Coconut water and coconut milk are vulnerable to food fraud because they can easily be diluted with other liquids and adulterated with undeclared ingredients, such as added sweeteners and dairy products, that are difficult for consumers to detect. The FoodChainID Food Fraud Database[i] (formerly the Decernis and United States Pharmacopeia [USP] database) contains 20 records related to coconut water, coconut milk and coconut milk powder. These include one story of a “coconut water” manufacturer that was found to be using water, sugar and flavoring to make the beverage, which contained no coconut at all.[ii]
Because the potential diluents and adulterants are cheap and easily available, the fraud is both profitable and easy to perpetrate. Organic coconut products are also at risk of having their organic status faked. Six of the Food Chain ID records are for products that were falsely labelled “organic.”
The addition of undeclared sweeteners is a commonly perpetrated fraud in coconut water. In the U.S., it has been estimated that 15% of all imported coconut waters are adulterated with added sugars that are not declared on the label.[iii] In 2021, Brazilian authorities confiscated 173,000 litres of coconut water with added sugars.[iv] Authorities in the United Kingdom reported that the presence of undeclared sugars in coconut water was “widespread” in 2017.[v]
Coconut milk and coconut milk powder have been adulterated with undeclared cow’s milk powder[vi] and with cornflour.[vii] Adulteration with dairy products poses a serious food safety risk to allergic consumers. To protect such consumers, food fraud risk mitigation activities for coconut milks, creams and yogurts should include regular monitoring for the presence of dairy allergens.
[vi] Centre for Food Safety The Government of Hong Kong Special Administrative Region, Food Alerts / Allergy Alerts – “Undeclared allergen (milk) found in prepackaged coconut milk powder.” Available at: https://www.cfs.gov.hk/english/whatsnew/whatsnew_fa/2017_177.html [Accessed 6 Oct. 2022]
[vii] Azlin-Hasim, S., Siang, Q., Yusof, F., Khairi Zainol, M. and Mohd, H. (2019). “Chemical Composition and Potential Adulterants In Coconut Milk Sold In Kuala Lumpur.” Malays. Appl. Biol, [online] 48(3), pp.27–34. Available at: http://journalarticle.ukm.my/14685/1/48_03_04.pdf [Accessed 6 Oct. 2022]
The demand for fish oil is increasing. It is packed full of heart-friendly omega-3 fatty acids, including the functionally important docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). Today, consumers are ever more aware of the health benefits of incorporating fish oil into their diets, such as lowering blood pressure and helping prevent heart disease. It is therefore no surprise that many companies are increasing their investments in providing high-quality fish oil supplements, such as those with value claims including single species, designated geographical origin, sustainability practices and traceability. And it’s a lucrative business: the industry was worth USD 11.95 Billion in 2021 and is expected to reach a value of USD 17.64 Billion by 2028—a CAGR of around 6.7%.
As the industry continues to grow, so does the risk of economic fraud. Fish oil itself varies depending on its source: fish from different regions—even within the same species—have different oil compositions, and, understandably, different price points depending on the quality. Individuals and illicit organizations are exploiting the growing demand by circulating adulterated, mislabeled products with sub-standard fish oil and/or misrepresented product origin for financial gain. More than ever, robust legislation is required, and there is a need for increasingly accurate and sensitive analytical techniques to verify the origin, authenticity and label claims of supplements, foods and beverages containing fish oils.
Traditional food integrity techniques can’t accurately distinguish the origin of fish oils from the same species. New approaches are needed to provide greater analytical depth and accuracy, and to ensure that consumers can trust brands, manufacturers are protected and governments can control the use of fish oil. Here we explore how gas chromatography isotope ratio mass spectrometry coupled with a mass spectrometer (GC-MS-IRMS) can overcome these challenges and allow analysts to confidently determine fish oil origin from a given species.
Fish Oils: A Tricky Catch for Authenticity Testing Laboratories
The existing approaches to determine fish oil authenticity, including gas chromatography-mass spectrometry (GC-MS) fatty acid profiling and untargeted fingerprint determination by spectroscopic techniques such as nuclear magnetic resonance (NMR) and near-infrared (NIR), are based on the compositional characteristics of the oils. While these compositions are important to understand, they do not reflect significant regional and geographic parameters. Yet gaining clarity on the geographical origin of fish oils from the same species is vital because the source of the fish oil can have significant financial implications. In particular, label claims that fish oils are derived from a certain geographical region can add value to the product. Confirming the fish oil origin also verifies traceability of the product and contributes to other important label claims including sustainability, health and safety. Therefore, knowing the origin of the fish oil and its authenticity helps to identify fraudulent practices that are used to boost product value.
Isotope Fingerprints Identify Regions and Processes
So, how can we better characterize fish oil? Compound-specific stable isotope analysis (CSIA) is an ideal solution. Fatty acids consist almost entirely of carbon and hydrogen. In fish, the natural variation of the isotopic ratios of these elements is influenced by the feed, the environment and the local habitat of a given population. CSIA can enhance fish oil testing by determining the stable isotopic values of individual fatty acids. Since isotopes vary with differing dietary sources, geographical regions of origin can be determined, even within the same species.
Recent advances in GC-IRMS allow the technique to provide the separation accuracy and detection resolution required to distinguish between different carbon and hydrogen isotopes in compositionally equivalent fatty acids, by CSIA. GC-IRMS works by separating compounds using gas chromatography, then analyzing carbon and nitrogen isotope fingerprints by combustion, and oxygen and hydrogen isotope fingerprints through pyrolysis. This approach enables the acquisition of isotopic information for each individual compound in the sample.
To further improve the capability of GC-IRMS, the set-up can be coupled to a single quadrupole mass spectrometer—GC-MS-IRMS—to allow structural determination and identification of compounds. With the hybrid system, the flow from the GC column is separated into two parts: the majority continuing for IRMS isotope analysis, with a minor portion for MS compound identification. The innovative design does not impact IRMS sensitivity, thereby gaining structural information without compromise. These system attributes mean that GC-MS-IRMS can determine the structure and isotope ratio of each fish oil compound. Using this method, analysts can generate accurate (close to the absolute value), repeatable and reproducible results.
The power of GC-IRMS is well-established in the industry. It has been embraced as a method to be standardized in food authenticity testing by several international bodies, including the European Committee for Standardization (CEN) and the German Chemical Society (GDCh), and is only made more powerful when coupled to MS. However, standardization requires successful method validation, demanding its specific investigation for fish oil characterization.
Separating Salmon and Comparing Cod
Fish oil labeling is centered around differentiating species and their geographical origins to support label claims. In a recent experiment, Thermo Fisher Scientific and Imprint Analytics worked together with Orivo to compare 30 salmon oils and 43 cod liver oils from the same species in different areas. These experiments were performed to demonstrate that isotopes can be used to identify the geographic origin of the samples, and to validate the method.
Samples were prepared by a derivatization procedure using CH3COCl in MeOH to obtain Fatty Acid Methyl Esters (FAMEs). Both carbon and hydrogen isotopes were measured for the samples (Table 1), and statistical analysis of the isotope data allowed the selection of certain parameters for the statistical model (Table 2). These then contributed to the discrimination of the given clusters for each model.
A number of principal functions (Fx) are generated in the analysis, integrating information from analytical parameters. Using different Fxs allows bivariate or multivariate illustrations, where F1 and F2 represent the largest amount of information available for the samples.
Table 1: List of the fatty acids (as FAMEs) screened and analyzed by GC-MS-IRMS.
Table 2: List of the fatty acids (as FAMEs) used in the statistical model.
Salmon
Most salmon products come from either Norway or Chile, and the two have significant price differences and values. It is therefore crucial that the label claims of any fish oil supplement can be verified. In the study, FAMEs were analyzed, and carbon and hydrogen isotope ratios determined using GC-MS-IRMS.
Discriminant analysis gave a correct prediction of 94.29% (Figure 1), showing that the two regional products could be clearly determined.
Similar to the salmon situation, Iceland and Norway have price discrepancies between products derived from each region’s cod. However, the two countries are physically very close, meaning there may be less extreme differences between the two diets and habitats, and therefore more similarity between isotopic fingerprints.
Despite the close proximity of the cod species, the multi-isotope method was able to discriminate the fish oil origin with a correct prediction of 97.22% (Figure 2). Based on this score, we can see the technique is highly accurate and reliable, making it a strong choice for fish oil determination.
GC-MS-IRMS Paves the Way for More Reliable Analyses of Fish Oil Authenticity
GC-MS-IRMS is a powerful technique that can determine the origin of fish oil by elucidating structure and isotope ratio. The study here shows the potential of GC-MS-IRMS in verifying the geographical origin of matrices with emerging commercial value and high adulteration risks—and validates the method, demonstrating that the resulting data provides conclusive answers about fish oil origins. Crucially, the technique is suitable even for products deriving from geographic regions close to one another.
We anticipate that isotope fingerprint analysis will continue to grow in the industry. With plans to use the technique to discriminate between different fish species underway, adopting GC-MS-IRMS methods into food analysis supports the need to uphold product authenticity and maintain consumer trust.
The authors kindly thank Orivo for collaboration on this study and providing the samples for analysis.
Eel fraud is big business. The illegal international trade in glass eels is estimated to be worth up to EUR3 billion[i].
Like most seafood, eel meat is vulnerable to food fraud. But, their poorly understood breeding patterns and endangered species status make eels particularly vulnerable to illegal trading and international smuggling.
A glass eel is a baby eel, and it is a heavily protected fish stock. It also makes a lucrative food fraud target: just one kilogram of smuggled glass eels can be turned into food products worth €25,000[ii].
Glass eels are attractive to food fraud criminals because they are an obligatory input to eel meat supply chains. Aquaculture fisheries produce around 95% of eel meat globally[iii], with most aquaculture production in China[iv]. Unlike other aquaculture animals, eels cannot successfully breed in captivity. This means that every eel raised for food was once a wild-caught glass eel.
Glass eels are in short supply. They are not allowed to be exported from Europe and there are strict restrictions on catch quotas and trade in other countries. One kilogram of live European glass eels in is worth €300/kg when first caught[v]. If those live eels are successfully smuggled out of Europe, they are worth €6,000/kg by the time they reach Asia[vi].
In April 2022, the U.S. Department of Justice indicted[vii] a major seafood distributor and eight of its employees for allegedly smuggling large quantities of glass eels from Europe to their eel-rearing facility in China, where the eels were raised, processed, then shipped to the U.S. as eel meat products. When intercepted by customs, the eel meat was found to be fraudulently described as American eel (Anguilla rostrata), which is a lawful import, rather than European eel (Anguilla anguilla).
Data analytics can reduce the risks of foodborne illness, improve collaboration among food processing and service teams and help identify food fraud. As technology has advanced, researchers, policy-makers and food safety professionals are finding new ways to collect, use and analyze data. Following are some of the latest advances in the field of data analytics and food safety.
Improving Risk Assessment Strategies
Data and tracking have long been integral components of food safety risk assessment. Today, researchers are combining big data, machine learning and microbial genomics to create next-generation quantitative microbial risk assessment (QMRA).
Researchers at the University of Maryland received funding from the United States Department of Agriculture National Institute of Food and Agriculture (USDA-NIFA) to support work that combines machine learning and computational analysis with genomic sequencing and data about foodborne pathogen characteristics. They intend to take advantage of big data available in the agriculture and food sectors and integrate data from food production, processing, food safety risk factors and genomic data to inform—and potentially transform—public health strategies to prevent foodborne diseases and speed response to outbreaks.
QMRA can be used to: predict the behavior and transmission of pathogens across food production, processing and supply chain; identify areas in the chain that could lead to contamination; and estimate the probability and consequences of adverse public health effects in the event that tainted products are consumed.
Abani Pradhan, associate professor in Nutrition and Food Science at the University of Maryland and lead investigator on this project, explains that this data analysis project should lead to better accuracy due to the inclusion of AI and genomics. “The sheer abundance of information by including molecular and genomic data available should increase the robustness of disease risk estimates by reducing the sources of uncertainty and variability in the QMRA model,” said Pradhan. “This is important because there are so many different species of each foodborne pathogen, and even within the same species, there are different variations or types called serovars.”
Pradhan’s team are starting with Salmonella, because it has more than 2,500 serovars, all of which have highly variable characteristics. How resistant a pathogen is to heat stress or antimicrobials, how infectious it is and how quickly it grows and spreads are all characteristics of the pathogen that can be partially explained by genomic data.
“The idea is to connect that genetic information with the characteristics of the pathogen to bridge the gap between the genes and the food safety aspects for consumers,” said Pradhan. “If we can use machine learning tools to understand the linkages between genotypes and phenotypes, based upon that we can determine which serovars are the most concerning so that we can focus our experimental work on those types and further strengthen our models to create a risk assessment that provides a more robust and complete picture of the risk for risk mitigation.”
Using Online Data To Detect Safety Issues
The U.S. has a robust regulatory and oversight system to identify foodborne threats. In 2019, researchers led by Adyasha Maharana of the Department of Biomedical Informatics and Medical Education, University of Washington, wanted to see if online consumer reviews might contain safety clues that could identify unsafe food products before official inspections or recalls occurred. They created a database linking Amazon food and grocery product reviews to product recall data from the FDA, and analyzed more than 1 million Amazon reviews featuring words like “sick,” “ill” and “foul.” The results showed that only 0.4% of the Amazon reviews containing those words were for recalled products.
The researchers also found synonyms for terms linked to FDA recalls in 20,000 reviews, although those products were still on the market. The researchers concluded that this “might suggest that many more products should have been recalled or investigated” and note their work could be used to aid regulators in determining which items to investigate.
A similar project, Google’s machine-learning algorithm FINDER (Food-borne Illness Detector in Real Time), uses search and location logs to identify restaurants that could be making people sick in real time. FINDER pulls data from people’s Google search queries for terms or symptoms that suggest they may have food poisoning. It then matches that information to Google location data logs to figure out which restaurants those individuals may have visited.
They tested this approach in Las Vegas and Chicago for four months in each city. The data analysis application helped food inspectors find 25% more unsafe restaurants compared to the previously used inspection method.
Neither of these case studies suggests regulators should do away with their more established procedures. However, combining this type of data analysis with existing strategies could further enhance safety.
Reducing Food Fraud
Many of today’s consumers want to know that the food they are eating comes from organic farms or was otherwise produced to certain standards. That’s why many restaurants now list which supply chain partners they use for specific menu items. This type of data reporting and sharing also offers improved food traceability. Having accurate information about where a food or beverage originated makes it easier to address and track problems when they do occur.
End-to-end traceability and real-time monitoring technologies continue to evolve, bringing new, more powerful tools that help providers at every link of the farm to table chain identify loss, theft and potential safety issues.
At the University of Adelaide, researchers improved upon current methods of detecting wine fraud by combining fluorescence spectroscopy and machine learning to determine a beverage’s molecular fingerprint. The team looked at Cabernet Sauvignon from three different wine regions. They found that their method could correctly authenticate the geographic origins of wine with 100% accuracy.
It is impossible to remove all food and beverage safety risks from the supply chain. However, successful applications of data analysis that help keep people safer are undoubtedly steps in the right direction. As more companies in the food and beverage industry adopt new data analysis tools, other interesting possibilities will become apparent. Even as things stand, the applications are full of promise.
GFSI notes that while there is some existing guidance that addresses fraudulent activities, there is a significant need for CCFICS, which deals with ‘horizontal’ issues, to develop definitions and update its guidance to better reflect current food fraud initiatives.
To support this work, Codex has created a dedicated working group, Chaired by the United States with co-chairs from China, the European Union, the Islamic Republic of Iran and the United Kingdom. GFSI acts as an official observer to Codex, providing input and recommendations on this work through its GFSI Codex Working Group. The group, which currently consists of representatives from Nestlé, PepsiCo, The Coca-Cola Company and Danone, plays a key role in underpinning GFSI’s Benchmarking Requirements and reinforcing Codex’s mandate of valuing collaboration, inclusiveness, consensus building and transparency.
The group is also observing to help ensure this work does not reinvent the wheel, but identifies, collects and utilizes existing work from experts within the scientific and academic industries and regulatory community that have been working on this topic for the past decade.
In regard to the feedback provided on the Codex Guidance on Food Fraud, the GFSI Codex Working Group stressed:
The importance of including industry as a key partner in managing food fraud
The need for clarity around the roles of respective Codex committees in the prevention and detection of food fraud, specifically around analytical and testing guidance to prioritize the detection of food fraud (i.e. the role of CCMAS – Codex Committee on Methods of Analysis and Sampling vs. the role of CCFICS in food fraud)
The importance of collaboration between all relevant stakeholders to manage food safety risks in the event of genuine food fraud incidents
The absolute need to include ‘feed for food producing animals’ in the scope of this work
The view that existing food safety processes and networks provide a good basis for managing communication of food fraud incidents and share good practices
To define numerous terms that are also being proposed, defined and considered with the development of agreed terms and conditions.
Codex is hoping to finish this work in 2024/2025. Between now and the last final draft, which is planned to be submitted for final approval to the Codex Alimentarius Commission, there will be multiple draft versions developed.
Undeclared allergens continue to be a big cause of food recalls. For allergen management practices to be effective within food companies, there must be a shared responsibility between food manufacturers, government agencies, regulators and consumers, says Guangtao Zhang, Ph.D., director of the Mars Global Food Safety Center. In a Q&A with Food Safety Tech, Zhang discussed key concerns related to undeclared allergens in food as well as the research that Mars is conducting to improve allergen management.
Food Safety Tech: The presence of undeclared allergens continues to be a hazard in the food safety space. Specific to peanut detection, what challenges is the industry facing?
Guangtao Zhang, Ph.D., director of the Mars Global Food Safety Center. All images courtesy of Mars.
Guangtao Zhang, Ph.D.: As food materials become more varied and complicated, food allergen management becomes increasingly complex. Robust, accurate and sensitive detection methods are essential to ensure consumer safety as well as compliance with regulatory standards for allergens in the food supply chain.
When you look at the regulatory aspects, detection methods go hand in hand. Firstly, there is a need to ensure that current standard detection methods used in regulatory control of consumer goods are validated for a range of complex food matrices to ensure neither over- nor under-estimation of allergen content occurs within a food supply chain. This is important because underestimation of allergen poses a significant food safety hazard to consumers, while overestimation of allergen can result in unnecessary product recalls, driving up product costs and food waste.
Secondly, validation and monitoring of the effectiveness of cleaning and handling practices in areas of potential cross contamination with allergen containing materials depend on reliable and robust quantitative food allergen test methods for their success. The more robust the testing protocols, the more we can improve our understanding of the risks associated with cross contamination of food allergens, potentially reducing the frequency of accidental contamination events.
It is also important to note that whilst the most common cause of undeclared allergen in the global food supply chain is through accidental contamination in raw materials or finished products, this is not the only method by which undeclared allergen may be found in a product.
For example, peanut flour may be used in economically motivated adulteration (EMA) food fraud cases. In 2018 the European Commission estimated that the cost of food fraud for the global food industry is approximately €30 billion every year. Due to its high protein content, peanut flour has been used as a bulking agent to raise the overall protein content of e.g., wheat flour, thus raising the ‘quality’, and therefore price, of lower value goods. The ability to effectively quantify peanut traces within complex products therefore has the potential to enable consumers of food products to further trust the safety of the food they eat.
ELISA (Enzyme linked immunosorbent assay) is the method used most frequently for peanut allergen detection in the food manufacturing industry because of its sensitivity and ease of use. However, it has disadvantages in certain settings. It is not currently validated for complex food matrices, as it is believed that the effects of both food matrices and food processing could result in an underestimation of peanut concentrations in thermally processed foods, leading to false negatives, as well as overestimation in complex food matrices, leading to false positives which are a potential food safety hazard to consumers.
Food Safety Tech: Tell us about the research that the Mars Global Food Safety Center is doing to help the industry with effective methods for peanut quantification.
Zhang: At the Mars Global Food Safety Center (GFSC) we believe that everyone has the right to safe food and that we have a responsibility to generate and share insights to help solve for global food safety challenges. We also know we can’t tackle these alone, which is why we collaborate with external partners. One of our focus areas is advancing understanding and knowledge sharing in peanut allergen detection. As part of that work, we are exploring methods of improving food safety via the development of advanced analytical methods to detect peanut allergen content, in the hopes that it will enable the food industry to expand on current preventative management protocols, including early detection methodologies, for faster response to future food allergen contamination events.
As part of our latest published research, we investigated the accuracy and sensitivity of ELISA-based test methods on raw and cooked wheat flour, wheat flour-salt and wheat flour-salt-oil matrices, which are common ingredients in the food industry. 10 ppm peanut was doped into each matrix during sample preparation. Recovery testing demonstrated that in all matrices the current industry standard ELISA method overestimated results with recoveries ranging from 49.6 to 68.6 ppm.
These findings prompted the development of a new confirmatory method based on liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) for peanut quantification. When subjected to the same validation testing programme the HPLC-MS/MS technique was demonstrably more accurate and sensitive, with a limit of quantification of 0.3 ppm and the detected peanut concentration ranging from 6.8 to 12.8 ppm for samples doped with 10 ppm peanut.
This work is a first step in the development of a new standard method for peanut detection in complex food matrices and could ultimately inform safer manufacturing Quality & Food Safety (Q&FS) processes across global supply chains to help ensure safe food for all.
The Lab Food Integrity Team at the Mars Global Food Safety Center.
Food Safety Tech: What projects are researchers at the Center working on to enhance allergen management as a whole?
Zhang: A successful allergen management program depends on rigorous control of allergenic foods and ingredients from all other products and ingredients at every step of the food production process, from raw material development to the delivery of final products. This means that for allergen management practices to be effective, they must be a shared responsibility between food manufacturers, government agencies, regulators and consumers.
At the Mars GFSC, we take a precompetitive approach to research, knowledge sharing and collaborations—this means we openly share insights and expertise to help ensure safe food for all. This is important in driving forward innovations, helping unlock solutions that may not have previously been possible.
We have shared our latest work both through an open access publication in Food Additives & Contaminants: Part A but also directly with regulatory bodies such as the FDA in the hopes of advancing knowledge in both food safety risk management and allergen management in complex flour-based media within global supply chains. In addition to this, this research contributes to a wider Food Safety Best Practice whitepaper focused on food allergen risk management currently under draft by the Mars GFSC, which will be published in collaboration with Walmart Food Safety Collaboration Center and the Chinese Institute of Food Science and Technology (CIFST) later this year.
We believe that global collaborations such as this are essential to improving food allergen management and mitigating food safety risks. Communication, training and knowledge sharing are core principles of the Mars GFSC and as such form a large part of our ongoing activities in this space. For example, we have hosted Food Allergen Management workshops in collaboration with Danone and Romer Labs focused on helping to raise awareness of current and future food allergen trends. At one such event in 2019, 100 participants from 16 food companies came together to promote food allergen management in the industry and ensure that the next generation of food integrity testing capability is relevant, practical, and directly applicable to the real-world problems experienced by manufacturers and processors throughout the supply chain.
Representatives of the Mars GFSC have also shared our insights externally at a number of international conferences as well as during a Food Enterprise Food Allergen Management Seminar on topics including effective allergen management procedures, our guiding principles for allergen managements at Mars, and shared our approach to encourage and share knowledge with other manufactures in this area.
We continue to support requests for technical insights, for example providing insights during a global consultation session on General Principles for Labeling of Prepackaged Food. This resulted in the addition of characterization requirements for possible allergenic substances, promoting the use of a recognizable naming system in ingredient lists that contain allergen warnings.
Food Safety Tech: Can you comment on additional work your team is doing in the area of food fraud?
Zhang: Food allergen risk management forms only one part of our wider food integrity focus at the Mars GFSC. We are committed to helping ensure food authenticity in an increasingly complex, global food supply chain through collaboration with global partners to develop new and improved tools and analytical methods that help protect the integrity of raw materials and finished products.
We have collaborated with researchers at Michigan State University to develop a Food Fraud Prevention Cycle roadmap (Introducing the Food Fraud Prevention Cycle (FFPC): A dynamic information management and strategic roadmap) which answered questions such as how to detect food fraud, how to start a food fraud prevention program, what to do in terms of testing, how much testing is enough, and how to measure success. Our intention in publishing this research was that the adoption of a holistic and all-encompassing information management cycle will enable a globally harmonized approach and the continued sharing of best practices across industry partners.
More recently, we completed an international collaboration tackling rice adulteration together with Queen’s University Belfast (QUB), Agilent Technologies, International Atomic Energy Agency (IAEA), China National Center for Food Safety Risk Assessment (CFSA), and Zhejiang Yangtze Delta Institute of Tsinghua University (Yangtze Delta). This work successfully developed a two-tier testing program, capable of rapidly screening the geographical origins of rice within the global supply chain (Food Fingerprinting: Using a two-tiered approach to monitor and mitigate food fraud in rice). By developing a tiered system, we could ensure that manufacturers use the right techniques for the right occasion, to maximize the information available in investigating food fraud at the best value. As part of this work, we have helped develop hands-on training in Ghana and inform best practice guidance to help build the foundations of a strong food safety culture in rice authenticity across the global supply chain.
Find records of fraud such as those discussed in this column and more in the Food Fraud Database, owned and operated by Decernis, a Food Safety Tech advertiser. Image credit: Susanne Kuehne
Cognac manufacturer Hennessy joined AuraBlockchain, a non-profit private blockchain for luxury brands that can be used to track the entire supply chain of a product. From raw materials to manufacturing to the consumer, digital timestamps are used to trace and record every step of the production process. Every product has a unique ID, with decentralized and unchangeable blockchain records. The consumer can check these records online to ensure authenticity of the purchased product.
Find records of fraud such as those discussed in this column and more in the Food Fraud Database, owned and operated by Decernis, a Food Safety Tech advertiser. Image credit: Susanne Kuehne
While blue is the most popular color around the world, not all blues are created equal and or belong into the food supply. The Rapid Alert System for Food and Feed in Europe mentioned a case of unauthorized food dye Sudan Blue II in a roasted corn snack food. Sudan Blue II, also known under the name Solvent Blue 35, is used to dye oils, solvents, alcohols, esters, hydrocarbon derivatives and other industrial chemicals, and is classified as carcinogenic and harmful to humans and the environment.
Find records of fraud such as those discussed in this column and more in the Food Fraud Database, owned and operated by Decernis, a Food Safety Tech advertiser. Image credit: Susanne Kuehne.
Iran is producing the lion share of saffron, the most precious spice, worldwide. One kilogram of saffron requires weeks of backbreaking work and the manual processing of around 170,000 flowers. Smuggling of what is also called “Red Gold”, and fraudulent and counterfeit saffron, are now million-dollar endeavors, as revealed by Europol and other investigations. From illegal food dyes like lead chromate, to herbs, to corn-on-the-cob strings, saffron is adulterated in many ways to enable fraudsters a participation in this $500 million market.
Find records of fraud such as those discussed in this column and more in the Food Fraud Database, owned and operated by Decernis, a Food Safety Tech advertiser. Image credit: Susanne Kuehne
Food fraud is rampant in pre-packaged and non-prepackaged meatballs, according to the Consumer Council in Hong Kong. A DNA investigation of beef meatballs revealed that 60% of samples contained pig derived meat, other samples contained chicken. None of the analyzed lobster ball samples showed any crustacean DNA. Consumers, especially ones with dietary or religious restrictions, are cautioned to check the ingredients lists of pre-packaged meatballs carefully and to be aware the some of the meatballs may contain undesired mystery meat.
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