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Global demand for soyabeans has increase dramatically especially over the past 30 years, which has led to concerns about lower-than-expected product quality, adulteration, illegal trade, and deforestation. Therefore, development of effective analytical regimes to determine geographical origin and hence traceability, have become a priority. This is the first review that investigates current analytical techniques coupled with multivariate analysis for determining the geographical origin of soybeans. The 10 analytical techniques in 3 main groups outlined above are assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. The contribution of chemometrics in in analysing complex data is also covered. Each of these methods has advantages and disadvantages of its own. For example, the major drawback of geochemical techniques is their instrumental and operational costs, which can be mitigated by spectroscopic methods at the expense of sensitivity. Gaps in application of some analytical techniques are also identified. 

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10951670268?profile=RESIZE_710xThe Food Authenticity Network has worked with Defra, FSA, FSS, NFCU, SFCIU and the Food Authenticity Centres of Expertise to agree a framework for a co-ordinated response from food authenticity Centres of Expertise to national / international food and feed fraud incidents / investigations.

Official controls of food and feed labelling and compositional standards involves the verification of labelled product information and requires a wide range of analytical and molecular biological techniques to be deployed, many with exacting instrumentation requirements and in-depth scientific interpretation of the datasets generated. In recognition that no single institution could field the complete range of such techniques with the required expertise in all of the food and feed commodity groups, a number of Food Authenticity Centres of Expertise (CoEs) have been acknowledged (see below for list and further information). 

It was envisaged that the virtual network of  CoEs would function in a similar way to a National Reference Laboratory by helping to ensure that authenticity testing methods employed are fit for purpose and offer expert advice to the food authenticity analytical community as required. 

A framework has been produced for collaboration of Food Authenticity Centres of Expertise to facilitate the formulation a collective technical view, in response to a request from UK Government, during an emergency food or feed fraud incident/investigation. A collective technical view will facilitate UK Government in making evidence-based decisions in a timely manner so as to minimise the impact on legitimate businesses and protect consumers.

The Framework is not a public document but the process flow diagram is presented above to illustrate the process agreed. An accessible version is available here.

The Framework flow chart and the accessible version have also been added to the CoE page on this network.

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10951401661?profile=RESIZE_400xBovine hard cheeses such as Asiago, Parmesan, and Romano are widely sold and consumed in pre-grated form for convenience, and any adulteration is difficult to detect visually. In this study, an analytical method was developed using a simple organic extraction to verify the authenticity of bovine hard cheese products by examining the lipid profile of these cheeses using proton Nuclear Magnetic Resonance (NMR) spectroscopy. Sample preparation by the extraction of lipid material from the cheese was achieved using deuterated chloroform ( (CDCl3 99.8% D), which avoided the time consuming preparation of derivatisation for gas chromatography of non-volatile compounds, hydrolysis of triacylglycerols for analysis of lipids in mass spectrometry. Ungrated samples of the hard cheese (Parmesan, Romano and Asiago) were analysed by proton NMR to ascertain a lipid profile of unadulterated cheese samples. A series of adulterated samples were prepared with vegetable oil adulteration in ranges from 5–60 weight%  using rapeseed, grapeseed, peanut, olive, high oleic sunflower, high oleic safflower, high linolenic safflower, soybean, and palm oils, and lipid profiles of these were determined by NMR. The ungrated cheeses were found to have a very consistent lipid profile from sample to sample, improving the power of this approach to detect vegetable oil adulteration.However, it also revealed that the palm oil adulterated samples yielded a lipid profile nearly identical to one of the authentic cheese samples. Quantification of the level of palm oil adulteration in these samples was achieved by generating calibration curves of two peak ratios.

In order to test out the method, 52 market samples of grated Parmesan, Romano, and Asiago cheese were purchased from retailers, restaurants, and public school cafeteria kitchens and analysed by NMR. It was found that 29% of all samples tested were certainly adulterated with palm oil. Palm oil is a clever adulterant owing to its similar lipid profile. Also, it is semi-solid at room temperature with a similar colour to cheese, and a low price compared to cheese. 

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Herbs and Spices are a group of foods highly susceptible to adulteration and fraud because of their high value. In this study, a new high performance liquid chromatography (HPLC) method with UV–vis detection was developed for the characterisation, identification and authentication of cinnamon, oregano, thyme, sesame, bay leaf, clove, cumin, and vanilla. This was achieved by the chromatographic separation of a methanol extract, and identification of 6 phenolic biomarkers (sesamol, eugenol, thymol, carvacrol, salicylaldehyde and vanillin) analysed on 87 samples of the 8 herbs and spices. The data was first treated by PCA (principal component analysis) followed by PLS-DA (partial least squares discriminant analysis) to give good classification between the 8 herbs and spices. The assay thus provides a relatively inexpensive, quality control and screening tool to ensure a correct assurance of the studied spices and herbs.

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The quality and quantity of the extracted DNA are two key aspects for a successful PCR (Polymerase Chain Reaction) amplification. Also, a reduction in time and cost required for DNA extraction are important. The aim of this study was to compare and optimise the performance of five different DNA extraction methods by boiling meat tissues from cattle, buffalo, sheep, goat, chicken, camel, horse and dog in PBS (Phosphate Buffer Saline), distilled water, alkaline lysis buffers 1, 2 or 3. The results indicated that the boiling of meat and its products in alkaline lysis buffers was the best method to extract crude DNA. The optimised crude DNA extraction protocol was coupled with PCR-RFLP (Restriction Fragment Length Polymorphism) analysis for meat species identification. The developed assay was tested on 53 commercial beef and mutton samples, out of which three samples were found to be adulterated.  

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10949009861?profile=RESIZE_400xAquacultured prawns were the second highest export of Bangladesh, but have now been renegated to seventh place. In the period from July to December 2022, 1,660kg of prawned were seized and destroyed by authorities because they were found to have been injected by a jelly-like substance to increase the water content. The injection was carried out after harvest, and by some traders in the supply chain. Exporters are worried that these incidents may reduce exports even more.

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Authentication of Incense Honey by Real-Time PCR

10948956673?profile=RESIZE_400x Incense honey, which is a unique nectar honey from the Azores archipelago, should contain over 30 % of pollen grains of the incense plant (Pittosporum undulatum Vent). In this study, a real-time PCR approach using a TaqMan probe to target the ITS region of P. undulatum was developed to specifically detect and quantify incense DNA in honey. The ITS marker developed and used for authenticating incense honey, showed high specificity against several plant species, including endemic species from the Azores archipelago, and high sensitivity, down to 0.01 pg of DNA, for P. undulatum. The method was successfully applied to 22 honey samples, from which incense DNA  was detected in all 9 monofloral incense honeys, and in 50 % of the multifloral samples from the Azores. Generally, the quantitative results for incense DNA were in good agreement with the melyssopalynological analysis, showing that all samples complied with their labelling, except for 2 multifloral honey samples that should have been classified as monofloral of clover and monofloral of incense.

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This paper proposes an innovative method based on AI, to reinforce traceability systems in detecting possible counterfeiting by product substitution. It is an item-based mass balance method that analyses the agreement of the traceability data flows not by using explicit (even stochastic) rules, but by exploiting the learning capabilities of a neural network. The system can then detect suspect information in a traceability data flow. The AI-based method was applied to a pork slaughtering and meat cutting chain case study, and used the weights of different cuts of a pork carcase as the training phase of AI. Any analogous carcase information along the supply line might indicate substitution or modification of the pork carcase cuts. 

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10948923475?profile=RESIZE_192XThe Food Standards Agency (FSA) Board agreed to a full organisational review of the NFCU, which had been given expanded activities in June 2018 from a strategic and tactical intelligence capability to a comprehensive response to criminal threats and vulnerabilities, including investigative capabilities. Selvarani Elahi, our Executive Director, was part of the review team that carried out the review from June to October 2022. The review collected evidence from 28 focus groups and 40 external stakeholders from the food industry and local authorities, as well as staff from the FSA/NFCU.  The Review reported 22 findings and made 5 recommendations:

  1. Clearer definition of the Unit’s purpose, with performance indicators aligned to its strategy.

  2. Using this enhanced clarity to assess ‘as-is’ capability, and then design and build the required ‘to-be’ position.

  3. Ensuring access to the latest tradecraft and capability within law enforcement to enhance capabilities.

  4. Nurturing of internal culture and improvements to internal career pathways.

  5. Better projection of the Unit, its food crime messaging and its successes.

  Read the News story here

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 10946299261?profile=RESIZE_710xThe European Commission's Joint Research Centre (JRC) has published its December 2022 Food Fraud Monthly Summary reporting food fraud incidents and investigations from around the world. These have been kindly represented as an infographic above by our Member Bruno Séchet, and thanks for allowing us to share it with the rest of the Network.

Also included in the Summary are two interesting articles. WHO (World Health Organization) has published the "WHO global strategy for foodsafety 2022-2030", which mentions food fraud as an important element to be taken into account in order to build more solid global food systems. Also AVICA (Information and Food Control Agency of Valencia, Spain) reported (in Spanish) 43 fraud cases in 2022, which is its first year of operation.

You can download the December Summary here   






















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Defra has published the final report of Project FA0185, which is a review of the state of the art of methods for offal species identification, and identify new methodology that is fit for purpose for offal identification in meat products. This will provide enforcement authorities with tools to verify labelling of offal in meat products and detect the undeclared presence of offal tissue in meat products – protecting consumers from food fraud.

The aim of the project was:  

To identify the most appropriate methodology of offal detection in meat products that is applicable to real-world scenario usage and general implementation to support UK enforcement. Once the method has been identified, future work will be drawn up to validate and disseminate the methodology.

The objectives of the research were:

  1. Make a comprehensive review of current capabilities and emerging technologies to determine offal. This will include a literature review (of both grey literature and peer-reviewed academic literature) and engaging with academia, instrument and technology manufacturers, the UK meat industry and enforcement agencies. The outcome will be a report of the information and a comparison of the methods or potential emerging technology.
  2. Determine critical requirements to identify end user needs. This will involve setting up a stakeholder group with Defra to review and determine the critical requirements of a method to detect offal in meat products.
  3. Design scenarios required by Government in support of industry, and a proof of concept analysis using the most promising approaches identified in Objectives 1 & 2. Firstly, taking into account intelligence from survey work by Defra and from wider government and stakeholders, the most appropriate offal types to focus on will be identified. Secondly, up to three of the most appropriate technologies identified in the first objective will be trialled at Fera with two different offals.
  4. The submit a final report with all the findings and data generated during the project including a comparison table of all the road tested technologies, a recommended method/test to determine the adulteration of food with offal and a scoping study to detail the future requirements to ensure the method is fit for purpose.

The project report can be downloaded here, and the link found in the Research Table under FA0185

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10944562296?profile=RESIZE_192X This Defra project FA0178 was a review of Point of Contact technology, through a food authenticity lens, with a specific focus on its application for verifying food fraud (i.e. to detect misrepresentation and mislabelling of foodstuffs). 

The objectives of the research project were to:

• Explore the availability of POC tests from a food authenticity testing perspective, the pipeline of test development, and the potential use of POC technologies currently employed in related arenas.
• Understand in which new situations POC tests could be applied. Any benefits should be quantified wherever possible.
• Understand the opportunities and limitations from using POC technology and the extent to which these limitations impact on the development of potential applications.
• Explore the maturity of underpinning support mechanisms (such as validation), and the challenges and opportunities of operationalising tests, particularly in relation to knowledge transfer, competence and training.

The report and 5 Annexes can be downloaded here, and you will also find the link on the Research Table on the website under FA0178.

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10944116089?profile=RESIZE_400xThe Food Authenticity Network is pleased to announce that the four FOOD AUTHENTICITY trademarks shown are now UK registered trademarks of LGC.

The trademarks cover Services undertaken in:

  • Class 41 - “Education; providing of training; entertainment; sporting and cultural activities; organising webinars; arranging and conducting of educational discussion groups; arranging and conducting conferences, seminars, workshops, and meetings.”
  • Class 42 - “Scientific and technological services and research and design relating thereto; industrial analysis and research services; design and development of computer hardware and software.”
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10943952272?profile=RESIZE_584xThe Food Authenticity Network is delighted to have been interviewed by Nick Hughes and be featured in this article in The Grocer that marks the tenth anniversary of the 2013 horsemeat issue, which rocked the food industry world-wide.

A decade on, there is widespread recognition that much of this trust has been rebuilt. Yet as we move into 2023, experts warn that a new perfect storm of factors is creating an ideal set of conditions for fraudsters to exploit. 

On the tenth anniversary of one of the industry’s darkest episodes, it’s timely to ask the question: is our food chain any safer from the risk of fraud?

“We have many more lines of defence [now],” says Emily Miles, CEO of the Food Standards Agency. “But that doesn’t mean there couldn’t be another scandal [like horsemeat].”

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The development of rapid in-situ tests without the need for sophisticated laboratory methods for screening ingredients/food for authenticity is increasing in use in the food industry. This study developed a rapid lateral flow immunoassay for identifying meat species in raw, heat processed and commercial meat products and offal. Firstly, a simple extraction protocol was developed for the efficient recovery of meat proteins, which was field-deployable. The extract was placed on a sandwich-format lateral flow immunoassay (LFIA) based on gold nanoparticles as labels and immunoglobulins (IgG and IgY) as biomarkers for meat species identification in raw and cooked meat mixes. The whole procedure for extraction and identification took only 15 minutes in total, and had a high sensitivity. The assay was validated both internally and inter-laboratory testing and by real-time PCR.  

Read the abstract here

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10936849884?profile=RESIZE_400x  IAEA have just published a book on AI accelerating nuclear applications, science and technology.  Chapter 5 deals with AI applications to food and agriculture, and in particular to food authenticity methods, food fraud detection and traceability. The advantages and limitations for AI, and ML (machine learning) applications are discussed in sample preparation and calibration involved with authenticity methodology, the advantages of data sharing, but with the proviso that data-driven decision-making is only as good as the data used. 

Read the abstract here and the full pdf version of the book and Chapter 5 in the above link


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Durum wheat is used for the manufacture of pasta. Declaration of geographical origin is an increasing regulatory requirement. In this study, authentic samples of durum wheat from different Italian, European, and non-European regions harvested in different years were collected, and their 87Sr/86Sr isotopic ratio determined. The samples were also analysed by inductively coupled plasma mass spectrometry (ICP‒MS) to determine 75 elements. A tiered approach was adopted in which the results of the 87Sr/86Sr analysis were input to a second step of support vector machine classification modelling (SVMC) based on the concentrations of 8 of the 75 elements ( Al, Mn, Mo, P, S, Ti, Y, and Zn). The model was validated against a blind set of samples. This predictive model was able to distinguish satisfactorily between Italian durum wheat and durum wheat from the rest of the world.

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Chris Elliott on BBC Radio 4 - Fighting Food Fraud

10930141894?profile=RESIZE_400x Chris Elliott was the the guest on BBC's Radio 4 programme - The Life Scientific talking about his life and his work on food fraud. The recording of the programme will be available for over a year from 10 January 2023. Chris was a founding director of the Institute for Global Food Security (IGFS) at Queens University Belfast. After the horsemeat fraud incident in 2013, he conducted an independent review of the UK food system. Following Recommendation 4 in the review report, LGC set up the Food Authenticity Network with funding from Defra. Chris also goes into details of the latest developments in fighting food fraud.

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10929285292?profile=RESIZE_584xThis paper describes a new non-targeted method (NTM) for distinguishing spelt from wheat, which aids in food fraud detection and authenticity testing. A spectral fingerprint was obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Neural network (CNN) models are built using a nested cross validation (NCV) approach neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. The method was validated using artificially mixed spectra from samples of processed spelt bread and flour, comprising of eleven untypical spelt, and six old wheat cultivars, which were not part of model building. The results showed that based on the same chemometric approach, the non-targeted method is reliable enough to be used on a wider range of cultivars and their mixes.

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10929257069?profile=RESIZE_710xFood Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. This paper gives an update on the application of Traceability 4.0 in the fruit and vegetable sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. A barrier to its implementation is that most of the advanced technologies have not yet gone beyond the laboratory scale, and hence have high implementation costs and lack of adaptability to industrial environments.

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