This PhD thesis (request a copy here) describes the application of a previously-published LC-MS analytical method for triglycerides in fats to build an authenticity classification model for oils and fats based upon their triglyceride profile.  The author reports good discrimination between different pure oils and also good discrimination when oil or sesame oil were adulterated with lower value oils.  The model was also used to discriminate aged and degraded oils, and those which had been heat-processed.  The author concludes that this fast, simple, robust and reliable method offers significant benefits in authenticating edible oils, evaluating oil degradation, and differentiating meat products from their fats. The method has excellent potential for universal use.

Part II - Food Fraud Prevention and Types of Fraud

Welcome! In support of the Food Authenticity Network, this blog series reviews key topics related to food fraud prevention. Watch here for updates that explore the definitions of food fraud terms and concepts.

This blog post builds on our previous review of the definition and scope of food fraud.

Food fraud was first clearly defined in 1820 by Frederick Accum in ‘A Treatise on Adulteration of Food and Culinary Poisons.’ Over the next two hundred years, the subject continued to be reviewed as a  food science or food safety problem, as by Wiley and other pillars of scholarship. Along the way, ‘someone else’ was relied upon to actually prevent the problem. The ‘someone else’ was never assigned.

Interdisciplinary areas of study converged over time, to enable the shift to focus on prevention. In the 1970s, criminology theory expanded from focusing on the criminal and punishment to prevention. In the 1980s, quality management became a separate area of business theory with a shift to understanding and reducing the root causes of problems. In the 2000s, risk management became more formalized, such as in ISO 31000 Risk Management, which focused on likelihood and consequence as well as risk and vulnerability. In the 2010s, Enterprise Risk Management expanded the resource allocation decision-making to evaluate not only how to mitigate but also to prevent problems. 

This holistic view of vulnerability applied criminology concepts to all criminal acts and all possible targets. For food products, that led to the need to define the ‘types of food fraud’ and the ‘types of products.’ If we are going to prevent food fraud, we need to consider all types of actions and products. This led to the holistic and all-encompassing definitions:

Type of Food Fraud & Definition (From various sources including GFSI and SSAFE):

• Adulterant-Substances (Adulterant/ Adulteration):
  • Dilution: The process of mixing a liquid ingredient with a high value with a liquid of a lower value.
  • Substitution: The process of replacing an ingredient or part of the product of high value with another ingredient or part of the product of lower value.
  • Concealment: The process of hiding the low quality of a food ingredient or product.
  • Unapproved enhancements: The process of adding unknown and undeclared materials to food products in order to enhance their quality attributes.
• Mislabeling or Misbranding: The process of placing false claims on packaging for economic gain.
• Grey market production/ diversion:
  • Gray Market: A market employing irregular but not illegal methods.
  • Theft: Something stolen and then covertly re-entered into commerce.
  • Diversion/ Parallel Trade: The act or an instance of shifting a product from one intended market to another, which is unauthorized but either legal or illegal.
• Counterfeiting (IPR): The process of copying the brand name, packaging concept, recipe, processing method, etc., of food products for economic gain.

The types of food fraud are intentionally broad – holistic and all-encompassing - to frustrate the criminal against action of any kind.

Watch out for the next blog in March, which will review fraud susceptibility of different types of products (e.g., raw materials or ingredients to finished goods in the marketplace).

If you have any questions on this blog, we’d love to hear from you in the comments box below.

References:

• https://publicdomainreview.org/collection/a-treatise-on-adulteration-of-food-and-culinary-poisons-1820
• Spink, J, and Moyer, DC, (2011) Defining the Public Health Threat of Food Fraud, Journal of Food Science, Volume 75 (Number 9), p. 57-63. (ISI 1.791; SJR 0.378), URL: https://ift.onlinelibrary.wiley.com/doi/full/10.1111/j.1750-3841.2011.02417.x
• Spink, John, Bedard, Brian, Bruner, Leon, Keogh, John, Scimeca, Joseph, Goodridge, Lawrence, Moyer, Douglas C, & Vasan, Akila (2019). International Survey of Food Fraud and Related Terminology: Preliminary Results and Discussion, Journal of Food Science,  84(10), 2705-271800, URL: https://ift.onlinelibrary.wiley.com/doi/full/10.1111/1750-3841.14705

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 January 2024.  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.  You can follow Bruno's LinkedIn feed here.

The authors of this study (open access) developed a chemometric classification model to distinguish true cinnamon from its potential adulterants, Cassia or Saigon cinnamons.  The model is based on simple and low-cost LC-UV analysis of four marker chemicals: eugenol, cinnamaldehyde, coumarin and cinnamic acid.  Sample pre-treatment was vortexing/sonicating with methanol followed by centrifugation.  Reference samples for the model were purchased from retail outlets rather than fully traceable sources; 25 samples of each type of cinnamon, including both sticks and powder.  The model was first constructed to differentiate pure powders.  Then the authors used an experimental design on a training set of in-house prepared mixtures (down to 1%/99% mixes) and a Partial Least Squares algorithm to model the classification of mixtures.  They found the model was linear and – in the case of true cinnamon mixed with either of the two adulterants – could discriminate adulteration down to 1%.  The model could not discriminate Cassia from Saigon cinnamons but the authors consider this a less important question.

Image from the published study.

A recent criminal conviction in the UK highlights the fraud risk from rogue employees.  Two Despatch Managers at 2Sisters Food Group, the country’s largest poultry supplier, were supplying another company, Townsend Poultry, with chicken. Townsend Poultry was not a customer of 2 Sisters Food Group and there were no records of any deliveries. The fraud was uncovered during an audit when Townsend Poultry appeared incongruous on the customer records.  Enquiries made with local hauliers used by the 2 Sisters Food Group confirmed there had been 84 deliveries from the 2 Sisters Food Group to Townsend Poultry, worth hundreds of thousands of pounds. The Despatch Managers had destroyed the records of those deliveries.

2 Sisters suffered the theft of £300K of stock over an extended period between 2019 and 2021.  This stock was then fed into the UK market with falsified or non-existent traceability records; a food safety risk.

Read the FSA statement here

Read the story here.

This paper (purchase required, free to IFST members) reports a quick, non-destructive technique to add to the panel of analytical tools needed to detect olive oil adulteration or mis-labelling.  This test is to detect addition of sunflower, rapeseed or corn oils.  It is based on electrochemical examination of the peak of alpha-tocopherol oxidation on a pencil graphite electrode (PGE).  There is no sample pre-treatment needed.  The authors prepared in-house oil mixes and were able to confidently discriminate “adulterated” samples at around 10% added non-olive oil. The method's relative standard deviation (RSD) was 20%, and the α-tocopherol in cold pressed olive oil cut-off value was 30.98 ± 12.57 nA.   The authors believe this is the first publication on utilisation of voltammetric techniques for the detection of olive oil adulteration using a PGE.

A survey is being undertaken as part of a project which is jointly funded by FSA and Defra to better-understand the capabilities and challenges related to the verification of geographical origin of food and feed to inform on future direction (Project Reference FS900435). The project is being undertaken by Fera Science Limited in York, UK.

The insight of all stakeholders with an interest in the geographical origin of food and feed is welcomed and will be invaluable to the project outcomes. Your participation in this project is invited by means of completing the questionnaire at the link below:

 https://forms.gle/zc9daRTvCAHjECQ68

Legal firm Hogan Lovells have published their predictions for US Food Law changes in 2024.  Whilst there is nothing specific to authenticity or claims, they do foresee a step-change in the extent of State level regulatory divergence and a step-change increase in class actions.  The headlines are

• FDA Setting Nutritional Guardrails for Foods on Multiple Fronts
• State Laws Run Amok
• Post-market Food Chemicals Surveillance: Can FDA Reassert Leadership and Stem the Rising Tide of State Action?  
• Significant New Rulemakings Could Mean Operational Changes for Meat & and Poultry Processors
• Heavy Focus on Heavy Metals to Continue Unabated
• FSMA Implementation Will Continue, But at a Slower Pace
• Some Inspections, With a Dash of Enforcement
• Guidance and Enforcement by the Federal Trade Commission (FTC)
• The Supreme Court Could Rewrite Administrative Law
• Class Action Litigation and Proposition 65

In this conference proceeding (free Computer Society membership needed plus USD19 article download fee) the authors use “big data” commodity price analysis as an indicator of possible food fraud incidence.  They calculate the expected price vs the actual price of each commodity and plot trends over time.  Where there is a sustained differential (e.g. “price is too good to be true”, or “price is over-inflated”) then the authors assume fraud.  They report surprisingly clear trends, and differences in different European countries.  For example, for oils and fats, there was evidence of price disparity in Belgium, Germany and Poland from 2021 until November 2022 when it was sharply corrected.  There was no such disparity in Italy and Spain.  They report correlations between different industry sectors within specific countries, most markedly illustrated by price disparities within Portugal.

Photo by Blogging Guide on Unsplash

Global reports of food fraud have been collated for over 40 years on the USP database under various ownership iterations (Decernis, FoodChainID).  The owners have always taken the approach that media reports, official reports or literature surveys are assessed by a team of analysts before logging, to ensure that fraud is genuinely the cause.  Thus the database holds relatively few entries but with relatively high confidence in the categorisation of each entry.

A summary of all entries has now been published in the open access literature.  Top of the list of “most adulterated foods” is dairy products.  This chimes with the recent annual summaries of “most adulterated foods” published on FAN’s website.  Patterns of food fraud do not appear to have changed over the past four decades.

This review article is primarily a literature search and list of relevant publications from the past 5 years rather than a critical or comparative review.  The authors cite publications that use a relatively rapid test method (either lab-based or point-of-use) coupled with chemometrics for categorising food.  They explain the principle of each analytical technique including spectroscopic techniques, ambient ionisation mass spectrometry, electronic sensors and isothermal amplification DNA techniques.  They then subdivide each into applications to categorise species/variety, quality attributes, or geographical origin of food.  They devote less time to listing different chemometric methods but do include a basic explanation of different methods such as PCA, HCA, PLS-DA, OPLS-DA, SVM, KNN, and PLSR.

Welcome! This new blog series[1] reviews key topics related to food fraud prevention. The first blog explores the definition of food fraud terms and concepts.

When considering any new subject, the most important starting point is to define the terms and the scope.

• 2011: Food fraud was first defined in a scholarly journal article in 2011 (Spink and Moyer, 2011).
• 2014: The Global Food Safety Initiative (GFSI), foundation for most of the world’s food safety management system standards, provided a similar key definition and scope.
• 2018: The International Standards Organization (ISO) published a definition of:
  • product fraud: “wrongful or criminal deception that utilizes material goods for financial or personal gain.” (ISO 22300:2018 updated from ISO 12931:2012)
• 2018: ISO 22000 Food Safety Management added a note that food fraud was to be considered as a root cause of food hazards.
• 2019: Spink et al conducted an International Survey of Food Fraud and Related Terminology                                                   
• 2023: The Food Authenticity Network published a review of global definitions of food fraud                                        
• Active: CEN and Codex Alimentarius have working groups that are actively developing their definitions of food fraud and related terms.

The simple definition is:

Food fraud is “intentional deception for economic gain using food”.

The scope of product fraud and food fraud is intentionally broad in order to cover all types of fraud.

Watch out for the next blog, which will review types of food fraud…...

If you have any questions on this blog, we’d love to hear from you in the comments box below.

About the author

John W Spink, Ph.D., is the Director and Lead Instructor for the Food Fraud Prevention Academy. Also, he is an Assistant Professor in the Department of Supply Chain Management (SCM) in the College of Business at Michigan State University (MSU). His food fraud prevention research focuses on policy and strategy to understand and prevent these supply chain disruptions and implement procurement best practices. He is widely published in leading academic journals and has helped lead national and global regulatory and standards activity. More recently, his teaching and research have expanded to supply chain disruption management and procurement best practices. He is also on the Advisory Board of the Food Authenticity Network. For more information please visit: www.FoodFraudPrevention.com

[1] Collaboration between Dr John Spink of Michigan State University and the Food Authenticity Network (FAN)

The UK National Food Crime Unit have a useful page "Food crime - guidance for businesses" which we have now added as a permanent link in FAN's "Guides" list, here.   The NFCU guidance is particularly aimed at small businesses.  It includes how to spot the signs of food fraud, what you can do to protect your own business, staff and customers, and how to report incidents.  There are links from the page to other useful resources.   

Photo by on

FAN are planning a series of short explainer videos to help you better navigate our website.  The first, on finding organisations that curate specific authenticity testing databases, was launched on social media today.  You can access or download the video here (hosted on a my own website because of file size limitation on FAN site).

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.

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.

Photo by PHÚC LONG on Unsplash

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.