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

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

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

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

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

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

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

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

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

Introduction to databases for non-targeted, multianalyte analytical methods

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Read full alert.

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

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

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

FSA-funded project: Review of current and emerging analytical methods for the testing of oil for authenticity (Project FS900520)

With funding from the UK Food Standards Agency, Fera Science Limited (Fera) in York, UK is currently undertaking a project to review the current and emerging analytical methods for testing edible oils and support the further development of analytical methods which will underpin and uphold the authenticity of edible oils in the supply chain. 

As part of the project’s evidence gathering, Fera would like to invite parties involved in sourcing, processing, and/or testing edible oils to participate in an online questionnaire. 

The fundamental mission of the FSA is food you can trust. The FSA strategy sets out FSA’s vision to ensure that the UK food system is safe, and that food is what it says it is. This involves building scientific capability in Public Analyst (PA) Official Laboratories (OLs) and working with Defra’s food authenticity programme to conduct research and development for analytical methods. Suitable analytical methods are required to ensure that food is what it says it is and to manage risk around food authenticity.

 As key stakeholders, your insight will help to inform FSA regarding issues in oil authenticity and future-proofed analytical tools to support both industry and regulators, while maintaining consumer confidence in our food. 

 Your participation will be very much appreciated and your views and insight will be invaluable to the project aims.

 A summary of key findings from the questionnaire will be included in the final report, but no sensitive information will be published.

Please complete the questionnaire here. If you have any questions, please contact info@fera.co.uk.

Your kind participation will be very much appreciated and your views and insight will be invaluable to the project aims.

Photo by Stephanie Sarlos on Unsplash

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.

Halloumi produced in Cyprus has a transitional exception until 2029 from the EU PDO regulations which stipulate that >50% of the milk content must be from sheep or goats.  This is because of the relatively low sheep/goat milk production on the island.  However, national Cypriot law still stipulates that the sheep/goat milk content must be >19% during this transition.  Major dairy companies on Cyprus have lobbied against this transitional law, arguing that it is unachievable without large scale import of sheep/goat milk powder.

It has been reported that a 2024 survey of one of the largest halloumi brands on sale in Cyprus found sheep/goat milk content at only 5%.  The same newspaper also reports that the regulators are working with Bureau Veritas on building a reference database of compositional parameters, to address longstanding analytical challenges in verifying the sheep/goat milk content of imported milk powder.

Photo by Ambitious Studio* | Rick Barrett on Unsplash

This study (open-access author’s link available until February 14, with thanks to Michele Suman for sharing) reports the development and validation of a non-targeted classification method for authenticity of dried oregano leaves by atmospheric pressure matrix-assisted laser desorption ionization mass spectrometry (AP-MALDI-MS).

The model was trained on 23 authentic oregano samples (sourced from a reputable company with full supply chain traceability - originated from Italy, France, Turkey, or Albania, harvested between 2019 and 2022) along with five pure adulterants (dried leaves of savory (Satureja montana), myrtle (Lagerstroemia indica), sumac leaves (Rhus coriaria), strawberry tree (Arbutus unedo), and olive tree (Olea europaea)), plus sixteen adulterated oregano samples, intentionally mixed with the above mentioned adulterants at ranges between 5 % and 60 %.

The most abundant signals were characterized by collision induced dissociation and library search, the spectral data were submitted to statistical analysis. A basal inquiry of the data by partial least squared discriminant analysis (PLS-DA) was carried out for the simple assessment of the discrimination capabilities of the ± AP-MALDI-MS signatures. The researchers then constructed two distinct random forest (RF) classifiers using the positive and negative most informative ions teased out by recursive feature elimination from the training sets. The aforementioned most significant variables (m/z values) were also merged by mid-level data fusion and used to build a third RF classifier.

They report that the cross-validations of the three RF classifiers achieved good outcomes as demonstrated by the satisfactory values of overall accuracy (84.9 %, 92.1 %, and 92.8 %, respectively). The three RF classifiers were tested on the hold-out data, which revealed reliable classifier performances (accuracy 80.1 %, 87.0 %, and 85.4 %).

Photo by 360floralflaves on Unsplash

This paper (purchase required) reports a method to differentiate pork gelatin from beef gelatin (down to 0.01% cross-contamination levels) based on the LC-MSMS analysis of 13 peptide marker ions (8 for bovine, 5 for porcine).  The authors report that their method was validated at three concentration levels and accurately identified the gelatin species in pharmaceutical capsules and gels.

LC-MSMS analysis of peptides provides an alternative approach to DNA testing, which has known difficulties in application to highly processed products like gelatin due to the low amount of viable DNA or distinctive fragments.  LC-MSMS is the approach described in a recent Defra research report which is referenced on the FAN research pages (scroll down table to FA0177).

A recent FAN blog described non-destructive impedance sensors as a tool to classify meat freshness.

In this paper (open access) the authors have used the same principle and developed a classification model for potato varieties based on the effect of their dry matter content on an electrical impedance sensor.  The test is destructive as the potato must be sliced.  The authors built a reference database from data from 9 cultivars (Actrice, Ambra, Constance, El Mundo, Fontane, Gaudi, Jelly, Monalisa and Universa) sourced directly from the grower.  These cultivars were chosen because they cover a wide range of dry matter content.  The authors collected multivariate analytical data from the impedance sensor; impedance magnitude and phase data along with derived parameters such as the minimum phase point of each spectrum, the ratio between the low- and high-frequency values of the impedance magnitude,  the dissipation factor, the distance between the zero and the maximum value of the Nyquist plot, and  the Cole model equivalent circuit parameters.

They conclude that machine learning methods for predicting potato dry matter and varieties, based on impedance data, can achieve an equivalent (sub-optimal) performance to conventional methods and that they hold promise for future improvement to surpass conventional methods. An improved deeper analysis could aim to reduce the root-mean-squared error and increase the coefficient of determination value, thereby enhancing the accuracy of dry matter data predictions. To achieve this, various techniques such as feature engineering, hyperparameter tuning, and advanced modelling approaches (e.g. convolutional neural networks) could be explored. The authors consider that alternate chemometric methods like the Kennard-Stone algorithm, which selects representative samples based on distance criteria, could lead to more robust dataset partitioning. Additionally, incorporating data fusion with results obtained through infrared spectroscopy could further improve the model’s performance.

Photo by Rodrigo dos Reis on Unsplash

This study (open access) investigated species substitution, mislabeling, and the sustainability of seafood products in the seafood markets of South China. 478 samples were purchased from retail markets in 11 cities across three provinces (Guangxi, Guangdong, and Hainan) between May 2021 and December 2023. Cytochrome c oxidase subunit I (COI) gene amplification was used to identify 156 fish species across 105 genera and 60 families. The researchers have published the correlation between genetic and taxonomical details.

The researchers used a combination of morphological and DNA barcoding methods to produce an atlas guide for these 156 economically important fish species.

Molecular identification revealed that 9.6 % (15/156) of fish species were mislabelled, with commercial fraud detected in three processed species: Hilsa kelee, Chelidonichthys kumu, and Argyrosomus japonicus. Some substitutions may have been unintentional.  3.8 % (6/156) of species identified were classified as threatened by the International Union for Conservation of Nature. The study also uncovered an example of illicit cross-border sales of fish products.

The authors conclude that their findings provide a technical reference for effective fish species identification and offer valuable insights into seafood market monitoring.

Photo by Dan Gold on Unsplash

Impedance is a complex Cartesian function describing the difference between an inputting and exiting sinusoidal electrical signal.  It can be depicted graphically as a plot (vector) of resistance vs reactance.  The linearity of this plot, and the angle of the vector, are distinctive.  In a sample of meat or fish, impedance is affected by the cell structure and the water content.  Both of which are an indicator of freshness.  An impedance sensor, comparing the result with a “normal” database, can therefore be used to detect unfresh meat or meat that has been prior frozen and defrosted without declaration.

This review (open access) describes published applications, comparing the technique with other approaches such as HADH Enzyme measurement (see FAN method explainers).  It concludes that the development of Impedance Sensor methods is now at a stage where the technique is ideal as a cheap, non-destructive inline check in the food industry, particularly if coupled with machine learning to spot unusual or anomalous samples.

Photo by Victoria Shes on Unsplash

The Soil Association (the UK's leading organic certification body) has issued an alert about fraudulent certificates from two named companies.  The alert notification includes a list of links to certification bodies and official websites where you can cross-check the veracity of certificates, in EU, US, GB and a number of other countries

This application note from Canadian testing company Purity-IQ builds upon published methods to describe the use of proton NMR in authenticity testing of herbs and spices.  Proton NMR, with non-targeted metabolomic profiling, can be used for botanical species authentication but also to detect product anomalies.  It is particularly useful for detecting dyes, as both natural and synthetic dyes tend to contain spectrally-distinctive aromatic ring structures.  In this application, the principle was demonstrated by the clear differentiation of paprika spiked with Sudan dyes, turmeric spiked with metanil yellow, and beet/grape extracts spiked with black rice extract.

Image from the application note.

The Institute of Food Science and Technology have published (here – open access) a new fact sheet on food crime and how to avoid becoming a victim.  IFST factsheets are intended to explain food science topics to consumers and small businesses in clear, concise terms.  This factsheet covers the types of potential food fraud, typical red flags that should raise warning signs, and confidential reporting lines if people have concerns.  It supplements and cross-references advice given by national regulatory agencies.

This E-seminar will introduce the viewer to the subject of sampling approaches for food analysis, focusing on those used by the UK Food Standards Agency (FSA).

Food sampling involves taking a sub-sample from a larger consignment to gain insight as to its composition. It performs an essential function in providing intelligence and evidence on the safety and authenticity of food and feed on the market, supporting enforcement action, where needed, to protect consumers. The process for undertaking sampling can be expensive and resource intensive, and therefore needs to be delivered in a coordinated and targeted manner to be effective in addressing identified gaps.

This e-seminar provides information to promote a better understanding of different sampling approaches that can be used in different situations.

This e-seminar was produced by the Joint Knowledge Transfer Framework for Food Standards and Food Safety Analysis, funded by the Food Standards Agency, the Department for Environment, Food and Rural Affairs, Food Standards Scotland and the Department for Science Innovation and Technology via the Government Chemist.

This eSeminar has been added to the eSeminar tab of the FAN Training section and can also be viewed here: