Food 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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Honey is classed as one of the food most susceptible to extension, adulteration and fraud. This review in the Journal of Apicultural Science examines the most used analytical methods for verifying the geographical and botanical origin of honey. These include long established melissopalynological analysis, the analysis of the mineral profile or chemico-physical parameters, to the current state-of-the-art technologies and methods including the metabolomic and genomic approaches, the blockchain or Internet of Things. The review discusses the advantages and limitations of these methods, and highlights the approach that many methods are used in combination because a combined approach usually leads to greater accuracy. 

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Tequila is a distilled spirit made from the blue agave plant, and its authenticity and traceability are usually determined by inspection carried out by the Mexican Tequila Regulatory Council. This review looks at the alternative means of authenticating Tequila with analytical methodology, and gives a critical analysis of the available techniques.The use of isotopic ratios stands out as the most robust technique, because it establishes the type of sugar source used and the maturation time of the manufacturing process.

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The purpose of this study is to show how authenticity limits businesses’ responses to competition in the food and drink sector.  The results of the study were based on a unique dataset of over 300 small- and medium sized breweries, and more than 1,300 beer drinkers in Franconia (Germany) to test the impact of authenticity on breweries’ reactions to competition within geographic communities. The results reveal that breweries tend to enlarge their product portfolio by introducing non-authentic products as a response to competition in geographic communities, while reducing their product diversity and engagement in non-authentic segments when preferences for authenticity prevail in the geographic community. Furthermore, in geographic communities where both  competition and preferences for authenticity are present, companies tend to keep their product portfolios narrow and withdraw non-authentic products even when product proliferation strategies would be more efficient to deal with competition.

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Walnut oil (WNO) is a high value vegetable oil with high nutritional value. This study developed a method based on excitation–emission matrix fluorescence (EEMF) spectroscopy coupled with chemometrics and ensemble learning to authenticate WNO, and semi-quantify the adulteration with other vegetable oils. A total of 711 vegetable oil samples were analysed by EEMF of which 426 samples were pure WNO. The classification models were established by chemometrics and ensemble methods. The ensemble methods achieved better classification performance, and PLS (partial least squares) regression was applied to semi-quantify the adulteration levels of walnut oil, especially with rapeseed oil (RSO).

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Cinnamon is a highly traded global spice, and commercial cinnamon (Cinnamon cassia) is often fraudulently replaced by other varieties (Cinnamomum zeylanicum, Cinnamomum loureirii, Cinnamomum burmanni). In this study, a miniaturised device which used three different  spectroscopic techniques, namely, ultraviolet–visible (UV–Vis), near-infrared (NIR) and fluorescence (FLUO) spectrometry was tested to authenticate cinnamon samples. After chemometrics were applied to the spectra, a correct classification rate by variety of 89%, 90% and 89% for UV–Vis, NIR, and fluorescence spectroscopy, respectively, was observed. 

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The Chinese Meat Research Centre has compiled 1,817 reports of fraud and adulteration in the meat and meat products sector recorded by the national and imported food inspection services, as well as media reports between 2012 and 2021. The study covered fresh and frozen livestock, poultry, and various processed meat products. The 1,817 reports of fraud/adulteration can be broken down by 670(33.72%) cases from domestic information of official sampling inspections, 773(38.90%) cases were from imported meat and meat products rejection notifications, and 544(27.38%) cases were reported by the media. The study breaks down the cases into the individual types of fraud including artifical enhacement (adding unapproved additives to enhance the quality of meat/meat products), substitution (substituting original or labelled meat with spoiled meat or by-products, or other meat species),  mislabelling (including missing information, changing durability dates), counterfeiting branded products, dilution (adding water usually by injection), illegal imports (no health or origin certification), and certification fraud (health certificate forgery or alteration). The study revealed that the highest number of fraud cases was by artifical enhancement, followed by illegal imports, and then substitution, the total of which accounted for 97% of fraud cases.

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 The U.S. Food and Drug Administration (FDA) conducted a survey of imported honey for exogenous sugars in 2021 and 2022. More than 70% of the honey consumed in the U.S. is imported. From January 2021 to March 2022, the FDA collected and tested 144 samples from either bulk or retail packaged shipments labelled as “honey”, to determine whether they contained undeclared added sugars. About 40% of the samples originated in India and Vietnam, the top two sources of honey imported by the U.S. The analysis used was the AOAC method for C4 sugars (cane sugar and corn syrups). Of the 144 imported samples collected and tested, the FDA found 14 (10%) to have carbon isotope values  atypical of authentic honey, and were considered non-compliant. When the FDA found a sample to be non-compliant, it refused entry of the shipment into the U.S. and placed the associated company and product on Import Alert, which means that for those products to be admitted into the U.S., the company is required to provide evidence to the FDA to overcome the non-compliance, such as the test results of a third-party laboratory, verifying that the product does not contain added sugars.

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The Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture began a coordinated research project in November 2013 which finished in September 2018, on "Accessible Technologies for the Verification of Origin of Dairy Products as an Example Control System to Enhance Global Trade and Food Safety". Nine countries took part in the project, and the report reviews the studies done in each of these 9 countries (Argentina, Bangladesh, China, Lithuania, Morocco, Russia Federation, Slovenia, Sri Lanka, and Singapore) which demonstrates the successful use of stable isotope and trace element analysis, sometimes using other nuclear and complementary techniques to verify the origin of dairy products. The project generated 17 peer reviewed publications, and the report consists of  the main publications from each of the 9 countries on dairy products, which have resulted directly from the research undertaken in the coordinated research project. The paper from China in the report deals with the verification of organic pork from conventional pork. 

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Saffron is a high value spice and hence susceptible to adulteration and fraud. In this study, a machine vision system based on smartphone image analysis and deep learning was used to detect saffron authenticity and quality. A dataset of 1869 images was created of 6 types of saffron/adulterants including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stems of saffron; sunflower; saffron stems mixed with food colouring; and corn silk mixed with food colouring. The deep learning system developed for grading and authenticity determination of saffron in images captured by smartphones and applied to these images, was a Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN). After applying further data augmentation and comparison against regular CNN-based methods and traditional classifiers, the results showed that the proposed LAII-v4 CNN approach gave an accuracy of 99.5%.

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Seafood species substitution is one of the most common types of fraud.This study aims to develop new assays based on DNA to identify fresh mackerel (Scomber spp.) and mackerel species in canned products. Primers were designed to identify a DNA mini-barcoding region suitable for species identification of 4 commercial mackerel species Scomber scombrus,Scomber japonicus,Scomber colias, and scomber australaticus in processed products. Also a new assay based on RPA coupled with the lateral flow visualisation was developed for the identification of the most expensive species of fresh mackerel (Scomber scombrus).  

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 A printed edition of a Special Issue on Novel Analytical Methods  for Food Analysis has just been published, which contains reprints of articles that have been published online in the open access journal "Foods". There are four articles in particular with relevance to food authenticity: 

1. Assured Point of Need Food Safety Screening: A Critical Assessment of Portable Food Analysers
2. Suitability of High Resolution Mass Spectrometry for Routine Analysis of Small Molecules in Food, Feed and Water for Safety and Authenticity Purposes: A Review
3. Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation
4. Food Authentication: Identification and Quantification of Different Tuber Species via Capillary Gel Electrophoresis and Real-Time PCR 

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 A joint German government and industry project (QSPEC) is developing a new generation of analytical instruments with a sensitivity almost comparable to NMR, but at a much lower cost based on new laser sources which generate quantum frequency combs.

The research is developing instrumentation based on a method that uses entangled photons to measure the substance to be analysed at one wavelength, and detect the information obtained from it at another wavelength. The first step is to generate an entangled photon pair consisting of a long-wavelength and a short-wavelength photon. The long-wavelength photon now interacts with the sample, changing its phase, and then the manipulated photon pair is then fed into another process in which yet another photon pair is generated, giving rise to quantum interference and a spectrum based on the bandwidth of the photon pairs. The short-wavelength photons act as carriers of compositional information. The resulting spectra gives a fingerprint of the food, and its authenticty can be determined by comparing this spectra with those from a dataset of authentic samples.

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Screening assays, for example, lateral flow assays (LFAs), can improve traceability, but often lack the required reliability. This paper gives an alternative approach for secure on-site compliance testing, using allergens as a case study. As a screening assay, a smartphone raw image analysis of the LFA gives an initial quantification of the proteins separated in the LFA . The proteins are then extracted from the LFA, and analysed by LC-MS (liquid chromatography-mass spectrometry) to quantify them more accurately. This approach was applied successfully to the allergenic proteins in peanuts and gluten.  

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The European Commission's Joint Research Centre (JRC) has published its November 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 is an interesting article by Euronews on the fight against fraud in the spice sector, which includes a reference to the work of JRC's Food Fraud Unit. There is also reference to an article in Nature, which summarises the current state of knowledge on organised crime (e.g. fraud, drug trafficking and forced labour) in the fisheries sector.  

You can download the November 2022 Summary here

 A criminal network (41 arrests) have been closed down by this joint operation, which involved the illegal sale of horsemeat, and linked to a number of crimes including food fraud, money laundering and document fraud concerning the untraceable sale of horsemeat on the Spanish, Belgian, German and Italian markets. The criminal network acquired horses across Spain either free or for Euros 100 because they were not fit for human consumption. They were slaughtered in an illegal abattoir, transported and given false veterinary documentation, and butchered before selling them, and generating illegal profits of about Euros 1.5 million.

. Read the Europol press release

Adulteration of meat products using offal is one of the routes of fraud. This paper describes a method developed to detect the presence of pork liver by identifying specific peptide markers from its trypsin digest using  liquid chromatography-mass spectrometry. Although 74 specific peptides were initially identified from thermally processed pork liver, after examining peptides derived from heat processed pate-type products, five specific peptides were chosen as authenticity markers to confirm the presence of pork liver in highly processed meat products.  

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This study used Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. A set of 147 samples of five vegetable oils from different brands were analysed by the three different spectroscopic methods. After chemometric analysis of the spectra, the total correct recognition rate of the training set and prediction set of FTIR spectra was 100% for both, and for Vis-NIR spectra was 100% and 97.6% based on the PLS-DA method. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method was only 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique, probably because of a small amount of fluorophores in the oils.

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In this study, a pocket-sized spectrometer and multivariate analysis were used for rapid authentication of coffee varieties (Arabica and Robusta) in three physical states (raw beans, roasted beans and powdered beans) to check mislabelling and fraud. Two main coffee varieties were collected from different locations in Africa, and the three physical states were scanned in the 740–1070 nm wavelength range. The spectral data were pre-treated with several  derivative-based methods followed by chemometric analysis to build the prediction models for coffee beans (raw, roasted and powdered).  The best results  obtained for raw coffee beans was an accuracy of 0.92 and efficiency of 0.82; for roasted coffee beans, an accuracy of 0.92 and efficiency of 0.87; while for roasted powdered coffee, an accuracy of 0.97 and efficiency of 0.97. The results reveal that for a more accurate differentiation of coffee beans, the roasted powder offers the best results. 

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REIMS is a direct tissue metabolic profiling technique used to accurately classify tissues using available mass spectral databases. This study was made to evaluate the reproducibility of the analytical equipment, methodology and tissue classification algorithms using a single-source reference material across four sites with identical equipment in the UK, Hungary, The Netherlands, and Canada. This was followed by each site analysing four different types of locally-sourced food-grade animal tissue.  Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69–100% correct classification for food-grade meat samples. The latter was caused by differences in animal tissue from local sources leading to significant variability in the accuracy of an individual site’s model. The  results inform future multi-site REIMS studies applied to clinical and food samples, and emphasise the importance of carefully-annotated samples that encompass sufficient population diversity.  

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