds), vintage identification, and geographical origin determination across various alcoholic beverages, with a particular focus on wine, Chinese baijiu, and beer.   It cites over 100 publications from the past 15 years.

The authors conclude that stable isotope analysis is a powerful tool for verifying the authenticity of alcoholic beverages, offering effective solutions to combat counterfeiting, mislabeling, and adulteration. They recommend that future studies should focus on understanding the ecological, biological, and hydrometeorological factors influencing isotope signatures and develop advanced multi-isotope and chemometric approaches to improve reliability. Expanding global databases and integrating emerging technologies such as artificial intelligence (AI) and machine learning will further enhance the effectiveness and accessibility of stable isotope techniques, ensuring safer and higher-quality alcoholic beverages for consumers worldwide.

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Laser Induced Breakdown Spectroscopy (LIBS) is a technique that can provide fast multi-elemental analysis , and hence food authenticity “fingerprints”, without requiring any sample preparation. It is an under-utilized technology in the food sector because of its high setup cost and lack of LIBS expertise in this sector.

This review (subscription required) gives an outline of LIBS fundamentals and instrumentation alongside published scope and capabilities of LIBS for detecting adulteration in various food samples.  The authors consider that LIBS is a pre-existing tool within the authenticity testing armoury which could be better utilised in the food sector..

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.

This pre-print (open access) reviews recent advances in electroanalytical methods.  These have the advantage, for food authenticity applications, that they are generally cost-effective and adaptable to field conditions. This review covers the application of these techniques across various food matrices, including olive oil, honey, milk, and alcoholic beverages.

The author reports that, by leveraging methodologies such as voltammetry and chemometric data processing, significant advancements have been achieved in identifying both specific and non-specific adulterants.

The review highlights novel electrode materials, such as carbon-based nanostructures and ionic liquids, which enhance sensitivity and selectivity. Additionally, electronic tongues employing multivariate analysis have shown promise in distinguishing authentic products from adulterated ones.

The integration of machine learning and miniaturization offers potential for on-site testing, making these techniques accessible to non-experts. Despite challenges such as matrix complexity and the need for robust validation, the author concludes that electroanalytical methods represent a transformative approach to food authentication.

This review (purchase required) covers vanillin quality control approaches including conventional, hyphenated, and sensory analyses. Markers to differentiate between authentic, synthetic, and adulterated vanilla are highlighted using hyphenated techniques. It includes discussion of carbon isotope ratio range to identify vanillin originating from biosynthetic (C3 plant), synthetic (petroleum) sources, or vanilla pods. Novel extraction methods typically provide greater selectivity, higher purity, shorter extraction times, and ecofriendly attributes compared to conventional methods. The authors report that the best methods include supercritical fluids (SCF) or natural deep eutectic solvents (NADES) that promoted higher yield of vanillin.

The review also highlights the promising avenue of biotransformation, the safest technique for the production of vanilla flavour components, tackling current challenges and emphasizing its potential to meet the market needs for authenticated and high-quality yields of vanillin.

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Paper-based analytical devices (PADs) have the potential for low-cost, rapid point-of-use testing with easier and cheaper fabrication than (for example) 3D laser-printed microfluidics.

This review states that it covers cutting edge applications for food authenticity analysis and includes a section on how close some of the applications are to commercialisation.  There is no detail in the publicly-available abstract as to what topics or applications the review covers.  Purchase of the article would be needed to ascertain its use or relevancy.  It is published in a reputable peer-reviewed journal.

This review (purchase required) and its associated recommendations is primarily aimed at regulators and competent authorities, but also has implications for food businesses.

The aim of was to consider food-related fraud prevention initiatives, understand what has worked well, and develop a series of recommendations on preventing food fraud, both policy related and for future research.

The authors found that reactive (including intelligence based) food fraud detection dominates over prevention strategies, especially where financial, knowledge, and time resources are scarce. First-generation tools have been developed for food fraud vulnerability assessment, risk analysis, and development of food fraud prevention strategies. However, examples of integrated food control management systems at food business operator, supply chain, and regulatory levels for prevention are limited.

They conclude that the lack of hybrid (public/private) integration of food fraud prevention strategies, as well as an effective verification ecosystem, weakens existing food fraud prevention plans. While there are several emergent practice models for food fraud prevention, they need to be strengthened to focus more specifically on capable guardians and target hardening.

This review (open access) covers technological and digital solutions to mitigate food fraud risk, concentrating on recent developments.  It categorises solutions as either systematic interventions (e.g. risk prioritisation databases, digital fraud prediction tools), fraud detection techniques (analytical test methods) or package-level technologies (e.g. traceability systems, anti-counterfeiting markers, RFID tags).

It concludes that a notable gap exists in converting laboratory based sophisticated technologies to tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (liBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms that are combined with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. with the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.