Paw San rice, also known as “Myanmar pearl rice”, is considered the highest quality rice in Myanmar. Methods for its authentication are one of the worldwide research priorities for specific regional foods identified by the Joint FAO/IAEA Centre for Nuclear Techniques and these new methods (here – free link for 50 days) is one of the outputs of that programme.

Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive.

In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice.

The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.

More than 1000 Volatile Organic Compounds (VOCs) have been identified in roasted coffee. They have been shown to be indicative of the roasting process, the type (Aribica or Rustica) and the specific variety and origin of the coffee bean.

In this paper (purchase required) the authors used an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify VOCs volatile as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). They reported that Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation. The partial Least-Squares Discriminant Analysis (PLS-DA) approach confirmed the PCA results. 24 VOCs were putatively identified, and 11 VOCs considered candidates as markers to detect coffee fraud, found exclusively in one type of adulterant: coffee husks, soybean, and rice.

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This dissertation from Florida State University describes designing and validating a species-specific PCR-lateral flow assay for Atlantic white shrimp (Litopenaeus setiferus) utilizing a miniaturized and cost-effective PCR instrument.  The selectivity was tested against 68 shrimp, prawn, and fish samples from 14 seafood species. L. setiferus was simultaneously amplified by the multiplex assay to give three visual bands, which distinguished it from other species having either one or two bands on the lateral flow stick.

The researcher also developed a specific red snapper assay, validating a rhPCR lateral flow assay where the thermotolerant RNase H2 enzyme was included in the PCR reaction to activate the red snapper-specific rhPCR primer. Amplicons generated in the duplex rhPCR reaction were detected using dual target lateral flow strips. The standardized assay was validated with 108 barcoded fish samples from 16 finfish species. Samples identified as Lutjanus campechanus or L. purpureus by DNA barcoding formed three distinct bands, while other fish species formed only two bands on the lateral flow strips. A minimum of 0.37 ng/μL crude DNA was needed to obtain a visible band on the lateral flow dip stick.

Both assays showed 100% specificity and took 90–120 minutes for completion. The researcher concludes that this confirms the suitability of PCR and rhPCR-lateral flow assays as an economical on-site tool for species authentication in the seafood industry.

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AcciMap is a systems-based technique used to analyse accidents that occurred within complex socio-technical systems. It has been used to drill into the causes of serious accidents in fields such as transportation or recreation, and also for applications such as identifying risk factors for child labour in supply chains.  More recently it has been used for food safety incidents and foodborne disease outbreaks.

This pre-publication paper is the first report on this methodology being used to analyse contributory factors in food fraud incidents.  The authors applied the model to the milk adulteration scandal (melamine) that emerged in China in 2008.  They coupled AcciMap with a classical Food Fraud Vulnerability Assessment approach (systemic weaknesses associated with opportunities, motivation and control measures).  Their model identified forty-eight contributory factors of influence grouped across six sociotechnical levels across the Chinese dairy system from government to equipment and surroundings. Lack of vertical integration (processes and communication) contributed to the failure. The authors conclude that, when viewed in a broader perspective, the melamine milk scandal can be linked to a series of human error and organisational issues associated with government bodies, the dairy supply chain, individual organisations and management decisions and individual actions of staff or processes.

Image from the publication

Last week the UK National Food Crime Unit (NFCU) rolled out their Food Crime Confidential whisteblower hotline to Northern Ireland with an official launch and series of publicity releases. The Food Crime Confidential number, 0800 028 11 80, can be used by anyone who has suspicion about food crime and wants to report it safely and confidentially to the NFCU. NFCU is a law enforcement arm of the Food Standards Agency.

Photo: FSA, Twitter/X

There are many species of cinnamon, with different commercial values, and so ground cinnamon is a perennial risk for substitution or adulteration of one species by another.  This article (open access) reviews analytical methods for cinnamon authentication.  It lists over 30 targeted methods, published from the 1980’s  to the current day.   The most common analytical markers in cinnamon authentication are cinnamaldehyde, coumarin, eugenol, and cinnamyl alcohol, but methods also include profiling of trace metal and mineral content.  The review also lists 18 untargeted methods using techniques such as NMR or IR coupled with chemometrics.  The authors conclude that most untargeted studies are limited to exploratory analysis and the ones employing classification chemometric methods are mostly devoted to the classifying pure samples of different species. More work is needed to develop methods to detect partial adulteration, which the authors consider to be a high risk in the current market.

Diagram from the article

The 7th session of the Codex alimentarius Committee on Spices and Culinary Herbs (CCSCH7), chaired by India, concluded its work on several important new add-ons to existing and new standards on small cardamon; allspice, juniper berry and star anise; and turmeric. It advanced the work on vanilla, still subject to additional inter-sessional work until the next session. It provided some clarifications about the labelling provisions for saffron on the country of harvest vs country of origin and recommended several methods of analysis. CCSCH7 agreed to start new work on four new standards on sweet marjoram; dried seeds – coriander; large cardamon; and on cinnamon with the help of specific electronic working groups. The USA also presented an advanced template for any new future proposed standard on spices and culinary herbs. The USA also agreed to prepare a discussion paper on trade data availability for spices and culinary herbs in general.

A very good summary of the session and outcomes is in a post by US law firm Keller & Heckman here.

The authors of this recent paper (open access) review the use of anti-counterfeiting measures in the supply chains of high value foods, such as foods with PDO or PGI designation.  They consider five classifications of anti-counterfeiting measures: mechanical solutions (labels, seals, anti-alteration devices), marking technologies (codes, holograms, covert inks, microtexts), digital traceability, electronic labels (RFID, NFD), and blockchain.  The authors cite examples and case studies where these measures have been used within five supply chain categories; milk & dairy products, meat & meat products, fish & seafood products, wines & spirits, olive oil.  They conclude that, of the available anti-counterfeiting tools, RFID tags and digital traceability systems are relatively underused in the highest risk supply chains.

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In this paper (purchase required) the authors make use of a recently developed ambient ionisation source for mass spectrometry.  Self Aspiration Corona Discharge Ionisation (SACDI-MS) enabled the direct measurement of volatile compounds from coffee, and when the authors coupled this with an Air Curtain Sampling Device it meant that, by removing interfering volatiles from neighbouring batches, they could design an in-line sensor suitable for use in a production environment.  The use of Deep Learning Algorithms with an Artificial Neural Network enabled them to compensate for other interfering peaks from environmental volatiles, often a problem in direct ionisation mass spectrometry.  They constructed a chemometric classification model using a reference set of coffees from 6 different geographic origins and proved that they could differentiate between them in a high-throughput, rapid production environment.  They conclude that this makes the approach ideal for in-line screening of coffee authenticity in situations when there is a consistent “expected” origin, used to train a classification model, that needs to be distinguished from substitution by “unexpected” origin coffee.

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European legislation permits the inclusion of non-cocoa vegetable fats up to 5% into milk chocolate recipes.  Anything beyond this is adulteration.

In this paper (purchase required) the authors report the use of proton NMR combined with chemometrics to discriminate between milk fats, cocoa fats and non-cocoa vegetable fats (“cocoa butter equivalents”, CBE).  They prepared known mixes (0-100%) of different fats.  They used both a targeted and an untargeted approach.  The targeted approach used the integrals of the signals belonging to ω-3, ω-6, ω-9, and saturated fatty acids.  The untargeted approach used the spectra as fingerprints.

The authors reported that the untargeted partial least-squares discriminant analysis model (PLS-DA) recognized the type of CBE in blends with sensitivities in prediction higher than 75%. The targeted PLS-DA model was capable of recognizing non-adulterated milk chocolate fats with 100% sensitivity and specificity in prediction. Conversely, low percentages in sensitivity were achieved for most of CBEs. Both targeted and untargeted PLS regression models efficiently determined the amount of CBE in blends. Fingerprinting models showed better results both in the classification and quantification of CBEs.  They conclude that this proves the applicability of ¹H NMR in milk chocolate quality control.

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New designated protected names came into force yesterday, under the UK/Japan trade agreement signed in 2021.

Within the UK, the following foods will now have protected descriptors and Geographical Indications

• Daiei Suika 
• Daisen Broccoli 
• Echizen Gani/Echizen Kani 
• Edosaki Kabocha 
• Futago Satoimo/Futago Imonoko 
• Hiba Gyu 
• Higashiizumo no Maruhata Hoshigaki 
• Hiyama Haishen 
• Ibuki Soba/Ibuki Zairaisoba 
• Iburigakko 
• Iwadeyama Koridofu/Iwadeyama Meisan Koridofu 
• Koge Hanagoshogaki 
• Kumamoto Akaushi 
• Matsudate Shibori Daikon 
• Mito no Yawaraka Negi 
• Monobe Yuzu 
• Nango Tomato 
• Okukuji Shamo 
• Ozasa Urui 
• Sayo Mochidaizu 
• Taisyu Soba 
• Tokyo Shamo 
• Toyama Hoshigaki 
• Tsunan no Yukishita Ninjin 
• Tsuruta Steuben 
• Yamadai Kansho 
• Yamagata Celery 
• Yatsushiro Tokusan Banpeiyu 
• Zentsujisan Shikakusuika 
• Hagi 
• Harima 
• Hokkaido 
• Mie 
• Nadagogo 
• Tone Numata 
• Wakayama Umeshu 
• Yamanashi

Within Japan, the following foods will now have protected descriptors and Geographic Indications

• Cornish Clotted Cream 
• Cornish Pasty  
• Anglesey Sea Salt/Halen Môn 
• Arbroath Smokies 
• Conwy Mussels 
• East Kent Goldings 
• London Cure Smoked Salmon 
• Lough Neagh Eel 
• Lough Neagh Pollan 
• Melton Mowbray Pork Pie 
• Orkney Scottish Island Cheddar 
• Pembrokeshire Earlies/Pembrokeshire Early Potatoes 
• Scotch Beef 
• Scotch Lamb 
• Single Gloucester 
• Staffordshire Cheese 
• Stornoway Black Pudding 
• Traditional Ayrshire Dunlop 
• Traditional Cumberland Sausage 
• Traditional Grimsby Smoked Fish 
• Traditional Welsh Caerphilly 
• Welsh Beef 
• Welsh Lamb 
• Welsh Laverbread 
• West Country Beef 
• West Country Lamb 
• Yorkshire Wensleydale 
• English Wine 
• English Regional Wine 
• Herefordshire Cider 
• Herefordshire Perry 
• Irish Poteen 
• Kentish Ale 
• Kentish Strong Ale 
• Somerset Cider Brandy 
• Welsh Wine 
• Welsh Regional Wine 

The UK government press release is here.

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The European Commission has published its first monthly report on EU Agri-Food Fraud. The stated intent of this free list of incident collations is to help both regulatory authorities and food businesses target their fraud defence activities to the highest risk areas.

The report collates all entries from iRASSF that have been categorised as “suspicious”.  It therefore includes official controls, border rejections, whistleblower complaints and media reports (unlike, for example, the JRC monthly food fraud collation which is fed purely by media reports).  It does not include “suspicions” which have not been communicated outside of the originating nation.

In January 2024, 277 “suspicions” are listed.  Read the January report here.

(image from the report)

This paper (purchase required) reports a simple screening method for vegetable oil adulterants (corn, sesame, soy, sunflower, almond, and hazelnut) in olive oil.  It is based on a multispecies DNA sensor that can be read with the naked eye. It is the first report of a DNA sensor for olive oil adulteration detection with other plant oils. The researchers have identified unique nucleotide variations which enable the discrimination of the seven plant species. Following a single PCR step, a 20 minute multiplex plant-discrimination reaction is performed, and the products are applied directly to the sensing device. The plant species are visualized as red spots using functionalized gold nanoparticles as reporters. The spot position reveals the identity of the plant species. The authors report that <5–10% of adulterant was detected with good reproducibility and specificity.

Image from the paper

The results of the FSA's most recent surveillance testing project of food and beverages are published here.  This covers retail samples (including online sales) purchased in October 2022 by various local authorities in England, Wales and Northern Ireland.  It included authenticity testing on 437 samples including meat and meat products, herbs and spices, basmati rice, coffee, cheese and olive oil.   97% were reported as authentic and two reported as inconclusive. The main commodities with authenticity issues identified were oregano with 13% of samples containing other leaf types and basmati rice with 10% of the basmati rice samples reported as having been adulterated with non-basmati rice varieties or in one instance having no approved basmati varieties at all.

FSS have published (here) their 2024-27 strategic plan for food crime prevention.  It is a high-level document and, although written for Scotland, the principles are applicable for regulators and enforcement bodies anywhere.  It describes a model for the identification, analysis and implementation of measures to reduce or prevent the ccurrence or re-occurrence of food crime and the identification and mitigation of related future food crime risks.

(image from the strategic plan)

In this proof-of-concept study (purchase required) the authors used direct-sampling ambient mass spectrometry (REIMS, sometimes called the “ion knife”) to build a classification model for different cuts of beef.  They analysed untargeted lipid profile data from a reference set of 125 authenticated samples purchased directly from an abattoir (25 each of ribeye, sirloin, brisket, shank and foreshank) from 12-month old bulls of the Pirenaica breed.  They used machine learning to select discriminatory features (11 fatty acids and 44 phospholipids).  They report good discrimination between different cuts of meat using the model.

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The 2023 BSI Supply Chain Risks and Opportunities Report is available for free download.

Food & beverage was the league-leading sector for supply chain theft, accounting for 22% of global reported thefts (up from 17% in 2022). 

Other watchouts include

• The reverberating impact of climate change impacts, a stark example being a trebling of virgin olive oil commodity price over the past 2 years.
• In the US, 48 ransomware attacks in the food and drink sector reported in 2023 by the FBI Crime Compliant Sector.

Image from the report

In this paper (purchase required) the authors build a Raman spectroscopy classification model to differentiate 5 brands of very similar dairy products.  The model was trained on 30 retail samples of each brand.  The authors reported significantly improved feature selection and model performance (particularly in detecting differences in dairy fat content, the key question for adulteration) when they used a data fusion approach of 4 different Machine Learning protocols rather than training using one protocol.  They recommend this fusion approach for constructing classification models.

In this paper (open access) the authors developed a rapid PCR-based test protocol for three species of tuna (Thunnus thynnus (Blue Fin Tuna), Thunnus albacares, and Katsuwonus pelamis) under simulated conditions for canned and flavoured products.  Home-made canned simulants were prepared by mixing each fish tissue of the three tuna species with salt, pepper, paprika, onion, oil, vinegar, and tomato followed by frying and boiling. DNA was then isolated from the home-made canned products. Binary mixtures were prepared using the isolated DNA in various percentages of adulteration that ranged from 1 to 100%.  DNA was extracted, followed by amplification by rapid small-scale PCR using species-specific primers.  The PCR products were hybridized (10 min) to specific probes and applied to the rapid sensing device. The signal was observed visually in 10–15 min using gold nanoparticle reporters.  The authors report that the method was reproducible and specific for each tuna species and 1% of tuna adulteration (in the isolated DNA) could be detected with the naked eye.

photo by Farhad Ibrahimzade on UnsplashP

This review article (purchase required) covers the recent application of CRISPR/Cas technology to food authenticity sensor applications.

The CRISPR/Cas system is a defence mechanism in bacteria and archaea against phage invasion.  It allows them to store fragments of invading DNA and use them as a guide to recognize and destroy future invasions. Bound to a CRISPR RNA (crRNA) that precisely matches the nucleotide sequence of the target (spacer sequences), the CRISPR-associated (Cas) protein is precisely navigated to these sequences. Through enzymatic processes, it cleaves target nucleic acids at specific sites.  This sequence selectivity underpins gene editing technology.  It is less well known that it can also be used to build selective analytical sensors.

The authors describe how, in recent years, CRISPR/Cas-based detection systems have been applied to precise DNA detection for food authentication. Such systems entail designing a guide RNA that complements the target DNA, triggering precise cleavage by the Cas enzyme and activating collateral cleavage. This leads to the cleavage of fluorescent probes, enabling detection. CRISPR/Cas-based detection systems can be a rapid and highly specific tool for identifying target DNA in food. Coupled with DNA amplification strategies, it has the potential to achieve sub-attomolar sensitivity for nucleic acid detection.

CRISPR/Cas assays are qualitative, but can be made quantitative by combining with digital sample partitioning. The sample is divided into multiple individual partitions, each containing a distinct number of biological entities (0, 1, 2, 3, etc.). Each partition reacts independently and the partition containing the target will produce an increased fluorescent signal, allowing absolute quantification using Poisson distribution.

The authors cite and review published examples, and conclude that these digital isothermal amplifications and dCRISPR methods are rapid and offer single copy sensitivity and high accuracy, making them a promising tool for accurate DNA quantification with great potential for application in authenticating food DNA.