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This study (open access) used literature reviews and stakeholder interviews to construct a generic supply chain map and identify fraud and food safety vulnerabilities for edible insects.  Safety concerns discussed include novel allergenicities and the effect of different processing methods on microbiological safety.  The main fraud risk discussed is the artificial enhancement of apparent protein content by adding an adulterant rich in nitrogen (as per the motivation for melamine adulteration of milk powder).

Conventional DNA authenticity analyses (RT-PCR) requires samples to be sent to a laboratory. Point-of-use tests, using isothermal amplification, are well characterised but are not in routine use (mainly due to cost and lower sensitivity).  This study (purchase required) compared four such amplification techniques for the identification of chicken DNA: loop-mediated isothermal amplification (LAMP), denaturation bubble-mediated strand exchange amplification (SEA), cross-priming amplification (CPA), and recombinase polymerase amplification (RPA).  The researchers focussed on the limit of detection, simplicity, amplification time and cost. The LAMP, CPA, and RPA primers all targeted the chicken mitochondrial cytochrome b gene. The SEA primers were provided by the SEA kit. The authors found that all methods showed good specificity to chicken.   0.1% chicken in mutton could be detected using LAMP and RPA methods. The authors considered that, although RPA costs 10 times more than LAMP, the system and primers of LAMP are far more complex. Therefore, they concluded that RPA is the most suitable method in multiplex detection, and LAMP is much better than the other three methods in single-plex detection.

Photo by Braňo on Unsplash

Legal firm Khaitan & Co have collated a useful 6-month summary of new Indian food safety regulation, FSSAI guidance, and regulatory consultations.  The summary period is from July to December 2023.  It can be accessed free here.

As the world's human population grows and climate change impacts food security, digital technologies are becoming increasingly critical for ensuring transparency, resilience, and fairness across the food system.

However, technology and digitalisation are often expensive and can be out of reach for small-scale farms and businesses. So, how can we ensure these technologies are available to all stakeholders, and the associated food is affordable for all consumers?

Here are four ways that technology can increase food system transparency, resilience and fairness (FAO report: Five ways science, technology and innovation are helping to transform the world's agrifood systems): 

1. Track food supply chains and collect secure data
2. Increase access to information and enable communication between stakeholders and with consumers
3. Monitor and predict changes to reduce and prevent risk
4. Connect small-scale producers and increase access to networks and services.

Read EIT-Food's blog on how can digital technologies increase food system transparency, resilience, and fairness?

The Agricultural and Food Chain Supply Act established a new regulator in the Republic of Ireland which came into force in December 2023.  The remit is to protect against unfair commercial terms in the Agri-food supply sector.  Some of the new industry obligations will make fraud mitigation mass-balance checks easier; for example the requirement for all buyers and suppliers within scope of the regulations to record sales volumes, costs and discounts and to supply them to the regulator on request.  The regulations apply when the buyer has large commercial muscle in comparison to the supplier (defined as a buyer turnover of < 2 million Euro when the supplier has a lower turnover).

Key prohibitions in the act include short notice cancellations (less than 30 days) for perishable products, acts of commercial retaliation against suppliers seeking to invoke their legal rights, buyers using suppliers’ trade secrets, late payment, refusal to confirm supply agreements in writing, and unilateral contract changes by the buyer.

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FAO, in partnership with Wageningen Food Safety Research (WFSR), has developed this technical background document to raise awareness of predictive early warning tools that can identify imminent and emerging food safety issues, and contribute to the prevention of food safety emergencies, while supporting the development of capacities for their use. 

The report also includes tools (Annex 3) that could be helpful in the prevention of food fraud - we are delighted that our blogs of the JRC's Monthly Food Fraud Reports, presented as visuals (created by our Member Bruno Séchet) gets a mention!.

Access the FAO report here.

In this study (open access), headspace solid-phase microextraction for sample extraction followed by untargeted gas chromatography coupled to high-resolution mass spectrometry (HS-SPME-GC-HRMS) for volatile compounds was used to build a classification model to discriminate saffron, safflower, calendula, capsicum and turmeric. (the latter four being potential adulterants of saffron).  The model was based on reference analysis of 38 authentic saffron (Crocus sativus L.) samples from different origins (Iran, Spain, Greece and Italy) 6 samples of turmeric (Curcuma longa L), 9 of calendula (Calendula officinalis L), 6 of capsicum (Capsicum annum L) and 4 of safflower (Carthamus tinctorius L., n = 4).  The instrument software was used to normalise the signals from the less concentrated or less responsive volatile compounds.  Unsupervised PCA and supervised PLS-DA gave a chemometric model that could clearly differentiate pure samples of all 5 species.

The researchers then sought specific volatile markers for each species using the pattern search function of MetaboAnalyst software. This function uses a template matching method and the results are expressed as a ranked list of variables with the Spearman correlation coefficient and p-value. They short-listed any compound with a Spearman correlation coefficient ≥ 0.80.  Tentative Identification of short-listed ‘markers’ was performed using mass spectra NIST 17 library. Only compounds with match factor ≥ 750 and relevant Kovats retention indexes (RI) relative to n-alkanes (C7–C30) were considered. The compliance of exact mass of detected ions (mass error < 5 ppm) and isotopic pattern were used to confirm the identification.

Once markers were identified they were used to build specific classification models to differentiate pure saffron from saffron adulterated with each specific species.  Models were built using in-house prepared mixes at 20, 10, 4 and 2% adulteration in each case.  The authors could successfully detect 2% adulteration with each of the 4 species modelled.

The EC Knowledge Centre for Food Fraud and Quality (the Joint Research Centre, “JRC”) have published their monthly collation of global food fraud media reports for December 2023.  Thanks, as always, for FAN member Bruno Sechet for formatting these into this infographic.  If you would like to join the JRCs mailing list to sign up for these monthly summaries then the link is here.

In March 2023, Dr Malcolm Burns, Head of the GMO Analytical Unit at the National Measurement Laboratory at LGC presented presented at the International Conference on GMO and New Genomic Techniques on 'Analytical strategies for detection of GMO's and NGT products- status and challenges'. The presentation explored some of the opportunities and challenges for the development of methods for the detection of NGT products.

You can now view Malcolm's presentation here.         
Note: all information given in the presentation was correct at the time of the presentation in March 2023.

This paper (purchase required) reports a proof-of-concept study to detect, with a point-of-use NIR scanner, the adulteration of ground almonds with apricot kernels .  The authors built a classification model by preparing their own ground almond from different almonds (120 samples) purchased at local markets and then preparing blends (up to 50%, in 2% intervals) with ground apricot kernel. They collected NIR spectra using a portable and benchtop spectrometer and analyzed the data by Soft Independent Modeling of Class Analogy (SIMCA) and Conditional Entropy (CE) with machine learning algorithms to generate a classification model. They used Partial Least Square Regression (PLSR) and CE with machine learning algorithms to predict the levels of apricot kernel in ground almonds. The authors reported that both SIMCA and CE algorithms combined with spectral data obtained from the spectrometers provided very distinct clusters for pure and adulterated samples (100% accuracy). Both units also performed well in predicting apricot kernels using PLSR with r_val>0.96 with a standard error prediction (SEP) 3.98%. They conclude that, based on the SIMCA, PLSR, and CE-based models, NIR spectroscopy showed great potential for real-time surveillance to detect apricot kernel adulteration.

Photo by Marcia Cripps on Unsplash

Spectrometric classification models are usually constructed by multivariate analysis of measurements from multiple samples from authenticated reference database.  In this study (open access) the authors used a simplified approach.  They tood a single measurement: the integrated IR spectrum between 3000-2800 cm².  They used factorial mixture design, on an Excel spreadsheet, to construct a calibration curve based only upon 3 reference samples: 100% Arabica, 100% Rustica and a 50/50 mix.  They then validated the curve using a range of other mix proportions, and concluded that it was suitable for detecting Rustica adulteration in “pure Arabica” down to 2.5%.  The authors propose this as a useful and cheap strategy for building specific classification models for the routine checking of adulteration in individual coffees that purportedly come from a consistent and well-characterised source.

In this publication (open access) the authors reviewed the potential frauds that could be applied to cultured meat, which of them could be detected by existing “conventional” meat test methods and controls, and which would require new authentication standards or testing.  They highlighted some threats which would require a new risk-management approach such as

• Use of conventional chicken meat in cultivated chicken nuggets
• Use of mouse myoblasts for cell sheet-based porcine meat
• 3D-printed steak produced by Company A using Wagyu-sourced muscle cell imitated by Company B with non-Wagyu-sourced muscle cells labelled as “Wagyu”
• Imitation of a plant protein scaffold-based cultivated meat by mixing conventional meat mush with extruded plant protein

The authors propose a scheme for establishing cultured meat authentication standards (pictured)

A recent study (purchase required), “Qualitative assessment on the chances and limitations of food fraud prevention through distributed ledger technologies in the organic food supply chain”, set out to answer three questions using structured qualitative research. 1) To what extent can Distributive Ledger Techonolgies (DLTs) help to prevent food fraud against the background of routine activity theory, by controlling target or offender or functioning as guardian, respectively? 2) How are stakeholders in the organic food industry familiar with DLTs today? 3) What is the role of the human factor i.e., what are potential hurdles for the practical implementation of technical feasible measures?.  Research methods included literature searches and stakeholder interviews.  The authors conclude that the reason DLTs have not seen widespread adoption in the Organic supply chain include valid concerns about data protection, costs, business models, consumer perceptions, and digital infrastructure.  They recommend ways in which these concerns could be addressed.

Photo by Egor Myznik on Unsplash

A recent study (purchase required) reports a new rapid method to classify whether “cream” is dairy, vegetable-based, or dairy adulterated with small amounts of vegetable oil.  The method requires no sample preparation, using rapid evaporative ionization mass spectrometry (REIMS, called the “ion knife” when originally developed for surgical diagnostics) to obtain a fingerprint of ions from lipids in the cream. 26 ions were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Then, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and small quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering.  The authors conclude that this artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream.

Photo by Daniela Chavez on Unsplash

This paper (purchase required) proposes a strategy to differentiate premium from blended honey based on the two-dimensional correlation spectroscopy (2D-COS) of Raman spectra combined with multiple deep learning techniques. A reference set of 700 Raman spectra of Manuka, acacia and multi-floral honeys were collected, and the corresponding synchronous, asynchronous and integrative correlation spectra were obtained. The t-distributed stochastic neighbour embedding (t-SNE) and partial least square regression (PLSR) were used to analyze one-dimensional spectra and 2D-COS image datasets of the same sample, demonstrating that 2D-COS can highlight the complex fingerprint features of samples and thus contribute to the spectral characterization. The authors report that a combination of synchronous 2D-COS and deep residual shrinkage networks (DRSN) achieved the best performance compared to the other models, with the root mean square errors of prediction (RMSEP) of 3.1166 for Manuka honey and 2.3188 for acacia honey, respectively. They propose that this would be sufficient to quantify the adulteration of Manuka honey with cheaper honeys using a technique that is rapid, relatively cheap and non-destructive.

Photo by Benyamin Bohlouli on Unsplash

There are four Protected Designation of Origin (PDO) descriptors of fortified wines in Andalucía (‘Condado de Huelva’, ‘Jerez-Xérès-Sherry’, ‘Manzanilla-Sanlúcar de Barrameda’, and ‘Montilla-Moriles’). Within each PDO, there are recognised different categories according to their particular winemaking conditions such as the ageing process (Fino and Manzanilla, Oloroso, Amontillado and Palo Cortado).  There is also a price premium dependent on the age of the wine.

This study (open access) aimed to differentiate volatile profiles of fortified wines obtained by headspace solid phase microextraction in conjunction with gas chromatography-mass spectrometry.  From the analysis of 104 reference samples, the authors used chemometric tools to identify the marker volatile compounds most related to fortified wine types. 28 marker volatile compounds gave enough information to discriminate by ageing process (biological, oxidative, or mixed) providing useful markers for the identification of each specific type of fortified wine. Among them, some esters were strongly related to biological ageing, aldehydes and acids to oxidative ageing, and lactones to mixed ageing. These volatile molecules involved in their differentiation could explain the unique organoleptic characteristics or attributes of these PDO fortified wines.

This study used the chemometric analysis of colloid profiles to classify milk. Colloidal nanosystems were separated by Field Flow Fractionation (FFF) working in saline carrier.  Rather than downstream detailed analysis of their composition, the FFF signals were measured directly.  There was minimal preprocessing . A set of 47 bovine milk samples was analyzed: a single analysis yielded a characteristic multidimensional colloidal dataset, that once processed with multivariate tools allowed simultaneously four different discriminations: fat content, thermal treatment, brand and manufacturing plant. The work represents the first attempt to identify milk sub-typologies based on colloidal profiles, and the most complete study concerning multivariate analysis of FFF fingerprint.  The authors recommend this as a sustainable technique, with limited pretreatment, non-toxic chemicals, and high throughput results.  They conclude that the analytical methodology is fast, green, simple, and inexpensive and could offer great help in the field of quality control and fraud identification.

This review covers recent applications of metabolomic techniques to identify non-Halal components in a food or pharmaceutical products. Examples include the detection of pig meat and the differentiation of chicken meat that is Zabiha (slaughtered by cutting the neck without detaching the spinal cord) from non-Zabiha (completely detaching the neck).  The paper highlights chemometric methods using data generated from small biological molecules (< 1500 kDA) measured by spectroscopic or chromatographic methods.  The authors conclude that metabolomics is a valuable tool in authenticating Halal products.

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 The UK Government Chemist Annual Review 2022 is published.

The Annual Review is a summary of referee casework, research projects, advice and impact of work carried out by the Government Chemist team during 2022.

Access the review here.

The US FDA have updated their guidance on implementation of the new CFR menu nutrition labelling requirements. They have invited comments on the guidance.  Closing date for comments to be taken into account in the next revision is 12 February 2024.

The menu labelling rules only apply to standard menu items offered by “covered establishments,” which are defined as restaurants and similar retail food establishments with 20 or more locations doing business under the same name and offering for sale substantially the same menu items, as well as restaurants and similar retail establishments that register to voluntarily subject themselves to the menu labeling requirements.  The rules require disclosure of calories on menu and menu boards, and require that other nutrition information (e.g., fat, sugar, protein) be available in written form on the premises and provided to the customer upon request.

The invitation to comment is here

The draft guidance (a Q&A format) is here

Photo by Alexandru-Bogdan Ghita on Unsplash