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This review (open access) provides an evaluation of microwave‐based systems (MW) in food applications, integrating both theoretical foundations and practical implementations. The fundamental principles of MW technology, including its theoretical background, sensing mechanisms, and imaging techniques, are discussed. The review then explores the applications of MW sensing and imaging in food analysis, encompassing contamination detection, moisture content evaluation, adulteration detection, quality control, and compositional assessment.

The advantages and limitations of MW systems for food applications are critically analyzed, along with an overview of commercial MW‐based technologies, relevant patent developments, and ongoing international research initiatives.

Finally, the future potential of MWS and MWI in the food industry is discussed, emphasizing their role in advancing real‐time, non‐invasive quality monitoring and strengthening food integrity.

The UK is actively developing a new Food Strategy, focusing on food security, health, environment, and the economy and published a policy paper on 15 July. 

Vision  

A healthier, more affordable, sustainable and resilient 21st century UK food system that grows the economy, feeds the nation, nourishes people, and protects the environment and climate, now and in the future.  

A healthier, more affordable, sustainable and resilient 21st century food system will deliver:   

• a thriving UK food sector that feeds a healthier and more productive UK population and enables economic growth
• a healthier population with reduced diet related ill-health, especially for children and vulnerable people
• better environmental outcomes on land and sea, enhancing nature and ecosystem services while reducing pollution, waste and greenhouse gas emissions
• improved resilience of the supply chain, with reduced impact of shocks and chronic risks on access to healthy and sustainable food.

Seed fraud, particularly the misrepresentation of rice paddy (unhusked rice grain) as rice seed, is a growing concern that threatens sustainability efforts.

This study (open access) proves the concept of using a portable NIR spectroscopic device, combined with chemometric analysis, for rapid onsite identification of rice seed and paddy varieties for real-time verification of seed authenticity.

A total of 280 rice samples, representing four varieties (Agra, Amankwatia, Legon 1, and Jasmine 85) across two categories (seeds and paddy), were analyzed.

After applying various pre-processing techniques and principal component analysis (PCA), the authors report that linear discriminant functions 1 and 2 revealed distinct clustering patterns for both the varieties and categories (rice seed and paddy). Among the classification algorithms used, Random Forest (RF) achieved 100 % accuracy for rice seed identification and 97.38 % for paddy identification in the test sets. Support Vector Machine (SVM) demonstrated 98.15 % accuracy in distinguishing between rice seed and paddy for detecting seed fraud.

The authors conclude that such a portable NIR device can reliably perform varietal identification and seed authenticity checks, including use by seed inspectors, farmers, and regulatory officers.

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This project conducted targeted surveillance sampling of retail food products for the Food Standards Agency (FSA) to identify emerging food safety risks and enhance the FSA’s intelligence on the food system. The programme was delivered in partnership with 3 Local Authority Official Food and Feed Laboratories (OLs) and 2 private OLs in England and Wales.

A total of 822 food samples from 24 different food commodity types were purchased from physical and online retailers in England, Wales, and Northern Ireland and were tested for compliance with relevant food regulations.

The samples were categorised into basket or frequently consumed foods, surveillance foods to inform the FSA’s knowledge of risk, and science and research foods to inform the FSA’s scientific knowledge and policy development. The overall findings showed that most foods were compliant with the testing and assessments undertaken.

Authenticity

A total of 260 samples were tested for authenticity and 94% were reported as authentic.

• When examined microscopically, 5 of the 30 oregano samples were found to contain other leaves in addition to oregano.
• Basmati rice has a unique aroma and flavour, and its cooking qualities make it a premium product. 13% of basmati rice samples tested were found to contain more non-Basmati rice varieties than is permitted.
• Out of the 30 pasta samples claiming to be made from durum wheat one was found to be unsatisfactory for authenticity with common wheat suggested to be present at a level greater than 3%.
• Also, 4 pork sausages were found to contain meat other than pork. The levels were low suggesting the presence was indicative of poor practice or cross contamination rather than deliberate inclusion. Similarly, one lamb mince ready meal contained other meat species, meaning the product was not what the buyer was expecting.

Composition

Compositional aspects of 405 samples were tested, and 87% were compliant.

• Compositional testing was conducted on orange juice which was found to be satisfactory in this regard. However, 23% of chicken ready meals and 23% of pork sausages contained less meat than declared on the label. Additionally, the fat content of milk was incorrect in 1 out of 5 samples tested.
• The claimed levels of caffeine in supplements were inaccurate in 18% of the samples tested. For olive oil samples, 17% did not match the defined profile for olive oils, and extraneous leaf matter exceeded permitted levels in 5 out of 30 oregano samples. Furthermore, 10% of fresh raw chicken samples contained undeclared added water.
• Levels of nitrates and nitrites greater than permitted were found in 3 samples of UK produced bacon. Additionally, 3 minced meat samples did not meet the claimed fat content or required collagen-to-meat protein ratios.
• A low alcohol drink was found to contain higher alcohol levels than claimed, and a non-dairy protein snack did not meet the claim related to protein content.
• Non-compliant composition in these 51 samples means that consumers are not receiving the products they expect or potentially pay a premium for.

This report has also been added to the 'Authenticity Surveys' part (2nd tab) of FAN's Research section.

The survey also reports on food safety related analytes.

This paper (open access) provides a comprehensive overview of emerging non-invasive techniques—such as fluorescence, near-infrared, mid-infrared, and Raman spectroscopy—for assessing meat quality and detecting adulteration.

The key novelty of this review is its integration of bibliometric analysis with a critical evaluation of advanced technologies aligned with the UN Sustainable Development Goals. Within the tabulated lists of published papers, the authors add their own 1-line opinion on the robustness of the underpinning database or chemometrics, and how near the work is to practical application.

The review highlights the potential of hybrid systems that integrate spectroscopy with chemometrics and machine learning to provide accurate, real-time, and sustainable meat authentication solutions. It also highlights research gaps such as the need for multi-adulterant detection models, standardized validation protocols, and open-access spectral databases.

The authors aim to align their commentary on innovation with regulatory and sustainability frameworks, including the UN Sustainable Development Goals.

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This study (purchase required) reports a targeted proteomics workflow that identified two peptide markers that could be used to identify chickpea protein in plant-based meat substitute food products.  Chickpea protein has been reported as a commodity with increasing supply and demand pressures as global demand increases.

The authors developed a high-resolution, targeted proteomics workflow for authenticating chickpea protein concentrates using LC-QTOF-MS/MS. Unlike broader spectral fingerprinting approaches such as spectroscopy techniques or nitrogen quantification, this method enables peptide-level specificity, allowing for robust detection in complex food matrices. The workflow used both in-gel and in-solution trypsin digestions

They report the discovery of two chickpea-specific legumin-derived peptides that were consistently detectable and unique among common plant, dairy, and other adulterant sources. To the best of the knowledge of the authors, these are the first peptides suggested for use of chickpea adulteration detection by any proteomics techniques.

They report that detection remained reliable even in commercial chickpea pasta samples containing about 20% total protein. 

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This paper (open access) reports the outcome of a study in which 104 cinnamon samples purchased at retailers in EU countries, have been investigated. The study showed that a high share of samples, 66.3%, either did not fulfil quality criteria set by international standards, were not compliant with European food safety legislation, were suspicious of fraud, or could be toxic for children due to a high content of coumarin. 

Substitution of Ceylon by Cassia cinnamon, so far the most recognised type of fraud, was not the problem most frequently detected in this study.  Many samples were classified as either strongly suspicious or suspicious, based upon being statistical outliers, but further investigation would be needed to confirm if adulterated. 

The authors report that the use of multiple analytical techniques, namely Energy Dispersive X-Ray Fluorescence, Head Space-Gas Chromatography-Mass Spectrometry, q-PCR, and Termogravimetric Analyses, was needed to cover the full range of irregularities detected in the study. 

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This comprehensive review (open access) covers methods such as near-infrared spectroscopy (NIR), Fourier-transform near-infrared spectroscopy (FT-NIR), mid-infrared spectroscopy (MIR), ultraviolet-visible spectroscopy (UV-Vis), Raman spectroscopy, laser-induced breakdown spectroscopy (LIBS), hyperspectral imaging (HSI), and digital and thermal imaging techniques.

The authors consider that HSI and other imaging systems are best suited for solid samples measured in reflectance mode. These techniques are ideal for analyzing products like eggs, meat, fish, seafood, and milk powder. On the other hand, spectroscopy methods such as Raman, NIR, and FTIR spectroscopy can be adapted for both liquid (e.g., milk) and solid samples. These methods allow measurements in reflectance, transmittance, or absorbance modes.

Spectroscopic methods provide detailed chemical composition analysis for precise identification of changes in food samples that could signal loss of freshness or adulteration. However, detailed preprocessing steps are required, and some methods, like FTIR and NIR, are affected by scattering phenomena in turbid samples. In contrast, HSI and other imaging systems are highly effective for providing spatial information. This makes them valuable for visualizing structural differences, such as changes in surface texture or temperature caused by microbial activity, improper storage, or the presence of adulterants.

The authors consider that digital imaging is the most cost-effective method, making it accessible for routine inspections. However, it requires good lighting and environmental conditions for optimal results. Additionally, digital imaging is limited to surface-level analysis and cannot detect internal defects, such as egg freshness. For such applications, thermal imaging is required, though it comes at an additional cost.

Denaturation, spoilage, or adulteration can impact animal protein-based food quality and cause changes in protein conformation and composition, as well as high absorbance and reflectance signals.

CNN-based models can further automate the extraction of high-level features from images. In cases where limited datasets are available, data augmentation techniques, such as rotation, flipping, and scaling, are employed to increase dataset diversity and improve model performance. Additionally, resampling techniques like SMOTE can be applied to address class imbalances by generating synthetic samples of minority classes, enhancing model predictability without overfitting.

Often, selecting the optimal Machine Learning and modeling approach is not straightforward, leading to the application of multiple methods to achieve the desired analytical outcome.  Models may perform poorly on new data due to model complexity, sample size and effect size. K-fold cross-validation is a common approach used in the studies reviewed in this paper. However, K-fold cross-validation assumes data point independence, which can lead to variability in results across different data splits. To mitigate this limitation, techniques such as stratified K-fold cross-validation  or Leave-One-Out cross-validation can enhance model generalizability. Similarly, mechanisms like ECA, LRN, conjugate gradient, and sequential minimal optimization methods can be applied to improve the robustness and generalizability of CNN-based models.

Photo by Victoria Shes on Unsplash

In 2009, the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (dMIQE) guidelines established standards for the design, execution, and reporting of quantitative PCR (qPCR) in research.

The expansion of qPCR into numerous new domains has driven the development of new reagents, methods, consumables, and instruments, requiring revisions to best practices that are tailored to the evolving complexities of contemporary qPCR applications.

Building on the collaborative efforts of an international team of researchers, updates, simplifications, and new recommendations to the original MIQE guidelines are presented, designed to maintain their relevance and applicability in the context of emerging technologies and evolving qPCR applications.

MIQE 2.0 has been added to FAN's Quality Section. Read MIQE 2.0 (DOI: https://doi.org/10.1093/clinchem/hvaf043).

The Government Chemist Annual Review for 2023 was presented to UK Parliament by the Parliamentary Under-Secretary of State for AI and Digital Government by Command of His Majesty. It was ordered by the House of Commons to be printed on 5 June 2025.

The Government Chemist Annual Review provides a summary of the work undertaken by the Government Chemist team, including highlights from the resolution of referee cases, advisory work and capability building activities. It includes work related to the in relation to food authenticity and safety, and includes an update on FAN. The review also details the impact of the work obtained though active engagement with a wide range of stakeholders.

Read full review.

The authors of this study (open access) used the results and datasets from 18 published projects and biobanks to build a database of bacterial metataxonomic data from fermented table olives.  The collated database contained database 442 samples of 16S rRNA bacterial profiles

They then compared three tree-based Machine Learning algorithms—Classification and Regression Tree, Random Forest (RF), and Extreme Gradient Boosting— to classify the origin or production process of the olives. They report that Machine Learning techniques can effectively classify bacterial profiles based on olive processing type, cultivar, country of origin, and isolation matrix. The Random Forest model achieved the highest accuracy, reaching 97% in the best cases, with a kappa coefficient above 0.8 for most categories.

They conclude that approach holds potential applications in the table olive sector and in other food products, where the industrial application of ML techniques to metataxonomic data could enhance traceability, authenticity, and quality control.

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The EU has updated its list (Delegated Regulation 2025/1184) of countries in which it considers controls against money laundering or terrorist financing are poor.  Any European business dealing with businesses in these countries is expected to enhance their financial due diligence checks and internal governance for contract review and sign-off.

Due diligence checking of new (and existing) suppliers is an essential fraud mitigation tool for any business, including food businesses.  The overall level of regulatory control in a given country, along with generic cultural attitudes to bribery and corruption, will inform this risk scoring.  A data source used by many businesses is the Corruption Perceptions Index league table published annually by Transparency International.

We have launched a new resource page (see 3rd tab) on our Food Crime webpages in order to collate estimates of the economic cost of food fraud.  If you are aware of other studies we can add to the three already cited then please let us know and we will add them to the page.

Estimates are inherently uncertain but the numbers are staggering.  Typically an annual $30 - $50 billion USD globally.  And that is just economic cost.  It is important to remember that food fraud often has a cost to human health or even life.

 Photo by Hossein Fatemi on Unsplash

Legal identity standards are an important benchmark for food authenticity.  They are set at a national (or EU) level, and can define everything from the minimum cocoa solids for chocolate to be branded as “chocolate” to the definition of a “meat pie”.  The difference between identity standards in different jurisdictions is a regular reason why internationally-traded foods are rejected as inauthentic. 

There are opposing trends in different parts of the world in terms of the scope and breadth of legal identity standards.  The EU has a wide range of Protected Geographic Indications, Protected Designations of Origin, and minimum specifications for many common foods.  The “Breakfast Directives”, covering jams, honey, fruit juices and milk, were tightened in 2024.  In the US, as part of the current national drive for deregulation, the FDA have just revoked 52 identity standards.  These mainly relate to foods that are seen as obsolete, or which have a very small commercial market within the US.  Meanwhile in India, where the scope of identity standards has been seen as narrower, the FSSAI have just tightened the norms for oils, sausages and colours.

With this continually evolving landscape, it is imperative that an exporting food company understands the identity standards of the territory where they are intending to sell.

See blog by legal firm Hogan Lovells on US deregulation of identity standards

See press report on India tightening of identity standards

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This work, originally presented at an Institute of Electrical and Electronic Engineers conference and now published in an IEEE journal (purchase required), provides an example of how a small team of researchers can develop a bespoke digital traceability system for the Agri-Food industry.  This provides an alternative approach to buying one of the distributive ledger systems available from large commercial software vendors.

The researchers developed a decentralized system for the agrifood supply chain that allows product traceability and quality assurance. System decentralization and privacy preservation were enabled through the combination of Self-Sovereign Identity (SSI), Decentralized Identifiers (DIDs), and Verifiable Credentials (VCs). DIDs provide stakeholders with complete control, eliminating the need for centralized identity providers. Role-based access control is facilitated through VC-Role, which defines the permissions of actors, and VC-Access, which ensures secure interactions with private blockchain channels.

The publication includes a description of the system architecture, DID and VC integration for access control, and a discussion of the QA requirements of the food industry.

The authors conclude that their system promotes traceability and ensures tamper-proof records of product quality. A proof of concept demonstrates the feasibility and potential impact of this approach in improving quality assurance.

This review (open access) covers food fraud in a wider context, including macro-economic motivation and opportunities for fraud.  The review has a particular focus on the Halal food sector within Islamic countries and communities.  After a discussion of modern “big data” analytical methods, such as spectroscopy and sequencing, it goes on to discuss point-of-use and real time testing approaches, sensors and internet-of-things, and predictive modelling  It concludes with the challenges in scaling some of these approaches, including inter-operability and data sharing, and makes a number of recommendations for capacity building in this field.  The authors propose a systems-level roadmap to bridge scientific innovation with regulatory and industrial application.

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This study (purchase required) assessed the nutrient composition and labelling accuracy of twenty-nine commercially available insect-based pet foods: twenty-four dog foods and five cat foods.  All were labelled as complete and balanced. Twenty were labelled as hypoallergenic. The products were analysed for proximate composition, essential amino acids, and mineral content (calcium, phosphorus, potassium, magnesium, copper, iron, zinc, selenium, mercury, and molybdenum) according to AOAC guidelines. The ‘hypoallergenic’ products were assessed for animal DNA using next-generation sequencing.

The results were compared with label declarations, considering nutritional and legal tolerances, as well as recommendations from FEDIAF and NRC for the intended species and life stages (g/1000 kcal ME). Heavy metals were compared to maximum tolerable limits from the FDA.

The analysis revealed that 22 products (76%) did not comply with declared nutritional values and tolerances for at least one nutrient, with nine products (31%) showing discrepancies in two or more; key issues were in crude fibre and metabolizable energy. Three products (10%) met FEDIAF’s recommendations, and seventeen (59%) met NRC’s recommendations. Only one (3%) adhered to both label and FEDIAF’s recommendations. Most nutritional inadequacies were seen in selenium, calcium, phosphorus, Ca/P ratios, and taurine, potentially posing health risks to pets.

Fifteen out of twenty (75%) hypoallergenic-labelled products complied with the labelled species.

Despite the potential benefits of insect-based pet foods, this study underscores the need for further research and stricter quality control to ensure safety and efficacy, ultimately improving pet nutrition and consumers’ trust.

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10 years ago, on the 14th July 2015, FAN was born - Happy 10th Anniversary to us! 🎉😍🏅🎂

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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