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One potential fraud is the undeclared bulking of meat products such as burgers and sausages with cheaper plant-based ingredients.  The authors of this paper (purchase required) describe a rapid (20 hours), robust and sensitive method for the detection of 23 foreign protein sources (alfalfa, buckwheat, broad bean, chia, chickpea, coconut, egg, flaxseed, hemp, lentil, lupine blue, maize, milk, pea, peanut, potato, pumpkin, rapeseed, rice, sesame, sunflower, soy, and wheat) in meat products using high performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS). It includes a new rapid defatting procedure, without carry-over effect and suitable for routine analysis, a robust protein extraction protocol (TRIS/HCl (1 M, pH 8.2) with 40% acetonitrile), suitable for various animal- and plant-based proteins including grain proteins, and new peptide markers for buckwheat and potato. An investigation of the influence of cooking (emulsion-type sausages), grilling (hamburger patties) and maturation (salamis) showed that the detection method is robust against different types of processing. The authors report that the limits of detection for all foreign protein sources were ≤ 0.02% protein and that no false-positive or -negative results were obtained.

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Honey authentication is an area where there is a continual “arms race” between analytical methods and methods of adulteration or other fraud. This review (open access) provides a comprehensive overview of honey authenticity challenges and related analytical methods. It describes direct and indirect methods of honey adulteration and the existing challenges in current detection methods and market supervision approaches. The authors focus on the advantages, disadvantages and scopes of integrated metabolomic workflows involving sample processing procedures, instrumental analysis techniques and chemometric tools. They discuss various improved microscale extraction methods combined with hyphenated instrumental analysis techniques and chemometric data processing tools. They conclude that the future of honey authenticity determination will involve the use of simplified and portable methods, which will enable on-site rapid detection and transfer detection technologies from the laboratory to the industry.

Earlier this year, the Food Standards Agency (FSA) commissioned the UK National Reference Laboratory for GMOs based at LGC (Teddington), to deliver a desk-based review of the current state-of-the-art associated with methods for the potential detection of (PBOs) in the food and feed supply chains.

Precision Bred Organisms represent organisms which possess genetic variability resulting from the application of modern biotechnology, which could also have arisen through traditional processes. In March 2023 the Genetic Technology (Precision Breeding) Act was passed in the UK, which brought forward primary legislation to amend the regulatory definition of a Genetically Modified Organism (GMO), to exclude from it those organisms that have genetic changes that could also have arisen through traditional processes.

The report, written by Malcolm Burns and Gavin Nixon, captures use of sector specific terminology and related international developments. A focus is given on the current scope and challenges for the analytical detection of specific DNA sequences alongside supportive traceability tools inclusive of reference materials and databases. The report provides a series of recommendations towards helping develop a framework for the traceability of PBOs as well as some of the future analytical challenges this presents.

The report will be of interest to scientists and analysts involved in developing molecular biology assays for the detection of small DNA sequence changes, government departments and related stakeholders involved in assessing the efficacy of methods for the traceability of PBOs, as well as to a broader audience (e.g., academia, industry, retailers, etc.,) who are interested in some of the scope and challenges that detection of PBOs may present.

The report can be found here and a pdf version here. 

Baltic salmon is relatively low value, because of concern about environmental contaminants, and can be fraudulently substituted for salmon from more premium origins.  These very pollutants have been used by a recent study (purchase required) as an analytical marker for geographic origin.  The authors used public data from national surveys of dioxins and PCB congeners in salmon to train a Machine Learning classification model.  The model could differentiate Baltic salmon from other origins (China, Chile, Canada, Norway, USA, and Vietnam) based on their analytical profile of chlorinated persistent organic pollutants.

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It has been reported that Spanish authorities have siezed 28 tonnes of food with tampered expiry dates.  The food is unfit for human consumption. Items were found in the facilities of companies in the provinces of Zaragoza, Valencia, and Almería, according to the Guardia Civil. Officials said meat products, frozen fish, and other items were sold with expired shelf life dates, manipulated labels, and irregularities in traceability. Eight people were arrested, and another 81 are being investigated for crimes, including fraud, public health offenses, and document falsification.

A masters thesis project sampled 106 retail packs of frozen shrimp (prawns) on sale in California and tested them for declared weight (i.e. excluding glaze), declared species and declared country of origin.  26% of samples had >20% glaze and 37% were legally underweight, rising to 57% in the super/extra colossal shrimp category. Ambiguous or incorrect species labelling was also observed in 37% of samples.  The vast majority (98%) of country of origin labels were verified as correct.

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The purpose of this review (purchase required) was to identify the most susceptible cheese type for fraud and the most commonly reported methods for evaluating fraud in all types of cheeses. The authors conducted a systematic review of the scientific literature. They conclude that Mozzarella cheese was most reported to be adulterated or at risk of adulteration.  The methods that were most used in detecting fraud were PCR and spectrometry methods, with less use being made of stable isotope, image analysis, electrophoretic, ELISA, sensors, near-infrared and NMR. The least used method was sensory evaluation.

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The authors of this study (here – purchase required) recommend a protocol for authenticity testing of processed meats that combines PCR with histology.  PCR indicates species substitution whilst histology indicates substitution with cheaper organs from the same species.  They reported that, for a market survey of 105 sausages and beef hams in Iran, neither technique on its own gave a true picture; in fact, by PCR alone, all of the sausage samples would have been reported as unadulterated.  It was only by combining the techniques that they found over 55% of the hams and 65% of the sausages to be adulterated, mainly with cheaper tissues from the same species.  It proves the continued value of histology, which requires experienced microscopists to interpret slides; an increasingly rare skill within routine analytical laboratories.

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A recent publication (here – open access) describes the development and validation of a smartphone reader to combat the previously reported fraud of adding red dye to cheaper species of tuna and passing them off as Thunnus thynnus (red tuna).  The red colour is associated with freshness in the minds of consumers.  The authors built a reference database of colourimetric readings (as CE XYZ data) using a spectroradiometer.  This classified tuna by species and/or by adulteration with beetroot extract.  They then used the pro camera on a commercial smartphone (Galaxy 7) to read test samples.  Reading with the smartphone required a dark room.  They used statistical functions to convert the camera’s RGB reading to CEXYZ and an achromatic reference sample to convert to CIELAB colour space to then match samples against the classification model.  They reported good matching, with a 0.6% false negative rate and 10% false positive rate for validation samples classified as adulterated or mis-labelled.

The Insider Business US TV documentary “11 of the most faked foods in the world” has now been posted on YouTube and is generating interest (2M views in 3 days).  They cover truffles, maple syrup, wasabi, Parmesan cheese, vanilla, caviar, honey, olive oil, Wagyu beef, coffee and saffron.  For each, they describe why it is a premium product and how it could be adulterated or substituted.  They do not discuss why they chose these foods as the “most faked” or justify the conclusion that consumer purchases in the US are “probably faked” but they do discuss examples and case studies.

Laser Photo-Acoustic Spectroscopy (LPAS) can be used to obtain a spectrum of dried herbs and then classification models can be built to detect adulteration.  It is analogous in this way to conventional Infra-Red spectroscopy.  The advantage of LPAS is the power of the source; a laser, vs a lamp.  In this publication (a preprint, not yet peer-reviewed) the authors report the development of an LPAS system trained and calibrated to detect olive leaf adulteration in oregano down to 20% and ideal for in-line use in an industrial setting.

A recent paper (purchase required) describes a feasibility study to use non-destructive ultrasonic inspection to detect olive oil adulteration with two other edible vegetable oils (sunflower and corn). Pulsed ultrasonic signals with a frequency of 2.25 MHz were used. Test samples were adulterated in variable percentages between 20% and 80%. The viscosity and density values were shown to correlate with acoustic parameters (ultrasound pulse velocity, frequency variables obtained from the Fast Fourier Transform, and attenuation) when measured at both 24 °C and 30 °C.  Acoustic parameters were able to discriminate adulteration at all of the percentages and mixes studies. The responses obtained through the parameters related to the components of velocity, attenuation, and frequency of the ultrasonic waves are complementary to each other. The authors concluded that classification of pure and adulterated oil samples is possible through non-destructive ultrasonic inspection.

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This paper (purchase required) describes the development of an array of 14 sensors based on colourimetric reactants immobilised on a paper or plastic support.  The advantage of this approach, rather than traditional “e-tongue” systems based on reactions in liquid solution, is that it enabled the development of a “dipstick” test that could be taken into the field rather than having to send test samples to a laboratory.  The authors report that they successfully used Machine Learning to train the system to discriminate between different botanical and local geographic origins of Iranian honey and also to discriminate when honeybees within these specific classifications had been illicitly fed with sugar syrups.  It shows the potential of a cheap field-based test which would be trained and used for verification testing at the beginning of honey supply chains where very specific "authentic" classification references are available, rather than relying on testing further up the chain when chemometric classification becomes much more diffuse and difficult.

Thanks again to FAN member Bruno Sechet of Integralim (www.integralim.net)  who has formatted the JRC monthly food fraud report as this pictorial infographic. 

The original report, along with those from previous months, can be found here. 

Remember that you can sign up on the JRC website to be notified when each report is published. 

An Australian survey has revealed a serious problem with mislabelling of seafood products.  More than 10% of retail and food-service samples tested were found to be mislabelled as to their species.  Shark and ray products had the highest occurrence of mislabelling at 35.9%. A specific issue was the mislabelling of protected species.  DNA barcoding revealed that in 18 instances, products incorrectly labelled as "flake" – or to a lesser extent as "shark" – were in fact holocephalians (i.e. chimaera fishes).  1 sample of spotback skate was mislabelled as "stingray" and 1 sample of school shark mislabelled as "gummy shark", both of which are critically endangered.  In total, 672 samples from supermarkets, fishmongers and restaurants were sampled across seven Australian states.  The study, by the Perth-based Minderoo Foundation (a privately-funded sustainability campaign group) was published in Nature and has been picked up by the media (example here)

A new vacancy for a Senior Prevention and Relationship Management Officer has arisen at the Food Standards Agency's National Food Crime Unit (NFCU).

You will lead and line manage a team ensuring delivery of Prepare, Prevent and Protect projects, enabling the NFCU to achieve the intended outcomes of its 4P strategic and tactical action plans. Working closely with the food industry, other regulatory partners and internal partners, within the NFCU and wider FSA, the team work to support identification and mitigation of known vulnerabilities and emerging threats and risks. You’ll nurture and encourage the team to develop and implement innovative methods and tactics, from conception to delivery, which protect food businesses and the public.

You'll be playing your part in keeping food safe and what it says it is, and protecting UK consumers from deceptive practices in the food sector. 

For further information / to apply: Senior Prevention and Relationship Management Officer - Civil Service Jobs - GOV.UK

The deadline is 3rd September 2023.

A recent article describes a novel approach using Machine Learning to detect large-scale fraudulent online sellers by recognising give-away signs in how they conduct their digital marketing. The strategy is scalable. The authors employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviours and transactions on a popular online marketplace platform. They combine bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings. Their best detection model obtained a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set, based on 45 selected features.

The use of calcium carbide to artificially ripen fruit such as bananas and mangoes is suspected to be prevalent in many countries.  It is cheap and effective.  It is also banned in most countries; calcium carbide is a carcinogen.  The authors of this paper (open access) have developed a cheap and simple test for calcium carbide that can be used a point-of-sale screening tool by enforcement officials or even consumers.  It is based on an instant colour change when free sulfahydryl groups (a ubiquitous impurity in calcium carbide) reacts with 5,5′-7 dithiobis-(2-nitrobenzoic acid.  This colour change is visible to the naked eye at calcium carbide concentrations typically used to ripen fruit.

It is 10 years since the Global Food Safety Initiative (GFSI) launched an interdisciplinary approach to detect food fraud and to understand and reduce the root causes.  This review article by Prof John Spink (purchase required) reflects on its evolution and impact.  He concludes that GFSI achieved momentum by establishing a food industry-wide definition and scope of the problem. Focusing on all types of fraud and for all products – not just incoming goods and adulterant-substances – created a holistic approach. Prof Spink describes this evolution in terms of the “hype cycle”, and concludes that food fraud prevention is is now in the ‘Scope of Enlightenment’ (e.g., processes and systems are simplified and optimized) and moving to the ‘Plateau of Productivity’ (e.g., standard operating procedures are adopted). GFSI, itself, is maturing and evolving through the ‘hype cycle.’ He predicts that the next ten years will include a more rigorous and thorough adoption of management systems with a continuous improvement process.

Saffron is one of the highest value food products and so often a target for adulteration.   Carthamus tinctorius L. (safflower stamens), dyed stamens of Nelumbo nucifera Gaertn. (lotus stamens) and stigmas of Zea mays L. (corn stigmas) have all been reported as bulking agents.  In this paper (purchase required)  the authors have developed a cheap and quick fluorescence probe which is selective to saffron and can be calibrated to detect dilution or bulking.  The three-dimensional (3D) fluorescence uses a hydrophilic hydrazine-naphthalimide functionalized chitosan (HN-chitosan) polymer probe. The amino functional group in the HN-chitosan probe react specifically with the oxygen-containing group of active ingredients in saffron. The authors applied four advanced chemometrics methods for the classification of saffron and adulterated saffron, and reported good performance in both training and prediction sets. They successfully applied the PLS regression model to the prediction of adulteration levels in saffron. They conclude that this strategy provides a new solution for rapid identification and quantification of adulteration in saffron.

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