Ghee, the glorious clarified butter leading a creamy buttery flavor to dishes and widely used in Asian cooking, is now also target for food fraud. Real ghee is based on pure butterfat extracted usually from cow’s milk, without impurities or additives. In Surajpole, India, an adulterated ghee operation was seized that used refined soybean oil and added butter flavor, which may have created a health hazard.
Last week Cannabis Industry Journal, a sister publication of Food Safety Tech, published its interview with AOAC International officials about the organization’s commitment to cannabis lab testing, where it sees this area headed in the future and the launch of its food authenticity and fraud program. AOAC first entered the realm of cannabis testing a few years ago and is making strides to get further involved with “methods regarding chemical contaminants in cannabis, cannabinoids in various foods and consumables, as well as microbial organisms in cannabis,” according to the article. AOAS also recently launched a food authenticity and fraud program to develop standards and methods geared toward economically adulterated foods. Read more about AOAC’s latest development on the food front as well as its push in cannabis lab testing in the article, “Spotlight on AOAC: New Leadership, New Initiatives in Cannabis and Food”.
Records involving wine fraud can be found in the Food Fraud Database. Image credit: Susanne Kuehne
Three arrests were made and at least 11,000 bottles of red wine labeled high-quality IGT Toscana wines have been seized in Italy for containing lower quality wines and fraudulent labeling, misrepresenting the wine’s geographic origin. The investigation was taken to the Europol level in conjunction with German and Italian law enforcement authorities.
These types of records can be found in the Food Fraud Database Image credit: Susanne Kuehne
One of China’s most famous health brands has been banned from making honey and issued a steep fine in China after selling expired honey. For a long time, the brand’s “Premium” honey was a supposedly safe alternative in China compared to “fake” honey, mixed with sugar syrup.
The food industry has been hard at work over the past few years implementing food fraud mitigation plans in response to Global Food Safety Initiative (GFSI) certification program requirements. GFSI defines food fraud as:
“A collective term encompassing the deliberate and intentional substitution, addition, tampering or misrepresentation of food, food ingredients or food packaging, labelling, product information or false or misleading statements made about a product for economic gain that could impact consumer health.” (GFSI Benchmarking Requirements, 2017)
GFSI then further defines the terminology of food fraud by citing seven categories (shown in the following diagram).
In the Food Fraud Database, we categorize food fraud records using the following terminology (with examples):
Dilution/substitution
Substitution of an entire fish fillet or partial dilution of olive oil with another oil
Artificial enhancement
Addition of melamine to artificially increase the apparent protein content of milk or the addition of coloring agents to spices
Use of undeclared, unapproved, or banned biocides
The use of chloramphenicol in honeybee populations (where not permitted) or the addition of hydrogen peroxide to milk
Removal of authentic constituents
The sale of “spent” spice powder (used in the production of an oleoresin) as a whole spice powder
Misrepresentation of nutritional value
Infant formula that does not contain the required nutritional content
Fraudulent labeling claims
Misrepresentation of label attributes related to production method (organic, kosher, halal, etc.)
Formulation of an entirely fraudulent product (using multiple adulterants and methods)
The sale of “100% apple juice” that consists of sugar, water, malic acid, flavor, and color
Other
This includes counterfeits, theft, overruns, etc.
Harmonization of food fraud terminology is frequently discussed, so I thought it might be useful to provide information on how our definitions relate to the GFSI terminology:
GFSI category “Dilution”: This category maps directly to our category dilution/substitution. The reason we combine these into one category is that the intent is the same: To replace the weight or volume of a product. This can occur either through partial or full substitution of a liquid product, a granulated product, or swapping an entire intact product such as a fish filet. One of the GFSI examples for substitution is “sunflower oil partially substituted with mineral oil”, which could just as accurately be described as dilution.
GFSI category “Substitution”: As noted above, this category maps directly to our category dilution/substitution. However, we would not consider the use of hydrolyzed leather protein in milk (one of the cited examples) to be dilution/substitution because it is not used to replace weight or volume. We would view that as artificial enhancement of the protein content of milk.
GFSI category “Concealment”: We do not include a category focused on concealment because all food fraud involves concealing some aspect of the true contents of the food. One of the examples cited in this category is “poultry injected with hormones to conceal disease.” The use of antibiotics, anti-fungal agents or other substances to reduce bacterial load or mask deterioration would be classified, in our system, as the use of undeclared, unapproved or banned biocides. The use of coloring agents on fruit to improve appearance would also be classified as artificial enhancement.
GFSI category “Mislabeling”: Since all food fraud is, to some extent, mislabeling, we reserve the use of the term fraudulent labeling claims to those label attributes that describe production processes (organic, kosher, etc.). With the exception of falsification of expiration dates, the other examples cited would not be classified by us as mislabeling. The sale of Japanese star anise, which is potentially toxic, as Chinese star anise (a different species) is dilution/substitution and a health risk to consumers. The sale of cooking oil that has been recovered from waste streams and illegally produced is also a form of substitution that poses a potential health risk to consumers.
GFSI category “Unapproved enhancements”: This GFSI category aligns nicely with our category artificial enhancement, and both examples cited are nicely illustrative of the concept, which involves the fraudulent addition of a substance specifically for its function (not as a replacement for weight or volume).
GFSI Category “Gray market production/theft/diversion”: The production and sale of food products through unregulated channels would all be classified in our category called other. Because these forms of food fraud involve the sale of food outside of regulatory control, prevention measures will generally be substantially different from the prevention of fraud within legitimate supply chains.
GFSI Category Counterfeiting: This GFSI category is similar to the gray market production/theft/diversion category in that it involves intellectual property infringement and production outside of regulatory control. It would similarly be classified in our other category.
A recently published paper advocates the inclusion of media reports as a source of information for assessing food fraud vulnerability.1 Those of us who maintain the Food Fraud Database could not agree more. We have been monitoring media reports for years and they are an important source of information in the database (accounting for 45% of all primary source references).
As I mentioned in last month’s post, there are challenges with using media reports to inform food fraud vulnerability. Many media reports are general discussions of the issue of food fraud and are not necessarily reporting new information. It may be difficult to filter out these types of reports without manual review. There may also be concerns about the validity of media reports on food fraud. This is the reason we implemented a classification for “weight of evidence” for incident records in the database. Overall, approximately 30% of the incident records in our database are classified as a “low” weight of evidence due to unverifiable data or a lack of corroborating reports. Some of our users choose to filter these out of their searches.
We have received requests for information about how the data in the Food Fraud Database compares with numbers reported in the paper. Table 11 in the paper described the top product categories, countries and type of fraud as reported in four food safety tracking systems.1 We have adapted that table below to data from the Food Fraud Database.
Product Category
%
Country of Origin
%
Type
%
Meat/Poultry
18
India
26
Dilution/substitution (misrepresentation of animal origin)
26
Seafood
16
China
9
Dilution/substitution (“other”)
19
Dairy Products
14
United States
9
Dilution/substitution with a non-food substance
14
Alcoholic Beverages
6
Columbia
6
Dilution/substitution (misrepresentation of botanical origin)
12
The most common food fraud records (“cases”) in the Food Fraud Database (2014-2015)
As shown in Table 11 in the paper, the top four products by number of articles in the media monitoring system (in 2014-2015) were meat, seafood, milk and alcohol. As shown above, when looking at data in the Food Fraud Database from 2014 and 2015, the top ingredient categories are very similar: Meat/Poultry, Seafood, Dairy Products, and Alcoholic Beverages. However, there was little agreement in the country of origin of the reported cases among any of the systems. For the Food Fraud Database (shown above), the top countries of origin in 2014–2015 were India, China, the United States and Colombia. According to the paper, the top countries of origin reported by the food fraud media monitoring system were Egypt, the United States, the U.K. and Saudi Arabia. The top country of origin reported by RASFF was China and by HorizonScan was the Czech Republic.
Table 4 reported the “types” of food fraud (which correspond to what we call “reasons for adulteration”) and the corresponding number of articles collected, which we have also adapted to the data in the Food Fraud Database below.
Types of Food Fraud in Records in the Food Fraud Database (2014–2015)
Type of Food Fraud
Number of Records
%
Dilution/substitution – misrepresentation of animal origin
212
26
Dilution/substitution (other)
159
19
Dilution/substitution with a substance not approved for use in foods
118
14
Dilution/substitution – misrepresentation of botanical origin
101
12
Unknown
87
11
Fraudulent labeling
64
8
Artificial enhancement of apparent protein content
58
7
Artificial enhancement with color additives
57
7
Other
41
5
Dilution/substitution – misrepresentation of geographic origin
40
5
Dilution/substitution – misrepresentation of varietal origin
28
3
Use of unapproved biocides (antibiotics, anti-fungal agents, preservatives, etc.)
21
3
Artificial enhancement (other)
7
1
Formulation of an entirely fraudulent product using multiple techniques and adulterants
2
0
TOTAL
828
*
* Greater than 100% because one record can have multiple types of associated fraud
It is not possible to make meaningful comparisons among the reported fraud “types” without harmonized definitions and standardization of data collection processes, as noted in the paper. A glance at Table 1 from the paper illustrates the variety of food fraud categorizations in use among the various systems.1 Generally, it is a challenge to directly compare any of the information coming from various sources such as RASFF, HorizonScan, the Food Fraud Database and others, due to the differences in the way data is collected, standardized and reported.
In contrast with foodborne illnesses, which are generally required to be reported to public health agencies, food fraud typically does not result in acute illness and is difficult to track. The nature of food fraud combined with differences in data tracking systems make it almost impossible to reconcile the data among the various systems. Regardless of which system is reporting, the reports are likely just a fraction of the true occurrence of food fraud; however, each can provide valuable perspective on risks to food safety (including those from food fraud). A holistic assessment of food fraud vulnerability should take into account a wide variety of information sources, including media reports.
Reference
Bouzembrak, Y., et al. (November 2018). Development of food fraud media monitoring system based on text mining. Food Control. Vol. 93. Retrieved from https://doi.org/10.1016/j.foodcont.2018.06.003
The end of the year is always a time of reflection. At Food Safety Tech, it is also a time when we like to share with you, our readers, the most popular articles over the last 12 months. Enjoy, and thank you to our loyal and new readers, as well as our contributors!
We developed a system that tracks food fraud records using four categories: Incidents, inference records, surveillance records and method records. Food fraud incidents are documented occurrences of fraud that include contextual information about location, perpetrators, timeframe, geographic location and other characteristics. In many ways, incidents are the gold standard of food fraud records. However, there have been unsubstantiated reports of food fraud that were subsequently discredited (such as the “plastic rice” scandal of a few years ago). For this reason, for every incident we capture, we assign a “weight of evidence” classification to provide our assessment of the strength of the evidence. For example, incidents reported directly by regulatory agencies with supporting documentation will generally be assigned a “high” weight of evidence classification.
We also work diligently to avoid “double counting” food fraud incidents, although at times this can be challenging. Incidents may be reported in multiple media outlets and, at times, the reports may not include enough information to determine if it is a new report or related to an issue already reported. We cross-reference the dates and locations of reports, along with information about the ingredients and adulterants, to help ensure that isolated food fraud cases are reported as one incident.
Expired (possibly rotting) eggs intended to be powdered and used in food production were discarded by regulatory authorities in India. A customer in China reported that expired carrots were being re-labeled with new dates. Adulterated milk was discovered in Pakistan. In Kenya, reports surfaced of sodium metabisulfite being used on meat to enhance its appearance. Finally, in the United States, two companies were indicted for importing giant squid and selling it domestically as octopus (which usually has a higher retail price) over a period of three years. A review of these reports illustrates how challenging it can be to collect and standardize food fraud information, especially when it is reported in media sources.
The company that produces the very popular flavored sparkling water brand LaCroix is facing a class action lawsuit that alleges false claims of the product being “all natural.” The suit alleges that certain flavor chemicals used in the beverage are, in fact, artificial ingredients. These flavor chemicals include limonene, linalyl propionate (linalool propionate), linalool and ethyl butyrate (ethyl butanoate). While these flavor chemicals can be synthesized, they are naturally occurring chemical constituents and can therefore be derived from natural sources.
The safety of the beverages is not at issue; this is a labeling question. The suit states that linalool is “used in cockroach insecticide,” which is inflammatory and misleading. Chemical compounds, including those used as food ingredients, naturally have multiple applications and this does not have any bearing on the question of whether they are safe to use in foods.
Presumably, the labeling issue of whether these flavor chemicals were naturally or synthetically derived will be addressed as the suit progresses. This suit does, however, highlight some of the challenges we have in tracking food fraud information related to flavors.
Flavors are big business. Appealing flavors enabled LaCroix to make unsweetened sparkling water explode in popularity. If you have been on the Institute of Food Technologists Annual Meeting expo floor, you have seen the prominent displays and creative food samples offered up by the big flavor houses. It is a competitive business and very proprietary. The FDA labeling requirements for flavors allow them to be listed generally as “spice,” “natural flavor,” or “artificial flavor” (or a combination of those). This makes tracking and standardizing public records of food fraud related to flavors challenging.
Our data includes more than 60 of food fraud related to flavors represented as “natural.” Most of these records are linked to vanilla extract or various essential oils. However, we have also captured a handful of records that address misrepresentation of synthetic flavor chemicals as naturally-derived. This includes records for linalool and ethyl butyrate, among others such as vanillin and linalyl acetate. However, none of these records describe publicly reported incidents of fraud for naturally-derived flavor chemicals. The records are based on peer-reviewed publications aimed at method development for authentication of natural flavors.
Added value claims such as “natural” tend to increase food fraud risk because the costs of production can be so much higher. While an ingredient like vanilla extract is certainly one example of this, we do not tend to see the same level of evidence of food fraud potential for naturally-derived flavor chemicals in public records. When our users need to conduct a food fraud vulnerability assessment for a natural flavor that is a proprietary blend of flavor chemicals, we suggest that they incorporate information from the entire natural flavors group into their assessment. Given the proprietary nature of flavor blends and FDA labeling requirements, it is not feasible for us to track every individual flavor blend in our database.
Fortunately, given the importance of flavors to the food industry, flavor companies have a vested interest in preserving their client relationships and public reputation by ensuring flavors labeled as “natural” qualify for that label claim.
Resources
The Decernis Food Fraud Database is a continuously updated collection of food fraud records curated specifically to support vulnerability assessments. Information is gathered from the scientific literature, regulatory reports, media publications, judicial records, and trade associations from around the world and is searchable by ingredient, adulterant, country, and hazard classification.
—Update— February 19, 2020: National Beverage Corp.announced dismissal of “all of the allegations contained in a prior lawsuit which challenged LaCroix’s natural ingredient labeling.” –END Update–
Food fraud happens in many ways, and it can be challenging to categorize the various methods of fraud. Dilution and/or substitution involves the intentional addition of an alternate product with the intent to replace weight or volume (olive oil, juices and fish are prone to this type of fraud). Artificial enhancement is the addition of a substance that is not intended to replace weight or volume, but to have a functional effect (such as the use of industrial dyes in spices). Certain forms of food fraud, such as theft/resale, counterfeit packaging, or overruns may not involve the addition of alternate ingredients. However, as customers and consumers, we would be taking a risk to trust the safety of any foods that are intentionally misrepresented.
Categories of methods by which food fraud happens (as defined in the Food Fraud Database1). Graphic courtesy of Decernis
While all forms of fraud can be considered “mislabeling” in one way or another, we consider fraudulent labeling claims to be defined as misrepresentation of a label attribute that implies a particular production technique. Examples include representing non-organic products as organically produced, the sale of foods as halal that do not meet the appropriate standards, changing poultry expiration dates, and labeling products such as eggs and Iberian ham as “free range.” In 2017, a company in Canada was fined for selling falsely labeled kosher cheese. More recently, in Malaysia, millions of products were seized based on the use of fraudulent halal labels.
We have compiled more than 300 records of food fraud involving the use of fraudulent labeling claims. The most common fraudulent claims identified in our records are shown in the chart below.
Fraudulent labeling claims based on records reported in the Food Fraud Database.1
Consumer interest in organic foods is increasing and NSF cites “added value claims” such as organic and free range as one of the important factors driving food fraud risk.2 There continues to be a need for robust analytical tools for the authentication of organic foods. However, recent research has indicated it may be unlikely that authentication of these food products can be can be achieved by a single analytical method or the measurement of a single marker.3,4 Given the technical complexity and cost of ensuring the authenticity of organic label claims through analytical testing, preventing this type of food fraud also requires strong supply chain management and trustworthy supplier relationships along with effective auditing programs.
References
The Decernis Food Fraud Database is a continuously updated collection of food fraud records curated specifically to support vulnerability assessments. Information is gathered from the scientific literature, regulatory reports, media publications, judicial records, and trade associations from around the world and is searchable by ingredient, adulterant, country, and hazard classification.
Inacio, CT and Chalk, PM. (January 2017) Principles and limitations of stable isotopes in differentiating organic and conventional foodstuffs: 2. Animal products. Crit Rev Food Sci Nutr.. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/25849871
Capuano, E., et al. (September 11, 2012). Analytical authentication of organic products: an overview of markers. Journal of the Science of Food and Agriculture. (Vol. 93) No. 1. https://doi.org/10.1002/jsfa.5914
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