An information-based risk analysis IT tool protecting the European food system(s).
Date: 2024-10-02
Institutions: Joint Research Centre
Subjects: agri-foodstuffs, artificial intelligence, data sharing, database, food fraud, food inspection, food policy, food systems, information system, international cooperation, research report, risk management, traceability

ISSN 1831-9424 

An information-based risk analysis IT tool 
protecting the European food system(s) 

Siligato, R., Bocchino, A., Ulberth, F. 

2024 

 EUR 31975 EN 

 
  
 
This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
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JRC138424 

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How to cite this report: European Commission, Joint Research Centre, Siligato, R., Bocchino, A. and Ulberth, F., An information-based risk 
analysis IT tool protecting the European food system(s), Publications Office of the European Union, Luxembourg, 2024, 
https://data.europa.eu/doi/10.2760/684532, JRC138424. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ................................................................................................................................................................................... 1 

Acknowledgements ............................................................................................................................................................ 2 

Executive summary ............................................................................................................................................................ 3 

1 

Introduction..................................................................................................................................................................... 5 

1.1 

The political and legal context ......................................................................................................................... 5 

1.2  EU initiatives to fight food fraud ..................................................................................................................... 8 

1.3  Global initiatives to fight food fraud .............................................................................................................. 9 

1.3.1 USA Food Safety Modernization Act ...................................................................................................... 10 

1.4  The Food Risk Matrix .........................................................................................................................................13 

1.5  Tools to fight food fraud and root causes (“drivers”) ............................................................................. 14 

2  Scope and objectives ................................................................................................................................................. 18 

3  Methodological approach .......................................................................................................................................... 19 

4  The needs of the different Commission Services............................................................................................. 20 

5  The tripartite structure of the project .................................................................................................................. 23 

5.1 Existing data and intelligence collection tools ............................................................................................ 24 

5.2 Possibilities to improve the existing data and intelligence collection tools..................................... 29 

5.2.1 EU wide registry of food fraud cases ................................................................................................... 29 

5.2.2 Modernised and enhanced food traceability ...................................................................................... 30 

5.3  Data analysis .......................................................................................................................................................34 

5.3.1 “Transaction ID Card” data elements .................................................................................................... 41 

5.3.2 The “Food ID Card”, a collection of all “Transaction ID Cards” .................................................... 44 

5.4 Model deployment ............................................................................................................................................... 47 

5.4.1 Insight capacity ............................................................................................................................................ 47 

5.4.2 Investigative capacity ................................................................................................................................50 

5.4.3 Anticipatory capacity ................................................................................................................................. 53 

5.5 Vulnerability analysis of supply chains ....................................................................................................... 54 

6 Remit and uncertainties of the ideal information-based risk analysis IT tool .......................................... 60 

6.1 Which products should be monitored?........................................................................................................... 60 

6.2 Which risks should be addressed? ................................................................................................................. 60 

6.3 Which illegal activities should be targeted? ................................................................................................. 61 

7  Conclusions .................................................................................................................................................................. 63 

References .......................................................................................................................................................................... 66 

List of abbreviations and definitions .......................................................................................................................... 76 

List of boxes ....................................................................................................................................................................... 78 

List of figures ..................................................................................................................................................................... 79 

List of tables ....................................................................................................................................................................... 81 

Annex 1: Data Science, AI and data quality ......................................................................................................... 82 

Data Science ........................................................................................................................................................... 82 

i 

Data quality .............................................................................................................................................................83 

Annex 2: The Transaction ID Card data items .................................................................................................... 86 

Product  .................................................................................................................................................................... 86 

Transaction price ................................................................................................................................................. 88 

Quantity ................................................................................................................................................................... 88 

Value adding features ........................................................................................................................................ 88 

Identification number .......................................................................................................................................... 90 

Means of transportation ..................................................................................................................................... 90 

Seller/Buyer ........................................................................................................................................................... 94 

Poverty and crime .................................................................................................................................... 99 

Corruption 

 ............................................................................................................................................. 100 

Country-based geopolitical risk ......................................................................................................... 101 

Checkpoint location............................................................................................................................................ 105 

Non-compliance .................................................................................................................................................. 105 

E-commerce ........................................................................................................................................................ 105 

Current Transaction ID (CTI) ........................................................................................................................... 105 

Backward Transaction IDs (BTIs) .................................................................................................................. 106 

Forward Transaction IDs (FTIs) ..................................................................................................................... 106 

Current market price  

 [Derived data item] ............................................................................................ 106 

Unit price 

   [Derived data item] .................................................................................................................. 107 

Future market price 

  [Derived data item] .............................................................................................. 107 

Non-compliance history 

   [Derived data item] ..................................................................................... 108 

Ease of detection 

    [Derived data item] .................................................................................................. 109 

Annex 3: The candidate food sector(s) ................................................................................................................ 110 

Annex 4: The legal bases for risk-based official controls ........................................................................... 116 

ii 

 
 
 
 
 
 
 
 
 
 
 
Abstract 

The Farm to Fork Strategy has recognised the necessity of intensifying efforts to combat fraudulent 
practices within the agri-food chain while simultaneously enhancing traceability and alert systems to 
improve coordination in addressing food fraud. 

A substantial volume  of food chain data is  already  accessible at European and international levels, 
enabling  a  shift  towards  a  digital,  risk-based  approach  to  safeguarding  the  food  system. 
Nevertheless,  this  data  is  dispersed  across  various  food  businesses,  competent  authorities  in 
Member States, and Commission services, and data sets are not always interoperable. 

The European Commission could harness its in-house resources, as existing databases, intelligence 
sources,  and  digital  tools,  to  aggregate  relevant  food  chain  information.  This  aggregation  can 
enhance  descriptive  analytics  for  visualizing  current  food  safety  and  fraud  issues,  diagnostic 
analytics  for  identifying  potential  underlying  causes,  and  predictive  analytics  for  more  effectively 
targeting risk-based official controls. 

The  report  recommends  incentivising  food  integrity  data  sharing  among  Member  States’  competent 
authorities and the European Commission, creating a public-private partnership for transitioning the 
supply  chain  to  digital  traceability,  and  developing  an  AI-driven  predictive  analytics  system  to 
support  targeting  control  activities  to  supply  chains  where  fraud  is  most  likely.  It  also  proposes  a 
project to assess supply chain vulnerabilities.  

1 

 
Acknowledgements 

The  cooperation  of  the  staff  in  the  European  Institutions  is  kindly  acknowledged.  The  following 
services participated. 

European Commission 

DG AGRI 

DG ESTAT 

DG JUST 

DG MARE  

DG RTD 

DG SANTE 

DG TAXUD 

JRC 

OLAF 

Other EU Institutions 

European Food Safety Authority 

Europol

Authors 

SILIGATO Riccardo  

BOCCHINO Andrea  

ULBERTH Franz  

2 

 
 
 
 
 
 
 
 
Executive summary 

Policy context 

The Farm to Fork Strategy acknowledged the need to scale up the fight against fraudulent practices 
while  enhancing  the  traceability  and  the  use  of  alerts  to  improve  coordination  on  food  fraud.  The 
Council Conclusions on the next steps how to better tackle and deter fraudulent practices in the 
agri-food chain underline that successful action to tackle food fraud needs to be based on rapid and 
effective  exchange  of  the  relevant  information,  appropriate  reporting  and  close  cooperation  and 
coordination between authorities within Member States as well as between the Member States and 
the Commission.  

Digital technologies and data analytics are best suited to address those demands. 

Key conclusions 

A  vast  amount  of  food  chain  data  is  already  available  at  the  European  level  and  internationally  to 
enable  the  transition  to  a  digital,  risk-based  approach  to  safeguard  the  food  system(s).  However, 
those data are scattered  among food businesses,  competent authorities in the  Member States and 
various  Commission  services.  In  addition,  databases  are  not  always  interoperable  requiring  time-
consuming manual manipulations to prepare them for data analysis.  

The  European  Commission  could  capitalise  on  the  in-house  availability  of  existing  databases, 
intelligence sources and digital tools by aggregating relevant food chain information to improve (a) 
descriptive  analytics  to  create  a  visual  overview  of  current  food  safety  and  food  fraud  issues,  (b) 
diagnostic  analytics,  which  could  identify  potential  underlying  drivers  of  those  issues,  and  (c) 
predictive  analytics  for  better  targeting  risk-based  official  controls.  Digital  tools  to  support  fraud 
investigations  already  exist  and  official  control  authorities  as  well  as  Commission  services  could 
profit from the experience law enforcement agencies have in using them. 

Main findings 

The protection of the European food system(s) requires a holistic approach by addressing suspicions 
and  problems  with  advanced  digital  tools.  The  digital  tools  proposed  in  this  report  would  scale-up 
the  fight  against  food  fraud  and  food  safety  issues,  while  providing  a  better  overview  of  the 
European food supply chains and their vulnerabilities.  

The report recommends:  

—  to train a machine learning model with a data set of historic fraud cases provided by the EU Food 
Fraud  Network  for  assessing  the  feasibility  of  using  predictive  analytics  for  identifying  cases 
being suspicious of food fraud; 

—  to  encourage  Member  States’  competent  authorities  to  notify  all  food  fraud  cases  detected  by 
application of Article 9(2) of the Official Controls Regulation to the EU Food Fraud Network using 
the appropriate iRASFF channel; 

—  to  motivate  and  incentivise  food  industry  to  share  food  fraud  data  and  intelligence  insights 
through  an  independent  data  trust  that  stewards  and  governs  the  sharing  of  data  among 
industry members as well as competent authorities; 

—  to set up a public-private partnership to encourage and incentivise food businesses to transition 
to end-to-end digital traceability systems that are interoperable and allow sharing of traceability 
data if requested by competent authorities; 

—  to create a well-defined technology roadmap for setting up an Artificial Intelligence (AI)-driven 
Information  Technology  (IT)  system  for  food  integrity  that  integrates  data  aggregation  and 
analysis  by  big  data  technologies  involving  experts  from  European  Commission  departments 
owning  relevant  datasets,  IT  professionals  (back-end  and  front-end  developers),  and  data 
scientists; 

3 

 
—  to initiate  a  project aimed  at  constructing generic supply chain maps for a selected number  of 
foods/ingredients where historical fraud incidence data indicate an increased risk, assess their 
vulnerability and identify critical points in the chain (vulnerability analysis critical control points). 

Related and future JRC work 

The  Knowledge  Centre  for  Food  Fraud  and  Quality,  operated  by  the  JRC,  is  a  suitable  partner  for 
Commission services regulating the food chain in their endeavour to enhance the protection of the 
European food system(s). The JRC could support the development of the required digital technology 
and the steering of expert groups for the vulnerability assessment of supply chains. 

4 

 
1 

Introduction 

1.1 

The political and legal context 

Food currently lies at the centre of political and societal discussions worldwide. Policy makers have 
to  integrate  different  and  often  conflicting  perspectives  from  many  interconnected  areas: 
agriculture,  fisheries  and  aquaculture,  health,  environment  and  biodiversity,  energy  and  mobility, 
trade,  sustainability  and  circular  economy,  research  and  innovation,  internal  market,  consumer 
protection. 

Following a series of food scandals at the end of the previous millennium, energising the publication 
of  the  White  Paper  on  food  safety1  by  the  European  Commission,  the  European  Union  (EU) 
established what is globally recognised as among the best food safety legislative frameworks. The 
overarching General Food Law2 framed the risk analysis paradigm3 (i.e. the separation between risk 
assessment,  risk  management  and  risk  communication)  and  the  precautionary principle4  around 
future  laws  and  policies  dedicated  to  food  safety  (e.g.  crop  protection,  animal  health  and  welfare, 
food additives, genetically modified organisms, biological hazards, etc...). The General Food Law also 
established  the  European  Food  Safety  Authority  (EFSA)5  with  the  role  of  providing  science-based 
risk assessment to help risk managers to make their decisions6. 

Although  in  the  last  20  years  the  EU  has  established  high  food  safety  standards,  food  fraud  had 
received little attention until the infamous “horsemeat scandal” in 2013, where products supposedly 
containing only beef were adulterated with horsemeat. Although this fraud did not pose a threat to 
consumer health, it had ample resonance as in many Member States horse is considered a pet, and 
not  a  commodity.  Consequently,  it  prompted  the  adoption  of  a  dedicated  Resolution  from  the 
European Parliament7 in 2014. The 2014 European Parliament Resolution highlights that:  

—  “there is no framework in place specifically to target food fraud”  

—  “the Commission has only recently identified food fraud as a new area of action” 

—  “national authorities tend to focus their controls on food safety and do not prioritise food fraud, 

often due to a lack of capacity and resources” 

The 2014 European Parliament Resolution also states that food fraud cases seem to be on the rise 
and  criminal  organisations  are  getting  increasingly  involved.  The  European  legislative  framework 
also lacks a clear and uniform definition of food fraud. 

At the moment, there is no legal definition of “food fraud” within the European legislative framework 
or within the Codex Alimentarius Guidelines. However, the European legislative framework provides 
clear references to food fraud and consumer protection: 

—  The  General  Food  Law2  protects  consumers’  interests,  as  they  shall  be  able  to  make  informed 
choices whenever buying any food product. The legislation aims at preventing food adulteration 
and misleading, fraudulent or deceptive practices. Food labelling and traceability are addressed 
as well. 

—  The Information Management System for Official Controls (IMSOC) Regulation8 states that fraud 
notifications  concern  any  “suspected intentional action by businesses or individuals for the 

1  

2  

3  

4  

5  

6  

7  

8  

European Commission (1999) White Paper on Food Safety [719 final] 

Regulation (EC) No 178/2002 of the European Parliament and of the Council of 28 January 2002 laying down the general principles and requirements of food law, establishing the European Food 

Safety Authority and laying down procedures in matters of food safety (OJ L 031 1.2.2002) 

Section IV of the Procedural Manual of the Codex Alimentarius Commission 26th edition. ISBN: 978-92-5-130341-2 

Article  191  of  the  Treaty  on  the  Functioning  of  the  European  Union  (OJ  C  202  7.6.2016).  Article  7  of  Regulation  (EU)  178/2002.  A  definition  is  available  here:  https://eur-

lex.europa.eu/summary/glossary/precautionary_principle.html.  

https://www.efsa.europa.eu/en 

The risk management part is under the responsibility of the decision-makers, usually the European Commission and the Member States. 

European Parliament resolution of 14 January 2014 on “Food crisis, fraud in the food chain and the control thereof” (P7_TA(2014)0011) 

Commission  Implementing  Regulation  (EU)  2019/1715 of  30  September  2019  laying  down  rules  for  the  functioning  of  the  information  management  system  for  official  controls  and  its  system 

components (the IMSOC Regulation) 

5 

 
                                                        
 
purpose of deceiving purchasers and gaining undue advantage therefrom, in violation of the 
rules referred to in Article 1(2) of Regulation (EU) 2017/625”. 

—  The  Food  Information  to  Consumers  Regulation9  states  that  food  information  shall  not  be 
misleading  as  regards  nature,  identity,  properties,  composition,  quantity,  durability,  country  of 
origin or place  of provenance,  method of manufacture or production,  health  effects/properties, 
special/peculiar  characteristics  (when  compared  to  similar  foods),  ingredients  not  included  in 
the  final  product.  Mandatory  food  information  shall  be  accurate,  clear,  and  easy  to  understand 
for  consumers.  Voluntary  food  information  shall  not  mislead  or  confuse  consumers,  and  be 
based on relevant scientific data. 

—  The  Official  Controls  Regulation  (OCR)10  instructs  Member  States’  competent  authorities  to 
perform their official controls on a risk basis and with appropriate frequency, taking into account 
also  the  likelihood  that  consumers  might  be  misled,  with  a  spotlight  on  nature,  identity, 
properties, composition, quantity, durability, country of origin or place of provenance, method of 
manufacture or production of food. 

The Directorate-General (DG) for Health and food safety (DG SANTE) published a working definition 
of food  fraud on their  webpage,  which  uses four criteria to describe  food fraud11: (i) violation  of EU 
rules, (ii) deception of consumers, (iii) economic gain, and (iv) intention. 

The academic community is also attempting to establish a consensus over the terminology related to 
food  integrity  and  authenticity,  as  summarised  in  peer-reviewed  scientific  publications12,13.  The 
European  Committee  for  Standardization14  is  working  as  well  on  an  official  standard  covering  food 
authenticity. 

The  OCR15  instructs  competent  authorities  to  perform  official  controls  on  a  risk  basis,  considering 
not  only  threats  to  food  safety  but  also  the  likelihood  that  consumers  might  be  misled  and  the 
intentional violations perpetrated through fraudulent or deceptive practices. Besides official controls 
in  the  area  of  food  and  feed  safety,  the  OCR  requires  competent  authorities  to  identify  possible 
intentional  violations  of  the  rules  perpetrated  through  fraudulent  or  deceptive  practices  (Article 
9(2)).  DG  SANTE  and  the  Joint  Research  Centre  (JRC)  have  jointly  published  a  technical  report 16 
presenting  challenges, opportunities and good practice  examples in  relation to the implementation 
of  Article  9(2)  of  the  OCR.  In  addition,  the  Commission  has  the  empowerment  to  designate  EU 
reference centres that shall support the activities  of  the Commission and of the Member States to 
prevent,  detect  and  combat  violations  of  the  rules  perpetrated  through  fraudulent  or  deceptive 
practices. So far, the Commission has not exercised this mandate. 

In  December  2019,  the  Council  of  the  European  Union  adopted  its  conclusions17  on  food  fraud.  The 
Council stressed that food fraud generates financial losses for both consumers and food operators, 
threatening the internal market, and often leading to public health risks. Food fraud can also be part 
of  organised  crime.  An  effective  strategy  to  tackle  the  phenomenon  includes  a  rapid  exchange  of 
relevant  information,  and  cooperation/coordination  between  Member  States’  authorities  and  the 
Commission. The Council called for a legal definition of food fraud, and for a strengthened system for 
exchanging  information  without  an  excessive  administrative  burden,  ultimately  developing  an 
integrated  strategy  against  food  fraud.  The  Council  underlined  similar  concepts  in  May  202118, 

9  

10  

11  

12  

13  

14  
15  

16  

17  

18  

Regulation (EU) No 1169/2011 of the European Parliament and of the Council of 25 October 2011 on the provision of food information to consumers 

Regulation (EU) 2017/625 of the European Parliament and of the Council of 15 March 2017 on official controls and other official activities performed to ensure the application of food and feed law, 

rules on animal health and welfare, plant health and plant protection products 

https://food.ec.europa.eu/safety/eu-agri-food-fraud-network/what-does-it-mean_en 

Spink,  J.,  Bedard,  B.,  Keogh,  J.,  Moyer,  D.  C.,  Scimeca,  J.,  &  Vasan,  A.  (2019).  International  survey  of  food  fraud  and  related  terminology:  Preliminary  results  and  discussion.  Journal  of  Food 

Science, 84(10), 2705-2718 

Robson, K., Dean, M., Haughey, S., & Elliott, C. (2021). A comprehensive review of food fraud terminologies and food fraud mitigation guides. Food Control, 120, 107516. 

https://www.cencenelec.eu/about-cen/ 
https://food.ec.europa.eu/horizontal-topics/official-controls-and-enforcement/legislation-official-controls_en#official-controls-regulation-eu-2017625 

Winkler, B., Maquet, A., Reeves-Way, E., Siegener, E., Cassidy, T., Valinhas De Oliveira, T., Verluyten, J., Jelic, M. and Muznik, A., Fighting fraudulent and deceptive practices in the agri-food chain, 

EUR 31436 EN, Publications Office of the European Union, Luxembourg, 2023, ISBN 978-92-68-00336-7 

European Council (2019) Next steps how to better tackle and deter fraudulent practices in the agri-food chain - Council Conclusions 15154/19 

EU's priorities for the 2021 United Nations Food Systems Summit - Council Conclusions 9335/21 (31 May 2021) 

6 

 
                                                        
 
mentioning  the  necessity  to  adequately  trace  food  throughout  the  food  chain  and  to  effectively 
combat food fraud. 

The  European  Commission’s  European  Green  Deal19  (2019)  is  the  comprehensive  policy  package  to 
achieve  climate  neutrality  by  2050,  embracing  various  policy  areas  under  the  responsibility  and 
influence of a plethora of actors and stakeholders, often holding diverging views. The  Farm to Fork 
Strategy20  (2020)  is  “at the heart of the European Green Deal aiming to make food systems fair, 
healthy and environmentally-friendly” 21.  The  Strategy  clearly  mentions  food  fraud  as  an  element 
jeopardising  the  sustainability  of  the  EU  food  systems,  where  the  European  Commission,  Member 
States,  Europol  and  other  EU  bodies  will  need  to  work  together  to  scale-up  the  fight  against 
fraudulent practices. The Farm to Fork Strategy also integrates food fraud within the EU’s fisheries 
control system and the enhancement of a dedicated traceability system.  

Another  priority  of  the  European  Commission,  A  Europe  fit  for  the  digital  age22,  established  the 
importance  of  digitalisation  with  a  clear  focus  on  data,  technology,  and  infrastructure.  In  its  White 
Paper  on  Artificial  Intelligence23  (2020)  the  Commission  states  that  “digital technologies such as 
Artificial Intelligence (AI) are a critical enabler for attaining the goals of the Green Deal”. In parallel, 
within the Communication on A European strategy for data24, data-driven innovation is presented as 
a great  opportunity to  contribute to the Green Deal.  Data are  also mentioned as a tool to ensure a 
more efficient fight against crime. Finally, the Communication announces the creation of a “Common 
European agricultural data space”  (e.g.  production  data,  supply  chain  data,  earth  observation  or 
meteorological data) and a “Common European data spaces for public administrations” (e.g. data use 
for improving law enforcement in the EU in line with EU law). 

The European Commission’s Promoting our European way of life25 initiative mentions fighting against 
organised crime26 (including counterfeiting, piracy and infringements of intellectual property rights 27) 
as a increasing priority.  

The  EU  Special  Eurobarometer  50528  highlighted  that,  regarding  food  fraud,  respondents  are 
concerned of being misled about the true qualities of food (61%) and the related potential of affecting 
their  health  (55%).  In  addition,  40%  of  respondents  consider  it  important  that  food  with  specific 
characteristics (e.g. labelled as “organic”) meets the EU standards. 

In March 2021 the European Commission published a Communication29 related to the action plans to 
promote  organic  food.  In  order  to  prevent  food  fraud  and  strengthen  consumer  trust,  the 
Commission commits to ensure a robust control system and to support the development of organic 
fraud  prevention  policies,  increase  cooperation  with  the  EU  Food  Fraud  Network  and  Europol, 
support Member States to reinforce import controls at the EU border, to strengthen traceability and 
transparency  (through  e.g.  digital  passports  and  new  digital  technologies),  and  to  develop  early 
warning systems using AI for data mining. 

In  October  2021  the  European  Parliament  adopted  the  Resolution  on  the  Farm  to  Fork  Strategy30, 
highlighting the importance of tackling fraudulent practices. Efficient traceability mechanisms in  the 
seafood  sector  are  pivotal  to  guarantee  food  safety,  transparency  and  to  tackle  illegal,  unreported 
and  unregulated  (IUU)  fishing,  calling  for  a  coordinated  approach.  The  Members  of  the  European 
Parliament stressed that  food  fraud  and counterfeiting mislead  consumers  and distort  competition 

19  

20  

21  

22  

23  

24  

25  

26  

27  

28  

29  

COMMUNICATION FROM THE COMMISSION - The European Green Deal [COM(2019) 640 final] 

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS A Farm 

to Fork Strategy for a fair, healthy and environmentally-friendly food system [COM(2020) 381 final] 

https://ec.europa.eu/food/farm2fork_en 

https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age_en 

European Commission (2020) White Paper on Artificial Intelligence - A European approach to excellence and trust. COM(2020) 65 final 

European Commission (2020) A European strategy for data. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the 

Committee of the Regions COM(2020) 66 final 

https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/promoting-our-european-way-life_en 

https://home-affairs.ec.europa.eu/policies/internal-security/organised-crime-and-human-trafficking_en 

https://home-affairs.ec.europa.eu/policies/internal-security/organised-crime-and-human-trafficking/counterfeiting_en 

European Commission, Directorate-General for Communication, Directorate-General for Health and Food Safety, (2021). Making our food fit for the future – Citizens’ expectations. 

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS ON AN 

ACTION PLAN FOR THE DEVELOPMENT OF ORGANIC PRODUCTION (COM/2021/141 final) 

30  

European Parliament resolution of 20 October 2021 on a farm to fork strategy for a fair, healthy and environmentally-friendly food system (2020/2260(INI) 

7 

 
                                                        
 
in the internal market. The European Parliament called on the Commission to work on a European 
force  against  food  fraud  to  enhance  coordination  between  the  different  relevant  national  agencies, 
and to increase traceability of the supply chain as a whole. 

In  April  2022,  the  European  Food  Safety  Authority  published  the  document  "Horizon scanning 
exercise on preparedness for future risk assessment requirements and possible challenges in 
regulatory science”31. Within Thematic area 5 (Sustainable food systems and food safety) food fraud 
is mentioned as an element of relevance to be monitored, especially when referred to an increase of 
fraud and food crimes linked to the development of sustainable food marking systems. 

In September 2022, the European Court of Auditors concluded that “the control systems in place to 
combat illegal fishing are partially effective; although they mitigate the risk, their effectiveness is 
reduced by the uneven application of checks and sanctions by Member States”32.  The  report 
recognizes  that  the  EU  scheme  has  the  most  comprehensive  coverage  in  terms  of  products  (all 
processed  and  unprocessed  wild-caught  marine  fish)  compared  to  the  USA  and  Japanese 
counterparts; the EU and USA schemes also have the broadest information requirements, increasing 
the  overall  level  of  detailed  traceability.  The  report  highlights  that  the  catch  certification  scheme 
established in 2008 has improved the traceability and reinforced import control, however “the lack of 
digitalisation  of  the  scheme  reduces  efficiency  and  increases  the  risk  of  fraud”,  posing  multiple 
challenges  (slower  processing  time  and  administrative  burden,  fraud  risk,  lack  of  information 
sharing, lost opportunity to automate control and cross-checks). Although Member States asked the 
Commission  to  develop  a  dedicated  IT  tool  (the  CATCH  database),  no  Member  State  seems  to  be 
using it.  

In October 2022, the European Parliament adopted a Resolution33 on EU aquaculture, reiterating the 
need  for  a  food  traceability  system  in  the  EU  which  “responds to consumer demands by providing 
information on where, when, how and what fish or aquatic food has been farmed, primarily to 
improve food safety but also to enable checks throughout the chain of both EU products and imports 
from outside the EU and to combat fraud;” furthermore, it “believes that this system should involve 
all actors in the value chain, so that they can collaborate with one another using digital systems, 
artificial intelligence and other technological innovations”.  In  addition,  the  European  Parliament 
“calls on the Commission to use digital systems and artificial intelligence to improve the traceability 
and sustainability of aquaculture products and to extend traceability to the feed used”. 

Food  fraud  includes  the  huge  financial  damage  it  causes  at  the  expenses  of  the  EU’s  finances. 
Therefore, to combat food fraud, it is not enough to consider only the science and policy frameworks 
(and  regulatory science) that  underpin current  food  law, but  the broad and  expansive areas of e.g. 
finance, taxation and customs are equally important. 

1.2  EU initiatives to fight food fraud 

Since  2013,  the  European  Commission  joined  forces  with  several  EU  agencies,  Member  States  and 
other  non-EU  countries  (i.e.  Switzerland,  Norway  and  Iceland)  by  establishing  the  EU  Food  Fraud 
Network34.  The  main  goals  are  collaboration  and  cooperation  by  exchanging  information  and 
coordinating actions, whenever there is a violation of the European agri-food legislative framework. 
As  an  example,  since  2015  the  members  of  the  EU  Food  Fraud  Network  can  benefit  from  the 
Administrative  Assistance  and  Cooperation  (AAC)35  System,  a  dedicated  IT  tool  recently  integrated 
within the Rapid Alert System for Food and Feed (iRASFF). 

The  Knowledge  Centre  for  Food  Fraud  and  Quality36,  operated  by  the  JRC,  was  launched  in  March 
2018 in order  to build  a  bridge  between science  and  policy. By combining laboratory analyses with 

31  

Paulović, T., Chartier, O., Zingaretti, M. C., Bertolozzi, D., Martino, G., Krüger, T., ... & Libbrecht, S. (2022). Horizon scanning exercise on preparedness for future risk assessment requirements and 

possible challenges in regulatory science. EFSA Supporting Publications, 19(4), 7297E. 

32  

European Court of Auditors, (2022). EU action to combat illegal fishing  – Control systems in place but weakened by uneven checks and sanctions by Member States. Special report 20, 2022, 

Publications Office of the European Union  

European Parliament resolution of 4 October 2022 on striving for a sustainable and competitive EU aquaculture: the way forward (2021/2189(INI)) 

https://ec.europa.eu/food/safety/food-fraud/ffn_en 

https://ec.europa.eu/food/safety/food-fraud/aas_en 

https://knowledge4policy.ec.europa.eu/food-fraud-quality/about_en 

33  

34  

35  

36  

8 

 
                                                        
 
market surveillance activities, media monitoring and management of commodity-specific databases, 
it disseminates knowledge within the European Institutions, to competent authorities in the Member 
States,  to  industry  stakeholders  and  to  the  public.  In  December  2018  and  November  2019  the 
Knowledge  Centre  for  Food  Fraud  and  Quality  organised  two  technical  meetings  with  Member 
States’  competent  authorities37.  Early  Warning  Systems  (EWSs)  and  compositional  databases 
received  special  attention;  all  Member  States  were  of  the  opinion  that  EWSs  would  improve  their 
capability to fight food fraud, although they are not in routine use in most of Member States. Member 
States agreed that: 

a)  a standardisation of food fraud definitions is necessary;  

b)  proactive EWSs should be developed at EU level for common use by authorities and  European 

Commission services, but operated by a dedicated EU Reference Centre;  

c)  collaboration  within  and  among  Member  States  and  with  the  European  Commission  needs 

improvement;  

d)  centres of competence (i.e. a network of official control laboratories in Member States) should 

share the workload of detecting fraud in the food chain. 

Food 203038 is the research and innovation policy developed by DG RTD to transform the European 
food system(s). As mentioned in Pathway 8 – Food Safety Systems of the Future39, the EU food safety 
standards would also benefit from new traceability technologies, digital innovation and authenticity 
systems, being areas that require further investments. Pathway 10 – Food Systems and Data clearly 
highlights  that  high-quality  data  is  the  currency  to  shape  the  future  data-driven  EU  food  systems, 
while  ensuring  that  food  is  safe  and  not  subject  to  fraud.  A  substantial  number  of  food 
traceability/authenticity related research projects were selected and received funding under Horizon 
Europe and other past research framework programmes. 

1.3  Global initiatives to fight food fraud 

At  the  global  level,  the  FAO/WHO  Codex Alimentarius  Committee40  considers  the  topic  of  food 
integrity and authenticity as an emerging global issue. A number of delegations to the Committee on 
Food Import and Export Inspection and Certification Systems stated to have experienced food fraud 
in  various  forms,  acknowledging  as  well  a  lack  of  adequate  analytical  methods  to  tackle  the 
problem. Besides stating what they consider as Economically Motivated Adulteration41 (EMA, wrongly 
utilised as a synonym of food fraud), the Committee states that:  

—  it is almost impossible for consumers to trace or confirm the integrity of a product; 

—  EMA is a criminal activity threatening the safety and health of consumers; 

—  EMA incidents negatively impact on economy, consumer confidence and businesses’ reputation; 

—  most of reported incidents are likely just a fraction of their true number. 

Codex  members  are  supporting  the  development  of  a  horizontal  fraud-specific  Codex  guideline, 
highlighting the linkages between food fraud and food safety. A Guidance is currently under drafting 
but should not increase the burden for food manufactures and competent authorities, or cause trade 
barriers.  Many  delegations  highlight  the 
importance  of  food  traceability  and  vulnerability 
assessments.  The  fight  against  food  fraud  faces  however  many  challenges,  e.g.,  limited  access  to 
information, lack of proper training, complexity of supply chains, and scarcity of effective detection 
techniques, protocols and databases. 

The  United  Kingdom  (UK)  Food  Standards  Agency  has  commissioned  the  elaboration  of  a  vision 
paper for information sharing in the food system to enable secure exchange of information through 

37  

38  

39 

40  

41  

https://knowledge4policy.ec.europa.eu/food-fraud-quality/technical-meetings-kc-ffq-1_en 

https://ec.europa.eu/info/research-and-innovation/research-area/environment/bioeconomy/food-systems/food-2030_en 

European Commission, Directorate-General for Research and Innovation, Fabbri, K., Froidmont-Görtz, I., Faure, U. et al., Food 2030 pathways for action – Research and innovation policy as a 

driver for sustainable, healthy and inclusive food systems, Fabbri, K.(editor), Ndongosi, I.(editor), Publications Office of the European Union, 2020 

https://www.fao.org/fao-who-codexalimentarius/home/en/ 

https://www.fda.gov/food/compliance-enforcement-food/economically-motivated-adulteration-food-fraud 

9 

 
                                                        
 
digital technology (data trust framework). Exchange of food fraud incidence data could be a use case 
for  such  a  framework.  Data  are  retained  by  the  owners  in  their  own  distributed  data  stores  and 
mediated  by a body that  eases the  exchange without seeing  the data itself42. A similar  approach is 
used by the UK Food Industry Intelligence Network43 to collect, collate, analyse and disseminate food 
chain information and intelligence  among member  organisations. Intelligence and  authenticity data 
are  aggregated  and  anonymised  by  an  independent  body  (a  law  firm)  and  periodically  shared  with 
the governmental institutions so that they better understand where risks from food fraud may lay in 
the  UK  Food  Industry.  The  UK  Food  Standards  Agency,  together  with  a  service  provider,  piloted  a 
study  on  the  use  of  AI  to  support  hygiene  inspection  of  food  establishments  by  prioritising 
businesses  that  are  more  likely  to  be  at  a  higher  risk  of  non-compliance  with  food  hygiene 
regulations.  Currently,  this  process  is  manual,  labour  intensive  and  inconsistent  across  local 
authorities. Using this AI-enabled tool is expected to benefit local authorities by helping them to use 
their limited resources more efficiently44. In another exploratory project the Food Standards Agency 
develops AI models to identify food and feed risks before they become a public health concern45.  

The Canadian Food Inspection Agency uses predictive analytics and data collected from the sampling 
and  testing  of  food  by  their  inspectors  and  other  partners  across  Canada.  Taking  into  account 
historical  data  and  trends  associated  with  certain  combinations  of  foods,  environmental  factors, 
populations and hazards, the tool will use machine learning to identify trends and patterns and warn 
officials when food safety issues are likely to occur46. 

The Singapore  Food Agency  leverages data  science to  ensure that food in  Singapore  remains safe 
for  consumption.  The  Agency  monitors  a  wider  variety  of  traditional  and  non-traditional  data 
sources,  ranging  from  scientific  literature  and  test  results  from  external  laboratories,  to  social 
media  posts  and  even  public  feedback  to  provide  early-warning  alerts  for  potential  food  safety 
issues47.  Also  the  Agency  shifted  to  a  targeted  data-driven  approach  for  food  safety-related 
inspections of food establishments, doubling the number of non-compliances detected48. 

The  Taiwan  Food  and  Drug  Administration  uses  an  ensemble  learning  model  for  risk  prediction  of 
imported foods to improve the efficiency and effectiveness of border controls49.  

1.3.1 USA Food Safety Modernization Act 

The United States of America (USA) Food and Drug Administration (FDA) launched in 2011 the Food 
Safety Modernization Act (FSMA)50 in order to shift the focus from responding to non-communicable 
foodborne  diseases  to  preventing  them.  The  FSMA  covers  different  perspectives  of  the  US  food 
systems;  however,  two  topics  are  of  high  relevance  for  this  project:  the  new  traceability 
requirements  (section  20451),  and  the  mitigation  strategies  against  intentional  food  adulteration 
(section  10652).  Vulnerability  assessments  and  mitigation  plans  are  required  under  the  FSMA  to 
protect food against intentional adulteration, although the focus of the FSMA Final Rule for Mitigation 
Strategies to Protect Food Against Intentional Adulteration is aimed at  preventing  acts intended to 
cause  wide-scale  harm  to  public  health,  including  acts  of  terrorism  targeting  the  food  supply  (i.e. 
food defence). 

42  

https://www.food.gov.uk/research/cutting-edge-regulator/food-data-trust-a-framework-for-information-sharing 

43  
44  

https://www.fiin.co.uk/ 
https://www.gov.uk/ai-assurance-techniques/fsa-developing-an-ai-based-proof-of-concept-that-prioritises-businesses-for-food-hygiene-inspections-while-ensuring-the-ethical-and-

responsible-use-of-ai 

https://www.digitalmarketplace.service.gov.uk/digital-outcomes-and-specialists/opportunities/16300 

https://inspection.canada.ca/inspect-and-protect/food-safety/more-sharing-more-safety/eng/1574450091524/1574450091977 

https://www.sfa.gov.sg/food-for-thought/article/detail/crunching-data-for-food-safety%27s-sake 

Singapore Food Statistics 2022. https://www.sfa.gov.sg/publications/sgfs 

Wu, L. Y., Liu, F. M., Weng, S. S., & Lin, W. C. (2023). EL V. 2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method. Foods, 12(11), 2118 

https://www.fda.gov/food/guidance-regulation-food-and-dietary-supplements/food-safety-modernization-act-fsma;  

https://www.fda.gov/food/food-safety-modernization-act-fsma/background-fda-food-safety-modernization-act-fsma 

https://www.fda.gov/food/food-safety-modernization-act-fsma/full-text-food-safety-modernization-act-fsma#SEC204 

https://www.fda.gov/food/food-safety-modernization-act-fsma/full-text-food-safety-modernization-act-fsma#SEC106 

45  

46  

47  

48  

49  

50  

51  

52  

10 

 
                                                        
 
Traceability 

The FSMA aims, inter alia, at improving traceability and requires food business operators (FBOs) to 
implement  mitigation  strategies  against  EMA  At  the  moment,  food  firms  in  USA  (except  farms  and 
restaurants) only need to maintain baseline record keeping covering one-step forward and one-step 
backward. However, such  requirements proved  to be insufficient to  track  efficiently  a specific  food 
chain  in  case  of  e.g.  outbreaks  or  recalls.  The  FDA  established  new  electronic  traceability 
requirements53 including: 

—  Key  Data  Elements  (KDEs):  commodity,  variety,  quantity,  location,  date,  FBO  name,  reference 

document type and number, etc.; 

—  Traceability  lot  code  (TLC):  a  descriptor,  often  alphanumeric,  used  to  uniquely  identify  a 
traceability  lot;  once  a  food  has  been  assigned  a  TLC,  the  records  required  at  each  CTE  must 
include that TLC; 

—  Critical  Tracking  Events  (CTEs):  harvesting,  cooling  initial  packing,  first  land-based  received, 
shipping, receiving, and transformation. FBOs could be requested to share such data quickly and 
easily  (i.e.  within  24  hours)  in  case  FDA  will  ask  for  such  information54.  Although  all  FBOs  are 
encouraged to utilise the new traceability classification, the traceability records are mandatory 
for  those  firms  (with  some  exemptions  for  e.g.  small  farms  or  restaurants)  “manufacturing, 
processing, packing, or holding (e.g. importing)” high-risk foods within the Food Traceability List 
(FTL) established by FDA according to a newly developed risk-ranking model (“The Model”). The 
FTL  includes:  cheeses,  shell  eggs,  nut  butter,  fresh  vegetables  (i.e.  herbs,  leafy  greens, 
cucumbers,  melons, peppers, sprouts, tomatoes, tropical  tree fruits, fresh cuts  fruits), seafood 
(finfish, crustaceans, bivalves), and ready-to-eat deli salads55.  

The  Model,  designed  to  be  flexible,  values  pairs  of  commodities  VS  hazards  according  to  seven 
parameters:  a)  frequency  of  outbreaks  and  occurrences  of  illnesses;  b)  severity  of  illnesses;  c) 
likelihood of contamination; d) the potential for pathogen growth, with consideration of shelf life; e) 
manufacturing  process  contamination  probability  and  industry-wide  intervention;  f)  consumption 
rate and amount consumed; f) cost of illness. 

Traceability  records  must  be  provided  within  24  hours  of  a  FDA  request,  usually  in  case  of  an 
outbreak, recall or threat to public health. 

Another highlighted benefit from improved electronic end-to-end traceability is the higher standards 
of  data  quality  automatically  gathered  and  collectively  assessed  by  FDA  authorities,  with 
opportunities to perform predictive analytics with AI as well. On 1 June 2021 FDA launched a public 
call  to  “to develop traceability tools that can be implemented in a scalable, cost-effective way for 
food operations of all sizes”56,57. The challenge covers tech-enabled traceability solutions appropriate 
for primary producers, manufacturers, industry, importers, distributors, retailers and foodservices. 

Intentional food adulteration 

The final rule on intentional food adulteration58 addresses mostly large companies, while exempting 
feed producers, farms and small food firms. The rule requires companies to create a  food defense 
plan that includes vulnerabilities and actionable process steps, mitigation strategies, procedures for 
food defense monitoring, and corrective actions and verification. The FDA has also published a Food 
Defense Plan Builder59 designed to help owners and operators of a food facility in the development 
of a food defense plan. 

Therefore, the FSMA approach to food fraud establishes a direct involvement of food companies in 
preventing  (through  risk-reducing  mitigation  strategies)  adulteration  activities  taking  place  within 

53  

54  

55  

56  

57  

58  

59  

https://www.fda.gov/food/food-safety-modernization-act-fsma/fsma-proposed-rule-food-traceability 

https://www.fda.gov/food/food-safety-modernization-act-fsma/fsma-final-rule-requirements-additional-traceability-records-certain-foods 

https://www.fda.gov/food/food-safety-modernization-act-fsma/food-traceability-list 

https://www.fda.gov/news-events/fda-voices/fda-seeks-innovative-food-traceability-tools-and-opens-dialogue-advancing-food-safety-technology 

https://precision.fda.gov/challenges/13 

https://www.fda.gov/food/food-safety-modernization-act-fsma/fsma-final-rule-mitigation-strategies-protect-food-against-intentional-adulteration 

https://www.fda.gov/food/food-defense-tools/food-defense-plan-builder 

11 

 
                                                        
 
their  premises,  rather  than  envisioning  a  public-private  collaboration  aiming  at  tackling  the  food 
fraud phenomenon (as in the case of the food safety strategy). 

New Era of Smarter Food Safety 

On  13  July  2020,  the  FDA  released  the  Blueprint for the New Era of Smarter Food Safety60.  The 
document  represents  the  collective  thinking  of  “FDA food safety experts, consumers, the food 
industry, technology firms, federal and state regulatory partners, our regulatory counterparts in 
other nations, and academia”. The blueprint frames the USA strategy for the next ten years, with the 
final  objective  of  creating  a  risk-based  prevention-oriented  regulatory  framework.  The  core 
elements are: 

—  Tech-enabled  traceability:  the  document  highlights  the  importance  of  e.g.  digitalisation,  AI, 
Internet  of  Things  (IoT),  blockchain,  sensor  technologies,  whole  genome  sequencing  and 
enhanced analytics. Standardisation and interoperability of data is key for successful and swift 
tracebacks and traceforwards along the affected food chain. 

—  Smarter tools and approaches for prevention and outbreak response: FDA is planning to utilise 
big-data  predictive  analytics  to  assess  the  information  gathered  through  the  new  traceability 
system, in order to e.g. foresee/mitigate the next vulnerabilities, strengthen root cause analysis 
procedures, or more easily recall from the market any contaminated product. Machine learning 
and AI represent valuable opportunities to be explored in this context. 

—  New  business  models  and  retail  modernization:  because  restaurants  and  other  retail 
establishments remain the most usual source of foodborne illness outbreaks, FDA is looking at 
new ways to protect consumers by changing behaviours and practices, with a special attention 
on  the  emerging  e-commerce,  new  delivery  models,  and  new  food  ingredients  and  production 
technologies. 

—  Food safety  culture: only by promoting  education, training  and intervention  strategies  all  along 
the USA food chain it shall be possible to reduce dramatically the burden of foodborne diseases, 
especially  with  the  help  of  dedicated  tools  and  user-friendly  technologies  (e.g.  smartphone 
apps). 

As the USA import 94% of its seafood supply, the FDA has run a pilot of its Artificial Intelligence (AI) 
Imported Seafood Pilot program61,62 in order to react quickly to imported seafood potentially posing a 
threat to public health. The pilot utilised Machine Learning to target seafood shipments. 

Next to the Artificial Intelligence Imported Seafood Pilot Program of FDA, PREDICT (Predictive Risk- 
based Evaluation for Dynamic Import Compliance Targeting) is a risk-based analytics tool FDA uses 
to electronically screen all regulated shipments imported or offered for import into the USA. It uses 
data mining, pattern discovery, and automated queries of FDA databases to determine the potential 
risk of a shipment63. 

60  

61  

62  

63  

https://www.fda.gov/food/new-era-smarter-food-safety/new-era-smarter-food-safety-blueprint 

https://www.fda.gov/food/cfsan-constituent-updates/fda-moves-second-phase-ai-imported-seafood-pilot-program?utm_medium=email&utm_source=govdelivery 

https://www.fda.gov/news-events/fda-voices/import-screening-pilot-unleashes-power-data-and-leverages-artificial-intelligence 

https://www.fda.gov/industry/fda-import-process/entry-screening-systems-and-tools 

12 

 
 
 
                                                        
 
1.4 

The Food Risk Matrix 

in  several 

The  EU  legislative  framework  does  not  provide  a 
legal  definition  of  “food  fraud”,  although  the  topic  is 
indirectly  addressed 
the 
academic  community  has  already  provided  an 
overview  of  the  terminology  available  on  the  topic. 
The  Food  Risk  Matrix64  (Figure  1)  describes  the 
artificial  boundaries  between  the  four  domains  and 
the associated risks affecting the food system(s): 

laws  and 

Figure 1: The Food Risk Matrix. 

—  Food quality risk: an unintentional act resulting in 
the  stated  or  required 

food  not  meeting 
attributes or standards; 

—  Food safety risk: an unintentional act that results 
in a food product that poses a health concern; 

—  Food  fraud  risk:  an  intentional  act  on  a  food 
product motivated economically and not intended 
to harm consumer health; 

—  Food  defense  risk:  an  intentional  act  on  a  food 
product  with  the  intention  to  harm  the  public 
health (e.g. terrorism). 

Source: Food Microbiology Academy. 

Providing assurance to consumers and other stakeholders about the safety, authenticity and quality 
of European food is pivotal to safeguard consumer health and guarantee the correct functioning of 
the  internal  market.  Incidents  related  to  food  safety,  quality,  fraud,  or  food  defense  often  can  be 
hardly  categorised  precisely  when  signalled.  Therefore,  the  lack  of  clarity  should  lead  to  the 
precautionary assumption that a non-compliance may potentially be a food fraud with intrinsic food 
safety  risks,  unless  proven  otherwise.  As  a  clear  categorisation  of  non-compliances  is  a  rather 
theoretical  exercise,  it  is  essential  to  address  any  non-compliance  applying  a  holistic  and 
standardised  approach,  regardless  of  the  category/categories  eventually  assigned  to  (sometimes 
after a lengthy investigation). For example:  

-  A non-compliance without food safety  risks  may affect  food quality or be  a  food fraud,  but the 
intention of the food business operators (FBOs)  may not be so easily proven.  A bottle of virgin 
olive  oil  mislabelled  as  extra-virgin  olive  oil  could  be  the  result  of  an  involuntary  loss  of 
organoleptic  properties  over  time  (e.g.  because  of  storage  conditions)  or  it  could  happen 
because  the  FBO  has  intentionally  sold  low-quality  olive  oil  as  a  more  expensive  one  for 
financial gain; 

-  A food fraud may be ultimately uncovered because of the food safety risks initially identified by 
food safety authorities. Only later national enforcement authorities are able to demonstrate the 
intention  and  the  financial  gain  of  the  fraudulent  FBO.  Replacing  nuts  with  cheaper  peanuts  (a 
known  allergenic  food)  without  correctly  labelling  the  product  may  have  severe  health 
consequences for consumers allergic to peanuts, but the enforcement authorities may uncover 
the  fraud  only  in  a  second  stage  of  the  investigation,  sometimes  after  many  consumers  have 
been already impacted by the fraud (initially regarded only as a food safety problem); 

-  A  non-compliance  with  food  safety  standards  may  relate  to  food  safety  or  food  defense,  but 
proving  the  intention  requires  an  internal  investigation  by  the  FBO.  Glass  pieces  found  in 
meatballs  may  be  the  result  of  an  unintentional  lack  of  attention  by  the  operators  within  the 
production plant, or the intentional sabotage performed by an employer for personal reasons or 
on behalf of third parties (e.g. retaliation or terrorism). 

64  

Spink  J,  Embarek  PB,  Savelli CJ et  al.  (2019)  Global  perspectives  on  food  fraud:  results  from  a  WHO  survey  of  members  of the International  Food Safety  Authorities  Network  (INFOSAN)  npj 

SciFood 3,12 

13 

 
                                                        
 
In  summary,  because  the  classification  of  the  food  risks  cannot  be  always  precise  and 
straightforward,  the  same  approach,  methodology  and  process  should  be  applied  to  every  non-
compliance  affecting  the  food  system(s),  not  to  ignore  or  underestimate  potential  threats  to 
consumer health. 

1.5 

Tools to fight food fraud and root causes (“drivers”) 

Strategies to detect and prevent food fraud broadly fall into two categories: scientific analysis to test 
the  authenticity  of  foods,  and  broader  mitigation  strategies  including  (i)  intelligence  gathering,  (ii) 
vulnerability assessments and (iii) economic analysis strategies. 

A widely accepted criminology theory, the “routine activity theory”, sees crime as the outcome of the 
convergence in time and place of (1) motivated offenders and (2) suitable targets in (3) the absence 
of  capable  guardians,  which,  translated  into  a  food  context,  represents  a  triangle  defined  by  (i) 
opportunities  (suitable  targets),  (ii)  motivation  (motivated  offenders)  and  (iii)  control  measures 
(guardianship)65.  Food  fraud  vulnerability  assessments  (FFVAs)  identify  weaknesses  or  flaws  that 
create opportunities for food fraud (Figure 2). 

Figure 2: Food fraud vulnerability assessment concept. 

Source: Food fraud vulnerability assessment (PwC, SSAFE). 

A report66 commissioned by the UK Department of Environment, Food and Rural Affairs considered 
as  root  causes  of  food  fraud  either  macro-economic  factors  (e.g.  drivers  of  commodity  price 
movements,  which  provide  an  opportunity  and  profit  incentive)  or  micro-economic  factors  that 
influence the individual decision of a fraudster. Main drivers of food fraud are67: 

–  Scarcity  of  raw  ingredients  can  drive  prices  up  and  increase  the  use  of  alternative 
ingredients  in  food  production.  The  concentration  of  retailers  into  global  chains  can  cause 
pressure on food prices, meaning suppliers may cut corners to compete for contracts; 

–  Consumer  demand  for  foods  with  special  characteristics,  such  as  provenance  and  ethics 

which are both expensive to source and easy to imitate; 

– 

The  length  and  complexity  of  global  food  supply  chains  can  lead  to  a  lack  of  traceability, 
making food fraud harder to detect; 

–  Criminals may use the internet to carry out illegal trade or pose as a legitimate business in 

order to infiltrate supply chains; 

–  Penalties for food-related crimes are generally lower than for other criminal activities. 

A  comprehensive  strategy  to  combat  food  fraud  would  not  only  try  to  detect  product  fraud  and 
identify and prosecute the perpetrator(s) but would also focus on preventive measures. Situational 
crime  prevention  theory  seeks  to  minimise  the  opportunities  and  increases  the  risks  to  deter 

65  

66  

67  

van Ruth, S. M., Huisman, W., & Luning, P. A. (2017). Food fraud vulnerability and its key factors. Trends in Food Science & Technology, 67, 70-75. 

https://randd.defra.gov.uk/ProjectDetails?ProjectId=20179 

https://post.parliament.uk/research-briefings/post-pn-0624/ 

14 

 
                                                        
 
potential  offenders68.  Until  recently,  public  policymaking  in  the  food  domain  has  concentrated  on 
protecting  public  health,  with  great  success.  However,  the  current  risk  analysis  approach  for  food 
safety has to be overhauled to deliver the evidence and guidance for developing policies to minimise 
food fraud incidents69.  

Several food safety management systems, which are widely applied  to  ensure  compliance with EU 
food law, recognised the importance of food fraud as a risk factor and included FFVAs and mitigation 
plans in pre-requisite programmes for certification (Table 1).  

Table 1: Benchmarking requirements for Global Food Safety Initiative recognised certification of food safety 
management systems. 

Food fraud vulnerability 
assessment 

Food fraud mitigation plan 

The  standard  shall  require  that  the  organisation  has  a 
documented  food  fraud  vulnerability  assessment  procedure  in 
place  to  identify  potential  vulnerability  and  prioritise  food  fraud 
mitigation measures 

The  standard  shall  require  that  the  organisation  has  a 
documented  plan  in  place  that  specifies  the  measures  the 
organisation has implemented to mitigate the public health risks 
from the identified food fraud vulnerabilities 

Source: Global Food Safety Initiative, 2018. 

EU  food  hygiene  legislation70  requires  FBOs  to  have  a  Hazard  Analysis  and  Critical  Control  Points 
(HACCP)  programme  in  place  but  does  not  prescribe  a  risk  assessment  targeting  potential  fraud 
opportunities. 

The EU General Food Law (Regulation 178/2002) defines traceability as the ability to track any food, 
feed,  food-producing  animal  or  substance  that  will  be  used  for  consumption,  through  all  stages  of 
production, processing and distribution. Although the main use of food traceability is to minimize the 
potential  health  risks  associated  with  unsafe  food  by  identifying  and  locating  the  contamination 
source,  it  can  provide  transparency  to  supply  chains.  Documented  traceability  in  turn  can  help 
establishing the authenticity of ingredients by tracking their origin and monitoring their handling and 
processing.  Records  typically  include  information  about  the  origin  of  the  ingredients,  production 
processes,  packaging,  and  distribution.  They  provide  transparency  to  supply  chains  and  enable 
stakeholders to identify and resolve issues efficiently. 

According to Article 18 of the General Food Law, traceability shall cover all stages of the food chain, 
from  production  to  distribution  (i.e.  from  farm  to  fork).  FBOs  should  apply  the  “one  step  forward  – 
one  step  backward”  principle,  i.e.  being  able  to  provide  on  demand  any  information  related  to  the 
suppliers and customers of a specific suspected product. 

In  order  to  satisfy  the  legal  traceability  requirements,  FBOs  should  keep  at  least  the  following 
information: 

—  Name, address of supplier, and identification of products supplied 

—  Name, address of customer, and identification of products delivered 

—  Date and, where necessary, time of transaction/delivery 

—  Volume, where appropriate, or quantity 

Major FBOs  usually  have in place traceability systems of various  forms, linking their supplies with 
the  final  products,  for  internal  purposes  and  to  quickly  recall  finished  products  in  case  of  need. 

68  

Lord, N., Spencer, J., Albanese, J., & Flores Elizondo, C. (2017). In pursuit of food system integrity: The situational prevention of food fraud enterprise. European Journal on Criminal Policy and 

Research, 23, 483-501. 

69  

Spink, J., Hegarty, P. V., Fortin, N. D., Elliott, C. T., & Moyer, D. C. (2019). The application of public policy theory to the emerging food fraud risk: Next steps. Trends in Food Science & Technology, 85, 

116-128. 

70  

Regulation (EC) No 852/2004 of the european parliament and of the council of 29 April 2004 on the hygiene of foodstuffs 

15 

 
                                                        
 
However, traceability data are scattered across private actors along the food supply chain in a non-
harmonised and fragmented manner, neither immediately available to one single operator within the 
supply chain nor to the authorities. In case of need, EU and national authorities have to contact each 
individual  FBO  to  track  problematic  lots  along  the  food  supply  chain.  Such  practice  delays  the 
identification  of  the  non-compliant  lot.  It  requires  considerable  efforts  to  manually  cross-check 
various data sources to create a coherent picture of the flow of the ingredients, the actors involved, 
and the relationship(s) among them. 

One  of  the  limitations  of  the  current  EU  traceability  systems  is  the  “one  step  forward  –  one  step 
backward”  principle,  which  does  not  provide  end-to-end  transparency  of  a  supply  chain.  Another 
weakness is the lack of a technology platform to aggregate data from diverse (manual or electronic) 
record  keeping  systems,  which  could  be  interrogated  by  operators  and  authorities  in  situations 
requiring  product  tracing  in  response  to  a  food  safety  or  fraud  issue.  Today’s  food  supply  chains 
might be complex and rarely transparent. Together with the clandestine nature of fraud, it makes it 
difficult to detect illegal manipulations of food, let alone to predict where fraud incidents may occur.  

Only the integration of various knowledge and intelligence sources can provide an indication of what 
is  happening  undetected  or  what  might  happen  in  the  future.  Intelligence  gathering  involves 
collecting and analysing information from various sources to identify and understand the methods, 
patterns,  and  actors  involved  in  fraudulent  activities  within  the  food  industry.  Advanced  data 
analytics, machine learning (ML), and AI can be employed to process and analyse large volumes of 
data,  helping  to  recognise  irregularities  in  the  data,  detect  potential  fraud  indicators,  and  highlight 
areas  that  require  further  investigation.  Intelligence  can  be  collected  from  open  sources  (e.g. 
Internet  and  media  reports),  in  specialised  databases  reporting  food  fraud  cases  (e.g.  iRASFF)  or 
databases that can be used to derive indicators related to geopolitical risks. 

For  example,  Bayesian  network  models  have  been  used  to  compute  probabilities  of  frauds  in  the 
food system. Among the drivers included in the Bayesian networks were price of the product, trade 
volume, country of origin, indices for perceived corruption, and price spikes. Another screening tool 
uses  import  statistics  and  time-series  analysis  tool  to  identify  statistically  relevant  changes  in 
commodity  flows  and  prices,  with  subsequent  analysis  of  the  flagged  cases  by  subject  matter 
experts. 

Authenticity  testing  using  a  broad  range  of  detection  methods  is  considered  as  the  ultimate  proof 
that a product is in its  original, genuine, verifiable and intended form  as declared and  represented 
(“true  to  the  name”).  As  food  testing  is  a  resource  intensive  process,  an  information  system  that 
directs  testing  activities  into  areas  where  fraud  incidences  are  more  likely  to  occur  would  be 
beneficial  to  control  authorities  as  well  as  honest  FBOs  and  their  service  providers.  Targeted 
surveillance  optimises  usage  of  resources,  economise  testing  and  increase  the  probability  of 
uncovering  illegal  activities.  Figure  3  visualises  the  interplay  of  different  knowledge  sources  and 
strands  of  activities  that  informs  an  EWS,  which  in  turn  enhances  the  efficiency  and  efficacy  of 
authenticity testing. 

16 

 
 
Figure 3: Elements of an integrated system for detection and mitigation of food fraud. 

Source: JRC. 

17 

Vulnerability/risk analysisFraud incidence historyTrade flowEconomic dataCountry risk profile...Supply chain knowledgeEarly Warning SystemTraceability recordsPredictive modelClassification of new caseAnalytical control and inspection 
 
2  Scope and objectives 

A comprehensive strategy to combat agri-food fraud not only attempts to detect product fraud and 
identify  and  prosecute  the  perpetrators,  but  should  also  focus  on  preventive  measures.  Horizon 
scanning  to  collect  weak  signals  of  irregularities,  validating  and  synthesising  them  into  actionable 
intelligence  can  play  an  important  role  in  this  respect.  The  knowledge  outcomes  can  inform  agri-
food fraud risk assessments and vulnerability analysis of supply chains, and help targeting control 
activities of competent authorities. It will help to increase the resilience of the agri-food system to 
fraudulent manipulations and create hurdles to deter fraudsters. Deployment of modern information 
and  communication  technology  that  can  combine  different  existing  data  streams  and  create  a  “big 
data”  pool  is  a  prerequisite  for  data-driven  discovery  of  irregularities,  indicative  of  possible  non-
compliance with the EU food law.  

An IT platform for integrating, synthesising and analysing information collected from different data 
sources  is  a  key  element  for  generating  warning  signals  and  trends  of  fraudulent  manipulations 
along  food  value  chains  to  support  the  fight  against  agri-food  fraud.  Furthermore,  the  platform 
would  not  only  be  used  as  an  EWS  but  boost  the  ability  of  services  to  trace  and  connect  relevant 
information of actual fraud cases in a more efficient way.  

Objectives 

The  main  study  objective  was  the  exploration  of  design  options  for  an  IT  platform  to  integrate  and 
analyse  agri-food  fraud  related  information  for  creating  early  warning  signals  of  fraud  and  to 
support handling of suspicious cases.  

To achieve the objectives, a scoping study for the design of an IT tool to integrate and analyse food 
integrity related information was undertaken, comprising of the following tasks: 

- 

Investigate  which  relevant  IT  systems  already  exist  for  collecting  and  analysing  relevant 
information related to food integrity, and for detecting fraud in the wider sense, taking account of 
the  price  of  commodities,  trends  of  imports  from  third  countries  and  administrative  measures 
put in place by EU Member States (e.g. border closures, redirection of trade flows);  

- 

Identify relevant data sources curated by various Commission services that can be used for data 
driven discovery of hidden connections;  

-  Collect  the  needs  of  Commission  services  for  AI  analytics  for  managing  agri-food  fraud  cases 

and predictive modelling to detect irregularities in the food supply chains they regulate;  

-  Formulate use cases in close cooperation with the concerned services and map out the desired 
functionalities of the IT tool to identify commonalities and potential synergies for optimal use of 
resources during software deployment/development;  

-  Evaluate  conditions  (e.g.  the  need  for  a  legal  basis)  to  enable  data  sharing  and  its  use  for  the 

creation of the IT system for food integrity;  

-  Assess  the  option  of  integrating  the  existing  IT  systems  for  exchanging  food  safety  relevant 
information  to  benefit  from  synergistic  effects  of  a  comprehensive  system  to  safeguard  food 
integrity;  

basic  design 

requirements 

for 

the 

-  Synthesise 

gathered 
development/deployment of the desired IT tool.  

information 

the 

into 

Producing the IT tool is outside the scope of the project. 

18 

 
 
3  Methodological approach 

The  report  collects  and  summarizes  the  needs  shared  by  experts  in  the  fields  of  food  fraud,  food 
safety, customs, trade, information technology and criminology, describing  the  functionalities of an 
ideal  IT  tool  which  could  theoretically  support  the  experts  in  their  daily  activities  to  scale  up  the 
protection  of  the  EU  food  systems.  Interviews  were  carried  out  separately  with  experts  from  the 
public and, in one case, from the private sector.  

Further interviews were carried out with colleagues across several European Commission services 
to understand which databases, IT tools and intelligence sources are already available at EU level to 
be  exploited  in  order  to  address  the  needs  shared  during  the  interviews.  In  most  of  the  cases  the 
information shared were confidential or EU-sensitive, therefore they have been recorded for future 
use but not referenced within this document. 

Desk  research  was  carried  out  to  identify  other  databases,  IT  tools  and  intelligence  sources  from 
public  or  international  organisations,  or  developed  by  the  private  sector.  Resources  from  private 
companies were recorded for internal use but not shared within this document in order not to give 
an undue advantage and visibility to some companies over others. 

Grey literature and academic papers were analysed to provide solid scientific bases to the report, to 
identify the  relevant  food fraud drivers  to  be addressed and to provide  concrete  recommendations 
on how to proceed with the next steps. Such desk research cannot be considered a comprehensive 
review or meta-analysis for reasons of time constrains and limited resources. 

The  European  legislation  and  the  Codex Alimentarius  documentation  has  been  scanned  and 
investigated  in  order  to  show  the  perspective  of  the  international  community  and  to  provide  legal 
bases to some recommendations within the text. 

19 

 
 
 
4  The needs of the different Commission Services 

Several interviews were carried out within Commission DGs and EU Agencies in order to understand 
the  needs  that  the  data-driven  IT  tool  should  cover,  and  the  functionalities  that  would  support  the 
daily activities in each DG. An overview of keywords extracted from the interview transcripts is given 
in Figure 4. 

Figure 4: Word cloud created from the interviews. 

Source: JRC. 

Considering all the needs expressed so far, an information-based risk analysis IT tool for protecting 
and monitoring the European food systems should be framed around three different work streams: 
data  collection,  data  analysis,  and  data  deployment.  In  simple  terms,  it  should  analyse  data  by  ML 
tools and/or AI to discover and flag irregularities (“warnings”) that require subsequent interventions 
by subject matter experts. 

The system should provide three main functionalities:  

1.  an  insight  capacity  giving  an  overview  of  non-compliances  in  real-time  (“what  is 
happening”), scaling up the existing food traceability system in the  EU, and mapping supply 
chains.  The  use  of  ML/AI  shall  support  EU  and  Member  States  officials  to  discover  hidden 
data connections in the existing databases that are difficult to uncover  manually, given the 
vast  amount  of  data,  especially  in  case  of  non-compliances  notified  by  national  food  fraud 
and  food  safety  authorities.  For  example,  at  the  moment  national  authorities  and  EU 
institutions  have  to  invest  a  considerable  amount  of  time  to  manually  reconstruct  the 
traceability  chain  of  non-compliant  product(s)  by  contacting  individually  FBOs,  whereas 
improved  interoperability  of  existing  databases  could  dramatically  cut  the  time  needed  to 
create  a  comprehensive  traceability  record  of  a  product,  which  is  key  for  successful  and 
swift trace-backs and trace-forwards along the affected food chain. 

2.  an  investigative  capacity  at  EU  and  global  level.  Too  often  fraudulent  companies  or 
investigators  by  e.g.  changing  names, 
individuals  evade  detection  and  deceive 
subcontracting  third  companies,  or  simply  by  selecting  specific  entry  points  in  the  supply 
chains more vulnerable than others. The system shall provide capabilities for: 

20 

 
 
●  matching  satellite  images  with  the  company  address  to  reveal  unauthorised/illegal 
premises or fake addresses, clearly not suitable to store food products according to the 
quantities declared; 

●  matching  scanned  documentation  with  templates  of  Certificate  of  Analysis  in  order  to 
reveal part of the documents manually removed or amended, thus hiding non-compliant 
values for specific contaminants (i.e. document forgery); 

●  matching  harvesting  areas,  claimed  on  e.g.  seafood  documentation,  against  their 

opening seasons and location; 

●  matching  logos  and  third-party  certificates  in  the  submitted  documentation  against 
authentic  labels,  photos  and  company  websites  and  official  registries,  revealing  non-
conformities and misalignments; 

● 

identifying  excessive  earnings  or  profit  increases,  matching  outliers  between  annual 
profit and company size;  

●  an  automatic  warning  in  case  national  authorities  mark  a  consignment  erroneously  as 

“valid” even if e.g. the documentary check is not satisfactory; 

●  hidden connections and suspicious patterns among buyers and suppliers, revealing the 
identity of often the same individual/company (the “bad players”) behind recurrent food 
crimes; 

●  wrong declaration for CN codes in order to pay less taxes or avoid detection. 

3.  an  anticipatory  capacity  (i.e.  predictive  analytics)  in  order  to  re-direct  risk-based  official 
controls in the Member States to specific food supply chains more vulnerable to frauds and 
safety  threats  (“what will happen?”).  The  use  of  digital  tools  shall  support  EU  and  Member 
States officials to not only train the IT system with data from selected databases and results 
from  real-world  investigations  (i.e.  supervised  learning),  but  also  with  potential  for  self-
learning  without  being  explicitly  programmed  via  training  (i.e.  unsupervised  learning), 
ultimately improving its predictive analytics capabilities. Model validation by subject matter 
experts  is  needed  only  the  first  time  a  new  pattern  is  uncovered,  thus  resulting  in  an 
automatic  signal  in  case  a  similar  anomaly  is  registered.  Relevant  parameters  to  model  a 
supply  chain  vulnerability  and  predict  future  non-compliances  should  include  at  least  the 
three  main  categories:  a)  prices  of  commodities;  b)  trends  of  EU  imports;  and  c) 
administrative measures between countries.  

The system shall be a public-public partnership, since the data are too sensitive to be shared with 
business operators. 

Relevant food integrity databases managed by the EU Institutions are not always user-friendly and 
lack important functionalities (e.g. query functions, data visualisation) and interoperability. They need 
to  be  modernised  before  integration  into  a  future  IT  system  that  should  be  more  agile,  efficient, 
frequently  updated,  aligned  with  the  users’  needs,  and  interconnected  to  other  IT  systems  of 
relevance. In addition to EU Institutions and Member States, also the competent authorities in third 
countries should be able to feed (but not necessarily access) the future IT system, being considered 
an important source of information and intelligence. 

The design of the new system should be flexible enough to cover the whole Food Risk Matrix (food 
fraud,  food  safety,  food  quality  and  food  defense)  in  case  of  need  and  future  interest.  The 
methodological  approach  and  the  architecture  of  the  future  IT  system  may  contribute  to  the 
development of similar approaches in areas unrelated to food. 

Data  quality  has  to  be  ensured  following  the  FAIR  principle  (Findable-Accessible-Interoperable-
Reusable)  even  if  the  data  is  only  available  to  authorised  users.  Multilingualism  and  standardised 
text  (e.g.  pre-generated  lists)  should  be  implemented  as  much  as  possible,  in  order  to  avoid  free 
text. 

The  system  should  improve  and  ease  information  sharing.  All  actors  potentially  interested  in  non-
compliances,  signals  and  trends  should  be  informed,  while  clearly  indicating  which  legislative 
framework  has  been  infringed  (e.g.  general  food  safety  legislation  or  the  organic  one).  At  the 

21 

 
moment,  the  same  non-compliance  notification  may  be  of  relevance  for  more  than  one  IT  system, 
but such information is often not shared (e.g. a non-compliance on organic food notified in TRACES 
that should be notified in OFIS as well). Notifications should be sent automatically by the system but 
validated/confirmed  manually  later  by  Member  States’  officials.  Fraudulent  operators  may 
potentially be beneficiaries of EU funding; therefore funding bodies should be informed automatically 
whenever  funded  entities  have  infringed  the  law  (e.g.  frauds  in  the  organic  sector  performed  by 
companies funded under the Common Agricultural Policy). Similarly, external auditors (e.g. in case 
of third party  certifications) should be informed  whenever there is a non-compliance detected in a 
company certified by their accredited auditing firm. 

A clear advantage of the development of such an IT tool would be a  better understanding of EU and 
global markets. Prices, quantities, trends, trade flows, are all of clear interest for many actors within 
the EU Institutions who perform market analysis. 

22 

 
 
 
5 

The tripartite structure of the project 

Addressing  the  needs  expressed  by  the  stakeholders  will  require  the  creation  of  an  integrated  IT 
infrastructure managed at EU level and supporting the competent and enforcement authorities in the 
Member  States.   There are a number  of tools and data source for building the  IT system  currently 
available.  In  addition,  suggestions  for  improving  the  functioning  of  current  systems,  which  would 
need  further  developmental  work  and/or  require  amendments  to  the  existing  legislation,  are  also 
offered. The suggestions are intended to trigger the discussion and attract resources to expand the 
current IT systems and to align it better with the needs of the user community.  

The core of the report is divided into three sections: data collection, data analysis, and data (model) 
deployment  (Figure  5).  Each  of  the  three  sections  encompasses  various  perspectives  (e.g.  legal, 
scientific,  political,  financial,  technical,  societal),  all  relevant  to  be  considered  holistically  for  the 
success and acceptance of the future IT system. 

Figure 5: Conceptual overview of the tripartite structure of the IT system. 

Source: JRC. 

In  summary,  the  ideal  IT  system  should  connect  all  relevant  data  sources  via  an  EU/supranational 
data warehouse (managed at EU level), analyse the data by deploying approaches/functions aligned 
with the needs described previously, and ultimately delivering the results to personnel of the EU and 
Member  States’  (a)  food  safety  authorities;  b)  police/enforcement  authorities;  c)  and  customs 
authorities. Data quality is pivotal for the success of the exercise (Annex 1). 

23 

 
5.1 Existing data and intelligence collection tools  

Food  systems  generate  different  types  of  data:  finance  (prices),  customs  (container,  export  and 
import destination, origin), traceability (lot number) and safety checks (official controls), to name a 
few.  Many  food  products  may  bear  Geographical  Indications  or  certification  scheme  logos  (e.g. 
organic) in addition to labelling requirements mandated by the EU legal framework. Member States’ 
control  authorities  perform  regular  checks  at  the  EU  borders  and  within  the  internal  market,  but 
their  database  are  mostly  not  interconnected  and  inter-operable,  meaning  that  data  cannot  be 
shared between the different Member States or between different competent authorities in a same 
Member State.  

Many  databases  are  not  freely  accessible  because  their  access  is  restricted  to  some  public 
authorities  or  they  may  be  available  after  paying  an  annual  fee.  However,  the  amount  of  data 
collectively available in some databases and intelligence sources is enough to build an effective and 
fully functional system in order to fulfil most of the needs expressed by end users.  

Examples of existing databases, intelligence tools and IT systems, which can be useful sources for 
creating a dedicated food integrity information system are summarised in  Table 2 (databases/tools 
may serve more than one sector). The databases managed by the private sector were not included in 
this report, although these have been taken into consideration for the overall analysis. 

Table 2: Summary of existing relevant databases, intelligence sources and IT systems. 

Sector 

Trade 

Acronym 

Name 

AFIS 

Anti-Fraud Information System 

Crisis Impact Analysis 

Owner 

OLAF 

DG TAXUD 

JRC Dashboard - Trade Flow Analysis for Structural Breaks 

JRC 

AMT/THESEUS  Automated Monitoring Tool/THESEUS 

COMEXT 

Comtrade 

Customs and imports 

AFIS-CSM 

Container Status Messages 

ICS2 

NCTS 

SURV3 

TARIC 

Import Control System 2 

New computerised transit system 

Surveillance 3 

Integrated Tariff of the European Union 

Agri-Food markets 

AGRIDATA 

Agri-food Data Portal 

AMIS 

ASAP 

Agricultural Market Information System 

Anomaly Hotspots in Agricultural Production 

OLAF/JRC 

DG Eurostat 

United 

Nations 

OLAF 

DG TAXUD 

DG TAXUD 

DG TAXUD 

DG TAXUD 

DG AGRI 

DG AGRI 

JRC 

EFSCM 

European Food Security Crisis preparedness and response 

DG AGRI 

Mechanism  

EUMOFA 

European  Market  Observatory 

for  Fisheries 

and 

DG MARE 

Aquaculture 

ISAMM 

Information System for Agricultural Market Management 

DG AGRI 

MARS Bulletin  —  Monitoring Agricultural Resources Bulletin  

JRC 

Alert and cooperation 

ADIS 

Animal Disease Information System 

networks 

OECD-FAO Agricultural Outlook 

CATCH 

EUROPHYT 

OFIS 

Organic Farming Information System 

iRASFF 

Rapid Alert System for Food and Feed 

TRACES 

TRAde Control and Expert System 

24 

OECD-FAO 

DG SANTE 

DG MARE 

DG SANTE 

DG AGRI 

DG SANTE 

DG SANTE 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Food Fraud 

Investigations 

Source: JRC. 

iRASFF-FF 

Administrative Assistance and Cooperation – Food Fraud 

DG SANTE 

EIS 

GetI 

Europol Information System 

Europol 

OLAF 

Some databases are already partially interconnected and they communicate with each other at some 
extent.  An  overview  of  connection  between  some  EU  and  global  databases  is  provided  in  Figure  6 
and Figure 7. 

25 

 
 
 
 
Figure 6: Data flows between some of the EU and national databases. 

Source: JRC. 

26 

 
 
Figure 7: Data flows between some of the EU, national and international databases. 

Source: JRC. 

Whereas  the  databases  and  IT  tools  with  a  high  data  granularity  may  potentially  feed  all  three 
capacities  fulfilled  by  the  proposed  IT  system  (insight,  anticipatory  and  investigative),  most 
intelligence sources and some databases are useful to build only the anticipatory capacity, as they 
work on trends and big numbers.  

Figure 8 classifies the various databases (excluding private databases), intelligence sources and IT 
tools  in  four  categories  according  to  two  parameters:  internal  market  vs  imports;  and  high  data 
granularity vs low data granularity. The same database, IT tool  or intelligence  source  may fall into 
more categories. 

27 

 
 
 
 
Figure 8: Classification of the databases, IT tools and intelligence sources according to the level of granularity 
and geographical coverage. While all sources may contribute to the anticipatory capacity of the ideal IT tool, 
only sources characterised by a high granularity may feed the insight and investigative capacities.  

Source: JRC. 

Most  of  the  information  required  to  build  the  anticipatory capacity  and  the  investigative capacity 
seem  already  available  (although  not  immediately  accessible)  in  one  or  more  of  the  analysed 
databases,  IT  tools  and  intelligence  sources.  The  insight capacity  is  fully  developed  in  terms  of 
imports of products entering the EU, which are possible to monitor with high granularity. However, 
the insight capacity in the internal market is compromised by the lack of three sets of crucial data: 

1. 

Traceability data of the food supply chain. While the EU possesses highly granular data for 
products of animal origin imported through a Border Control Post, whatever happens in the 
internal  market  is  not  directly  accessible  to  public  authorities.  FBOs  have  the  legal 
obligation  to  record  only  their  suppliers  and  their  customers;  the  authorities  may  request 
such data in the context of an investigation, but the process can be inefficient and data are 
not  always  available  in  time  and  in  sufficient  quality.  As  a  consequence,  public  authorities 
are oblivious to the connection between FBOs (the “food supply maze”) and, if needed, they 
do not have a clear overview of the full traceability, affecting as well part of the investigative 
capacity. 

2.  Laboratory analyses and authenticity tests performed by the food industry. Collectively, the 
amount  of  analysis  performed  by  the  food  industry  dwarfs  those  performed  by  competent 
authorities  in  the  Member  States.  In  line  with  several  food  defence  guidelines,  the  food 
industry (especially companies with a high turnover) usually invests large resources to build 
trustworthy supply chains, especially when a new supplier is assessed at the beginning of a 
contract.  Those  data  are  not  shared  with  governmental  authorities,  therefore  frauds 
affecting  the  food  industry  are  taken  care  of  internally  by  the  food  industry  (between 
supplier  and  buyer),  with  the  consequence  that  a  criminal  operator  may  affect 
simultaneously  several  other  FBOs  because  those  affected  do  not  communicate  between 
themselves  nor  to  a  third  neutral  knowledge-broker.  For  reasons  of  preserving  its 
commercial strategy, the food industry is reluctant to share information about its suppliers.  

3.  Data  from  official  inspections  performed  by  competent  authorities  in  the  Member  States. 
Although  many  Member  States’  competent  authorities  publish  annual  reports  or  news  on 
their websites, Member States are obliged to share only cross-country (i.e. affecting several 

28 

 
  
Member States) non-compliances, in terms of food fraud (iRASFF,  OFIS). There is a lack of 
overview  of  the  full  range  of  national  non-compliances  and  the  related  data,  which  may 
range  from  authenticity  tests  to  criminal  investigations.  In  addition,  the  same  non-
compliance may be dealt with differently according to the authority who is in charge of the 
case: customs, food safety agencies and the police approach the same case differently and 
do  not  necessarily  communicate  with  each  other,  compromising  potentially  the  success  of 
the  investigation.  In  addition,  a  tighter  coordination  between  the  three  different  national 
authorities (customs, food safety, and police) is necessary.  

5.2 Possibilities to improve the existing data and intelligence collection tools 

5.2.1 EU wide registry of food fraud cases 

Whereas it seems unrealistic to access in the near future the data held by the food industry, Member 
States may more easily agree to share records of national non-compliances (without compromising 
police  investigations)  via  the  existing  iRASFF  system.  The  relevant  data  scattered  across  various 
national databases, IT tools and intelligence sources could be integrated in a supranational EU data 
warehouse  by  utilising  data  mapping  tools  and  application  programme  interfaces  (APIs)  without 
reshaping  and  disrupting  the  national  databases.  The  process  developed  by  the  SIGMA  project, 
where several Member States’ competent authorities send national data to a dedicated database via 
APIs (Figure 9) managed by the European  Food  Safety Authority, is  a good example  for automated 
data mapping and integration. 

Figure 9: Data submission from European countries to the European Food Safety Authority: the SIGMA project 
approach. 

Source: SIGMA Consortium, 2022. Data submission from European countries to EFSA: the SIGMA project approach. Source: 
EFSA supporting publication 2022:EN-7254. https://www.efsa.europa.eu/en/supporting/pub/en-7254 

29 

 
 
Recommendation 1 

A  comprehensive  overview  of  past  fraud  cases  is  an  indispensable  resource  for  conducting 
vulnerability  assessments  of  supply  chains.  By  analysing  them,  patterns  related  to  e.g. 
commodity, geography, means  of  transportation,  etc.,  and  potential  risk  areas  within the supply 
chain  can  be  identified.  Machine  learning  algorithms  can  be  used  to  enhance  the  capability  of 
detecting  anomalies  associated  with  food  fraud  and  to  train  models  to  recognise  risk  factors 
where fraudulent activities are likely to happen. 

The  developed  models  can  then  be  applied  to  real-time  cases  to  detect  sudden  deviations, 
anomalies,  or  suspicious  activities  that  may  indicate  potential  fraud.  These  models  are  ‘self-
learning’  as  they  use  new  cases  to  fine  tune  the  model  parameters  to  improve  their  predictive 
ability. 

(Pseudo)anonymised  dataset  of  historic  fraud  cases  may  be  used  to  train  a  machine  learning 
model  for  assessing  the  feasibility  of  using  predictive  analytics  for  identifying  cases  being 
suspicious of food fraud. The JRC could be entrusted to carry out this feasibility study as it has 
proven  expertise  in  data  analytics  and  food  fraud,  both  operating  within  the  European 
Commission IT environment.  

Recommendation 2 

Having enough data (‘big data’) is a fundamental prerequisite for data-driven analytics because it 
enhances the accuracy, reliability, and depth of insights derived from data. It is especially crucial 
in  fields  like  machine  learning,  where  data  is  the  primary  driver  of  model  performance  and 
predictive  power.  A  larger  dataset  helps  machine  learning  models  to  better  understand  the 
underlying  patterns  and  relationships  in  the  data  to  make  more  accurate  predictions  or 
classifications. 

Current  EU  legislation  provides  that  Member  States  can  share  cross-border  violations  of  the 
agri-food  chain  legislation  via  the  Alert  and  Cooperation  Network.  However,  notification  of 
(potential) food fraud cases transmitted to the EU Food Fraud Network have primarily a cross-
border dimension, which means that a comprehensive overview of food fraud cases in the EU is 
not available.  

Therefore,  Member  States’  competent  authorities  could  be  encouraged  to  notify  all  food  fraud 
cases detected by application of Article 9(2) of the OCR to the EU Food Fraud Network using the 
appropriate  iRASFF  channel,  including  those  having  only  a  national  dimension  (at  the  moment 
there  is  only  the  legal  obligation  to  notify  cross-country  cases).  This,  together  with  data 
analytics, will result in a better understanding of fraud risks in the EU food supply.  

Likewise,  the  food  industry  shall  be  motivated  and  incentivised  to  share  food  fraud  data  and 
intelligence insights through an independent data trust that stewards and governs the sharing of 
data among industry members as well as competent authorities. Access to food fraud data held 
by  industry  would  greatly  enlarge  the  available  data  set  that  can  be  used  for  descriptive, 
diagnostic and predictive analytics. 

5.2.2 Modernised and enhanced food traceability 

Regulation (EC) No 178/2002 mandates FBOs to adopt a “one step back—one step forward” approach. 
However, it does not contain requirements regarding the form and structure of traceability systems 
and procedures. Consequently, the EU lacks a centralised traceability system, instead relying on the 
commitment of the FBOs to share data in case of need.  

30 

 
 
 
 
 
Although the  concept  of a  linear food supply  chain71 is usually  used  to describe  the actors along  a 
food supply chain, the connections between food and ingredients, individuals and companies is much 
more complex, non-linear and intertwined, resembling a network difficult to represent and visualize 
(Figure 10). 

Figure 10: a simplified structure of the food supply chain. 

Source: JRC. 

Competent authorities have to identify  where a non-compliance started, regardless if it concerns a 
food safety or  food  fraud incident. Currently,  the  authorities’ officers must manually draw the food 
network under investigation, trying to gather all relevant information in a timely manner, and aiming 
at  identifying  which  “node”  of  the  network  may  be  the  problematic  one.  Obviously,  a  digitalised 
traceability  system  has  the  potential  to  ease  the  burden  of  trace-back  investigations  to  a  large 
extent by automatically linking stakeholders in a supply chain.  

Blockchain  technology  has  emerged  as  a  promising  solution  for  enhancing  food  traceability, 
improving  the  flow  of  information  and  providing  transparency  of  supply  chains.  The  immutable 
records  allow  stakeholders,  including  consumers  (via  QR  codes),  to  verify  the  records  associated 
with the food they purchase. However, as also highlighted by FAO72, blockchain does not seem to be 
the best candidate to build a solid governmental traceability system.  In particular, it cannot prevent 
unscrupulous actors from changing or manipulating lots prior to or after data being recorded on the 
blockchain because the lot is traced through a marker on the product packaging (e.g. a barcode or 
QR  code)  and  not  the  physical  product  itself.  Blockchain  participants  are  usually  anonymous, 
therefore  impeding  any  further  action  by  authorities  in  case  of  need.  For  this  reason,  public 
databases  and  IT  systems  are  seen  as  more  aligned  with  the  needs  of  EU  and  national  services, 
without taking into account blockchain technology. 

The  European  Parliament  Research  Service  has  published  a  comprehensive  report73  addressing 
blockchain  with  eight  case  studies,  including  blockchain  applications  in  the  food  industry.  As 
blockchain  technologies  provide  benefits  when  “reliance on a single authority able to operate this 
infrastructure is not feasible or not desired”, the future traceability system may better profit from a 
managed database operated and owned by the European Institutions and the EU Member States, as 
highlighted by Chart 174 of the FAO document72. By using any blockchain technology, no single actor 
has full control of the system, but also the different technologies are often not interoperable, and no 
clear  standards  have  been  agreed  so  far.  Several  of  the  20  policy  options  proposed  in  the  report 
pertain  to  supply  chains  where  the  European  Commission  is  encouraged  to  provide  funding  for 
technology  adoption  and 
the 
recommendations,  the  report  also  states  that  the  expected  benefits  are  not  unique  to  blockchain 
technology but rather derive from the digitalisation of supply chain logistics processes.  

the  concerned  sectors.  Notwithstanding 

implementation  by 

71  

72  

73  

https://ec.europa.eu/info/sites/default/files/food-farming-fisheries/farming/documents/food-supply-chain_en.pdf 

Sylvester, G. (2019). E-agriculture in action: Blockchain for agriculture. Opportunities and Challenges. ISBN 978-92-5-131227-8 (FAO) 

European Parliament, Directorate-General for Parliamentary Research Services, Tcholtchev, N., Lämmel, P., Frazzani, S. (2021). Blockchain for supply chains and international trade – , European 

Parliament 

74  

Adapted from the original flowchart created by National Institute of Standards and Technology (NIST). Page 42 of: Yaga D, Mell P, Roby N, Scarfone K (2018) Blockchain Technology Overview. 

NISTIR 8202 

31 

 
 
                                                        
 
the 

future 

infrastructure  will 

infrastructure.  The  Forum  and 

As  part  of  the  EU  Strategy  to  create  a  blockchain  ecosystem,  the  European  Commission  and  the 
European  Union  Intellectual  Property  Office  (EUIPO)  have  co-organised  the  EU  Blockathon  2018 
competition,  resulting  in  the  establishment  of  the  Anti-Counterfeiting  Blockathon  Forum75,  “a 
network  of  people  and  organisations  working  together  to  shape  and  deliver  the  future  anti-
interconnect 
counterfeiting 
organisations,  enforcement  authorities  and  citizens  to  help  the  transport  and  proof  of  authentic 
goods and address the challenges of counterfeiting”. The winner of a following contest developed the 
best  proposal  of  a  blockchain-based  digital  infrastructure  for  authenticating  products  and 
exchanging  data  between  the  EU  intellectual  property  offices,  governments,  customs  authorities, 
manufacturers,  retailers,  logistics  operators  and  customers76,  focusing  on  Intellectual  Property 
Rights  (IPR)  infringements.  The  final  goal  was  the  creation  of  a  product  authentication  system 
almost impossible to breach of corrupt. As highlighted in the “Blockchain Use Case” document77, the 
creation  of  blockchain  systems  can  benefit  enforcement  authorities  and  customs  as  well.  For 
example,  tokenised  goods  (with  proven  authenticity)  may  have  a  swift  passage  through  customs 
checks, or enforcement authorities may be alerted in case the good integrity is at risk, e.g. through 
permissioned  applications  sending  automatic  notifications  to  rights  holders  and  authorities, 
potentially  recording  even  customs  actions.  This  future  blockchain  architecture  should  be 
complementary  and  compatible  with  existing  systems/databases  managed  by  the  governmental 
authorities,  without  being  an  additional  burden  for  customs,  enforcement  authorities  and  rights 
holders.  Especially  the  customs  authorities  would  be  capable  of  comparing  the  information  stored 
within  the  tokenised  goods  (e.g.  shipping  routes)  and  the  documents  provided  by  carriers  and 
focusing  controls  on  specific 
importers, 
consignments, containers and/or goods. Food safety authorities may be alerted if e.g. the recorded 
temperature  during  transportation  were  not  compliant  with  the  hygiene  requirements,  or  if  the 
product  has  been  opened  illegally  and  potentially  adulterated  while  on  route.  The  new  traceability 
system may benefit from the “tokenisation” in the authenticity layer, i.e. the process of producing a 
token.  Tokens  are  assets  that  allow  information  and  value  to  be  transferred,  stored,  and  verified 
efficiently  and  safely,  usually  produced  by  the  rights  holders  in  the  first  place.  Other  actors  of  the 
food  value  chain  (e.g.  transport,  enforcement  and  provenance)  add  optional  and  supplementary 
features and information linked with the tokenised goods. As highlighted in the Blockathon Report 78, 
“blockchain  has  clear  potential  […],  as  it  is  a  technology  that  can  track  and  trace  a  product 
throughout its supply chain”. 

identifying  misalignments  and 

irregularities  and 

A lesson learned from blockchain technologies to be imported into the future traceability system is 
the  possibility  for  companies  to  disclose  voluntarily  some  information  to  consumers  (e.g.  origin, 
production  methods,  etc…),  in  order  to  build  trust  and  confidence  as  currently  allowed  by  many 
blockchain applications. Some FBOs  already allow their  consumers to  obtain  extra  non-mandatory 
information  (e.g.  origin,  production  method)  by  simply  scanning  a  QR  code.  By  giving  such 
opportunity  within  the  future  institutional  traceability  system,  consumers  would  reward  the  most 
transparent  operators  across  the  food  supply  chain,  potentially  spreading  such  practices  without 
any legal imposition. 

75  

76  

77  

78  

https://euipo.europa.eu/ohimportal/en/web/observatory/blockathon 

https://euipo.europa.eu/ohimportal/en/web/observatory/blockathon/acbi 

https://euipo.europa.eu/tunnel-web/secure/webdav/guest/document_library/observatory/documents/Blockathon/Blockathon-Forum_Blockchain-Use-Case.pdf 

https://euipo.europa.eu/tunnel-web/secure/webdav/guest/document_library/observatory/documents/Blockathon/Blockathon_Report.pdf 

32 

 
 
 
                                                        
 
Box 1: examples of studies on blockchain performed in third countries. 

A report produced by researchers from the Deakin University mentions that “blockchain tends to be 
oversold as a ‘guarantee’ of product authenticity and anti-counterfeiting in general, but it is not the 
ultimate solution to the problem of product fraud because there is no guarantee as to the integrity of 
the data that a blockchain contains.”79  

The  UK  Food  Standards  Agency  also  investigated80  the  potential  impact  of  blockchain  technologies 
on  food  chains.  The  participants  of  the  two  projects  highlighted  “real  potential  benefits  to 
improvements  in  safety  standards  and  quality  of  food  throughout  many  value  chains”  (e.g.  provide 
data  only  once  and  re-use)  when  applying  blockchain  technologies.  However,  they  also  expressed 
concerns about initial financial investments, sharing confidential and/or commercially sensitive data 
with  competitors,  and  the  fear  of  a  single  entity  dominating  the  governance  of  such  distributed 
systems. The Food Standards Agency report highlights that “it should not be the responsibility of a 
regulator such as Food Standards Agency to set up and define and manage such a chain as the 
scope could increase to beyond regulation”,  thus  reducing  the  expectations  that  blockchain 
technologies  (considered  anyway  still  immature)  could  replace  or  support  governmental  and 
regulatory systems or databases. 

Recommendation 3 

Leveraging the digital transformation for improving product traceability is not only beneficial for 
resolving  food  safety  crises;  it  makes  supply  chains  also  more  transparent,  accountable,  less 
susceptible  to  fraud,  and  enables  more  effective  identification  of  vulnerabilities.  On  top,  digital 
traceability  can  enhance  consumer  trust  in  the  food  they  buy.  International  standards  outlining 
the principles and basic requirements for the design and implementation of traceability systems 
are already in place, among them ISO 22005 and the  GS1 Global Traceability Standard, including 
the  GS1  Electronic  Product  Code  Information  Services  (EPCIS),  providing  transparency  as 
products  move  physically  along  an  entire  supply  chain  (“what,  when,  where,  why  and  how”  of 
products). A wide range of IT systems built on internationally accepted principles are available on 
the market to aid traceability implementations.  

It is recommended to set up a Public-Private Partnership to encourage and incentivise FBOs to 
transition  to  end-to-end  digital  traceability  systems  by  facilitating  traceability  research  and 
development expenditure. The initiative should be primarily directed towards small hold farmers 
and  small-medium  enterprises  to  increase  their  capacity  to  participate  in  digital  traceability 
solutions.  Funding  should  be  provided  for  the  adoption  of  already  existing  digital  technology  to 
ensure that requirements for certain previously agreed key data elements are met, systems are 
interoperable,  and  traceability  data  can  be  provided  on  request  to  competent  authorities  via  an 
API. This can substantially reduce the authorities’ lag time to respond to food chain incidents by 
providing them a transparent view of the affected supply chain.  

A  dedicated  Working  Group  representing  food  public  and  private  chain  stakeholders  should  be 
set  up  to  elaborate  and  agree  the  required  key  data  elements  as  well  as  the  necessary  IT 
infrastructure at national as well as at supranational level. 

79  

80  

Smith M, Ashraf M, Austin C, and Lester R (2021) Product fraud: Impacts on Australian agriculture, fisheries and forestry industries. ISBN 978-1-76053-169-0 

Insights and Learnings from exploring the use of Blockchains (2021) https://www.food.gov.uk/research/research-projects/food-data-trust-a-framework-for-information-sharing 

33 

 
 
 
 
 
                                                        
 
5.3  Data analysis 

As the  complexity of the  worldwide food  supply  chain grows, the  food sector  needs  to incorporate 
data-driven  solutions  by  expanding  the  usage  of  information  technologies  that  facilitate  the 
gathering, sharing, and analysis of data. 

The  European  Commission  and  competent  authorities  collect,  collate  and  store  an  impressive 
amount  of  food  chain  data  in  their  repositories.  Those  data  can  be  mined  by  powerful  cloud 
computing  AI  tools  to  extract  insights  that  can  be  used  effectively  to  visualise  connections  and 
patterns not recognisable with traditional statistical methods. 

Data science is today considered a separate science embracing competencies belonging to different 
traditional  academic  sectors,  not  being  simply  a  branch  of  mathematics.  There  are  two  goals  in 
analysing data81: 

—  Inferring  how  the  response  variables  are  associated  to  the  input  variables  (generative 
modelling),  where  data  and  signals  are  analysed  with  methodologies  to  visualise  them  and 
detect  patterns,  groups,  trends  and  outliers  or  to  confirm  hypotheses  with  appropriate  testing 
methods;  

—  Predicting  what  the  responses  are  going  to  be  to  future  input  variables  (predictive modelling), 

where the algorithm also self-learns how to recognise complex non-linear correlations. 

Building the insight capacity into the state of food integrity in the EU requires (1) data integration, (2) 
visualisation, (3) analytical tools and (4) real-time collaboration82. 

Data  aggregation/integration  across  relevant  data  sources  and  informatics  tools  must  combine  all 
relevant data elements into a single shared view to create a more complete picture of an emerging 
or  ongoing  event (Figure 11). This will allow  a faster  recognition of existing  problems and generate 
new knowledge that will contribute to latency reductions. The SIGMA–Animal Disease Data Model of 
the  European  Food  Safety  Authority  is  an  example  for  aggregating  data  known  to  be  already 
collected by several Member States, which can serve as a blueprint.  

Figure 11: Data aggregation process. 

Source: https://brightdata.com/blog/web-data/data-aggregation 

81  

82  

Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745-766 

Greis, N. P., & Nogueira, M. L. (2017). A data-driven approach to food safety surveillance and response. In Food protection and security (pp. 75-99). Woodhead Publishing 

34 

 
 
 
 
                                                        
 
A  visualization  tool  not  only  provides  a  graphical  representation  of  data  that  is  more  easily 
interpreted,  but  can  also  be  used  as  a  problem-solving  tool.  For  example,  dashboards,  charts, 
graphs,  tables,  and  other  graphical  elements  to  visually  represent  data  in  (near)  real-time.  This 
allows users to understand patterns, trends, and anomalies at a glance (Figure 12). 

Figure 12: an example of a visualisation tool. Monthly Fair Prices of the THESEUS repository of the JRC, which is 
in support of the anti-fraud work of the EU Customs and other relevant services in the European Institutions. 

Source: JRC. 

35 

 
 
 
 
Analytical Tools can be used to assess the likelihood of a food integrity incident from fused data  in 
order  to  guide  response  actions  (e.g.  inspection)  followed  by  sampling  and  analysis  of  high  risk 
products (Figure 13).  

Figure 13: Heat map depicting intrinsic risk and its dynamics for certain agri-food commodities based on trade 
flow data. 

Source: Fera ltd. 

AI-enabled  data  analytics  for  food  safety  has  been  used  for  example  to  detect  pathogens  in  food, 
model  food  spoilage  due  to  microbial  growth  and  activity,  flag  the  presence  of  contaminants,  and 
grade the freshness of certain foods. In addition, data-driven algorithms have not only the ability to 
detect  but  also  to  predict  general  food  safety  hazards  as  well  as  identify  the  most  important 
influential  factors  and  the  relationships  between  these  factors  and  the  presence  of  food  safety 
hazards83,84,85.  iRASFF  data  were  frequently  used  as  input  for  data-driven  food  safety  risk 
prediction86,87,88.  Consequently,  regulatory  authorities  and  food  safety  risk  assessment  bodies  such 
as  the  European  Food  Safety  Authority  are  becoming  increasingly  interested  in  using  AI  based 
analytics to make and keep the food supply safe89. 

By  contrast  to  data-driven  tools  for  food  safety,  only  a  few  AI  based  initiatives  offer  predictive 
analytics for food fraud, which are either based on a combination of various data sources, such as 
historic  iRASFF  data,  meteorological  data,  certain  World  Bank  Indices,  or  trade  flow  data. 
Researches  from  Wageningen  University  and  Research  (WUR)  pioneered  the  use  of  Bayesian 
Network  modelling  and  historical  RASFF  data  combined  with  indicators  taken  from  EUROSTAT, 
European  Food  Safety  Authority,  FDA,  World  Bank,  etc.,  as  input  for  predicting  the  type  of  food 
fraud90,91. The gained insights can assist competent authorities in formulating surveillance strategies 

83  

Wang, X., Bouzembrak, Y., Lansink, A. O., & van der Fels‐Klerx, H. J. (2022). Application of machine learning to the monitoring and prediction of food safety: A review. Comprehensive Reviews in 

Food Science and Food Safety, 21(1), 416-434 

84  

85  

86  

Qian, C., Murphy, S. I., Orsi, R. H., & Wiedmann, M. (2023). How can AI help improve food safety?. Annual Review of Food Science and Technology, 14, 517-538 
Benefo, E. O., Karanth, S., & Pradhan, A. K. (2022). Applications of advanced data analytic techniques in food safety and risk assessment. Current Opinion in Food Science, 100937 
Nogales, A., Díaz-Morón, R., & García-Tejedor, Á. J. (2022). A comparison of neural and non-neural machine learning models for food safety risk prediction with European Union RASFF data. 

Food Control, 134, 108697. 

87  

Bouzembrak, Y., & Marvin, H. J. (2019). Impact of drivers of change, including climatic factors, on the occurrence of chemical food safety hazards in fruits and vegetables: A Bayesian Network 

approach. Food control, 97, 67-76 

88  

Liu, N., Bouzembrak, Y., Van den Bulk, L. M., Gavai, A., van den Heuvel, L. J., & Marvin, H. J. (2022). Automated food safety early warning system in the dairy supply chain using machine learning. 

Food Control, 136, 108872 

89  

90  

PwC EU Services & Intellera Consulting (2022) Roadmap for action on Artificial Intelligence for evidence management in risk assessment. EFSA supporting publication 2022:EN-7339. 120pp.  
Marvin, H. J., Bouzembrak, Y., Janssen, E. M., van der Fels-Klerx, H. V., van Asselt, E. D., & Kleter, G. A. (2016). A holistic approach to food safety risks: Food fraud as an example. Food research 

international, 89, 463-470 

91  

Bouzembrak, Y., & Marvin, H. J. (2016). Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling. Food Control, 61, 180-187 

36 

 
 
                                                        
 
focused  on  specific  products  that  might  be  more  susceptible  to  fraud  based  on  the  nature  and 
source of the product, pricing, shifts in product demand, etc. 

The  Bavarian  State  Office  for  Health  and  Food  Safety  (LGL  Bayern)  developed  ISAR  (Import 
Screening for the Anticipation of Food Risks), meanwhile used by other German State (‘Länder’) food 
authorities, to improve hazard identification and to implement more targeted food control measures. 
Input  data  are  captured  from  the  German  Foreign  Trade  Statistic.  For  each  commodity,  monthly 
information  on  the  import  volume  and  the  related  commodity  price  is  available  for  each  country 
exporting  to  Germany.  Time  series  analysis  is  then  applied  to  obtain  predictions  of  expected 
commodity  prices  and  import  volumes  in  future  months.  Unexpected  changes  in  the  series  create 
signals  and  shifts  that,  after  confirmation  by  subject  matter  experts,  can  trigger  measures  to 
mitigate  a  food  safety  or  food  fraud  issue.  The  proprietors  of  the  system  identified  a  couple  of 
opportunities to improve the performance of the system, e.g. by including notifications of the iRASFF 
databases together with other relevant indices pertaining to the geopolitical and social environment 
of exporting countries.  

Media  observations  collected  by  a  specific  application  of  the  European  Media  Monitor  (MedISys)  is 
used by the European  Food Safety Authority  for the  detection of emerging  risks in the food  chain92 
and in combination with text mining it can also serve as an early warning tool to show global trends 
and  developments  in  food  fraud  activities93,94.  The  JRC  produces  the  freely  available  Food  Fraud 
Monthly Reports, based on screening of relevant media reports collected via MedISys95. 

Next  to  MedISys-FF  and  the  Food  Fraud  Monthly  Reports,  other  EU  IT  tools  are  of  relevance  for 
predictive food integrity analytics: 

—  AFIS-CSM:  the  software  a)  compares  import-export  information  from  maritime  shipping 
companies  with  single  administrative  documents  (SADs)  provided  by  exporters,  b) 
it 
reconstructs  the  itinerary  of  containers  and  c)  checks  for  discrepancies  in  information  flows. 
For  example,  an  exporter  may  declare  a  different  origin  to  pay  less  import  duties  or  to  evade 
sanitary controls targeting specific third countries. For some cases information is available even 
when monitoring road or train transport until the container is unloaded in the EU territory. 

—  AMT/THESEUS: the algorithm a) identifies imports with a price too low compared to the average 
market  value  (even  considering  the  origin  and  the  destination),  b)  it  uncovers  systemic 
underpricing/overpricing, c) it estimates the fair price and fair weight, and d) it visualizes spikes, 
trends and levels for commodities of interest. For example, a fraudster based in a third country 
may export to the EU sugar syrup mislabelled as honey with an import price much cheaper than 
standard  honey;  as  a  consequence,  the  system  may  spot  the  outlier  because  of  the  price  “too 
good to be true”. 

—  JRC Dashboard - Trade Flow Analysis for Structural Breaks: the dashboard highlights structural 
changes  in  trade  flows  (specifically  imports)  by  utilising  data  from  the  Surveillance  database. 
For  example,  if  a  specific  commodity  from  a  specific  country  is  subject  to  increased  sanitary 
controls because of past non-compliances in terms of food safety, the trade of that commodity 
may  be  redirected to  neighbouring countries  to  be  exported  to the EU  while  avoiding the  more 
stringent sanitary scrutiny (i.e. by changing export location). As another example, the dashboard 
may identify the replacement of a specific product with a cheaper (but functionally similar) one 
by monitoring the quantities of the latter imported in higher amounts.  

—  Monitoring Agricultural Resources Bulletin (MARS): Since 2011 the Bulletin is published monthly 
and provides: a) crop yield forecasts for the ongoing season; and b) information on the condition 
of crops and on weather conditions affecting crop growth and development. Two series of MARS 
Bulletins are published: “JRC MARS Bulletin – Crop monitoring in Europe” (every month, focused 
on  EU  Member  States  and  neighbouring  countries)  and  “JRC  MARS  Bulletin  -  Global  outlook” 

92  

93  

94  

European Food Safety Authority; Development of web monitoring systems for the detection of emerging risks. EFSA Journal 2009; 7(10):1355. [50 pp.].  

Bouzembrak, Y., Steen, B., Neslo, R., Linge, J., Mojtahed, V., & Marvin, H. J. P. (2018). Development of food fraud media monitoring system based on text mining. Food Control, 93, 283-296 

Marvin, H. J., Hoenderdaal, W., Gavai, A. K., Mu, W., van den Bulk, L. M., Liu, N., ... & Bouzembrak, Y. (2022). Global media as an early warning tool for food fraud; an assessment of MedISys-FF. 

Food Control, 137, 108961 

95  

https://knowledge4policy.ec.europa.eu/food-fraud-quality/monthly-food-fraud-summary-reports_en 

37 

 
                                                        
 
(twice  during  the  growing  season,  on  Turkey,  Ukraine,  Kazakhstan,  Russia  and  North  Africa). 
Detailed  Information  on  the  MARS  crop  yield  forecasting  system,  as  well  as  relevant  datasets, 
are publicly accessible via the Agri4Cast Toolbox. 

—  DG  TAXUD  Crisis  Impact  Analysis:  The  dashboard  focuses  on  exports  (values  and  quantities), 
monitoring the impact of  sanctions and conflicts  while analysing market  elasticity. It uses data 
within  the  Surveillance  database  and  it  provides  circumvention  analysis  by  modelling  trade 
flows of exported goods from the EU to selected destination countries. For example: some goods 
are prohibited to be exported from the EU to a specific country under embargo, but companies 
may  utilise  third  countries  as  transit  intermediaries  to  ultimately  ship  the  goods  to  the 
sanctioned country. 

Commercial  softwares  used  by  law  enforcement  agencies  could  be  deployed  for  data  mining, 
traceability, analysis of unstructured data, unveiling of hidden connections, and forecasting.  

Based  on  the  outcomes  of  the  technology  mapping  exercise  and  a  cost-benefit  analysis,  senior 
management can decide whether the system should be implemented to improve the integrity of the 
EU food supply chain. 

Recommendation 4 

Data-driven analytics to support informed decision making offers new opportunities to enhance 
food  integrity  by  analysing  ‘big  data’  to  identify  potential  risks,  patterns,  trends,  and  areas  for 
improvement.  Creating  big  data  involves  the  accumulation  and  processing  of  large  volumes  of 
information from various sources. Machine learning can identify subtle patterns and correlations 
in  large  datasets  that  might  not  be  apparent  through  traditional  analysis.  This  can  help 
authorities to identify emerging risks more effectively and to support the design of appropriate 
risk-based monitoring schemes.  

Creating  and  managing  big  data  requires  careful  planning,  resources,  and  expertise  in  data 
management, analysis, and relevant technologies. It  is also important to  have  a clear objective 
for  collecting  and  analysing  big  data  to  ensure  that  the  efforts  contribute  meaningfully  to  the 
objective of the exercise. 

The European  Commission is mandated  to  collect large volumes  of  food chain  data; other data 
sets  held  by  Commission  services  could  be  of  use  as  well.  However,  the  data  sets  are  often 
dispersed  and  lack  interoperability.  Aggregating  these  data  sets  has  the  potential  to  generate 
the required ‘big data’ to facilitate data-driven decision support processes. 

The JRC recommends to create a well-defined technology roadmap for setting up an AI-driven IT 
system for food integrity that integrates data aggregation and analysis by big data technologies 
involving  experts  from  European  Commission  departments  owning  relevant  datasets,  IT 
professionals  (back-end  and  front-end  developers),  and  data  scientist.  The  roadmap  should 
consider the need for: 

- 

computing 
computationally intensive tasks; 

infrastructure 

(cloud  computing,  high  performance  computing) 

for 

-  building and implementing APIs for data aggregation from diverse sources and tools for 

data cleaning, transformation, and standardisation before storage;  

-  appropriate data storage solutions to accommodate large data sets; 

-  a  collaborative  environment  where  data  scientists,  IT  professionals  and  food  chain 

experts collaborate on machine learning models and analytics pipelines; 

- 

- 

compliance  with  data  protection  regulations,  particularly  when  handling  personal  or 
sensitive information. 

training for IT professionals and data scientists to proficiently manage cloud resources 
and big data technologies. 

38 

 
 
Predictive modelling can be utilised to cover food fraud as well as food safety risks. However, it is 
sensible to assume all food fraud may be a potential risk to public health unless proven otherwise 96. 
Specific  models  trained  on  different  data  sources  have  to  be  built  if  both  aspects  of  food  integrity 
shall be covered. 

For  example,  with  the  values  already  available  a  product  coming  from  a  country  with  a  high 
corruption index and low food safety index would already be marked as vulnerable in terms of both 
food  fraud  and  food  safety.  However,  after  the  finalisation  of  dedicated  food  risk  vulnerability 
assessments (FRVA, see below), the proposed system may also be able to mark a product in powder 
form,  which  is  generally  more  vulnerable  to  food  fraud  (as  adulterants  can  be  more  easily  mixed 
with  the  authentic  product),  but  maybe  less  to  food  safety  risks  (because  microorganisms  cannot 
reproduce easily in a dry environment). 

The previous step should deliver information to an EU database already harmonised and structured, 
inter-operable  and  comparable,  and  legally  available  and  accessible  to  staff  (EU  and/or  Member 
States). The digital IT tool should be able to identify and correct irregularities, gaps or errors in the 
input data, with suggestions to be validated by experts before proceeding with the data analysis.  

Data are expected to be delivered continuously to the digital IT tool. Member States’ operators and 
inspectors, the EU Institutions, and possibly the food industry should be able to insert input data in a 
semi-automated, multilingual and standardised format. The (food) product shall be characterised by 
a number of information entries (an “ID Card”) to be provided by public and private actors along the 
food chain, and later collected by the new EU database.  

Developing a user-friendly data collection system across the EU (top-down), co-developed together 
with all stakeholders (bottom-up), but still complete and informative, will be key for the success of 
such information-based risk analysis IT system.  

Figure 14: Data items in a data model. 

Source: Black A., Nederpelt P. van. (2020) Dictionary of dimensions of data quality (3DQ) Dictionary of 60 Standardised 
Definitions. DAMA-NL. 

Figure  14  shows  the  most  common  terminology  associated  with  databases.  “Records”  may  be 
considered as one of the “ID cards” of a food product. Each record will include a series of data items 
(filled  with  data  values)  gathered  from  the  individual  databases  and  associated  with  the  general 
attributes co-developed with bottom-up and top-down approaches.  

96  

Elliott, C. (2014). Elliott Review into the integrity and assurance of food supply networks-Final report: A national food crime prevention framework. PB 14192 

39 

 
                                                        
 
 
The  new  EU  database  should  include  several  attributes  organised  in  hierarchical  levels.  Although 
data  items  are  shown  as  sequential  cells  in  Figure  14,  not  necessarily  all  data  items  should  be 
treated  equally  from  a  hierarchical  perspective,  and  therefore  a  multi-layered  record  may  provide 
additional benefits in the context of the new EU database (Figure 15). 

—  Data values inserted/chosen manually by the users/operators (e.g. food inspectors, national and 
EU  authorities,  FBOs)  are  considered  the  1st  level  category.  The  objective  is  to  minimize  the 
number  of  inputs  falling  into  this  category  to  reduce  manual  mistakes,  limiting  in  parallel  the 
administrative burden for the operators. In order to collect inputs harmonised and interoperable 
across all Member States, a suitable solution would be a series of options to choose from (i.e. 
several lists) whenever possible, in the native language of the user (i.e.  multilingual - available 
in all 24 EU languages, and possibly  additional  ones  from third countries). Inputs falling  within 
this category may also include non-compliances uncovered through official controls or internal 
audits from the private sector.  

—  Each  data  value  in  the  1st  level  category  would  be  automatically  associated  with  a  2nd  level 
category  of  data  values,  pre-compiled  by  national  and  European  authorities  and  linked 
unequivocally only to a single 1st level data value. Similarly, even lower level categories may be 
envisioned  according  to  the  needs.  For  example  (Figure  15):  the  identity  of  a  FBO  (1st  level 
category)  is  automatically  associated  with  pre-compiled  data  as  e.g.  the  country  of  origin  (2nd 
level  category),  that  implies  some  parameters  as  corruption  index,  etc…  (3rd  level  category).  In 
other  words: when users introduce (directly or indirectly) a  specific data  value, all data values 
hierarchically  inferior  and  associated  with  it  (the  associated  “package”)  never  change  and  are 
introduced automatically into the data record. 

Figure 15: A simplified example of hierarchical data record, with the different levels of categories for each data 
item. Each text box represents one data item. 

Source: JRC. 

The project may profit from the creation of a dedicated Working Group in order to establish: 

—  The  data  attributes  within  each  data  record.  Not  all  data  items  should  be  filled,  however  there 
should be the possibility to utilise such data model independently from the source of information 
(i.e. database), thus being  flexible, versatile  and coherent  with the  requests and needs of  each 
actor of the food supply chain; 

40 

 
—  The nomenclature to be utilised for each data item; 

—  The categorisation and hierarchy of data items, according to the principles of speed, minimalism, 

multilingualism and automatism. 

The Working Group should include especially those actors who will provide and will use/analyse the 
data  records.  Overall,  the  data  model  established  should  be  easy  to  be  filled,  multilingual, 
informative, interoperable, and complete. 

However,  an  effective  and  valuable  data  record  would  be  more  complex  than  what  is  described 
above. This report frames the architecture of two “ID Cards”: 

—  The “Transaction ID Card” is a collection of information related to a specific transaction between 

two actors of the food chain.  

—  The “Food ID Card” is the collection of all Transaction ID Cards connected to a specific product 

and related ingredients.  

The  possibility  of  having  a  harmonised  set  of  data  values  within  an  EU  database  will  provide  the 
foundation  for  further  analysis.  In  summary,  each  data  value  provides  useful  information  not  only 
utilised  to  describe  an  individual  transaction  of  a  specific  food  product,  but  also  to  be  further 
processed by a digital IT tool to calculate the probability that the concrete food lot/consignment may 
be subject to non-compliances. 

5.3.1 “Transaction ID Card” data elements 

The “Transaction ID Card” is a collection of data items related to a specific transaction between two 
actors of the food chain. Similarly to a line connecting two dots within a network, such record would 
provide selective data on a specific event that took place in a specific time (and place, possibly).  It 
contains several data items hierarchically organised as shown in Table 3. As previously mentioned, 
the  operator  shall  insert  only  the  information  within  then  1st  level  category,  while  the  rest  of  the 
Transaction ID Card (i.e. lower hierarchical levels) will automatically be filled by the digital IT tool. In 
addition, there are some data items that are not just simply referred to a single data item of higher 
hierarchical  level,  but  that  stem  from  the  interconnection  of  different  data  items  (of  various 
hierarchical levels) contained in the Transaction ID Card itself. As a consequence, such “derived data 
items”  (Figure  16)  do  not  need  to  be  inserted  manually  by  the  operator  but  are  provided  and 
calculated by the IT system automatically. The data items in Table 3 provides in [square brackets] the 
proposal on which classification, list, options or format to use for the data values. 

Table 3: Data items of the Transaction ID Card. 

1st level category 

2nd level category 

3rd level category 

(to  be  filled  manually  by  human 
personnel) 

[automatically  linked  to  a 
specific 1st level category] 

[automatically linked to a 
specific  2nd 
level 
category] 

Date [DD/MM/YY] 

Product [TARIC] 

Transaction Price [€] 

Quantity [units, kg, l] 

qualities 

Physical 
and 
processing level [whole, pieces, 
powder, liquid] 

Refrigeration [Y/N] 

Supply situation 

41 

 
 
 
 
 
 
 
 
 
 
Value adding features 

[organic, halal, kosher, conventional] 

Identification number  

[lot number, tag number] 

Means  of  transportation  [air, water, 
road, train] 

Seller [ID number; e.g. LEI number] 

Age [months] 

Economic health 

Firm size 

Recidivism 

Address  

[Street address, house number, 
Zip code, UN/LOCODE Country]  

Owner 

Certifications 

Sector competition level 

Supply chain position 

Poverty  and  crime  [GDP 
per  capita  growth  at 
NUTS3 level] 

Corruption  [Corruption 
Perception Index] 

Country-based 
geopolitical risk 

Buyer [ID number; e.g. LEI number] 

Age [months] 

Economic health 

Firm size 

Recidivism 

Address  

[Street address, house number, 
Zip code, UN/LOCODE Country]  

42 

Poverty  and  crime  [GDP 
per  capita  growth  at 
NUTS3 level] 

Corruption  [Corruption 
Perception Index] 

Country-based 
geopolitical risk 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Owner 

Certifications 

Sector competition 

Supply chain position 

Checkpoint location 

Address  

[Street address, house number, 
Zip code, UN/LOCODE Country] 

Poverty  and  crime  [GDP 
per  capita  growth  at 
NUTS3 level] 

Corruption  [Corruption 
Perception Index] 

Country-based 
geopolitical risk 

Non-compliance [Y/N] 

Typology of non-compliance 

E-commerce [Y/N] 

Current Transaction ID (CTI) 

Backward Transaction IDs (BTIs) 

Forward Transaction IDs (FTIs) 

DERIVED DATA ITEMS 

[automatically  inserted  by  the  digital 
IT tool] 

Current market price [€/(U/kg/L)] 

Unit price [€/(U/kg/L)] 

Future market price [€/(U/kg/L)] 

Non-compliance history [%] 

Ease of detection 

Source: JRC. 

43 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 16: Derived data items: Current market price; Unit price; Non-compliance history; and Ease 
of detection. 

Source: JRC. 

Annex 2 provides a detailed list of all data items of the Transaction ID Card and an analysis of the 
associated  food fraud drivers according to  some scientific and  economic literature. The data items 
collected in the Transaction ID Card, given their high granularity, do not only provides a solid base to 
increase  the  full  traceability  of  the  food  supply  chain  (the  “insight capacity”  and  “investigative 
capacity”)  but  also  feeds  the  vulnerability  analyses  to  be  performed  in  relation  to  the anticipatory 
capacity.  

5.3.2 The “Food ID Card”, a collection of all “Transaction ID Cards” 

The  “Food  ID  Card”  is  a  collection  of  all  Transaction  ID  Cards  related  to  a  specific  product  in  a 
specific time and place (i.e. a specific point within the supply chain). The Transaction ID Cards can be 
visualised as “pearls of a necklace” (Figure 17). In reality, more chains would converge into a single 
Food ID Card.  

The  Food  ID  Card  should  include  the  vast  majority  of  data  items  to  be  used  by  the  digital  IT  tool, 
however  a  minority  of  derived  data  items  (e.g.  supply  chain  length  and  supply  chain  complexity) 
could be indirectly associated to (and calculated for) each Food ID Card (Figure 16). All previous and 
following Transaction ID Cards of the ingredients used to produce a specific product would thus be 
visible to the human operator.  

44 

 
Figure 17: A simplification of the Food ID Card. Each Food ID Card is a collection of Transaction ID Cards linearly 
linked through specific data items. The last Transaction ID Card is opened, showing the several data items 
within. The data items highlighted by the blue dotted line provides the same information in both transaction ID 
Cards, connecting them. In reality, more chains would converge towards a single Food ID Card. As a general 
rule, the number of Transaction ID Cards contained within a Food ID Card will increase with the level of 
processing and with the number of actors involved in that specific chain. 

Source: JRC. 

Supply chain length and supply chain complexity – [Derived data item] 

One  of  the  available  definitions  for  “supply  chain”  is  the  network  of  organisations  involved  in 
different  mechanisms  and  activities,  with  links  to  the  previous  and  subsequent  steps  that  produce 
value in the form of goods and services conveyed to the consumer97. 

The threat that a long and complex supply chain represents to the authenticity of foodstuff is widely 
recognised  in  the  literature98,99,100,101,102,103,104,105,106.  This  is  because  long  supply  chains  decrease 
transparency,  visibility  and  trust  as  fraudsters  can  exploit  supply  chain  vulnerabilities  introducing 
illegitimate  goods  into  the  regular  market107.  As  a  matter  of  fact,  the  increasing  number  of  steps 
before  foodstuff  reaches  the  consumer  increases  the  supply  chain  opacity  as  it  is  much  harder  to 
monitor  the  good’s  composition  and  provenience108.  In  other  words:  the  more  actors  have  been 
involved  in  the  production  of  a  certain  good,  the  higher  chances  of  food  fraud  being  committed  by 
some  of  those  actors.  Shorter  supply  chains  are  thus  less  vulnerable  given  the  minor  number  of 
actors. 

A supply chain monitoring system is mostly not in place to control and register each  step between 
the producer and the consumer to ensure the legitimacy of the product. Its implementation may be 
useful  to  recollect  foodstuff  composition  and  origin,  reducing  fraud  opportunity  as  the  information 
available would deter adulteration.  

97  

98  

99  

Christopher, M. (2016). Logistics & supply chain management. Pearson Uk. 

Roberts, M. T., Viinikainen, T., & Bullon, C. (2022). International and national regulatory strategies to counter food fraud. Food & Agriculture Organization. 

Codex Alimentarius Commission. (2017). Discussion paper on food integrity and food authenticity. CX/FICS 17/23/5. Prepared by Iran with assistance from Canada and the Netherlands, Codex 

Committee on Food Import and Export Inspection and Certification Systems (CCFICS) (23rd Session), Mexico City, Mexico, 1-5 May 2017. 

100  

Lotta, F., & Bogue, J. (2015). Defining food fraud in the modern supply chain. Eur. Food & Feed L. Rev., 10, 114. 

101  

Manning, L., & Monaghan, J. (2019). Integrity in the fresh produce supply chain: solutions and approaches to an emerging issue. The Journal of Horticultural Science and Biotechnology, 94(4), 413-

421. 

102  

Manning, L. (2016). Food fraud: Policy and food chain. Current Opinion in Food Science, 10, 16-21. 

103  

Morehouse, J. E., & Cardoso, L. (2011). Consumer product fraud–how to stop the fraud now. Supply Chain Quarterly. 

104  

Tibola, C. S., da Silva, S. A., Dossa, A. A., & Patrício, D. I. (2018). Economically motivated food fraud and adulteration in Brazil: Incidents and alternatives to minimize occurrence. Journal of Food 

Science, 83(8), 2028-2038. 

105  

Lotta, F., & Bogue, J. (2015). Defining food fraud in the modern supply chain. Eur. Food & Feed L. Rev., 10, 114. 

106  

Kennedy, S. (2008). Why can't we test our way to absolute food safety?. Science, 322(5908), 1641-1643. 

107  

Stadler, R. H., Tran, L. A., Cavin, C., Zbinden, P., & Konings, E. J. (2016). Analytical approaches to verify food integrity: Needs and challenges. Journal of AOAC International, 99(5), 1135-1144. 

108  

Sarpong, S. (2014). Traceability and supply chain complexity: confronting the issues and concerns. European Business Review. 

45 

 
 
                                                        
 
Figure 18: Examples of Supply Chain Length (SCL) and Supply Chain Complexity (SCC) calculated for the 
node/transaction of interest (yellow circle). A) Section A represents a one-ingredient food product sold from 
one actor of the food chain to others, without further processing. B) Section B represents a multi-ingredient 
product, with the ingredients delivered by multiple suppliers to a single processor. C) Section C symbolizes the 
most common situation, where single ingredients and highly processed ingredients contribute to the final 
product. The longest supply chain is outlined in green, whereas other two shorter supply chains are outlined in 
red. Red circles represent nodes/transactions not belonging to the supply chains of the node/transaction of 
interest (in yellow). 

Source: JRC. 

The  derived  data  item  “Supply  Chain  Length  (SCL)”  may  be  defined  as  the  maximum  number  of 
consecutive  nodes  that  compose  the  supply  chain  of  a  specific  node.  In  section  A  of  Figure  18  the 
number  of  nodes  leading  to  the  node  of  interest  is  four,  plus  the  node  of  interest:  the  SCL  is 
calculated  as  five.  On  the  other  hand,  the  “Supply  Chain  Complexity  (SCC)”  may  be  defined  as  the 
total  number  of  nodes  contributing  to  the  production.  In  section  B  of  Figure  18.  The  SCC  is  six, 
whereas the  SCL is  only two because  five suppliers contribute to  the  node of  interest but there is 
only  one  step  between  them  and  the  node  of  interest.  Finally,  section  C  of  Figure  18  highlights  the 
difference between SCL and SCC. The maximum number of consecutive contributing nodes is five (as 
shown in green), but the total number of contributing nodes is sixteen.  

Taking into account both variables may be useful to capture two different aspects of a specific food 
supply  chain:  SCL  represents  the  exchanges  throughout  the  supply  chain,  hence  opportunities  of 
adulteration by the different economic agents, whereas SCC may capture the number of ingredients 
that  compose  the  product,  hence  the  opportunities  of  adulteration  of  each  ingredient.  The 
combination  of  the  two  variables  may  enable  the  proposed  IT  tool  to  understand  comprehensively 
these two aspects of a food supply chain. 

46 

 
 
 
5.4 Model deployment 

The expected outputs of the IT system fall within two main categories: 

—  “What  is  happening?”:  An  overview  of  non-compliances  and  trade  flows  of  commodities  within 
the  EU,  employing  dashboards  showing  geographical  maps,  heat  maps,  charts  to  represent 
trends, comparisons, patterns, distributions and time-series of data in near-real time to provide 
a situation report. This functionality is intended to assist users in quickly and effectively making 
informed  decisions  about  which  mitigation  measures  to  implement.  To  trace  back  a  non-
compliant  lot,  competent  authorities  could  request  to  access  the  digital  traceability  records  of 
the  concerned  FBOs,  via  an  Application  Programming  Interface  (API),  if  available.  In  this  case, 
the  system  could  show  the  linkages  between  non-compliant  ingredients  and  the  derived 
processed products, as well as the connections between FBOs. Ideally, all the connected nodes 
along the food network will become visible, providing a better understanding of the dimension of 
the non-compliance and speeding up recall or withdrawal of all affected products. 

—  “What  will  happen?”:  An  AI  algorithm  (e.g.  machine/deep  learning)  will  be  trained  to  recognise 
patterns  according  to  a  variety  of  data  sources  identified  by  vulnerability  analysis  of  certain 
supply  chains  (e.g.  market  prices,  non-compliances,  corruption  level,  etc.)  to  predict  potential 
non-compliances.  The  objective  will  be  to  support  Member  States’  competent  authorities  in 
setting  up  more  effective  and  efficient  surveillance  and  official  control  programmes  to  protect 
the food supply  chain  from food safety  and food fraud incidences. The results  of investigations 
and official controls should be fed back to the algorithm to improve its predictive power.  

The vulnerability analysis shall rely on the quantitative values associated with the “Food ID card” 
data  items  of  the  product.  Such  values  are  either  derived  from  internationally  recognised 
indexes,  or  alternatively  forged  by  a)  an  expert  group  that  would  translate  qualitative 
judgements  into  quantitative  values,  and/or  b)  the  proposed  IT  tool  shall  self-learn  how  much 
weight  to  attribute  to  each  input  in  order  to  best  tailor  its  predictions.  For  example,  the  ML 
approach  may  decide  to  associate  a  higher  weight  to  the  price,  rather  than  the  geographical 
origin, according to the history of that commodity as recorded from past cases. In other words, 
the digital IT tool may consider the price as a  better predictor of vulnerability  compared to the 
geographical origin. 

The  objective  will  be  to  support  Member  States’  control  authorities  (the  main  actors  who  can 
perform enforcement actions) to target certain containers/products approaching the EU border, 
or specific companies/products/individuals within the internal market. However, the outputs will 
ultimately be checked by a multidisciplinary centre of competence. 

5.4.1 Insight capacity 

Principles of the future traceability system 

The  EU  lacks  a  centralised  traceability  system,  relying  on  the  information  provided  by  the  FBOs 
along  the  food  supply  chain  in  case  of  need.  A  solid  traceability  system  would  highly  scale-up  the 
preparedness to safeguard consumer health, and it can easily contribute to fight any food fraud or 
criminal action within the internal market. 

Although  the  concept  of  a  linear  food  supply  chain109  is  usually  utilised  to  describe  the  network  of 
FBOs contributing to shape the global food systems, the connections between food and ingredients, 
individuals and companies is highly more complex, non-linear and intertwined, resembling a chaotic 
network quite difficult to represent and visualize at once (Figure 10). For example, many operators in 
the food service industry are small-scale businesses dealing with a large number of products and 
ingredients, whereas on the contrary e.g. an olive oil producer is usually a medium-sized enterprise 
dealing with many suppliers delivering a single product. 

The  new  traceability  system  should  immediately  provide  the  authorities  with  a  clear  overview  of 
such food maze, virtually taking few minutes to trace any source along the food supply chains, and 

109  

https://ec.europa.eu/info/sites/default/files/food-farming-fisheries/farming/documents/food-supply-chain_en.pdf 

47 

 
                                                        
 
delivering  a  fast  targeted  response  before  more  non-compliant  products  might  affect  other 
consumers. Therefore, some core principles need to be established framing the functionalities of the 
future IT tool: 

—  Any non-compliant product/ingredient (e.g. fraudulent, unsafe) transmits the non-compliance to 
the  other  derived  products/ingredients  forward  along  the  chain;  however  the  opposite  is  not 
necessarily true, i.e. if a product containing more than one ingredient is non-compliant, not all its 
ingredient  are  non-compliant.  The  non-compliance  status  moves  forward,  and  not  backward, 
along the food supply chains. 

—  Any information associated with the “Transaction ID Card” is automatically carried along the food 
supply  chain,  and  associated  with  the  “Food  ID  Cards”  of  the  derived  food  products.  In  other 
words: the ID of each product automatically contains also the information of its ingredients, thus 
it  is  not  necessary  to  contact  the  other  actors  in  the  supply  chain  to  retrieve  that  information. 
The system should deliver user-friendly visualization tools in this regard. 

Figure 19: A simplified food network of the ingredients required to prepare a pizza margherita. Arrows 
represent connection between supplier and consignee. Circles represent a specific lot of food/ingredient. Red 
shapes represent non-compliant sections of the food network.  

Source: JRC.  

As  an  example,  Figure  19  shows  the  simplified  version  of  the  food  network  related  to  a  pizza 
margherita.  Each  circle/node  represents  a  specific  lot  of  ingredient,  while  arrows  show  the  flow 
supplier-consignee.  During  an  official  control  by  food  safety  authorities,  a  specific  lot  of  the 
ingredient  “mozzarella”  is  found  contaminated  with  high  levels  of  melamine:  with  the  proposed  IT 
tool, the authorities may ask the FBO to immediately withdraw from the market the non-compliant 

48 

 
 
 
lots of mozzarella and derived lots of pizzas, because they can  swiftly trace which processed food 
product  contains  the  non-compliant  ingredients.  Additionally,  the  authorities  have  at  their  disposal 
all the information covering the lots of ingredients (e.g. milk, salt, citric acid, rennet) used to produce 
that specific lot of mozzarella, thus they can start investigating if the contamination happened in the 
factory  producing  mozzarella,  or  if  the  contamination  was  carried  by  a  non-compliant  lot  of 
ingredients.  

First  advantage:  the  new  traceability  system  would  guarantee  higher  speed  in  finding  the  data 
needed  to  investigate  any  non-compliance,  without  being  subject  to  the  data  availability  from  the 
supply chain actors upon request. Products suspected of being unsafe or fraudulent could be more 
easily identified and withdrawn from the market by FBOs. 

Following  the  example  from  the  previous  section,  the  authorities  are  still  unaware  if  the 
contamination  started  in  the  factory  producing  mozzarella,  or  it  was  a  consequence  of  a  non-
compliant lot of ingredients. Figure 20 shows the previous food network (from Figure 19) surrounded 
by the blue dashed line as part of a wider food network. Once again, each circle/node represents one 
lot  of  ingredients,  whereas  the  arrows  represent  the  flow  supplier-consignee.  Each  FBO  may  be 
responsible  for  multiple  nodes  (i.e.  producing  more  than  one  food  product),  and  each  node  may 
contribute to more  than  one node (i.e.  each lot of ingredients may  be used in  many  lots  of derived 
food products), adding complexity to the whole network. 

Figure 20: An example of food network. Arrows represent any connection between supplier and consignee. 
Circles represent a specific lot of food/ingredient. Red shapes represent non-compliant sections of the food 
network. The dashed purple triangle highlights the node/product in common between the non-compliant 
sections of this food network. The area surrounded by the blue dashed line represent the food network drawn in 
the previous Figure 19. 

Source: JRC.  

The EU and national authorities need to identify where the non-compliance started, regardless if it 
concerns  food  safety  and/or  food  fraud.  Different  sections  of  the  food  network  (marked  in  red  in 
Figure 20) show the same non-compliance almost simultaneously in the proposed IT system.  

—  Nowadays:  the  authorities’  officers  must  manually  draw  the  food  network  under  investigation, 
trying  to  gather  all  relevant  information  in  a  timely  manner,  and  aiming  at  identifying  which 
“node” of the network may be the problematic one;  

—  In the future: with the help of the proposed IT tool, the non-compliant lot of ingredients (marked 
with the dashed purple triangle in Figure 20) which triggered the risk (by “contaminating” other 

49 

 
nodes  of  the  food  chain)  is  automatically  signalled  to  food  inspectors.  It  is  the  only  node  in 
common  to  all  non-compliant  sections  of  the  food  network  already  discovered  by  food 
inspectors;  notably,  some  of  the  nodes  not  coloured  in  red,  although  possibly  non-compliant, 
may not yet be uncovered along the food chain by official controls, and therefore are displayed in 
green.  

Identifying  the  nodes  which  trigger  the  non-compliance,  disrupting  all  the  food  chain,  is  pivotal  to 
help official authorities to better target investigations and controls on certain lots and FBOs.  In real 
food networks this exercise may be very challenging, given the high level of complexity and number 
of  lots  of  ingredients.  Therefore,  human  personnel  alone  may  not  be  able  to  identify  the  most 
vulnerable nodes by simply drawing manually the food network and tracing the movement of goods.  

Second  advantage:  the  new  traceability  system  would  automatically  identify  hidden  connections  in 
the food network, supporting authorities to better uncover which FBOs and/or which food products 
are  responsible  for  several  non-compliances  identified  along  their  supplier-buyer  chain,  without 
depending on the manual labour of personnel from EU and national authorities. 

Previously,  the  report  classified  the  databases  and  intelligence  sources  in  four  macro-categories 
(Figure 8) according to data granularity and geographical coverage (internal market VS imports). The 
insight  capacity  is  fed  by  those  sources  with  high  granularity,  required  to  connect  single 
products/lots and uncover the network of transactions in the internal market and at the EU border. 

At  the  moment,  data  from  FBOs  are  shared  only  upon  request  by  Member  States’  competent 
authorities  (e.g.  in  case  of  a  food  safety/food  fraud  non-compliance  and  subsequent  opening  of  an 
investigation),  and  data  from  Member  States’  competent  authorities  are  not  shared  with  the  EU  or 
other  Member  States’  competent  authorities  unless  a  non-compliance  affects  more  than  one 
Member  State  (data  shared  through  iRASFF).  As  a  consequence,  not  enough  granular  information 
are  available  on  the  EU  food  supply  chain,  limiting  the  data-mining  possibilities  of  the  proposed  IT 
system. 

Third advantage: the collection of all traceability-related data in a single centralised database would 
improve the interoperability and availability of data currently fragmented across different public and 
private databases, providing granular information for the proposed IT system  to  better perform its 
anticipatory capacity. 

5.4.2 Investigative capacity 

The  investigative capacity  capitalizes  on  the  same  approach  and  IT  architecture  of  the  insight 
capacity as described in the previous section. Whereas the insight capacity focuses on products and 
their  related  non-compliances,  the  investigative capacity  exploits  the  full  traceability  potential  to 
identify rogue FBOs who could recurrently jeopardise the integrity of the food supply chain. For this 
reason,  also  the  insight  capacity  is  fed  by  those  databases  and  intelligence  sources  with  high 
granularity  (Figure  8),  required  to  connect  FBOs  and  uncover  the  network  of  transactions  in  the 
internal market or at the EU border. 

In  the  ideal  scenario  (i.e.  the  availability  of  the  “Transaction  ID  Cards”  and  “Food  ID  Cards”),  the 
proposed  IT  tool  would  apply  the  same  methodology  on  the  new  EU  database  to  cover  both  the 
insight capacity and the investigative capacity; therefore, no further programmes would be needed. 
Figure 19 and Figure 20 focused on product traceability, however the same Figures are still relevant 
for the investigative capacity if, conceptually, “products” are replaced with “FBOs”.  

Figure 21 shows how the proposed IT tool would specifically exploit the structured and unstructured 
data available in the new EU database to investigate fraudulent and criminal activities affecting the 
food system(s). The most relevant functionalities include: 

—  Connecting FBOs who share common interests and are involved in recurring frauds, especially 
when  group  of  people  responsible  for  criminal  activities  are  not  immediately  obvious  from  the 
official  data.  Examples  of  data  sources  include  mostly  social  media  (e.g.  Facebook,  Twitter, 
YouTube).  

—  Signalling  unrealistic  increases  in  revenues  in  short  time.  If  a  company  has  significantly 
increased  its  profits  as  compared  to  the  average  of  other  companies  in  the  same  business 

50 

 
sector, the system should identify such outlier and signal it to the authorities. Excessive profits 
may result from fraudulent operations, thus requiring increased scrutiny.  

—  Assessing  addresses  and  geographical  locations  according  to  the  suitability  of  the  declared 
establishment  for  food  business  operations.  For  example,  a  rogue  FBO  may  misdeclare  the 
address of a private apartment on a certificate, but the system would compare the pictures from 
e.g. Google Maps with a set of parameters typical of food establishments, thus signalling to the 
investigators a suspicion on the official documentation.  

—  Unveiling forged certificates and documentation by comparing them with commercial and public 
templates. A FBO working in the organic sector may receive laboratory analyses for pesticides 
(using  standard  kits  commercially  available)  registering  some  residues  levels  not  compliant 
with  the  organic  legislations.  A  fraudster  could  manually  or  digitally  alter  the  certificate  to 
remove  the  non-compliant  values,  but  although  a  human  inspector  may  have  difficulties  to 
recognise a single missing row among tens of lines, the system would automatically identify the 
missing data items by comparing the submitted documentation with templates stored internally 
or on online databases. 

—  Recognise logos and third party certificates (e.g. organic, Geographical Indications) on products 
and documentation,  comparing them with the information  available  on the company  website or 
registries  from  official  authorities  (e.g.  EUIPO).  The  system  would  automatically  signal  if  the 
submitted  documentation  declares  some  products  as 
the 
company/registration website does not mention at all the possibility to buy organic products, or 
no organic logos/certification number is publicly available on the website. 

“organic”,  even 

though 

—  Scrutinize customs declaration with other data sources (company website, other documentation) 
to uncover misdeclaration of country of origin or CN codes in order to avoid risk-based official 
controls  carried  out  on  products  coming  from  specific  third  countries  or  products  subject  to 
temporary increased scrutiny. A fraudster may alter customs declaration to pay less taxes or to 
avoid sanitary controls.  

51 

 
Figure 21: Functionalities covered by the investigative capacity. A) Connecting individuals through social 
networks, finding hidden linkages between their companies. B) Monitoring revenues of FBOs, highlighting 
suspicious increases. C) Comparing addresses of facilities and companies with real pictures from e.g. Google 
Maps, unveiling facilities not resembling FBOs. D) Comparing laboratory analyses with those provided by 
testing companies and certification databases, unveiling modified or removed rows from the submitted 
documentation. E) Comparing logos and certifications on the labels or documents with the company website, 
revealing inconsistencies between products sold online and products declared. F) Comparing CN codes 
declared on customs declarations with other documentation and company websites, unveiling misdeclaration 
of CN codes to pay less duties or to avoid specific sanitary controls.  

Source: JRC; Google Maps. 

52 

 
 
 
5.4.3 Anticipatory capacity 

The  anticipatory capacity  covers  those  steps  before  a  non-compliance  is  discovered  and  the 
investigation starts. The anticipatory capacity supports and optimizes risk-based official controls to 
highlight  suspicions  and 
investigated  through  the 
identify  possible  non-compliances  later 
functionalities  covered by  the insight capacity  and the investigative capacity. The data accumulated 
during  the  inspections  and  investigations  will  help  to  improve  the anticipatory capacity  and  better 
tailor  it  to  real  cases  through  a  feedback  mechanism.  Several  functionalities  described  previously 
for  the investigative capacity  may  be  applied  in  both  steps:  to  start  an  investigation  or  during  an 
investigation. 

Analytics activities are classified in three macro-categories110 according to the decision-making goal 
outcome: 

—  Descriptive analytics,  highlighting  relationships  amongst  data.  Analysts  may  discover  casual 

connections not evident or obvious if manually investigated; 

—  Predictive analytics,  applying  statistics-  or  ML-  based  techniques  to  predict  future  trends  and 
patterns. It is pivotal that the organisation continues to accumulate data to verify the efficacy of 
the predictive algorithms; 

—  Prescriptive analytics,  based  on  predictive  analytics,  and  suggesting  the  best  (i.e.  resource-
efficient)  course  of  actions  in  order  to  achieve  a  specific  outcome.  In  the  ideal  scenario, 
responses would be automatic following new inputs. 

The  proposed  IT  tool  should  support  EU  and  national  authorities  in  identifying  the  most 
suspicious/vulnerable  products  in  the  internal  market  or  imported  into  the  EU  territory,  tailoring 
official controls accordingly through an impartial and risk-based approach. Notably, as highlighted in 
the OPSON IX Report111, “Europol and Interpol managed to develop predictive models with indicators 
of possible shortage of raw materials”. It would be wise to profit from such previous experiences by 
integrating them in the context of this project. The long-term goal should be the decrease in number 
and impact of non-compliances in the internal market and at the EU border. 

In  the  previous  sections,  the  report  classified  databases  and  intelligence  sources  according  to  the 
level  of  granularity  (Figure  8).  The  anticipatory capacity  may  feed  on  all  databases,  intelligence 
sources  and  IT  tools  already  available,  but  especially  on  the  data  items  of  the  “Food  ID  Card” 
associated with quantitative values to be assessed and compared (the “Food ID Card” data items in 
Annex 2 were chosen also because relevant as drivers). Given the vast diversity of data theoretically 
available, the anticipatory capacity should be able to predict vulnerabilities of specific supply chains 
according  to  e.g.  production  levels,  natural  conditions,  FBOs  identity,  geopolitical  crises,  sanitary 
epidemics, market prices, country-specific factors, market trends, administrative barriers, to name a 
few. Some examples (sometimes inspired by real cases) include: 

—  The  war  in  Ukraine  has  created  a  global  shortage  of  wheat  and  sunflower  oil,  but  the  request 
from consumers is unchanged. It is  reasonable to believe that  those two supply chains  (wheat 
and  sunflower  oil)  are  now  under  pressure  and  more  vulnerable,  therefore  potentially  more 
targeted by fraudsters given the need to fill a market void while prices rise. 

—  A  FBO  who  has  never  sold  organic  olive  oil,  who  has  never  been  certified  by  an  accredited 
certification  body,  and  who  has  never  advertised  organic  olive  oil  on  the  company  websites, 
suddenly submits official documentation mentioning organic products delivered to a buyer in the 
olive oil supply chain. 

—  Sesame seeds from a specific third country were found to be highly contaminated with residue 
levels  not  compliant  with  the  EU  legislation.  As  a  result,  increased  risk-based  official  controls 
have  been  redirected  towards  that  commodity  imported  from  that  specific  third  country.  The 
exporter FBO has thus decided to avoid the official controls either by a) changing CN codes or b) 
shipping the products (with correct CN codes) from a neighbouring country. The proposed IT tool 

110  

111  

Spink, J., Elliott, C., Dean, M., & Speier-Pero, C. (2019). Food fraud data collection needs survey. npj Science of Food, 3(1), 1-8.  

https://www.europol.europa.eu/publications-documents/operation-opson-ix-%E2%80%93-analysis-report 

53 

 
                                                        
 
would signal such patterns by a) showing changes of market trends in similar products from the 
same country (e.g. less sesame seeds, more pumpkin seeds) or b) showing decreased imports 
of  sesame  seeds  from  the  third  country  under  scrutiny  coupled  with  higher  imports  of  the 
sesame  seeds  from  neighbouring  countries.  In  other  words,  the  proposed  IT  tool  would  signal 
redirections of trade flows. 

—  A specific lot is sold at 67% of the average market price because the rogue FBOs has replaced 
beef  with  cheaper  and  unsafe  horse  meat  without  declaring  it  on  the  label.  The  ideal  digital  IT 
tool  would  identify  such  outlier  according  to  the  analyses  of  all  transaction  prices,  thus 
redirecting official controls towards the suspicious lot “too good to be true”. 

—  An animal disease has devastated beef production in third countries, and it could spread to other 
countries if left unchecked. So far, the EU has managed to contain the disease by nullifying the 
imports  of  contaminated  lots.  The  ideal  digital  IT  tool  would  increase  the  risk-based  official 
controls  on  specific  beef  imports  given  the  global  animal  health  situation,  but,  as  soon  as  the 
disease is over, the official controls would decrease accordingly. 

—  The  yearly  production  of  Manuka  honey,  specifically  made  in  Australia  and  New  Zealand,  is 
estimated at e.g. 2 800 tons. In June the EU has already imported 1 500 tons of Manuka honey, 
and  worldwide  1  300  tons  of  Manuka  honey  have  already  been  traded  in  third  countries.  While 
approaching the maximal global production, the IT tool would signal imports of Manuka honey as 
suspicious, given that the traded quantities may exceed the estimated yearly production. In other 
words, it would signal if high-value products are traded in unrealistic quantities. 

—  Products  are  produced  in  a  country  X  characterised  by  high  corruption  levels,  weak  control 
systems, recurrent non-compliances and low gross domestic product (GDP) per capita. Provided 
the  other  variables  are  the  same,  products  produced  and  exported  from  country  X  would  be 
considered  more  vulnerable  to  fraud  and  safety  non-compliances  compared  to  products 
produced  in  a  country  Y  scoring  better  for  the  parameters  under  examination.  Consequently, 
risk-based official controls would inspect more often products from country X rather than from 
country Y. 

Finally,  the  results  of  investigations  and  official  controls  (supported  by  the  insight capacity  and 
investigative capacity)  should  feed  inputs  back  to  the  IT  algorithm  in  order  to  align  itself  to  reality 
and  improve  the  predictive  power  of  the  anticipatory capacity.  Therefore,  the  IT  tool  should  be 
capable of: 

—  Signalling  vulnerabilities  on  a  mix  of  vulnerability  factors  a priori  established  and  inserted  by 

human personnel; 

—  Suggesting potential vulnerability factors resulting from data meta-analysis, assessed by human 
personnel  (to  verify  causation  besides  correlation)  and  later  feeding  back  on  the  digital  IT  tool 
for further refinement. 

In both cases, the most important question remains unsolved: how much weight should be assigned 
to  each  specific  vulnerability  factor?  Could  the  machine-learning  algorithm  establish  the  weight  of 
each value independently? 

5.5 Vulnerability analysis of supply chains 

Food safety management systems based on HACCP principles are widely applied by FBOs to ensure 
that food is safe to consume and it complies with regulatory requirements. ISO issued the ISO 22000 
Food  Safety  Management  series  of  standards.  Together  with  a  number  of  private  standards  (e.g. 
FSSC 22000, IFS Food, BRCGS Global Food Safety), they form the basis for certification of FBOs by 
third party auditors to document the availability and functioning of a system to ensure the safety and 
quality  of their products. Several  of those standards  cover  food fraud as well and require  FBOs to 
conduct and document a food fraud vulnerability assessment. 

Identifying  food  fraud  risks  and  vulnerable  points  in  a  supply  chain  and  the  data  sources  that 
qualitatively  or  quantitatively  characterise  the  risks  are  key  elements  for  a  data-driven  predictive 
analytics system.  

54 

 
Vulnerability  analysis  systematically  considers  the  factors  that  create  vulnerabilities  in  a  supply 
chain,  i.e.  where  food  fraud  is  more  likely  to  occur112.  Multiple  food  fraud  vulnerability  assessment 
tools  have  been  developed  to  support  FBOs  in  their  endeavour  to  safeguard  the  supply  chain  (e.g. 
SSAFE). By adopting criminological theory and using vulnerability assessment tools, companies can 
assess  vulnerability  for  food  fraud  on  both  company  and  supply  chain  levels.  In  addition, 
vulnerability  analyses  for  certain  high  risk  food  supply  chains  already  exist  in  the  scientific 
literature.  

Some examples of vulnerability analyses already performed include: 

-  Regarding  FBOs,  the  industry  segment  and  the  tier  are  better  predictors  of  vulnerability  than 
company size or its geographical location. The influence of the business size varies much more 
with the company’s location. The FBOs a) at the end (e.g. catering, retail) of the animal products 
supply chains, b) larger sized, and c) in Africa and Asia seem the most vulnerable to fraud113. The 
higher  vulnerability  of  casual  dining  food  services114  (compared  to  mass  caterers)  was  also 
confirmed,  although  elsewhere  manufacturing  was  identified  as  the  main  point  of  adulteration 
(as the food becomes indistinguishable from its original form)115. 

-  Due  to  the  nature  of  milk,  biological  hazards  (i.e.  pathogenic  micro-organisms  as  Listeria  or 
Salmonella) are the most common threats to the integrity of the dairy supply chain116. Chemical 
(e.g.  antibiotics,  aflatoxins,  heavy  metals)  and  physical  contaminants  pose  much  lower  threats 
for  dairy  products.  The  most  common  milk  fraud  issues  include  addition  of  water  to  milk  and 
addition of illegal (and often unsafe) materials as e.g. milk powder, urea, cane sugar, melamine, 
formalin,  caustic  soda  and  detergents.  Cheese  and  yoghurt  are  mostly  affected  by  fraudulent 
documentation. 

- 

- 

The  vulnerability  to  fraud  in  the  spice  chain  is  overall  perceived  as  medium  vulnerable117,  with 
higher  vulnerability  scores  for  technical  opportunities  (grinding  the  spices  easily  mask 
adulterants  within)  and  economic  drivers  (spices  being  expensive  commodities).  Key  risks 
include  adulteration,  detection  difficulty,  prices,  and  market  competition.  Recently  the  JRC  has 
published  the  results  of  an  EU  wide  coordinated  control  plan  to  establish  the  prevalence  of 
fraudulent practices in the market of herbs and spices118; the overall rate of suspicious samples 
was 17%, ranging from 48% in the oregano supply chain to 6% for paprika/chilli. 

The  organic  sector  covers  credence-based  products  (similarly  to  kosher  and  halal)  highly 
vulnerable  to  frauds.  Some  of  the  most  recurrent  drivers  are  the  price  asymmetry  between 
organic and conventional food products (with consequent economic pressure and motivation to 
substitute, mislead or deceive) and the ethical culture of the organisation119, although overall the 
organic supply chains were perceived slightly less vulnerable than conventional chains120 due to 
fewer opportunities for fraud and the presence of more adequate controls. 

-  A recent study on seafood121 reported the most recurrent frauds based on geographic location or 
supply  chain  node.  The  most  prevalent  fraud  type  is  species  adulteration  with  illegal  or 
unauthorized veterinary residues (especially from Asia), ruminant DNA in fishmeal and various 
adulterants. The second most recurrent fraud is the chain of custody abuse, with health marks 
or  certificates  being  absent,  improper,  or  fraudulent.  Aquaculture  farming  was  found  to  be  the 
most vulnerable supply chain node, followed by import/export, and then processing. The report 

112  

113  

114  

115  

116  

117  

118  

Manning, L., & Soon, J. M. (2019). Food fraud vulnerability assessment: Reliable data sources and effective assessment approaches. Trends in Food Science & Technology, 91, 159-168 

van Ruth, S. M., & Nillesen, O. (2021). Which company characteristics make a food business at risk for food fraud?. Foods, 10(4), 842 

van Ruth, S. M., van der Veeken, J., Dekker, P., Luning, P. A., & Huisman, W. (2020). Feeding fiction: Fraud vulnerability in the food service industry. Food Research International, 133, 109158 

Soon, J. M., & Abdul Wahab, I. R. (2022). A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration. Foods, 11(3), 328 

Montgomery, H., Haughey, S. A., & Elliott, C. T. (2020). Recent food safety and fraud issues within the dairy supply chain (2015–2019). Global Food Security, 26, 100447 

Silvis, I. C. J., Van Ruth, S. M., Van Der Fels-klerx, H. J., & Luning, P. A. (2017). Assessment of food fraud vulnerability in the spices chain: An explorative study. Food Control, 81, 80-87 

Maquet, A., Lievens, A., Paracchini, V., Kaklamanos, G., De La Calle Guntinas, M.B., Garlant, L., Papoci, S., Pietretti, D., Ždiniaková, T., Breidbach, A., Omar Onaindia, J., Boix Sanfeliu, A., Dimitrova, T. 

and Ulberth, F., Results of an EU wide coordinated control plan to establish the prevalence of fraudulent practices in the marketing of herbs and spices, EUR 30877 EN, Publications Office of the 

European Union, Luxembourg, 2021, ISBN 978-92-76-42979-1, JRC126785 

119  

Manning, L., & Kowalska, A. (2021). Considering fraud vulnerability associated with credence-based products such as organic food. Foods, 10(8), 1879 

120  

van Ruth, S. M., & de Pagter-de Witte, L. (2020). Integrity of organic foods and their suppliers: Fraud vulnerability across chains. Foods, 9(2), 188. 

121  

Lawrence, S., Elliott, C., Huisman, W., Dean, M., & van Ruth, S. (2022). The 11 sins of seafood: Assessing a decade of food fraud reports in the global supply chain. Comprehensive Reviews in Food 

Science and Food Safety, 21(4), 3746-3769. 

55 

 
                                                        
 
also provides the most recurrent frauds in specific continents; for example species substitution, 
species  adulteration,  fishery  substitution,  illegal  processing  and  undeclared  product  extension 
are strongly associated with Europe and North America and with the top end of the supply chain. 

-  A  recent  food  fraud  vulnerability  assessment  in  the  Chinese  Baijiu  supply  chain122  came  to  the 
conclusion that the main food fraud drivers are numerous technical opportunities (lack of know-
how  on  detection  capabilities),  strong  economic  drivers  (the  raw  material  price,  the  specific 
composition or properties of raw materials, and the competition level in the supply chain), and 
insufficient control measures (raw material counterfeiting detection systems are mainly used by 
producers, while most retailers do not test the authenticity of raw materials). 

Official  controls  may  already  profit  from  the  knowledge  available  to  tailor  risk-based  official 
controls, which can later-on be improved once a data driven IT system provides more insight. 

The  Routine  Activities  Theory  uses  three  elements  to  explain  crimes:  the  motivated  offender,  the 
suitable target, and the lack of guardianship123,124, therefore an environment less vulnerable hinders 
fraudulent  activities.  Based  on  this  theory  FFVAs  aim  at  identifying  opportunities  for  fraud  in  a 
supply  chain,  searching  for  motivations  for  supply  chain  stakeholders  to  commit  fraud  and 
assessing the efficiency and effectiveness of controls put in place (deterrents). 

Food chains obviously differ in their specificities and characteristics and the associated risk factors 
will  therefore  vary  as  well.  However,  certain  factors  will  pertain  in  general  and  they  have  been 
already  described  in  the  literature124.  Based  on  this  academic  insight,  PwC  together  with  SSAFE 
categorised  them  into  three  main  groups  (Table  4),  which  they  have  used  to  compose  a  self-
assessment  questionnaire  directed  primarily  at  FBOs  but  a  number  of  elements  are  also  of 
relevance for competent authorities to identify fraud risk in a particular food chain. 

Table 4: Indicators for the three key elements opportunities, motivations, and control measures and their 
numbering used in the food fraud vulnerability assessment. 

Opportunities 

Motivations 

Control measures 

of 

adulteration 

raw 

12.  Supply 

and 

pricing 

raw 

31.  Price asymmetries 

1. 

 Complexity 
materials 

2.  Availability  technology  and  knowledge 

to adulterate raw materials 

3.  Detectability adulteration raw materials 

materials 
13.  Valuable 

components 
attributes raw materials 

or 

32.  Fraud  monitoring  system  raw 

materials 

14.  Economic 
company 

conditions 

own 

33.  Verification of fraud mon. system 

raw materials 

4.  Availability  technology  and  knowledge 

15.  Organizational  strategy  own 

34.  Fraud  monitoring  system  final 

to adulterate final products 

company 

products 

5.  Detectability adulteration final products 

16.  Ethical  business  culture  own 

35.  Verification  of  fraud  monitoring 

6.  Complexity of counterfeiting 

company 

17.  Criminal 
company 

system final products 

offences 

own 

36. 

Information 
company 

system 

own 

7.  Detectability of counterfeiting 

18.  Corruption  level  country  own 

37.  Tracking and tracing system own 

8.  Production lines/processing activities 

19.  Financial strains supplier 

company 

company 

38. 

Integrity 
employees 

screening 

own 

9. 

Transparency chain network 

20.  Economic conditions supplier 

39.  Ethical  code  of  conduct  own 

company 

10.  Historical evidence fraud raw materials 

21.  Organizational 
supplier 

strategy 

40.  Whistle blowing own company 

11.  Historical evidence fraud final products 

22.  Ethical 

business 

culture 

supplier 

41.  Contractual 
supplier 

requirements 

23.  Criminal offences supplier 

42.  Fraud control system supplier 

122   Wang, Y., Liu, J., Xiong, Y., Liu, X., & Wen, X. (2023). Food Fraud Vulnerability Assessment in the Chinese Baijiu Supply Chain. Foods, 12(3), 516. 

123  

Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American sociological review, 588-608 

124  

van Ruth, S. M., Huisman, W., & Luning, P. A. (2017). Food fraud vulnerability and its key factors. Trends in Food Science & Technology, 67, 70-75 

56 

 
 
                                                        
 
24.  Victimization of supplier 

43.  Mass balance control. supplier 

25.  Corruption 

level 

country 

supplier 

44.  Tracking  and 
supplier 

tracing  system 

26.  Economic conditions sector 

45.  Social control chain network 

27.  Criminal offences customer 

46.  Fraud control industry 

28.  Ethical business culture sector 

47.  National food policy 

29.  Historical  evidence  branch  of 

48.  Law enforcement local chain 

industry 

30.  Level of competition in sector 

49.  Law enforcement chain network 

50.  Fraud contingency plan 

Source: SSAFE, PwC (2017). 

Many  of the key factors/drivers  of  food  fraud  can be  described in qualitative  or  quantitative  terms, 
which are accessible via existing data sources. For example, socio-economic and geopolitical data 
can be provided by Eurostat, FAO, World Bank, Transparency International, UN Development Reports, 
The Economist, The International Country Risk Guide, etc., which are relevant for assessing the fraud 
risks  at a higher level,  e.g. for an  exporting country,  a particular supply chain,  or  a combination of 
both.  

Indexes at a country level for augmenting the predictive analytics for food fraud could include: 

—  The governance index of the country  

—  Whether there is a legal system regulating the food chain in the country  

—  The GDP of the country  

—  The economic growth of the country  

—  The supply chain index of the country  

—  The political risk index (PRI) of the country  

—  The human development index of the country  

—  Global Innovation Index of the country 

A  group  of  experts  can  help  in  translating  qualitative  statements  into  quantitative  metrics,  as 
devised by NSF Safety and Quality UK Limited125 (Figure 22 and Figure 23). 

125  

Jack, L. (2015). Risk modelling of food fraud motivation:'NSF fraud protection model'intelligent risk model scoping project FS 246004 

57 

 
 
 
 
 
 
 
 
 
 
                                                        
 
Figure 22: Profit Metric and criteria. 

Source: NSF Fraud Protection Model125. 

Figure 23: The NSF model for ranking food fraud risks. 

Source: NSF Fraud Protection Model125. 

58 

 
 
 
 
 
 
 
Each product category or topic to be covered requires substantial resources to: 

—  understand the legislation and policies in place; 

—  identify relevant databases; 

—  gather scientific evidence and assessments; 

—  consult stakeholders, especially those who will provide inputs (e.g. data and documents); 

—  establish the IT resources, both physical (e.g. servers) and digital (e.g. algorithms, programmes); 

—  assign personnel for monitoring and follow-up. 

Recommendation 5 

A substantial number of scientific publications describes attempts to identify vulnerability factors 
and  food  fraud  drivers  for  specific  commodities  and  tiers  of  the  supply  chain.  However  a  more 
targeted  approach  is  needed,  as  most  of  the  published  literature  is  primarily  directed  towards 
food  businesses  and  not  much  consideration  is  given  how  competent  authorities  can  use  it  for 
vulnerability assessment of complete supply chains and trading routes.  

To  bridge  this  gap  it  is  recommended  to  initiate  a  project  aimed  at  constructing  generic  supply 
chain  maps  for  a  selected  number  of  foods/ingredients  where  historical  fraud  incidence  data 
indicate  an  increased  risk.  Next,  the  project  team,  including  supply  chain  experts,  should  asses 
their  vulnerability  and  identify  critical  points  in  the  chain  (vulnerability  analysis  critical  control 
points, VACCP). Based on the VACCP, relevant data sources characterising the food integrity risks 
should  be  identified,  located  inside  as  well  as  outside  of  the  EC.  The  project’s  outcome  would 
serve as a pre-requisite for the successful implementation of Recommendations 1 and 3 as both 
would  profit  from  the  inclusion  of  socio-economic  and  geopolitical  indicators  in  the  descriptive 
and  predictive  models.  Conversely,  the  vulnerability  assessment  could  benefit  from  the  work 
conducted  in  Recommendations  1  and  3,  because,  during  the  data  mining  phase,  valuable, 
previously  unknown,  and  potentially  actionable  patterns  of  risk  factors  may  surface.  Including 
them  together  with  the  associated  indicators  could,  in  turn,  improve  the  predictive  ability  of  the 
model (i.e. self-learning capacity of the model).  

Given  the  plethora  of  food  products  available  to  consumers,  the  complexity  of  global  supply 
chains and the multitude of potential hazards compromising food integrity, the generic VACCP of 
supply  chains  and  the  associated  predictive  risk  model  can  only  include  food  ingredients  that 
have a high risk of being the target for fraudulent manipulations. The  Annual Report of the Alert 
and  Cooperation  Network,  the  annual  summaries  of  the  JRC  Food  Fraud  Monthly  Reports  and 
commercial  databases  can  be  used  for  ranking  ingredients  on  the  basis  of  past  food  integrity 
incidences.  To  further  prioritise  them  for  inclusion  in  the  programme  risk  ranking  should  be 
applied.  Models  for  ranking  food  safety  risks  quite  often  use  two  dimensions  where  the  risk 
likelihood  (probability)  is  plotted  against  its  impact  (severity).  The  NSF  Fraud  Protection  Model 
builds  on  such  an  approach  but  uses  “Likelihood  of  Detection”  and  “Profit”  instead  as  metrics 
(Figure 23). Ingredients/products situated in the right upper quadrant (highly profitable, less likely 
to be detected) would constitute the primary targets of fraudsters.  

59 

 
 
 
 
 
6 Remit and uncertainties of the ideal information-based risk analysis IT tool 

A broad dialogue within the European Institutions and with the industry and civil society is needed to 
address  some  critical  questions,  set  up  boundaries  and  establish  working  modalities  before 
consolidating any future IT system. 

6.1 Which products should be monitored? 

There  is  a  need  to  frame  which  commodities/products  to  be  covered  and  addressed  through  the 
ideal  digital  IT  tool.  There  is  no  clear  definition  of  food  fraud  at  EU  level.  However,  Article  2(21)  of 
Commission Implementing Regulation (EU) 2019/1715 states that: 

‘fraud notification’ means a non-compliance notification in iRASFF concerning suspected intentional 
action by businesses or individuals for the purpose of deceiving purchasers and gaining undue 
advantage therefrom, in violation of the rules referred to in Article 1(2) of Regulation (EU) 2017/625; 

The legislative framework covered by Article 1(2) of Regulation (EU) 2017/625 includes food and food 
safety, genetically modified organisms, feed and feed safety, animal health and welfare, plant health, 
plant  protection  products,  organic  food,  and  EU  quality  schemes126,  some  not  covered  by  any 
previous  section  of  this  report.  In  addition  to  scientific  evidences,  a  political  decision  is  needed  to 
establish  up  to  which  limits  and  topics  the  functionalities  and  coverage  of  the  ideal  digital  IT  tool 
should be enlarged to. A concrete opportunity could be starting with the core “food” commodities (i.e. 
destined  to  human  consumption),  slowly  expanding  to  other  topics  according  to  the  priorities 
decided at political level, and by integrating the lessons already acquired along the way.  

6.2 Which risks should be addressed? 

The ideal digital IT tool described so far may potentially be utilised to cover all aspects of the Food 
Risk  Matrix  (food  quality,  food  fraud,  food  safety,  and  food  defense).  Some  non-compliances  are 
mostly  financial,  with  no  risks  for  consumer  health.  On  the  opposite,  some  food  safety  non-
compliances do not necessarily bring obvious financial gains to the fraudsters. 

By focusing only on financial risks, health (i.e. food safety) risks may be ignored, and vice versa. In 
case such system would be used by customs authorities, police offices and food safety agencies as 
well, the relative importance of financial risks VS health risks, and how to integrate the various risk 
typologies  in  the  same  IT  system,  should  be  established  at  political  level.  However,  it  seems  a 
shared desire to cover both food fraud and food safety. As stated by Prof. Chris Elliott, “it is sensible 
and prudent to assume all food fraud may be a potential risk to public health unless proven 
otherwise”127. 

For now, two possibilities could be hypothesized for the predictive analytics algorithm (Figure 24): 

—  The  ideal  digital  IT  tool  provides  a  vulnerability  indicator  for  financial  risks,  and  a  separate 
indicator for health risks. Therefore, each product/transaction will carry at least two indicators, 
and it will be the responsibility of national authorities’ officials to decide if focusing on products 
vulnerable to high financial risks/low health risks, or vice versa, without any other indication or 
direction from the ideal digital IT tool. 

Or 

—  A priori the programmers establish  within the ideal digital IT tool  a certain weight for financial 
risks, and another weight for health risks, according to political choices rather than via ML. Such 
parameters are weighted and integrated into a single parameter/value, giving the possibility for 
national  authorities’  officials  to  have  a  clear  ranking  of  most  vulnerable  products,  taking  into 
account all risks (e.g. financial and health). 

In case it is established at political level to combine all risk indicators into a single value, it could be 
still  relevant  to  visualize  the  single  indicators  (financial  and  health)  separately,  because  some 
agencies and institutions may only be interested in assessing and addressing a specific risk typology 

126  

https://agriculture.ec.europa.eu/farming/geographical-indications-and-quality-schemes/geographical-indications-and-quality-schemes-explained_en 

127  

Elliott, C. (2014). Elliott Review into the integrity and assurance of food supply networks-Final report: A national food crime prevention framework. PB 14192 

60 

 
                                                        
 
(e.g. DG TAXUD is mostly interested in monetary fraud indicators, whereas the European Food Safety 
Authority mostly in food safety risks). 

This report focuses on the ideal digital IT tool required to predict vulnerabilities mostly falling under 
the financial aspect. In case  of  a political  will to predict and  rank  health  risks according to frauds, 
products,  exposures,  etc…,  the  European  Food  Safety  Authority  is  the  most  authoritative  choice  to 
assess if health risks may be given priorities, and to provide such ideal ranking (and indicators) of 
the most relevant drivers for food safety issues. 

Finally, it is worth mentioning that the official controls in place in many Member States mostly focus 
on food safety aspects, rather than fraudulent ones. Therefore, a risk-based official control systems 
should profit from the already established  framework available to deal with food safety threats. As 
noted by DG SANTE128, “EU countries have some arrangements in place to deal with fraud threats in 
the agri-food chain, but official controls focusing on fraudulent and deceptive practices are not yet 
systematically in place across all control areas.” 

Figure 24: the two aspects of food integrity, where a non-authorised treatment or ingredient substitution 
introduces a food safety hazard. The € symbol represents financial risks (i.e. monetary losses), whereas the 
heart represents health risks (i.e. food safety threats). Risk severity ranks from 1 (very low) to 10 (very high). 

6.3 Which illegal activities should be targeted? 

Source: JRC. 

The European legislative framework and the Codex Alimentarius lack a clear definition of food fraud. 
Therefore, this report drafts the layout of a system covering a subject so far undefined from a legal 
perspective.  Two  steps  may  be  necessary  to  be  taken  if  any  future  project  will  follow  the  present 
report: 

—  Establishing the legal definition of food frauds in the EU legislation; 

—  Deciding which frauds should be covered by the future information-based risk analysis IT tool. 

The  food  fraud  scientific  community  has  repeatedly  tried  to  establish  and  classify  food  frauds129; 
however,  some  criminal  activities  are  still  lacking  clarity  and  are  not  considered  as  fraudulent 

128  

European Commission, Directorate-General for Health and Food Safety, (2022). Report from the Commission on the overall operation of official controls carried out in Member States (2019-2020) 

to ensure the application of food and feed law, rules on animal health and welfare, plant health and plant protection products – , Publications Office of the European Union 

129  

Robson, K., Dean, M., Haughey, S., & Elliott, C. (2021). A comprehensive review of food fraud terminologies and food fraud mitigation guides. Food Control, 120, 107516. 

61 

 
                                                        
 
activities in some classifications (e.g. smuggling; black and grey market; low hygiene conditions; IUU 
fishing).  

62 

 
7  Conclusions 

This report provides an overview of European and international databases, intelligence sources and 
IT  tools  covering  food  integrity,  namely  the  safety  and  authenticity  of  food,  which  protects  the 
interests of honest food chain stakeholders, including consumers.  

The needs of the different European Commission services have been captured through interviews of 
staff  dealing  with  the  development  and  implementation  of  food  chain  policies.  From  the  interviews 
three  main  capacities  (insight,  investigative,  anticipatory)  emerged,  which  a  data-driven  decision 
system should possess to scale up the protection of the European food system(s).  

Food-related data are collected separately and in a mostly non-standardised manner by various EU 
and national actors, according to different legal bases. Data interoperability, accessibility, reliability 
has to be addressed and improved to serve as a solid foundation for further data analytics by AI to 
flag  anomalies  in  supply  chains.  This  information,  together  with  relevant  socio-economic  and 
geopolitical  indicators  and  specific  supply  chain  knowledge,  would  support  regulatory  bodies  to 
assess the situation and, if necessary, choose the most appropriate mitigation measures.  

Substantial  investments  in  computing  infrastructure  and  human  capital  are  required  to  modernise 
the current IT landscape inside and outside the EC.  

Centre of competence on food integrity 

Member  States  have  already  expressed  their  wishes  for  the  creation  of  a  centre  of  competence 
which  could  scale-up  the  fight  against  food  fraud  and  protect  the  integrity  of  the  European  food 
systems130. The creation of a dedicated Interservice Group with representatives of the interested DGs 
and EU offices (namely JRC, AGRI, SANTE, MARE, TAXUD, OLAF, RTD) could be the starting point for 
Commission  services  to  share  information,  needs  and  contact  points.  In  addition  to  policy  officers, 
such a centre of competence requires the involvement of technical officers with knowledge on food 
fraud, food  safety, criminology, data analysis,  economics, markets, customs,  food law, statistics, to 
name a few. The involvement of other relevant authorities and agencies (e.g.  European Food Safety 
Authority, Europol) would provide a strong added value for the success of the centre of competence, 
especially from a technical perspective. 

The  centre  of  competence  will  be  best  placed  to  assess  the  food  fraud  and  food  safety  drivers, 
trends  and  vulnerabilities  already  known,  producing  food  fraud  vulnerability  assessments  and 
monthly reports by analysing e.g. market prices and trends, geopolitical developments, recent food 
safety  scandals,  and  ultimately  supporting  national  inspectors  to  redirect  sanitary  and  customs 
controls towards specific products in the internal market and at the EU border through a qualitative 
(but not quantitative) analysis. 

Pilot project on seafood 

At the beginning, a pilot project may be set up with the seafood sector (Annex 3), given the relevance 
of the sector and the well-established controls, in particular regarding fishing catches and imports, 
including the measures to fight against IUU fishing. Seafood commodities are subject to fraudulent 
practices  and  food  safety  risks,  and  the  EU  is  heavily  dependent  on  imports  of  fish  and  fishery 
products.  

As  the  EU  cannot  access  all  the  data  required  to  build  a  full  traceability  of  all  supply  chains,  it  is 
worthy to focus on a specific step of the food supply chain on imports, on which the EU has access to 
highly valuable and granular data. Several databases and intelligence sources are already focusing 
on  food  imported  at  the  EU  border:  Surveillance  3,  COMEXT,  AFIS-CSM,  AMT,  THESEUS,  TRACES, 
iRASFF, CATCH, DG TAXUD Crisis Impact Assessment. 

Food ID Card 

As the development of a comprehensive “Food ID Card” is hindered by the gaps in the information on 
all transactions taking place within the internal market, such prototype could focus on collecting the 
data required to build the “Transaction ID Cards” of imported products. The JRC can access several 

130  

https://knowledge4policy.ec.europa.eu/food-fraud-quality/technical-meetings-kc-ffq-1_en 

63 

 
                                                        
 
databases managed by other Commission services without bringing about any change to the current 
legislative framework. 

The creation of a Food ID Card template, and the related Transaction ID Cards, is an ambitious and 
resource-demanding exercise. It is however pivotal for the protection of the European food systems 
for a number of reasons: 

- 

It provides a complete overview of trade data at regional, national and global level, fostering 
the creation of data-driven policies and unveiling trends for foresight exercises. The Covid-
19  crises  and  the  Russian  aggression  against  Ukraine  has  demonstrated  that  some  of  our 
globally interconnected food supply chains are more vulnerable  than  expected. Uncovering 
such vulnerabilities with data-driven tools is the first step to reduce dependencies of the EU 
for critical/essential products. 

-  Besides food frauds, and despite of high levels of protection experienced by EU citizens, food 
safety risks are  a  constant threat  to consumer  health. The  availability of  the complete  and 
digitalised EU food network in the hands of experienced EU and national food inspectors is 
the most valuable asset to quickly identify and withdraw unsafe products from the market, 
ultimately  reducing  morbidity  and  mortality  levels  among  EU  citizens,  while  spotting 
establishments uncompliant with the food hygiene legislation and wrongdoers. 

-  Food  fraud  activities  involve  significant  economic  gain.  There  is  a  growing  interest  from 
criminal  cartels  in  “agropiracy”  because  of  the  increasing  financial  opportunities.  Targeted 
and  swift  intervention  will  be  facilitated  with  improved  traceability  and  quickly  available 
information. 

Also,  in  times  of  war,  bioterrorism  may  become  a  stronger  threat  to  EU  citizens.  Bioterrorists  or 
hostile  nations  may  target  food  supply  chains  compromising  food  security  or  even  using  food 
commodities as vectors for biological or chemical attacks.  

Cooperation with stakeholders 

A  wise  strategy  would  capitalise  on  the  framework  already  in  place  to  tackle  food  safety  non-
compliances and upscale it against other risks in the Food Risk Matrix (i.e. food fraud, food quality, 
and food defense). Therefore, it is crucial to liaise with the several stakeholders involved in different 
aspects of food fraud and food safety: 

—  Member  States  collect  all  data  on  non-compliances  affecting  their  national  food  system(s).  At 
the  moment  they  are  legally  obliged  to  share  cross-country  cases  with  other  Member  States 
and EU institutions for non-compliances on food fraud, food safety and the organic sector, with 
no obligation to share national cases. On the other hand, most of police investigations (national 
and international) are shared with Europol on a voluntary basis. 

—  A  dialogue  with  Member  States’  representatives  (food  safety  authorities,  customs,  police 
authorities, Ministries) is necessary to increase cooperation to tackle all risks in the Food Risk 
Matrix.  Willingness  to  share  data  on  national  non-compliances  and  investigations  is  the  first 
step  to  strengthen  and  enrich  the  future  EU  database,  and  ultimately  feed  and  empower  more 
consistently  the  three  capacities  (insight,  investigative  and  anticipatory)  of  the  proposed  IT 
system. 

—  Different aspects of the European food system(s) fall under the remit of several  DGs of the EC. 
Setting up an Interservice  Group,  and at later stage  a dedicated  centre of competence  on  food 
integrity,  will  allow  for  the  management  of  EU  food  systems  in  a  comprehensive  and  more 
efficient manner. 

—  The  civil society and the industry, in particular producers, manufactures, traders, retailers and 
consumers. The involvement and support from these stakeholders is necessary to proceed and 
scale-up  the  protection  of  the  most  vulnerable  European  food  sector(s).  Especially  the 
stakeholders  with  economic  interests  (producers,  manufactures,  traders,  and  retailers)  will 
benefit from mutual sharing of information.  

—  In parallel, a dialogue should be fostered with their representatives at EU and national level to 
build their own private traceability system (e.g. managed by umbrella organisations) to speed up 

64 

 
information sharing in case public authorities need records covering one-step forward and one-
step backward for their investigations.  

Food risk vulnerability assessments (FRVAs) 

In the context of the anticipatory capacity, in the best-case scenario the IT tool relies on quantitative 
values associated with each data item in order to build vulnerability profiles (in terms of food fraud 
and  food  safety)  for  each  product/transaction  inside  the  system.  Some  data  items  in  the  “Food  ID 
Card” and “Transaction ID Card” are already associated with quantitative values (e.g. prices, GDP per 
capita, Corruption Index, quantities); while others are only related to qualitative factors.  

Translating the qualitative information in quantitative values in terms of vulnerability will have to be 
performed  by  a  dedicated  expert  group  who  will  widen  the  scope  of  the  currently  used  FFVAs  in 
order  to  cover  all  aspects  of  the  Food  Risk  Matrix,  resulting  in  the  newly  called  food  risk 
vulnerability assessments (FRVAs). The expert group should assign values not only for drivers and 
vulnerabilities in terms of food fraud risks, but also in terms of food safety risks. The EU legislative 
framework  and  international  guidelines  already  encourage  to  perform  risk-based  official  controls 
(Annex 4). 

The expert group should be composed mostly by technical experts from the food industry and from 
academia,  supervised  (and  in  some  cases  joined)  by  staff  working  in  the  centre  of  competence  on 
food integrity, capitalising on the experience from “The Model” developed by the US FDA FSMA and 
other international initiatives (Section 1.3.1). 

65 

 
 
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List of abbreviations and definitions 

Abbreviations 

Definitions 

AAC 

AI 

API 

BTI 

CN 

CTE 

CTI 

DG 

EFSA 

EMA 

EORI 

EPCIS    

EU 

EUIPO   

EWS 

FBO 

FDA 

FFVA 

FRVA 

FSMA 

FTI 

FTL 

GDP 

GI 

GIIN 

HACCP   

HS 

IMSOC   

IoT 

IPR 

ISAR 

ISIC 

IT 

ITGS 

ITRE 

IUU 

Administrative Assistance and Cooperation 

Artificial Intelligence 

Application Programming Interface 

Backward transaction ID 

Combined Nomenclature 

Critical Tracking Events 

Current Transaction ID 

Directorate-General 

European Food Safety Authority 

Economically Motivated Adulteration 

Economic Operators Registration and Identification 

Electronic Product Code Information Services 

European Union 

European Union Intellectual Property Office 

Early Warning System 

Food business operator 

Food and Drug Administration 

Food Fraud Vulnerability Assessment 

Food Risk Vulnerability Assessment 

Food Safety Modernization Act 

Forward Transaction ID 

Food Traceability List 

Gross domestic product 

Geographical Indication 

Global Intermediary Identification Number 

Hazard Analysis and Critical Control Points 

Harmonised System 

Information Management System for Official Controls 

Internet of Things 

Intellectual property rights 

Import Screening for the Anticipation of Food Risks 

International standard industrial classification 

Information Technology 

International trade in goods statistics 

European  Parliament’s  Committee  on  Industry,  Research 
and Energy 

Illegal, unreported and unregulated 

76 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
JRC 

KDE 

LEI 

ML 

NACE 

NUTS 

OCR 

PRI 

Joint Research Centre 

Key Data Element 

Legal Entity Identifier 

Machine Learning 

Nomenclature statistique des activités économiques dans la 
Communauté européenne 

Nomenclature of Territorial Units for Statistics 

Official Controls Regulation 

Political risk index 

RASFF   

Rapid Alert System for Food and Feed 

SAD 

SCC 

SCL 

SSR 

TLC 

UK 

UNECE   

USA 

VACCP   

WCO 

WUR 

WWF 

Single Administrative Document 

Supply Chain Complexity 

Supply Chain Length 

Self-sufficiency ratio 

Traceability lot code 

United Kingdom 

United Nations Economic Commission for Europe 

United States of America 

Vulnerability Analysis and Critical Control Points 

World Customs Organization 

Wageningen University & Research 

World Wildlife Fund 

77 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of boxes  

Box 1: examples of studies on blockchain performed in third countries. ...................................................... 33 

78 

 
 
 
List of figures 

Figure 1: The Food Risk Matrix....................................................................................................................................... 13 

Figure 2: Food fraud vulnerability assessment concept....................................................................................... 14 

Figure 3: Elements of an integrated system for detection and mitigation of food fraud. .......................... 17 

Figure 4: Word cloud created from the interviews. ............................................................................................... 20 

Figure 5: Conceptual overview of the tripartite structure of the IT system. ................................................. 23 

Figure 6: Data flows between some of the EU and national databases. ........................................................ 26 

Figure 7: Data flows between some of the EU, national and international databases. ............................. 27 

Figure 8: Classification of the databases, IT tools and intelligence sources according to the level of 
granularity and geographical coverage. While all sources may contribute to the anticipatory capacity 
of the ideal IT tool, only sources characterised by a high granularity may feed the insight and 
investigative capacities. ................................................................................................................................................. 28 

Figure 9: Data submission from European countries to the European Food Safety Authority: the 
SIGMA project approach. ............................................................................................................................................... 29 

Figure 10: a simplified structure of the food supply chain. .................................................................................. 31 

Figure 11: Data aggregation process. .......................................................................................................................... 34 

Figure 12: an example of a visualisation tool. Monthly Fair Prices of the THESEUS repository of the 
JRC, which is in support of the anti-fraud work of the EU Customs and other relevant services in the 
European Institutions. ..................................................................................................................................................... 35 

Figure 13: Heat map depicting intrinsic risk and its dynamics for certain agri-food commodities based 
on trade flow data. ........................................................................................................................................................... 36 

Figure 14: Data items in a data model. ....................................................................................................................... 39 

Figure 15: A simplified example of hierarchical data record, with the different levels of categories for 
each data item. Each text box represents one data item. ................................................................................... 40 

Figure 16: Derived data items: Current market price; Unit price; Non-compliance history; and Ease of 
detection. ............................................................................................................................................................................. 44 

Figure 17: A simplification of the Food ID Card. Each Food ID Card is a collection of Transaction ID 
Cards linearly linked through specific data items. The last Transaction ID Card is opened, showing 
the several data items within. The data items highlighted by the blue dotted line provides the same 
information in both transaction ID Cards, connecting them. In reality, more chains would converge 
towards a single Food ID Card. As a general rule, the number of Transaction ID Cards contained 
within a Food ID Card will increase with the level of processing and with the number of actors 
involved in that specific chain. ..................................................................................................................................... 45 

Figure 18: Examples of Supply Chain Length (SCL) and Supply Chain Complexity (SCC) calculated for 
the node/transaction of interest (yellow circle). A) Section A represents a one-ingredient food 
product sold from one actor of the food chain to others, without further processing. B) Section B 
represents a multi-ingredient product, with the ingredients delivered by multiple suppliers to a 
single processor. C) Section C symbolizes the most common situation, where single ingredients and 
highly processed ingredients contribute to the final product. The longest supply chain is outlined in 
green, whereas other two shorter supply chains are outlined in red. Red circles represent 
nodes/transactions not belonging to the supply chains of the node/transaction of interest (in yellow).
 ................................................................................................................................................................................................ 46 

Figure 19: A simplified food network of the ingredients required to prepare a pizza margherita. 
Arrows represent connection between supplier and consignee. Circles represent a specific lot of 
food/ingredient. Red shapes represent non-compliant sections of the food network. ............................ 48 

Figure 20: An example of food network. Arrows represent any connection between supplier and 
consignee. Circles represent a specific lot of food/ingredient. Red shapes represent non-compliant 

79 

 
sections of the food network. The dashed purple triangle highlights the node/product in common 
between the non-compliant sections of this food network. The area surrounded by the blue dashed 
line represent the food network drawn in the previous Figure 19. .................................................................. 49 

Figure 21: Functionalities covered by the investigative capacity. A) Connecting individuals through 
social networks, finding hidden linkages between their companies. B) Monitoring revenues of FBOs, 
highlighting suspicious increases. C) Comparing addresses of facilities and companies with real 
pictures from e.g. Google Maps, unveiling facilities not resembling FBOs. D) Comparing laboratory 
analyses with those provided by testing companies and certification databases, unveiling modified or 
removed rows from the submitted documentation. E) Comparing logos and certifications on the 
labels or documents with the company website, revealing inconsistencies between products sold 
online and products declared. F) Comparing CN codes declared on customs declarations with other 
documentation and company websites, unveiling misdeclaration of CN codes to pay less duties or to 
avoid specific sanitary controls. .................................................................................................................................. 52 

Figure 22: Profit Metric and criteria. .......................................................................................................................... 58 

Figure 23: The NSF model for ranking food fraud risks. ...................................................................................... 58 

Figure 24: the two aspects of food integrity, where a non-authorised treatment or ingredient 
substitution introduces a food safety hazard. The € symbol represents financial risks (i.e. monetary 
losses), whereas the heart represents health risks (i.e. food safety threats). Risk severity ranks 
from 1 (very low) to 10 (very high). ............................................................................................................................... 61 

Figure 25: A schematic depiction of an AI system. ................................................................................................ 82 

Figure 26: Difference between HS, CN and TARIC nomenclature. The same commodities (“Peas” and 
“Beans”) are classified up to 6 digits in the Harmonised System (upper part), up to 8 digits in the 
Combined Nomenclature (middle part), but the level of granularity in the classification of the same 
products increases up to 10 digits in the TARIC nomenclature (lower part). ............................................... 87 

Figure 27: Top categories by number of seized articles infringing IPR in 2020. The y-axis represents 
the percentage of seized goods; the x-axis represents the categories of seized goods. ......................... 91 

Figure 28: Detained articles infringing IPR by means of transport (2017-2020). The y-axis represents 
the amount of seized goods in millions; the x-axis represents the means of transport. ........................ 92 

Figure 29: Share of global food miles by transport method. The y-axis represents the means of 
transport; the x-axis represents the percentage of global food miles. ......................................................... 92 

Figure 30: An example of Country Risk classification as published by amfori (Countries’ Risk 
Classification 2022). ........................................................................................................................................................ 103 

Figure 31: EU supply balance (2019, live weight equivalent, food use only). ................................................ 114 

80 

 
 
List of tables 

Table 1: Benchmarking requirements for Global Food Safety Initiative recognised certification of food 
safety management systems. ........................................................................................................................................ 15 

Table 2: Summary of existing relevant databases, intelligence sources and IT systems. ....................... 24 

Table 3: Data items of the Transaction ID Card. ....................................................................................................... 41 

Table 4: Indicators for the three key elements opportunities, motivations, and control measures and 
their numbering used in the food fraud vulnerability assessment. ................................................................. 56 

Table 5: List of FAOSTAT commodities and the related Self-Sufficiency Ratio (SSR) values, from the 
lowest SSR value to the highest, related to the EU. ............................................................................................. 111 

Table 6: Self-sufficiency rates by seafood commodity group in 2019. ............................................................. 112 

Table 7: EU-27 self-sufficiency rates by agriculture commodity group in 2021. .......................................... 113 

81 

 
 
 
 
Annex 1: Data Science, AI and data quality 

Data Science 

Data  science  is  today  considered  the  fourth  approach  to  scientific  discovery  (together  with 
experimentation,  modelling,  and  computation),  and  a  separate  science  embracing  competencies 
belonging to different traditional academic sectors, not being simply a branch of mathematics. Data 
scientists integrate formal statistical theory, software development and database theory. Therefore, 
data  scientists  are  versatile  in  designing  and  improving  data  collection/gathering,  statistical 
analysis,  data  visualization  and  interpretation,  and  in  coding  innovative  IT  programmes  tailored  to 
accomplish such goals. There are two goals in analysing data131: 

—  Predicting what the responses are going to be to future input variables; 

—  Inferring how nature is associating the response variables to the input variables. 

Machine predictions provide probabilistic results and a corresponding risk score. 

The  European  Commission  has  already  started  to  address  the  potentialities  and  risks  brought 
forward by AI132. According to the Ethics Guidelines for Trustworthy AI133, prepared by the High-level 
Expert  Group  on  Artificial  Intelligence,  a  trustworthy  AI  should  be  lawful,  ethical  and  robust.  The 
seven  key  requirements  for  a  trustworthy  AI  are:  (1)  human  agency  and  oversight;  (2)  technical 
robustness  and  safety;  (3)  privacy  and  data  governance;  (4)  transparency;  (5)  diversity,  non-
discrimination and fairness; (6) environmental and societal well-being; and (7) accountability. 

Figure 25: A schematic depiction of an AI system. 

Source: High-level expert group on artificial intelligence. (2019). Ethics guidelines for trustworthy AI. 

The JRC has published a report defining what AI is134, identifying four commonalities in the assessed 
AI definitions: 

—  Perception of the environment, including the consideration of the real world complexity; 

—  Information processing: collecting and interpreting inputs (in form of data); 

—  Decision  making  (including  reasoning  and  learning):  taking  actions,  performance  of  tasks 
(including adaptation, reaction to changes in the environment) with certain level of autonomy; 

—  Achievement of specific goals: this is considered as the ultimate reason of AI systems. 

131  

Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745-766. 

132  

https://ec.europa.eu/digital-single-market/en/artificial-intelligence 

133  

High-level expert group on artificial intelligence. (2019). Ethics guidelines for trustworthy AI. 

134  

European Commission, Joint Research Centre, Samoili, S., López Cobo, M., Gómez, E. (2020). AI watch – Defining artificial intelligence : towards an operational definition and taxonomy of artificial 

intelligence, Publications Office 

82 

 
                                                        
 
The  baseline  AI  definition  is  the  one  established  by  the  High-level  Expert  Group  on  Artificial 
Intelligence:  

“Artificial intelligence (AI) systems are software (and possibly also hardware) systems designed by 
humans that, given a complex goal, act in the physical or digital dimension by perceiving their 
environment through data acquisition, interpreting the collected structured or unstructured data, 
reasoning on the knowledge, or processing the information, derived from this data and deciding the 
best action(s) to take to achieve the given goal. AI systems can either use symbolic rules or learn a 
numeric model, and they can also adapt their behaviour by analysing how the environment is 
affected by their previous actions. 

As a scientific discipline, AI includes several approaches and techniques, such as machine learning 
(of which deep learning and reinforcement learning are specific examples), machine reasoning 
(which  includes  planning,  scheduling,  knowledge  representation  and  reasoning,  search,  and 
optimization), and robotics (which includes control, perception, sensors and actuators, as well as the 
integration of all other techniques into cyber-physical systems).” 

The  European  Parliament’s  Committee  on  Industry,  Research  and  Energy  (ITRE  committee)  has 
published  a  comprehensive  study  highlighting  challenges  and  opportunities  for  using  Artificial 
Intelligence135. The AI applications fit, broadly speaking, in two macro-categories: a) enhancement of 
the performance and efficiency of industrial processes; and b) human-machine collaboration (which 
relates to the project outline in this document). The food sector is not among those considered at the 
forefront in applying AI computational algorithms, although Europe shows competitive strengths in 
industries like automotive, healthcare, financial services, energy, tech and media. 

Machine  learning  is  considered  a  sub-discipline  of  AI,  defined  as  “where computers programs 
(algorithms) learn associations of predictive power from examples in data. Machine learning is most 
simply the application of statistical models to data using computers.”136  The  most  wide-spread 
machine-learning  approaches  are  supervised learning,  unsupervised learning,  and  reinforcement 
learning. Machine learning utilises methods not based on a priori assumptions about the distribution 
of  data,  but  on  finding  patterns  autonomously  in  difficult  situations.  Notably,  machine  learning  can 
also reduce the baseline need for data harmonisation. 

It is worth concluding by stressing the potential ethical, societal and legal controversies in using AI-
based machine learning algorithms for decision-making, security and law enforcement purposes137. 
For example, the GDPR Regulation states that ‘’The data subject shall have the right not to be subject 
to a decision based solely on automated processing, including profiling, which produces legal effects 
concerning him or her or similarly significantly affects him or her”’138, protecting against misuse or 
abuse of citizens’ personal data. The European Parliament Research Service has published as well a 
Study specific on biases and AI139. 

Data quality 

Data  quality  is  the  degree  to  which  dimensions  of  data  meet  requirements.  Data  quality  (inputs)  is 
pivotal  to  guarantee  solid  results  from  subsequent  analysis  (outputs).  The  most  common  twelve 
dimensions of data quality are140: 

—  Accuracy (i.e. correctness of data values): the degree of closeness of data values to real values.  

—  Availability:  the  degree  to  which  data  can  be  consulted  or  retrieved  by  data  consumers  or  a 

process.  

135  

Eager,  J,  (2020)  Opportunities  of  Artificial  Intelligence,  Study  for  the  committee  on  Industry,  Research  and  Energy,  Policy  Department  for  Economic,  Scientific  and  Quality  of  Life  Policies, 

European Parliament, Luxembourg. https://www.europarl.europa.eu/thinktank/en/document.html?reference=IPOL_STU(2020)652713 

136  

Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of global health, 8(2). 

137   Wynsberghe, A. V. (2020). Artificial intelligence: From ethics to policy. Panel for the Future of Science and Technology. 

138  

Art. 22.1 of Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC 

(General Data Protection Regulation, GDPR) 

139  

European  Parliament,  Directorate-General  for  Parliamentary  Research  Services,  Miguel  Beriain,  I.,  Jiménez,  P.,  Rementería,  M.  (2022).  Auditing  the  quality  of  datasets  used  in  algorithmic 

decision-making systems – , Publications Office of the European Union 

140  

http://www.dama-nl.org/data_quality/ 

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—  Clarity: the ease with which data consumers can understand the metadata. 

—  Completeness of records (i.e. coverage): the degree to which all required records in the dataset 

are present.  

—  Completeness of data values: the degree to which all required data values are present. 

—  Consistency: the degree to which data values of two sets of attributes (within a record, within a 

data file, between data files, within a record at different points in time) comply with a rule.  

—  Currency: the degree to which data values are up to date. 

—  Punctuality:  the  degree  to  which  the  period  between  the  actual  and  target  point  of  time  of 

availability of a dataset is appropriate. 

—  Timeliness: the degree to which the period between the time of creation of the real value and the 

time that the dataset is available is appropriate. 

—  Traceability: the degree to which data lineage is available.  

—  Uniqueness: the degree to which records occur only once as a record in a data file.  

—  Validity: the degree to which data values comply with rules.  

Other dimensions141 of data quality cover: 

—  Access security: the degree to which access to datasets is restricted.  

—  Accessibility: the ease with which data can be consulted or retrieved.  

—  Appropriateness: the degree to which the format is suitable for use.   

—  Ability to represent null values: the degree to which a format allows null values in an attribute.  

—  Coherence: the degree to which datasets can be combined. 

—  Comparability  of  populations:  the  degree  to  which  data  values  representing  two  populations 

have the same definition and are measured in the same way. 

—  Comparability over time: the degree to which data values over time have the same definition and 

are measured in the same way. 

—  Compliance with laws, regulations, or standards: the degree to which data and datasets are in 

accordance with laws, regulations, or standards. 

—  Confidentiality:  the  degree  to  which  disclosure  of  data  should  be  restricted  to  authorized  data 

consumers. 

—  Credibility:  the  degree  to  which  data  values  are  regarded  as  true  and  believable  by  data 

consumers. 

—  Equivalence: the degree to which attributes stored in multiple datasets are conceptually equal. 

—  Granularity: the degree to which a single characteristic is subdivided in attributes. 

—  Integrity: the degree of absence of data value loss or corruption. 

—  Interpretability: the degree to which data are in an appropriate language and units of measure. 

—  Latency: the period of time between the point when the data is created and the  point when it is 

available for use. 

—  Linkability: the degree to which records of one data file can be correctly coupled with records of 

another data file. 

—  Metadata  compliance:  the  degree  to  which  the  data  values  are  in  accordance  with  their 

definition, format specification and value domain. 

141 

Adapted  from:  Black  A.,  Nederpelt  P.  van.  (2020)  Dimensions  of Data Quality  Dimensions.  Research  paper.  DAMA-NL;  and  Black  A.,  Nederpelt  P.  van.  (2020) Dictionary of  dimensions  of  data 

quality (3DQ) Dictionary of 60 Standardized Definitions. DAMA-NL 

84 

 
 
 
 
 
                                                        
 
—  Naturalness:  the  degree  to  which  the  composition  of  datasets  is  aligned  with  the  real-world 

objects that they represent. 

—  Objectivity: the degree to which the data values are created in an unbiased manner. 

—  Obtainability: the degree to which the data can be acquired. 

—  Plausibility: the degree to which data values match knowledge of the real world. 

—  Portability  (Data):  the  degree  to  which  data  can  be  installed,  replaced,  or  moved  from  one 

system to another while preserving the existing quality. 

—  Portability (Format): the degree to which a format can be applied in a wide range of situations. 

—  Precision:  the  degree  of  accuracy  with  which  data  values  are  recorded  or  classified,  OR  the 

degree to which the error in data values spreads around zero (in statistics). 

—  Reasonability: the degree to which a data pattern meets expectations. 

—  Recoverability: the degree to which datasets are preserved in the event of incident.    

—  Redundancy: the degree to which logically identical data are stored more than once. 

—  Referential integrity: the degree to which data values of the primary key of one data file and data 

values of the foreign key of another data file are equal. 

—  Relevance:  the  degree  to  which  the  composition  of  datasets  meets  the  needs  of  the  data 

consumer. 

—  Reliability: the closeness of the initial data value to the subsequent data value. 

—  Reproducibility: the degree to which a dataset can be recreated with the same data values. 

—  Reputation: the degree to which data are trusted or highly regarded in terms of their source or 

content. 

—  Retention period: the period that datasets are available until they can or must be deleted. 

—  Value: the degree to which data provide advantages from their use. 

—  Variety: the degree to which data are available from different data sources. 

—  Volatility: the degree to which data values change over time.  

An  important  absent  from  the  above  list  is  the  concept  of  “Interoperability”.  Interoperability 
describes  the  ability  of  two  components  or  systems  to  exchange  data  and  utilise  efficiently  those 
data.  The  concept  of  interoperability  has  been  also  addressed  in  the  context  of  the  Sustainable 
Development Goals142. 

According  to  the  Cambridge  dictionary143,  interoperability  is  “the degree to which two products, 
programs, etc. can be used together, or the quality of being able to be used together.” 

According  to  Merriam-Webster144,  interoperability  is  “the  ability of a system (such as a weapons 
system) to work with or use the parts or equipment of another system”. 

142  

https://unstats.un.org/capacity-building/meetings/UNSD-DFID-SDG-Open-Data-Bangladesh/documents/Day-2-Interoperability.pdf 

143  

https://dictionary.cambridge.org/it/dizionario/inglese/interoperability 

144  

https://www.merriam-webster.com/dictionary/interoperability 

85 

 
 
 
 
                                                        
 
Annex 2: The Transaction ID Card data items 

Product 

The  classification/nomenclature  of  foods  is  highly  variable  among  the  various  EU  databases  and 
systems. Consequently, this section will focus only on the many food nomenclatures utilised in the 
European Institutions, but  given  the high  number of  entities (sometimes thousands), no Annex  will 
be provided with the details of each single classification. 

—  The RASFF Portal145 classifies entries in 37 product categories covering food, feed, live animals, 
water  and  food  contact  materials.  The  categories  are  quite  generic  and  broad,  and  are  not 
organised hierarchically in sub-categories; therefore, the RASFF food classification may not be 
the  best  choice  for  a  digital  IT  tool  that  has  to  identify  specific  vulnerabilities  in  specific  food 
chains.  For  example,  the  category  “Fats  and  oils”  covers  olive  oils  and  other  vegetable  oils  as 
well,  the  seconds  often  used  to  adulterated  the  firsts.  Aggregating  statistics  for  both  products 
(the  adulterant  and  the  adulterated)  is  not  effective  to  identify  vulnerabilities  specific  for  e.g. 
olive oil. 

The RASFF nomenclature is also used in the JRC Food Fraud Monthly Reports146. 

—  The EFSA  FoodEx2  system147 is the  food nomenclature  that complements the  Standard Sample 
Description data model. It was originally designed for risk assessment purposes (i.e. evaluating 
the exposure of consumers to specific foods); it contains around 4 400 terms related to food and 
around 700 terms related to feed, distributed across seven hierarchies148. 

—  Regulation 1333/2008149, covering food additives, established a food classification system150 of 168 

terminologies distributed across four hierarchies.  

—  The Opson IX Report151 nomenclature includes 15 broad categories not organised hierarchically. 

—  The  World  Customs  Organization  (WCO)152  Harmonised  System  (HS)153  is  a  multipurpose 
international  product  nomenclature,  updated  every  5  years,  based  on  six  digits  (Chapters, 
Headings,  Subheadings)  comprising  more  than  5000  commodity  groups154.  It  is  a  classification 
utilised by all WCO countries (more than 200 countries) covering over 98 % of the merchandise 
in international trade.  

—  The  DG  TAXUD  Combined  Nomenclature  (CN)155  further  expands  the  WCO  HS  and  contains 
thousands of codes used for two purposes: Common Customs Tariff and the EU’s external trade 
statistics.  CN  codes  are  also  used  for  internal  trade  statistics.  Most  trading  nations  apply  a 
similar  nomenclature.  CN  codes  are  updated  every  year  by  amending  Annex  I  to  the  basic 
Council  Regulation  (EEC)  No  2658/87156  and  they  arrive  up  to  8  digits  distributed  across  four 
hierarchies.  Part  of  Surveillance  data  use  the  CN  codes,  because  exports  usually  use  the  CN 
codes. Part of the IMSOC systems use the CN codes as well.  

—  The  DG  TAXUD  TARIC  nomenclature157,158  is  a  subdivision  of  the  previous  CN  nomenclature, 
improving the granularity up to 10 digits. Out of 99 Chapters, TARIC shows 24 Chapters covering 
the food sector (Chapters 1-24) hosting in total 5 253 codes distributed across five hierarchies. 
TARIC  codes  are  used  for  all  imports  into  the  EU,  and  more  rarely  for  some  exports;  as  a 

145  

https://webgate.ec.europa.eu/rasff-window/screen/search 

146  

https://knowledge4policy.ec.europa.eu/food-fraud-quality/monthly-food-fraud-summary-reports_en 

147  

https://www.efsa.europa.eu/en/data/data-standardisation 

148  

European Food Safety Authority (EFSA). (2015). The food classification and description system FoodEx 2 (revision 2) (Vol. 12, No. 5, p. 804E) 

149  

Regulation (EC) No 1333/2008 of the European Parliament and of the Council of 16 December 2008 on food additives 

150  

https://webgate.ec.europa.eu/foods_system/main/?event=categories.search 

151  

EUROPOL (2021) Opson IX Report. https://www.europol.europa.eu/publications-documents/operation-opson-ix-%E2%80%93-analysis-report 

152  

http://www.wcoomd.org/ 

153  

http://www.wcoomd.org/en/topics/nomenclature/overview/what-is-the-harmonized-system.aspx 

154  

http://www.wcoomd.org/en/topics/nomenclature/instrument-and-tools/hs-nomenclature-2022-edition/hs-nomenclature-2022-edition.aspx 

155  

https://taxation-customs.ec.europa.eu/business/calculation-customs-duties/customs-tariff/combined-nomenclature_en 

156  

Council Regulation (EEC) No 2658/87 of 23 July 1987 on the tariff and statistical nomenclature and on the Common Customs Tariff 

157  

https://taxation-customs.ec.europa.eu/business/calculation-customs-duties/customs-tariff/eu-customs-tariff-taric_en 

158  

https://ec.europa.eu/taxation_customs/dds2/taric/taric_consultation.jsp?Lang=en&Expand=true&SimDate=20220715 

86 

 
                                                        
 
consequence,  part  of  Surveillance  data  use  the  CN  codes,  and  THESEUS  uses  fully  the  TARIC 
codes  for  detailed  analyses.  Part  of  the  IMSOC  systems  uses  the  TARIC  codes  as  well.  TARIC 
codes are updated every day. 

 Figure 26: Difference between HS, CN and TARIC nomenclature. The same commodities (“Peas” and “Beans”) 
are classified up to 6 digits in the Harmonised System (upper part), up to 8 digits in the Combined Nomenclature 
(middle part), but the level of granularity in the classification of the same products increases up to 10 digits in 
the TARIC nomenclature (lower part). 

Source: JRC; TARIC database. 

The  TARIC  nomenclature  (10  digits)  seems  the  best  option  to  classify  food  commodities  in  detail, 
being  more  accurate  and  updated  daily.  In  case  such  level  of  granularity  is  not  available,  the  CN 
codes (8 digits) may be the second best choice. Notably, some of the richest EU databases already 
utilise TARIC or CN codes for their classification, simplifying any envisioned future harmonisation. 

Physical qualities and processing level  

Product  fraud  is  generally  easier  to  achieve  with  liquid,  ground,  prepared  and  powdered  products 
rather  than  whole  foods159,160,  and  complex  foods  with  multiple  ingredients  generally  offer  greater 
fraud opportunity than simple, single-ingredient products. For example, processing techniques in the 
spice  chain  (grinding,  chopping,  milling)  represent  a  great  risk  of  adulteration  (being  hidden  in 
ground  or  crushed  material)161;  similar  observations  can  be  applied  to  the  fish  supply  chain,  as 
recognizable  external  morphological  features  are  typically  removed  when  the  fish  is  filleted  or 
otherwise processed162 (i.e. it is easier to declare the wrong species of a fish fillet compared to the 
whole  fish).  Frozen  food  is  also  more  vulnerable  to  fraud,  as  inspectors  may  have  difficulties  to 
check  the  central  part  of  a  lot  completely  frozen,  potentially  hiding  adulterated  products.  For  this 
reason,  a  specific  Working  Group  should  establish  a  specific  quantitative  ranking  to  the  different 

159  

Jack, L. (2015). Risk modelling of food fraud motivation:'NSF fraud protection model'intelligent risk model scoping project FS 246004. 

160  

Power, A. C., Jones, J., NiNeil, C., Geoghegan, S., Warren, S., Currivan, S., & Cozzolino, D. (2021). Whatʼs in this drink? Classification and adulterant detection in Irish Whiskey samples using near 

infrared spectroscopy combined with chemometrics. Journal of the Science of Food and Agriculture, 101(12), 5256-5263. 

161  

Silvis, I. C. J., Van Ruth, S. M., Van Der Fels-klerx, H. J., & Luning, P. A. (2017). Assessment of food fraud vulnerability in the spices chain: An explorative study. Food Control, 81, 80-87. 

162   Wong, E. H. K., & Hanner, R. H. (2008). DNA barcoding detects market substitution in North American seafood. Food Research International, 41(8), 828-837 

87 

 
 
                                                        
 
qualities of products in order to provide numerical inputs to the ideal digital IT tool; for example, a 
fish fillet would be valued quantitatively more risky/vulnerable than a whole fish. 

Refrigeration 

The need of being refrigerated during the transportation or storage phases presents critical safety 
risks  in  case  the  cold  chain  is  interrupted.  Although  such  data  item  does  not  relate  much  to  food 
fraud, in case the ideal digital IT tool will also cover the food safety aspects it would be relevant to 
notice if specific products require refrigeration or not. 

Supply situation 

The  local,  regional  and  global  supply  situation  for  a  specific  product  influences  the  probability  of 
being  targeted  by  fraudulent  practices:  if  the  demand  is  higher  than  the  offer,  there  is  a  vacuum 
economically  advantageous  to  be  filled.  The  Covid-19  crises  or  the  Ukrainian  war  have  shown  that 
more globalised and longer supply chains are more vulnerable to external shocks and unexpected 
shortages.  Several  databases,  intelligence  sources  and  IT  system  already  over  the  global  market, 
although it remains difficult to understand how to quantify this data item. 

Transaction price 

In this section, transaction price means the price as indicated in that specific lot, e.g. in a cargo. 

Food fraudsters are agents who sell an adulterated product to the victim trying to convince them of 
its  authenticity163.  This  kind  of  good  is  called deceptive product164  and,  in  this  category,  consumers 
might look for lower prices, but they would not buy an outright fake good163. 

Food fraudsters  have the  maximization  of profits  and minimization of  costs  as objective 165,166 hence 
criminal enterprises will participate in the market also adopting strategies similar to the legitimate 
economic agents and one of those is price competition163. 

Prices have been under the scope of OLAF using the JRC-developed algorithm THESEUS to prevent 
import  duties  evasion,  value-added  tax  (VAT)  fraud  and  trade-based  money  laundering.  The 
algorithm identifies price outliers in trade data by defining the product “fair price” and comparing it 
to  the  analysed  cargo.  This  detection  process  have  been  proved  to  be  reliable  in  a-posteriori 
controls167.  The  integration  of  this  methodology  may  benefit  the  development  of  the  ideal  digital  IT 
tool as the prices “too good to be true” can be detected and stopped. 

Quantity 

The  quantity  of  products  (usually  kilograms,  litres  or  units)  within  a  specific  consignment  is 
the  probability  of  being  adulterated.  Fraudsters  can  more  easily  hide 
correlated  with 
fraudulent/unsafe products in larger orders, e.g. at the end of a container, where official controls are 
more difficult to be implemented. As a general rule, the smaller the amount purchased, the  higher 
the probability to detect non-compliances, because with high quantities it is more difficult to perform 
sampling efficiently. Therefore, high-quantity lots are more vulnerable to food fraud. 

Value adding features 

Products  may  have  certain  attributes  implying  a  relevant  price  premium.  In  the  case  of  food 
production, there are three categories of goods:  

163  

Moyer, D. C., DeVries, J. W., & Spink, J. (2017). The economics of a food fraud incident–Case studies and examples including Melamine in Wheat Gluten. Food Control, 71, 358-364. 

164  

OECD (2008), "The markets for counterfeit and pirated goods", in The Economic Impact of Counterfeiting and Piracy, OECD Publishing, Paris 

165  

Everstine,  K.,  Abt,  E.,  McColl,  D.,  Popping,  B.,  Morrison-Rowe,  S.,  Lane,  R.  W.,  ...  &  Chin,  H.  B.  (2018).  Development  of  a  hazard  classification  scheme  for  substances  used  in  the  fraudulent 

adulteration of foods. Journal of food protection, 81(1), 31-36. 

166  

SSAFE. (2017). Food Fraud vulnerability assessment tool. https://www.ssafe-food.org/tools. (Accessed 19 December 2020). 

167  

Arsenis, S., Perrotta, D., & Torti, F. (2015). The estimation of fair prices of traded goods from outlier-free trade data. Tech. Rep. EUR 27696 EN, JRC-100018, European Commission, Joint Research 

Centre, Publications Office of the European Union, Luxembourg. 

88 

 
                                                        
 
—  Search goods: the consumer can verify the quality of the item before purchasing it, hence there 

is no information asymmetry; 

—  Experience goods: the consumer can determine the quality only after the transaction; 

—  Credence goods: the buyer cannot fully ascertain the quality of the good even after buying it168.  

This  asymmetric  information  and  the  average  higher  prices  combine  in  a  very  high  fraud 
vulnerability124. As a matter of fact, organic food has a price premium of around 20-24%169 whereas 
Geographical  Indications’  prices  are  123%  higher  compared  to  their  conventional  counterparts170. 
Since the consumers are willing to pay more, then credence goods are more exposed to fraud124,171.  

The analysed scientific literature identifies the following features as the most relevant: 

—  Organic goods: their demand  has been  steadily growing172 reaching in the EU  the total value of 
44.8  billion  of  euros  in  2020173.  Organic  farming  has  a  higher  benefit/cost  ratios  of  20-24% 
compared to conventional farming174 and is promoted through institutional trust (documentation, 
certificates,  logos,  etc…).  The  regulation  governing  the  sector  is  stratified.  The  Codex 
Alimentarius  Guidelines175  and  the  International  Federation  of  Organic  Agriculture  Movements 
Basic Standards supply the minimum international standard on organic production worldwide176. 
At the same time, each area and country has its own regulation177 and private organizations can 
determine  additional  requirement  exceeding  the  EU  legislation178.  It  is  clear  how  the  label 
“organic”  might  entail  different  production  standards  and  the  identification  of  all  the  different 
characteristics  is  challenging,  even  with  analytical  tests177.  Furthermore,  purchases  are  made 
based  upon  institutional  trust,  i.e.  logos,  certification  and  standards,  throughout  the  supply 
chain171,179.  This  type  of  transaction  is  deeply  exposed  to  fraud  as  criminals  might  place  organic 
symbols  on  conventional  product  with  no  possibility  of  identification,  as  within  organic 
characteristics there is e.g. access to grazing or the space available to an animal171. Such frauds 
damage consumers, organic producers who  are  unable  to  compete at lower  prices, and  finally 
the trust in the organic symbols177, endangering a farm management and food production system 
created to incentivize environmental sustainability and animal welfare180. 

—  Halal goods: the halal market is increasing worldwide. At European level the Muslim community 
is growing too181 due to immigration and higher birth rate182, hence in the future the relevance of 
this market will grow183. Given the product characteristics, halal goods are credence goods since 
the  Islamic  dietary  laws  refer  to  the  nature,  origin  and  processing  methods  of  foodstuff184.  No 
analytical  method  can  measure  animal  welfare,  the  ritual  slaughter  method,  treatment  and 
separation  of  halal  animals,  etc.  in  all  the  stages  of  the  supply  chain185,  hence  the  quality 

168  

OECD. (2010). Appellations of Origin and Geographical Indications in OECD Member Countries: Economic and Legal Implications.  

169  

Crowder, D. W., & Reganold, J. P. (2015). Financial competitiveness of organic agriculture on a global scale. Proceedings of the National Academy of Sciences, 112(24), 7611-7616. 

170  

EUIPO.  (2016).  Infringement  of  protected  geographical  indications  of  wine,  spirits,  agricultural  products  and  foodstuffs  in  the  European  Union.  https://euipo.europa.eu/tunnel-

web/secure/webdav/guest/document_library/observatory/documents/Geographical_indications_report/geographical_indications_report_en.pdf 

171  

Darby, M. R., & Karni, E. (1973). Free competition and the optimal amount of fraud. The Journal of law and economics, 16(1), 67-88. 

172  

Capuano,  E.,  Boerrigter‐Eenling,  R.,  van  der  Veer,  G.,  &  van  Ruth,  S.  M.  (2013).  Analytical  authentication  of  organic  products:  an  overview  of  markers.  Journal  of  the  Science  of  Food  and 

Agriculture, 93(1), 12-28. 

173  

Statista. (2022). Organic retail sales value EU 2004-2020. Retrieved 7 June 2022, from https://www.statista.com/statistics/541536/organic-retail-sales-value-european-union-europe-statistic/ 

174  

Crowder, D. W., & Reganold, J. P. (2015). Financial competitiveness of organic agriculture on a global scale. Proceedings of the National Academy of Sciences, 112(24), 7611-7616. 

175  

Dumea, A. C. (2013). Factors influencing consumption of organic food in Romania. The USV Annals of Economics and Public Administration, 12(1 (15)), 107-113. 

176  

Essoussi,  L.  H.,  &  Zahaf,  M.  (2009).  Exploring  the  decision‐making  process  of  Canadian  organic  food  consumers:  Motivations  and  trust  issues.  Qualitative  Market  Research:  An  International 

Journal. 

177  

van Ruth, S. M., & de Pagter-de Witte, L. (2020). Integrity of organic foods and their suppliers: Fraud vulnerability across chains. Foods, 9(2), 188. 

178  

Janssen, M., & Hamm, U. (2014). Governmental and private certification labels for organic food: Consumer attitudes and preferences in Germany. Food Policy, 49, 437-448. 

179  

Lee, H.  J.,  & Hwang,  J.  (2016).  The  driving role  of  consumers’  perceived  credence  attributes  in  organic  food  purchase decisions:  A  comparison of  two groups of  consumers.  Food quality  and 

preference, 54, 141-151.  

180  

Council  Regulation 

(EC)  No 834/2007  of  28 June  2007  on  organic  production  and 

labelling  of  organic  products  and 

repealing  Regulation 

(EEC)  No 2092/91, 

http://data.europa.eu/eli/reg/2007/834/2022-01-01 

181  

Lever, J., & Miele, M. (2012). The growth of halal meat markets in Europe: An exploration of the supply side theory of religion. Journal of Rural Studies, 28(4), 528-537. 

182  

The Economist. (2010). Cut-throat competition. https://www.economist.com/business/2009/09/17/cut-throat-competition. 

183  

Lever, J., & Miele, M. (2012). The growth of halal meat markets in Europe: An exploration of the supply side theory of religion. Journal of Rural Studies, 28(4), 528-537. 

184  

Verbeke, W., Rutsaert, P., Bonne, K., & Vermeir, I. (2013). Credence quality coordination and consumers' willingness-to-pay for certified halal labelled meat. Meat science, 95(4), 790-797. 

185  

Van der Spiegel, M., Van der Fels-Klerx, H. J., Sterrenburg, P., Van Ruth, S. M., Scholtens-Toma, I. M. J., & Kok, E. J. (2012). Halal assurance in food supply chains: Verification of halal certificates 

using audits and laboratory analysis. Trends in Food Science & Technology, 27(2), 109-119. 

89 

 
                                                        
 
assessment  is  impossible  even  after  the  purchase184.  Often,  the  main  violations  of  halal 
prescriptions  is  the  substitution  of  oil  with  lard  perpetrated  by  food  manufacturers  (since  the 
latter  is  cheaper  than  the  former),  or  the  addition  of  adulterants  to  more  valuable  goods  to 
increase  the  quantity,  reduce  the  cost  or  other  fraudulent  purposes186.  Halal  counterfeiting  is 
also one of the most frequent violation in the meat sector151.  

—  Kosher goods: the case is very similar to the halal market. It is impossible to determine whether 
the Jewish dietary laws were respected as also for kosher foods the rules cover the production, 
preparation and consumption. For this reason, together with the consumers’ willingness to pay a 
premium  to  ensure  the  respect  of  the  religious  prescription187,  kosher  foodstuff  is  deeply 
exposed to fraud124. 

—  Protected  Designation  of  Origin,  Protected  Geographical  Indication  and  Traditional  Speciality 
Guaranteed products: these quality labels were created by the EU to promote specific products 
associated  with  a  certain  area  of  production  with  associated  characteristics170,188,189.  Each  label 
has  specific  requirements  regarding  the  connection  with  the  connected  geographical  area,  e.g. 
Protected Designation of Origin requires products to be produced, processed and prepared in a 
circumscribed area whereas Protected Geographical Indication requires that at least one of the 
steps  takes  place  in  that  area190.  Food  authenticity  can  be  proven  using  analytical  techniques 
performed  in  the  laboratories  of  some  regulatory  authorities191  but,  given  the  price  premium 
compared to their counterparts124,170,192,193, they are particularly exposed to fraud. 

Identification number 

The  data  item  “Identification  number”  refers  to  all  numerical  tags  specific  for  each  individual 
consignment. Examples may include the lot number, the delivery tracking number or livestock tags. 

Means of transportation 

In  a globalised  world,  the  modes  of transportation used in logistics  represent  a vulnerable  step in 
the supply chain.  

No  scientific  review  on  which  transportation  modality  is  the  most  used  for  food  fraud  has  been 
identified.  Hence,  the  study  may  profit  from  the  information  available  for  other  better  explored 
markets, i.e. drugs and counterfeit goods. 

The  transport  sector  is  recognised  to  be  one  of  the  economic  sectors  most  vulnerable  to  criminal 
infiltration194,195,196,197,198.  Analyses  could  be  found  for  only  few  methods  of  transport,  but  there  are 
some  indications  that  all  are  as  exposed  to  crime  infiltration197.  This  is  because  transportation  is 
characterised  by  a  dynamic  context195  and  is  functional  to  illicit  activities199.  Transport  firms  can 

186  

Fadzlillah, N. A., Man, Y. B. C., Jamaludin, M. A., Rahman, S. A., & Al-Kahtani, H. A. (2011). Halal food issues from Islamic and modern science perspectives. In 2nd international conference on 

humanities, historical and social sciences (Vol. 17, pp. 159-163). Singapore: IACSIT Press. 

187  

Havinga, T. (2010). Regulating halal and kosher foods: different arrangements between state, industry and religious actors. Erasmus L. Rev., 3, 241. 

188  

European Commission. (2013). Quality schemes explained. https://ec.europa.eu/info/food-farming-fisheries/food-safety-and-quality/certification/quality-labels/quality-schemes-explained_en 

189  

Grunert, K. G., & Aachmann, K. (2016). Consumer reactions to the use of EU quality labels on food products: A review of the literature. Food Control, 59, 178-187. 

190  

Dias, C., & Mendes, L. (2018). Protected designation of origin (PDO), protected geographical indication (PGI) and traditional speciality guaranteed (TSG): A bibiliometric analysis. Food Research 

International, 103, 492-508. 

191  

Camin, F., Boner, M., Bontempo, L., Fauhl-Hassek, C., Kelly, S. D., Riedl, J., & Rossmann, A. (2017). Stable isotope techniques for verifying the declared geographical origin of food in legal cases. 

Trends in Food Science & Technology, 61, 176-187. 

192  

Danezis, G. P., Tsagkaris, A. S., Brusic, V., & Georgiou, C. A. (2016). Food authentication: state of the art and prospects. Current Opinion in Food Science, 10, 22-31. 

193  

Hajdukiewicz,  A.  (2014).  European  Union  agri-food  quality  schemes  for  the  protection  and  promotion  of  geographical  indications  and  traditional  specialities:  an  economic  perspective.  Folia 

Horticulturae, 26(1), 3. 

194  

Fijnaut, C. (Ed.). (1998). Organized crime in the Netherlands. Martinus Nijhoff publishers. 

195  

Savona, E., & Berlusconi, G. (2015). Organized crime infiltration of legitimate businesses in Europe: A pilot project in five European countries. Final report of project ARIEL–assessing the risk of 

the infiltration of organized crime in EU MSS legitimate economies: a pilot project in 5 EU countries. 

196  

Dandurand, Y. (2021). Tow Truck Wars (p. 26). Global Initiative Against Transnational Organized Crime’s. https://globalinitiative.net/wp-content/uploads/2021/06/Tow-Truck-Wars-How-organized-

crime-infiltrates-the-transport-industry-GITOC.pdf 

197  

van Duyne, P., van Dijk, M., von Lampe, K., & Newell, J. L. (2005). Organised crime economy: managing crime markets in Europe. Wolf Legal Publishers. 

198  

Savona, E. U., & Riccardi, M. (2018). Mapping the risk of Serious and Organised Crime infiltration in European Businesses: Final report of the MORE project. Transcrime-Università Cattolica del 

Sacro Cuore, Milan. 

199  

Savona, E. U., & Riccardi, M. (Eds.). (2015). From illegal markets to legitimate businesses: The portfolio of organised crime in Europe. Trento: Transcrime – Università degli Studi di Trento. 

90 

 
                                                        
 
function as facilitators200, moving illegal and counterfeit goods throughout the globe using the same 
legitimate distribution means used for legal goods195,201,202.  

Given  the  low  availability  of  extensive  studies  on  the  topic,  a  2020  EU  report  on  IPR  is  used  as  a 
proxy.  Foodstuff  covered  by  IPR  was  one  of  the  most  seized  counterfeit  good  (Figure  27).  Overall, 
almost  82%  of  the  all  seized  goods  were  imported.  This  percentage  is  similar  to  those  of  the 
previous years203. Almost 50% of all the seized counterfeit goods were shipped via sea, whereas the 
road transport (courier or postal traffic) contributed for 31% and air traffic for 7%. Taking into account 
the  years  2017-2020,  the  preferred  transportation  methods  were  sea  and  road  (Figure  28).  Similar 
results are visible in Figure 29, showing the share of global food miles by transport method. 

Figure 27: Top categories by number of seized articles infringing IPR in 2020. The y-axis represents the 
percentage of seized goods; the x-axis represents the categories of seized goods. 

Source: European Union Intellectual Property Office. (2021). EU enforcement of intellectual property rights: Results at the EU 
border and in the EU internal market 2020. Publications Office. 

200  

Klima, N. (2011). The goods transport network’s vulnerability to crime: opportunities and control weaknesses. European Journal on Criminal Policy and Research, 17(3), 203-219. 

201  

Kennedy, J. P., & Wilson, J. M. (2017). Liabilities and responsibilities: ocean transportation intermediaries (OTIs) and the  distribution of counterfeit goods. Maritime Economics & Logistics, 19(1), 

182-187. 

202  

Kleemans, E. R. (2007). Organized crime, transit crime, and racketeering. Crime and Justice, 35(1), 163-215. 

203  

European Union Intellectual Property Office. (2021). EU enforcement of intellectual property rights: Results at the EU border and in the EU internal market 2020. Publications Office.  

91 

 
  
  
                                                        
 
Figure 28: Detained articles infringing IPR by means of transport (2017-2020). The y-axis represents the amount 
of seized goods in millions; the x-axis represents the means of transport. 

Source: European Union Intellectual Property Office. (2021). EU enforcement of intellectual property rights: Results at the EU 
border and in the EU internal market 2020. Publications Office. 

Figure 29: Share of global food miles by transport method. The y-axis represents the means of transport; the x-
axis represents the percentage of global food miles. 

Source: Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 
360(6392), 987-992. 

Road transport 

The  increasing  competitive  pressure197,  companies’  poor  financial  condition,  and  contacts  with 
professional  criminals  can  push  the  transport  firm  to  participate  in  illegal  activities  in  order  to 
increase  revenues200.  Often,  organised  crime  members  start  the  connection  exploiting  firms’ 
economic condition, in order to create a situation of mutual dependence204. 

204  

Kleemans, E. R., & de Poot, C. J. (2008). Criminal careers in organized crime and social opportunity structure. European Journal of Criminology, 5(1), 69–98. 

92 

 
 
 
                                                        
 
The sector’s start-up costs are significant and firms can rapidly fall under financial pressure. Small-
medium  enterprises,  in  particular,  can  enter  a  phase  of  distress  if  there  are  no  orders,  whereas 
bigger companies have an easier access to credit and can compensate more easily to such market 
fluctuations200.  

Truck driving is characterised by a high degree of autonomy and mobility, which are two important 
features needed to carry out criminal activities. In fact, they are independent and  rarely supervised, 
hence  creating  conditions  for  carrying  out  illegal  traffics.  Often,  drivers  come  in  contact  with 
criminal groups through social ties during their road life, e.g. at rest areas205. 

Air transport 

This  way  of  transport  is  mainly  explored  in  the  literature  referring  to  drug  trafficking,  whereas  a 
wide  literature  for  goods  smuggling  could  not  be  found.  The  criminal  groups’  modus operandi  is 
different  in  airports  compared  to  e.g.  ports.  In  the  latter,  the  main  function  is  to  transport  not 
persons but goods, whereas in the former there is an enormous flow of goods and persons together. 
This translates in different modes of drug smuggling: in airports organised crime groups mainly use 
drug mules or hide it thanks to internal staff support205, whereas in ports they hide commodities in 
deck  cargos  or  containers196.  In  fact,  in  the  two  settings  criminal  groups  rely  on  the  major  flow 
occurring in the logistical centre in order to conceal their loads205.  

Maritime transport 

Ports are crucial  centres  of the global supply chain. In 2018  almost 60% of food travelled by sea 206 
(Figure 47). In Italy, drug seizure (i.e. the most valuable smuggled good) took place for the 78.7% at 
the border, of which 98.1% at the maritime one.  Organised crime groups do not choose ports based 
on  territorial  control,  but  on  the  support  that  they  can  gather207.  Hence,  port  surveillance  is 
fundamental for preserving the integrity of global trade flow from criminal activities208.  

In  seaports,  informal209  and  criminal  activities  are  entwined  with  the  operation  process.  Indeed, 
illegal economy runs on the same track as that of global economic exchanges 210 and the closeness 
between  legal  and  illegal  flows  creates  professional  contacts  and  work  environment  that  can  be 
used as breeding ground for criminal activities211,212 both in the legal and illegal economy213. Organised 
crime  groups  need  a  door  (e.g.  a  corrupted  person)  to  secure  their  shipments,  and  seaports 
represent the physical spaces that provide the needed market access for their goods. Being a border 
area,  the  port  fosters  a  certain  set  of  conventional  rules  and  standards,  creating  “static 
relationships”  that  preserve  internal  dynamics,  including  criminal  governance.  At  the  same  time, 
seaports  are  space  for  global  trade.  Thus,  they  tend  to  be  outward  looking,  by  promoting  trade 
speed, fluidity and smoothness in business relationships214, including criminal ones215. 

Ports  are  complex  systems  characterised  by  misaligned  interests  of  governmental  agencies  (e.g. 
Port Authority, Customs Agency), private companies and regulatory entities (e.g. Ministry of Interior, 
Ministry  of  Infrastructure,  local  authorities)214,  where  informality  of  agreements  among  actors  and 
exchange of information with unconventional but established manners prevail215. Indeed, port labour 
is  characterised  by  militancy,  casual  and  close-knit  communities  where  the  workforce  has  its 

205   Madarie, R., & Kruisbergen, E. W. (2020). Traffickers in Transit: Analysing the Logistics and Involvement Mechanisms of Organised Crime at Logistical Nodes in the Netherlands: Empirical Results 

of the Dutch Organised Crime Monitor. In Understanding Recruitment to Organized Crime and Terrorism (pp. 277-308). Springer, Cham. 

206  

Poore, J., & Nemecek, T. (2018). Reducing food’s environmental impacts through producers and consumers. Science, 360(6392), 987-992. 

207  

Direzione Centrale per i Servizi Antidroga. Relazione Annuale. 2021, p. 376. 

208  

Eski, Y. (2016). Policing, Port Security and Crime Control: An Ethnography of the Port Securityscape (1st ed.). Routledge 

209  

The  informal sector  is  broadly  characterised  as  consisting of  units  engaged in  the  production  of goods  or  services  with the  primary  objective  of generating employment  and incomes  to the 

persons  concerned.  These  units  typically  operate  at  a  low  level  of  organisation,  with  little  or  no  division  between  labour  and  capital  as  factors  of  production  and  on  a  small  scale.  Labour 

relations - where they exist - are based mostly on casual employment, kinship or personal and social relations rather than contractual arrangements with formal guarantees. 

210  

Sciarrone, R., & Storti, L. (2019). Le mafie nell'economia legale. Scambi, collusioni, azioni di contrasto (pp. 1-198). il Mulino 

211  

Kleemans, E. R., & Van de Bunt, H. G. (2008). Organised crime, occupations and opportunity. Global Crime, 9(3), 185-197. 

212  

Mars, G. (2016). Locating deviance: crime, change and organizations. Routledge. 

213  

UNODC (2013) Combating transnational organized crime committed at sea. Issue Paper. United Nations, New York 

214   Wilmsmeier, G., & Monios, J. (2015). The production of capitalist “smooth” space in global port operations. Journal of Transport Geography, 47, 59-69. 

215  

Sergi, A., & Storti, L. (2020). Survive or perish: Organised crime in the port of Montreal and the port of New York/New Jersey. International Journal of Law, Crime and Justice, 63, 100424. 

93 

 
                                                        
 
written  and  unwritten  rules216  and  this  defines  them  as  logistical  infrastructure  and  strategic 
locations ideal for criminal groups to operate in217. 

Railway transport 

Figure 28 shows the relevance of railways in foodstuff shipment; their usage for drug trafficking is 
well  established218,219.  On  the  other  hand,  there  is  a  limited  use  of  them  for  counterfeit  goods 
transport.  No  relevant  scientific  analysis  of  the  characteristics  of  this  mean  of  transport  in  the 
context of food fraud has been identified, nor of its use for illicit traffics. 

In  conclusion,  a  comparative  analysis  of  the  criminal  infiltration  and  usage  between  all  means  of 
transport could not be found, but this brief overview shows how it is necessary to take into account 
this variable. The routes taken in the internal market will be arduous to control given the absence of 
internal  borders,  thus  reinforcing the necessity  of improving the internal traceability  within the EU 
food supply chain. On the other hand, controlling imports may already ensure a good coverage, given 
the hints derivable from the EUIPO report. The latter covers solely products subject to IPR, but their 
data could be used as proxy for the entire phenomenon. Furthermore, control systems are already in 
place to achieve this task as the AFIS-CSM (developed by OLAF) and Import Control System 2 (ICS2) 
(developed  by  DG  TAXUD).  Hence,  incorporating  these  methods  will  help  achieving  already  a  high 
level of coverage. 

Seller/Buyer 

The  Transaction  ID  Card  covers  any  transaction  business-to-business  taking  place  between  two 
FBOs  in  the  food  supply  chain.  It  is  not  intended  to  cover  business-to-consumers  transactions,  at 
least  in  principle.  There  exists  several  possibility  to  connect  identifiers  and  code  to  a  single 
company: 

—  The  Economic  Operators  Registration  and  Identification  (EORI)220  number  is  an  EU  registration 
and identification number for the economic operators who import or export in  or out of the EU. 
The EORI number is valid throughout the EU and it is used as a common reference number for 
interactions with the customs authorities in any Member State. The EORI number consists of two 
parts: the Member State country code, and a unique code/number. 

The  EORI  number  has  two  pitfalls:  it  does  not  apply  to  economic  operators  in  third  countries 
(unless  they  intend  to  lodge  a  customs  declaration,  an  Entry  Summary  Declaration  or  an  Exit 
Summary Declaration), and it only applies to the EU economic operators who have to deal with 
Customs.  

—  The Legal Entity Identifier (LEI)221, launched by G20 in 2011, is a unique global identifier for legal 
entities  involved  in  financial  transactions  (e.g.  companies  or  governmental  entities).  The  LEI 
codes  are  based  on  the  ISO  17442  standard  and  include  20  digits  (letters  and  numbers).  Many 
regulatory bodies mandate LEIs in their legislation. 

—  The Global Intermediary Identification Number (GIIN)222 is a 19-character  code  which  states the 
approval  of  foreign  financial  institutions,  financial  institution  branches,  direct  reporting  non-
financial  foreign  entities,  sponsoring  entities,  sponsored  entities,  and  sponsored  subsidiary 
branches. The GIIN code is assigned by the FATCA registration system and must be confirmed on 
regular basis. 

216  

Bottalico, A. (2020). Towards a common trajectory of port labour systems in Europe? The case of the port of Antwerp. Case Studies on Transport Policy, 8(2), 311-321. 

217  

Ruggiero, V. (2013). Organised and transnational crime in Europe. In The Routledge Handbook of European Criminology (pp. 174-187). Routledge. 

218 

European Monitoring Centre for Drugs and Drug Addiction and Europol (2019), EU Drug Markets Report 2019, Publications Office of the European Union, Luxembourg 

219   World Drug Report 2021 (United Nations publication, Sales No. E.21.XI.8). 

220  

https://taxation-customs.ec.europa.eu/customs-4/customs-procedures-import-and-export-0/customs-procedures/economic-operators-registration-and-identification-number-eori_en 

221  

https://www.lei-worldwide.com/ 

222  

https://www.irs.gov/businesses/corporations/fatca-registration-and-ffi-list-giin-composition-information 

94 

 
 
                                                        
 
—  The  VAT  identification  number223  identifies  taxable  persons  (businesses)  or  non-taxable  legal 
entities  in  many  countries  around  the  globe.  Every  EU  country  issues  its  own  national  VAT 
number  format.  VAT  numbers  are  used  to  identify  the  tax  status  of  the  customer,  the  place  of 
taxation and the invoices. 

Given  the  G20  endorsement  and  the  global  scope,  the  LEI  code  seems  the  best  candidate  to  be 
chosen as preferred identifier for the Food ID Card. However, the data item may also list the other 
identification codes provided by the other systems, in order to deliver a more precise assessment of 
a  FBO  and  to  connect  the  various  databases  who  may  feed  the  overarching  EU  database  for 
Transaction ID Cards, ultimately increasing its interoperability features. 

Age 

The  longevity  of  a  FBO  can  be  measured  in  days,  months  and  years.  Given  that  established  FBOs 
have  theoretically  a  higher  credibility  with  their  buyers  and  final  consumers,  it  is  probable  that 
newly established FBOs may more probably deliver fraudulent products e.g. in case a company is set 
up for a specific number of transactions before being closed and leave no traces within the supply 
chain. “Months” is therefore the most preferred option to measure the activity of FBOs: as a general 
principle to be discussed in specific working groups, the longer a FBO is active, the less probable it 
is that it will commit fraudulent activities. 

Economic health 

Through time there has been strong attention to the relationship between the economic health and 
the criminal behaviour of the firms. The topic has been of interest for economists, criminologists and 
organization theorists and all of them contributed from their own point of view. Economists explain 
the behaviour using the Beckerian model of expected benefit from criminal activity versus expected 
costs  of  punishment:  given  any  prior  poor  economic  performance,  crime  increases  the  short-run 
profitability of the firm and increases the short-run expected chance of job loss, thus the expected 
gain from crime224. This is because the theoretical cost of government sanctions may be perceived to 
be  smaller  than  the  cost  deriving  from  the  firm  bankruptcy225;  in  other  words:  better  to  risk  a  fine 
rather  than  close  the  business.  Organization  theorists  argue  that  pressure  from  the  top  of  the 
hierarchy  to  keep  profits  may  lead  employees  to  commit  crimes,  whereas  criminologists  mention 
that  pressure  on  individuals  being  part  of  a  struggling  organization  may  naturally  lead  some 
individuals to criminal behaviour on behalf of the firm to save their job224. Therefore, it is clear how 
all the fields of study have analysed the phenomenon and underline its importance.  

The  relevance  of  this  variable  is  recognized  also  in  the  more  specific  field  of  food  fraud  as  the 
possibility  of  economically  motivated  adulteration  will  undoubtedly  increase  in  a  firm  with  poor 
financial  condition226.  In  the  literature  there  are  multiple  indicators  of  financial  distress:  liquidity 
ratios,  leverage  ratio,  financial  leverage,  and  cash  flow227.  A  panel  of  experts  in  business  analysis 
may precisely identify the variable(s) to consider in the ideal digital IT tool. 

Firm size 

The firm size is one of the most important factors for white-collar crimes228 and in particular fraud 
detection229,230,231.  In  the  food  sector,  firm  dimension  influences  significantly  firm’s  perception  over 
fraud vulnerability, with larger enterprises perceiving themselves as more exposed to fraud302,232 for 

223  

https://taxation-customs.ec.europa.eu/vat-identification-numbers_en 

224  

Alexander, C. R., & Cohen, M. A. (1996). New evidence on the origins of corporate crime. Managerial and Decision Economics, 17(4), 421-435. 

225  

Baysinger, B. D. (1991). Organization theory and the criminal liability of organizations. BUL Rev., 71, 341. 

226  

Luo, J., Wang, X., Li, H., Xiao, S., Gao, Y., & Li, L. (2016). Analysis of motivation and reason for Economically Motivated Adulteration in food company. Science and Technology of Food Industry, 37(5), 

281-282. 

227  

Dirman, A. (2020). Financial distress: the impacts of profitability, liquidity, leverage, firm size, and free cash flow. International Journal of Business, Economics and Law, 22(1), 17-25. 

228  

Sutherland, E. (1949). White Collar Crime New York. Holt, Rinehart. 

229  

van Ruth, S. M., Luning, P. A., Silvis, I. C., Yang, Y., & Huisman, W. (2018). Differences in fraud vulnerability in various food supply chains and their tiers. Food Control, 84, 375-381. 

230  

Persons, O. S. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research (JABR), 11(3), 38-46. 

231  

Junger, M., Wang, V., & Schlömer, M. (2020). Fraud against businesses both online and offline: Crime scripts, business characteristics, efforts, and benefits. Crime science, 9(1), 1-15. 

232  

Holtfreter, K. (2005). Is occupational fraud “typical” white-collar crime? A comparison of individual and organizational characteristics. Journal of Criminal Justice, 33(4), 353-365. 

95 

 
                                                        
 
multiple reasons (e.g. higher number of suppliers, larger orders, higher reputation, and international 
supply  chains302).  On  the  other  hand,  for  these  same  reasons  larger  corporations  have  better 
controls  compared  to  small-medium  enterprises233  and  are  also  less  exposed  to  competition  (a 
phenomenon potentially driving the firm to commit fraud234,235).  

Summarizing, larger firms may be more prone to receive adulterated products from their suppliers, 
but have more controls dedicated to fraud mitigation since criminal events are recognised to be the 
most  harmful  financially236.  The  literature  does  not  identify  the  result  of  these  two  opposite  forces 
determining the most fraudulent firm size. 

Regarding the  measurement  of this variable, there are multiple possibilities. In the literature, total 
assets, sales, profits, market capitalization are the most common and number of employees and net 
assets  when  the  others  are  not  available.  The  choice  of  measurement  always  depends  on  data 
availability237.  
As data source, it may be resourceful to connect the new EU database with that of the Chambers of 
Commerce  of  the  Member  States  to  provide  the  necessary  corporate  data  for  firms  located  in  the 
internal market. For those in third countries, already existing high quality private databases on firm 
characteristics may be used. 

Recidivism 

The majority of discovered food frauds are perpetrated by legitimate actors operating within the food 
supply  chain  and  partially  by  organized  crime  groups238.  The  offences  rate  varies  between  isolated 
episodes  to  structured  criminal  groups  active  in  the  sector239.  This  means  that  the  problem  of 
recidivism must be tackled to prevent professional fraudsters from repeatedly polluting the market. 
In fact, if food fraud is  considered  apart from the food safety standpoint, the main drivers  for  food 
fraud are profit maximization240 or cost minimization241,242. 

The majority of food frauds occurs in the regular supply chain, hence assimilating food frauds to any 
other corporate crime. The latter type of felony is perpetrated routinely in the everyday life243 and an 
analysis has shown that a history of past corporate crimes (the fraud incident history) implies a 19 
times higher likelihood of illegal behaviour in the future244. 

This  shows  the  necessity  of  keeping  track  of  previous  offences  committed  by  a  firm,  monitoring 
other factors (e.g. address and responsible person) to unmask any type of concealment.  

The  measurement  of  recidivism  is  a  complicated  issue.  In  a  scenario  in  which  a  firm  has  multiple 
plants  and  one  of  them  is  reported  to  be  more  fraudulent  than  the  others,  then  measuring  the 
recidivism  propensity  at  firm  level  will  damage  the  other  plants  and  the  aggregated  level  of 
riskiness  may  be  below  the  alert  level,  letting  the  adulterated  foodstuff  enter  the  market.  On  the 
other hand, the data for the firm-plant combination can be difficult to obtain. 

In  addition  to  that,  the  fraud  dimension  has  to  be  taken  into  account.  Given  the  same  number  of 
incidents  per  year,  the  ideal  digital  IT  tool  shall  take  into  account  the  incident  size.  Hence,  as  a 
measure  of  recidivism  the  ideal  digital  IT  tool  may  use  two  different  variables:  the  frequency  of 

233  

Silvis, I. C. J., Van Ruth, S. M., Van Der Fels-klerx, H. J., & Luning, P. A. (2017). Assessment of food fraud vulnerability in the spices chain: An explorative study. Food Control, 81, 80-87. 

234  

235  

Yang, Z., Zhou, Q., Wu, W., Zhang, D., Mo, L., Liu, J., & Yang, X. (2022). Food fraud vulnerability assessment in the edible vegetable oil supply chain: A perspective of Chinese enterprises. Food 

Control, 138, 109005 
Yan, J., Erasmus, S. W., Toro, M. A., Huang, H., & van Ruth, S. M. (2020). Food fraud: Assessing fraud vulnerability in the extra virgin olive oil supply chain. Food Control, 111, 107081. 

236  

Gatzert, N. (2015). The impact of corporate reputation and reputation damaging events on financial performance: Empirical evidence from the literature. European management journal, 33(6), 

485-499. 

237  

Dang, C., Li, Z. F., & Yang, C. (2018). Measuring firm size in empirical corporate finance. Journal of banking & finance, 86, 159-176. 

238  

Lord, N., Flores Elizondo, C. J., & Spencer, J. (2017). The dynamics of food fraud: The interactions between criminal opportunity and market (dys) functionality in legitimate business. Criminology & 

Criminal Justice, 17(5), 605-623. 

239  

Rizzuti, A. (2021). Organised food crime: an analysis of the involvements of organised crime groups in the food sector in England and Italy. Crime, Law and Social Change, 1-20. 

240  

Ulberth, F. (2020). Tools to combat food fraud–a gap analysis. Food Chemistry, 330, 127044. 

241   Mu, E., & Carroll, J. (2016). Development of a fraud risk decision model for prioritizing fraud risk cases in manufacturing firms. International Journal of Production Economics, 173, 30-42. 
242  

Everstine,  K.,  Abt,  E.,  McColl,  D.,  Popping,  B.,  Morrison-Rowe,  S.,  Lane,  R.  W.,  &  Chin,  H.  B.  (2018).  Development  of  a  hazard  classification  scheme  for  substances  used  in  the  fraudulent 

adulteration of foods. Journal of food protection, 81(1), 31-36. 

243   Mihret, D. G. (2014). National culture and fraud risk: exploratory evidence. Journal of Financial Reporting and Accounting. 
244  

Baucus, M. S., & Near, J. P. (1991). Can illegal corporate behaviour be predicted? An event history analysis. Academy of management Journal, 34(1), 9-36. 

96 

 
                                                        
 
recidivism and its impact. The former shall be calculated using the average yearly violations over a 
given  time  span,  whereas  the  latter  shall  use  the  average  market  value  of  the  seizure  calculated 
over the seizures that took place in the same or in a different time period. The interval length shall 
be decided by a panel of expert in the subject.  

Address 

The  data  item  “Country”  is  recurring  in  several  items  within  the  ID  card  of  a  food  product.  The 
number of times that more data items “Country” are repeated within the “Food ID Card” is related to 
the complexity of the supply chains: intuitively, short supply chains will be characterised by less data 
items “Country” because less actors will move the product from one location to another (even within 
the same Country). 

Having  a  standardised  list  to  classify  Countries  is  a  plus  to  perform  data  analysis  and  potentially 
visualize  the  movement  of  the  food  products.  The  following  Country  classifications  have  been 
identified: 

—  ISO 3166245: developed to define internationally recognized codes of letters and/or numbers to be 
used when referring to countries and their subdivisions. However, it does not define the names 
of countries – this information comes from United Nations sources. Below a screenshot copied 
from their website for a better overview: 

—  UN/LOCODE Code List by Country and Territory:  the  United  Nations  Economic  Commission  for 
Europe (UNECE)246 has established their list of Country codes247 based on ISO 3166. Each Country 
is also characterised  by codes specific  for  many territories  and municipalities, thus increasing 
the granularity and detail of the data item. This classification is the one utilised by AFIS-CSM and 
IMSOC. 

—  EUROSTAT:  EUROSTAT  is  using  several  classifications  almost  overlapping  with  the  previous 
UN/LOCODE  one,  with  minor  changes  for  specific  areas.  There  are  few  inconsistencies  but 
EUROSTAT is working to solve them.  

●  An old classification248 utilised by THESEUS, which is going to be dismissed and will not 

be relevant anymore. 

●  Geonomenclature249  alias  “GEONOM” 

[Commission  Implementing  Regulation  (EU) 
2020/1470 of 12 October 2020 on the nomenclature of countries and territories for the 
European statistics on international trade in goods and on the geographical breakdown 
for other business statistics250], utilised by the Surveillance and COMEXT databases. It is 

245  

https://www.iso.org/iso-3166-country-codes.html 

246  

https://unece.org 

247  

https://unece.org/trade/cefact/unlocode-code-list-country-and-territory 

248 

https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?sort=-

1&file=comext%2Fbulk_download%2FCOMEXT_METADATA%2FCLASSIFICATIONS_AND_RELATIONS%2FCLASSIFICATIONS%2FENGLISH%2FPARTNERS-ISO.txt 

249  

https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-gq-22-004 

250  

https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32020R1470&qid=1657635392530 

97 

 
 
                                                        
 
used  for  trade  statistics,  it  is  very  detailed  and  it  is  updated  annually  to  incorporate 
multiple  needs  for  change.  The  International  trade  in  goods  statistics  (ITGS)251  Geonom 
(alpha  or  numerical  version)  is  used  to  “code”  the  reporter  and  partner  dimensions  of 
the detailed trade data. 

●  SCL GEO codes252: used for geopolitical entities, and in line with the EU Interinstitutional 
Style Guide. The work is based on various international sources: Interinstitutional Style 
Guide,  Nomenclature  of  Territorial  Units  for  Statistics  (NUTS)  classification,  ISO  3166 
country codes (3166-1 alpha-2), and UN standard country and area codes classification. 
The country codes correspond to the ISO 3166 classification with the exception of United 
Kingdom where the code "UK" was adopted (instead of "GB") and Greece where the code 
"EL"  was  adopted  (instead  of  "GR")  in  order  to  comply  with  the  Interinstitutional  Style 
Guide. The source of the regional codes for EU is the NUTS classification of ESTAT. From 
this GEO code List, EUROSTAT has derived simplified versions for the purpose of some 
specific statistics, i.e.:  

  Country/region of birth 
  Country of birth of father 
  Country of birth of mother 
  Country of birth of parents 
  Country of cabotage 
  Country, in which the controlling enterprise is located 
  Country of destination 
  Country/region of loading/embarking 
  Country of registration 
  Country of residence 
  Country of transit 
  Country/region of unloading/disembarking 
  Country/region of work 
  Country of citizenship 
  National and international organizations and institutions 
  Geopolitical entity (partner) 

This more general Eurostat online code list (SCL GEO) is used to code the reporting and 
partner countries (or geopolitical entities) of the aggregated data and also the datasets 
about trade in goods statistics by enterprise characteristics statistics. 

●  NUTS classification253: a hierarchical system for dividing up the economic territory of the 
EU and the UK, plus some other third countries close to the EU. It includes three levels: 
NUTS  1  (major  socio-economic  regions),  NUTS  2  (basic  regions  for  the  application  of 
regional  policies)  and  NUTS  3  (small  regions  for  specific  diagnoses).  Eurostat  has 
established  a  link  between  postcodes  and  NUTS  level  3  codes  in  order  to  exploit 
information  which  originally  is  coded  only  by  postcodes254.  As  the  NUTS  classification 
lacks a global overview, it will not be analysed further. 

—  TARIC255: The codes are not per se obsolete, but the usage of TARIC as reference is obsolete. 

Notably, not only the country codes change from one database to another, but also the name of the 
country itself may vary according to each classification. 

https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:International_trade_in_goods_statistics_(ITGS) 

251  
252 

https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=DSP_NOM_DTL_VIEW&StrNom=CL_GEO&StrLanguageCode=EN&IntPcKey=48517890&IntKey=48517890&StrLayoutCode=

HIERARCHIC&IntCurrentPage=1 

253  

https://ec.europa.eu/eurostat/web/nuts/background 

254  

https://ec.europa.eu/eurostat/web/nuts/correspondence-tables/postcodes-and-nuts 

255  

https://circabc.europa.eu/ui/group/0e5f18c2-4b2f-42e9-aed4-dfe50ae1263b/library/8a818a13-6bbb-451a-a749-ed8a2f7e0d4f/details 

98 

 
                                                        
 
From a preliminary analysis, even though differences are minors, the best compromise for country 
classification  is  the  one  offered  by  UN/LOCODE,  as  it  is  almost  identical  to  the  one  used  by 
Surveillance  and  Geonomenclature,  while  increasing  the  granularity  beyond  the  national  level 
(region and municipality) even in third countries. 

Maritime  trade  costs  tripled  compared  to  the  pre-pandemic  average.  Therefore,  import/export  of 
products  on  long  distances  may  have  become  more  subject  to  fraud  as  mislabelling  of  country  of 
origin  (e.g.  for  luxury  products  from  overseas).  Quantifying  the  distance  covered  by  the  products 
may provide an insight regarding the risk of fraud on origin labelling. 

Poverty and crime 

Socio-economic  drivers  of  crime  have  been  widely  explored  in  the  economic  literature.  In  1942, 
Shaw  and  Mackay256  established  that  poverty  and/or  social  deprivation  had  the  highest  correlation 
with crime. In the 1960s, Fleisher257 analysed the individual decision-making process proving the role 
of income on individual criminal decisions. The author stated that low income theoretically increases 
the propensity to commit crime as the legal earning alternative is much lower than the criminal one, 
and the potential cost of punishment is relatively low since the punishment (e.g. prison) will impose 
them lower costs because their future earnings will be modest too. This reasoning was successively 
modelled  by  Becker258,  followed  by  Ehrlich259;  as  they  applied  the  conventional  economic  model  to 
individual  criminal  choices.  In  this  model,  individuals  decide  to  commit  crimes  based  on  expected 
costs  and  expected  benefits.  The  higher  the  outcome  of  any  illegal  activity  compared  to  legal 
alternatives, the higher will be the incentive to commit a crime. On the other hand, if individuals face 
tougher punishments, then they will have a lower motivation. Applying the model to food frauds:  

—  The lower the income, the higher the probability to commit fraud; 

—  The higher the earning (in relation to the income), the higher the probability to commit fraud; 

—  The higher the punishment, the lower the probability to commit fraud; 

—  The higher the chances to be detected, the lower the probability to commit fraud. 

The  consequences  of this  proposition are that given  a  certain punishment,  people who  would  earn 
the most from a criminal act compared to lawful behaviour, will be the most incentivized to commit a 
crime. This angle can be useful when scanning the internal market for fraud. In a given country the 
punishment  for  illegal  activities  will  be  the  same,  but  low-income  areas  will  have  higher  relative 
returns to crime. 

Empirical  studies  have  also  explored  the  topic.  Buonanno’s  review260  shows  that  poverty  is  one  of 
the factors most closely related to criminal activity.  

Crime  encompasses  different  typologies,  but  the  most  relevant  category  to  the  present  study  is 
fraud. Economic literature  has identified several  economic variables that  function as fraud drivers, 
the most cited being GDP per capita. 

Most  studies  focused  on  the  impact  of  fraud  on  economic  growth,  conversely  less  attention  was 
dedicated  to  the  opposite  causal  direction.  Nevertheless,  recent  studies  show  a  negative  relation 
between  economic  development  and  fraud  occurrence261,262,263,264.  As  a  matter  of  fact,  low  economic 
growth  produces  low  income  for  people  and  low  revenues  for  firms.  Stricter  budget  constraints 
increase the chance and number of fraudulent endeavours committed in order to achieve their goals, 

256  

Shaw, C. R., & McKay, H. D. (2010). Juvenile delinquency and urban areas: A study of rates of delinquency in relation to differential characteristics of local communities in American cities (1969). In 

Classics in environmental criminology (pp. 103-140). Routledge. 

257  

Fleisher, B. M. (1966). The effect of income on delinquency. The American Economic Review, 56(1/2), 118-137. 

258  

Becker, G. S. (1968). Crime and punishment: An economic approach. In The economic dimensions of crime (pp. 13-68). Palgrave Macmillan, London. 

259  

Ehrlich, I. (1973). Participation in illegitimate activities: A theoretical and empirical investigation. Journal of political Economy, 81(3), 521-565. 

260  

Buonanno, P. (2003). The socioeconomic determinants of crime. A review of the literature. 

261  

Türedi, S., & Altıner, A. (2016). Economic and political factors affecting corruption in developing countries. Int. J. Eco. Res, 7(1), 104-120. 

262  

Omidi, M., Min, Q., & Omidi, M. (2017). Combined effect of economic variables on fraud, a survey of developing countries. Economics & Sociology, 10(2), 267. 

263  

Achim, M. V., Borlea, S. N., Găban, L. V., & Cuceu, I. C. (2018). Rethinking the shadow economy in terms of happiness. Evidence for the European Union Member States. Technological and economic 

development of economy, 24(1), 199-228. 

264  

Ahmad, B., Ciupac-Ulici, M., & Beju, D. G. (2021). Economic and Non-Economic Variables Affecting Fraud in European Countries. Risks, 9(6), 119. 

99 

 
                                                        
 
in  the  case  of  firms,  or  satisfy  personal  needs,  in  the  case  of  individuals264.  Taking  into  account 
economic  growth  (measurable  using  GDP per capita growth rate)  means  capturing  not  only  the 
economic conditions in one precise year, but also its dynamic through time.  

In conclusion, the relevance of economic drivers for the prediction of criminal behaviour, particularly 
in  the  case  of  fraud,  is  well  established  both  in  theoretical  and  empirical  studies.  This  variable  of 
interest  can  be  significantly  important  to  safeguard  the  internal  market.  Furthermore,  in  case  of 
economic  crisis,  especially  if  asymmetric  within  the  same  country,  taking  into  account  economic 
outcome and its change can be relevant. The measurement of economic conditions can be performed 
using GDP per capita at NUTS3 level for Member States, which  can  be  calculated dividing the GDP 
variable  by  the  average  population  at  NUTS3  level  available  on  Eurostat.  In  fact,  using  the  “GDP  at 
current  market  prices  by  NUTS  3  regions”  or  [nama_10r_3gdp]  database  from  Eurostat  and  the 
“Population  on  1  January  by  broad  age  group,  sex  and  NUTS  3  region”  or  [demo_r_pjanaggr3] 
database  the  GDP  per  capita  can  be  obtained.  For  example,  the  GDP  for  the  Arr.  De  Bruxelles 
Capitale  in  2020  is  provisionally  83846.54  and  the  corresponding  population  is  1223364,  hence  the 
GDP per capita is of 83846.54/1223364 = 0.06853768788 million per capita, hence 68537.68 euros per 
capita.  

For  third  countries,  the  data  can  be  used  at  country  level  and  it  is  available  on  the  World  Bank 
database. Because economic growth is a relevant variable, GDP per capita growth at NUTS3 level for 
Member States  can  be  used  as  a  measure  especially  in  case  of  economic  crisis,  since  it  can  hit  a 
country asymmetrically. 

Corruption 

Corruption  is  a  facilitator  to  all  illegal  trades265.  The  link  between  corruption  and  fraud  is  widely 
recognised in the literature266,267. Even though  each type  of  fraud  has its own characteristics264, the 
presence of corruption is common to all the different typologies, food fraud included268,269. Corruption 
is  thus  instrumental  to  illicit  trades  as  it  avoids  controls  and  prevents  or  reduces  punishment,  if 
illegal goods are caught. The facilitation of illicit trades does not happen only at logistics hubs, but 
also along the supply chain265. 

In this analysis, the corruption variable can be useful for both the internal market and imports. In the 
first  case,  the  higher  the  corruption  level  of  a  Member  State,  the  higher  the  probability  that  any 
control  on  the  supply  chain  can  be  bypassed.  In  the  second  case,  also  the  potential  corruption  at 
border posts must be considered. 

The  goods  crossing  borders  must  comply  with  the  legislation  of  both  the  destination  and  origin 
countries and the mix with social and organizational factors makes border control particularly prone 
to  corruption270.  Furthermore,  the  occasions  are  multiplied  as  border  law  enforcement  officials 
process  a  larger  number  of  transactions  compared  to  the  average  street  policeman271.  Organised 
crime  is  among  the  actors  most  benefiting  from  this  setting.  Some  researchers 272  believe  that 
corruption  is  one  of  the  defining  characteristics  of  organised  crime.  However,  in  general  there  is 
wide  consensus  over  the  deep  connection  between  organised  crime  groups  and  corruption270,273,274. 
This shows the relevance of the corruption variable in this analysis as organised crime groups play 
a significant role in the criminal sector of food fraud275.  

265  

Shelley, L. I. (2018). Corruption & illicit trade. Daedalus, 147(3), 127-143. 

266   Mihret, D. G. (2014). National culture and fraud risk: exploratory evidence. Journal of Financial Reporting and Accounting. 

267   Mu, E., & Carroll, J. (2016). Development of a fraud risk decision model for prioritizing fraud risk cases in manufacturing firms. International Journal of Production Economics, 173, 30-42. 

268  

van Ruth, S. M., Huisman, W., & Luning, P. A. (2017). Food fraud vulnerability and its key factors. Trends in Food Science & Technology, 67, 70-75. 

269  

Spink, J., Fortin, N. D., Moyer, D. C., Miao, H., & Wu, Y. (2016). Food fraud prevention: policy, strategy, and decision-making–implementation steps for a government agency or industry. CHIMIA 

International Journal for Chemistry, 70(5), 320-328. 

270  

Jancsics, D. (2019). Border corruption. Public Integrity, 21(4), 406-419.  

271  

Parayno Jr, G. (2013). Combatting corruption in the Philippine customs service. Corruption and anti-corruption, 204. 

272  

Finckenauer, J. O. (2005). Problems of definition: what is organized crime?. Trends in organized crime, 8(3), 63-83. 

273 

Gamba, A., Immordino, G., & Piccolo, S. (2018). Corruption, organized crime and the bright side of subversion of law. Journal of Public Economics, 159, 79-88. 

274  

European Commission, Directorate-General for Migration and Home Affairs, Gounev, P., Bezlov, T. (2013). Examining the links between organised crime and corruption – , Publications Office.. 

275  

Europol. (2021). Operation Opson IX – Analysis report. https://www.europol.europa.eu/cms/sites/default/files/documents/opson_ix_report_2021_0.pdf 

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The big challenge of analysing corruption is in its measurement as the phenomenon is intrinsically 
hidden.  The  vast  majority  of  the  literature  uses  measurements  based  on  corruption  perception276 
while  the  others  use  measurements  that  objectively  identify  the  corruption  levels  but  they  are 
tailored to their settings, hence having poor external validity. 

Measuring corruption using perception-based statistics entails that if in two countries with the same 
objective level of corruption, one with higher awareness than the other, then the proxy will capture a 
lower corruption level in the country with lower corruption awareness. Although, theoretically, this 
analysis might be biased, no other proxy is available given the intrinsic characteristics of corruption. 
It is necessary to rely on their vast use in the literature, conscious of the inherent flaws. There are 
multiple indicators used in the literature that try to capture the corruption phenomenon: 

—  The  Global Corruption Barometer277,  realised  by  Transparency  International,  surveys  ordinary 
citizens on their corruption perception in the public sector. It is not available for all the countries 
in the world and for those available the latest year may vary. 

—  The Country Policy and Institutional Assessment278 is developed by the World Bank and assesses 
the  transparency,  accountability  of  the  public  sector  and  corruption.  Each  country  is  assessed 
base  on  16  criteria  developed  by  the  World  Bank  staff.  This  index  is  available  mainly  for  sub-
Saharan countries. 

—  The Corruption Perception Index279  is  realised  by  Transparency  International  surveying  experts 
and  business  people  on  the  corruption  levels  in  the  public  sector.  The  index  aggregates 
corruption  indexes  produced  by  professional  institutions  with  documented  methods.  It  is 
available  for  180  countries  and  it  is  available  for  all  the  years.  Furthermore,  the  statistical 
methodology has been reviewed by the JRC280 showing the soundness of its procedure. The only 
flaw found was corrected since the 2018 edition. 

Given the characteristics of each of the mentioned indexes, the usage of the Corruption Perception 
Index is recommended. The data are readily available and easy to export, the methodology is proofed 
by a reliable institution and data are updated. The inherent flaw of being a perception index is due to 
the  nature  of  the  measured  phenomenon  but  its  usage  in  the  literature  proves  its  usefulness  to 
capture corruption. Furthermore, after a comparison covering the available countries and years, the 
Global Corruption Barometer and the Country Policy and Institutional Assessment indexes are fairly 
comparable.  Some  differences  are  present,  but  may  be  due  to  the  higher  variability  of  latter 
compared to the former. 

Country-based geopolitical risk 

The analysis of country-based risk factors may be useful to assess food fraud vulnerabilities281 for 
ingredient  sourcing  countries.  In  particular,  laws  and  enforcement  may  represent  a  serious 
disincentive  for  fraudsters282  as  opaque  and  unclear  legislation  together  with 
insufficient 
enforcement offer fraud opportunities268. It is clear that, following the Beckerian model of crime, it is 
possible to predict that tougher laws and stricter controls may increase the expected costs of crime 
as deterrence surges, although penalties for fraud-related crimes are generally lower than for other 
criminal activities. 

A  valuable  source  that  can  be  used  to  evaluate  the  country-based  risk  is  the  Countries’  Risk 
Classification283  developed  by  amfori284  (Figure  30),  a  stakeholder  organisation  of  over  2  400 
retailers,  importers,  brands  and  associations  from  more  than  40  countries.  The  Countries’ Risk 

276  

Olken, B. A. (2009). Corruption perceptions vs. corruption reality. Journal of Public economics, 93(7-8), 950-964. 

277  

https://www.transparency.org/en/gcb 

278  

https://cpia.afdb.org/?page=home 

279  

https://www.transparency.org/en/cpi/2021 

280  

Álvarez-Díaz, M., Saisana, M., Montalto, V., & Moura, C. T. (2018). Corruption Perceptions Index 2017 Statistical Assessment. European Commission Joint Research Centre Technical Report. 

281  

Everstine, K., Popping, B., & Gendel, S. M. (2021). Food fraud mitigation: strategic approaches and tools. Food Fraud, 23-43. 

282  

De Lange, E. (2013). Food crisis, fraud in the food chain and the control thereof. European Parliament report 2013/2091(INI). Brussels: European Parliament. 

283  

https://www.amfori.org/resource/country-risk-classification-2022  

284  

https://www.amfori.org/content/about-amfori 

101 

 
                                                        
 
Classification  is  based  on  the  Worldwide  Governance  Indicators  developed  by  the  World  Bank.  The 
latter institute identifies six dimensions of governance: 

—  Voice and Accountability 

—  Political Stability and Absence of Violence/Terrorism  

—  Government Effectiveness  

—  Regulatory Quality 

—  Rule of Law  

—  Control of Corruption 

All  these  indicators  capture  the  governance  ability  of  the  204  fully  analysed  (plus  9  partially 
assessed) countries. The indicators are updated with a lag of around two years, but - as testified by 
the report itself - it is rare that countries change its classification. In this analysis, the overall score 
may  be  useful  to  summarise  the  characteristics  of  the  country,  but  the  six  individual  factors 
identified by the World Bank may be taken into account as well.  

Other parameters worthy to be considered by the ideal digital IT tool include285: 

—  The governance index of the country286 

—  Whether there is a legal system of the food in the country287 

—  The GDP of the country288 

—  The economic growth of the country289 

—  The supply chain index of the country290 

—  The political risk index of the country291 

—  The human development index of the country292 

—  Global Innovation Index of the country293 

—  The press index of the country294 

—  The food safety level of the country295 

285  

A holistic approach to food safety risks: Food fraud as an example 

286  

http://info.worldbank.org/governance/wgi/#home 

287  

https://data.worldbank.org/indicator/IC.LGL.CRED.XQ 

288  

https://data.worldbank.org/indicator/NY.GDP.MKTP.CD 

289  

https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG 

290  

https://www.fmglobal.com/#!year=2015&idx=Index&handler=map 

291  

https://www.prsgroup.com/ 

292  

https://hdr.undp.org/data-center 

293  

https://www.globalinnovationindex.org/Home 

294  

https://rsf.org/en/index 

295  

https://impact.economist.com/sustainability/project/food-security-index/ 

102 

 
                                                        
 
Figure 30: An example of Country Risk classification as published by amfori (Countries’ Risk Classification 
2022). 

Source: amfori. 

Owner 

The legal person responsible for a business. Often the same criminal can open multiple companies 
to  hide  transactions,  or  they  can  close  a  company  after  having  deceived  buyers  and  customers 
before  opening  a  new  company  with  virtually  no  connection  to  the  previous  one.  Being  able  to 
identify the network of companies and sub-companies  related to  single individuals  would certainly 
help in unmasking organised criminal networks and their activities in the food supply chain. 

Certifications 

A  FBO  may  request  an  accredited  private  company  to  inspect  and  release  a  certification  as 
established  nationally  or  internationally  by  private  standards.  The  world  of  private  certification 
systems is immense, however there are some certifications that are internationally recognised and 
highly appreciated by the actors within the food supply chain.  

Although  private  controls  by accredited  certification  organisms cannot replace  the official  controls 
operated by public authorities, there is consensus that a FBO certified under recognised certification 
systems  may  be  less  prone  to  commit  non-compliances  compared  to  those  never  inspected  by 
third-party  audits  or  public  official  controls.  FBOs  who  use  to  follow  guidelines  and  rules 
internationally established may be also less prone to unintentional non-compliances on food quality 
and food safety.  

Such  approach  is  also  supported  internationally.  The  Codex  Alimentarius  Guidelines  state  “The 
frequency and intensity of controls by inspection systems should be designed so as to take account 
of risk and the reliability of controls already carried out by those handling the products including 
producers, manufacturers, importers, exporters, and distributors”296. 

Sector competition level 

The level of competition may influence firm propensity to commit fraud268,301,297. Indeed, high market 
competition draws firms’ margins down; hence, many enterprises may have difficulties having price 
advantages  and  accomplishing  their  financial  goals.  Markets  characterized  by  fierce  competition 
may push firms to commit frauds to survive301. 

Measuring  the  market  sector  competition  level  and  its  evolution  through  time  may  give  the 
opportunity  to  identify  a  more  vulnerable  market  or  a  market  that  is  experiencing  the  entrance  of 

296  

Article 31 of Codex Alimentarius (2010) Guidelines for the Design, Operation, Assessment and Accreditation of Food Import and Export Inspection and Certification Systems. CXG 26-1997. 

297   Manning, L., Smith, R., & Soon, J. M. (2016). Developing an organizational typology of criminals in the meat supply chain. Food Policy, 59, 44-54. 

103 

 
 
                                                        
 
new  relevant  firms,  hence  increasing  the  competition.  The  most  accepted  measure  of  market 
concentration is the Hefindahl-Hirshman Index calculated by squaring the market share of each firm 
present in the market and then summing the resulting values298. The problem of this measure and of 
any other market share measure is in the laboriousness of the definition of market boundaries. Both 
the  geographical  area  and  the  relevant  products  have  to  be  taken  into  account.  In  fact,  it  is  not 
possible  to  hypothesize  that,  for  example,  there  are  no  transportation  costs.  This  means  that  for 
each firm the relevant market may not be necessarily the whole internal market or national market 
within each Member State. Regarding the relevant products, for example butter and margarine may 
be  thought  to  be  substitutes  if  consumers  can  substitute  one  for  the  other  in  response  to  a  small 
and permanent change in relative prices. 

In  conclusion,  if  in  theory  the  sector  competition  level  would  be  a  very  strong  predictor  for  fraud 
vulnerability,  it  is  impossible  to  measure  and  update.  Hence,  it  cannot  be  practically  used  in  this 
analysis.  

Supply chain position 

There  is  scientific  evidence  showing  that  actors  at  different  supply  chain  stages  have  different 
exposure  to  fraud299.  There  are  different  analyses  focusing  their  assessment  along  three  factors: 
opportunities, motivations, and control measures. The results do not agree on which position in the 
supply  chain  is  the  most  vulnerable.  Some  studies  believe  that  it  is  the  wholesalers  and  traders 
(followed by retailers and food processors) as they have the least adequate control measures and, 
being  in  the  middle  of  the  supply  chain,  are  more  prone  to  pass  to  the  next  step  the  fraudulent 
product300. Other studies provided evidence that retailers risk the most, as producers are generally 
more  aware  of  the  goods,  processes  and  the  motivation  behind  adulteration301.  Finally,  studies 
carried out in specific markets have shown that milk processors in the Netherland feel to be more 
exposed  than  farmers302,  whereas  in  Canada  the  exposure  to  fraud  is  perceived  to  be  increasing 
throughout the supply chain303. 

The results shown discuss the vulnerability to fraud victimization in supply chain tiers. In the effort 
to  prevent  such  crimes,  it  is  necessary  to  intervene  in  the  level  before  the  victimized  one.  The 
industry  segments  most  exposed  to  fraud  may  change  also  based  on  the  market  in  which  they 
operate.  For  example,  the  supply  chain  involved  in  the  production  of  animal  by-products  is  more 
exposed compared to the livestock market302. 

In  order  to  keep  track  of  the  position  in  the  supply  chain  and  the  operational  sector,  the 
Nomenclature statistique des activités économiques dans la Communauté européenne  (NACE)  for 
the firms based in the Member States and the International standard industrial classification (ISIC) 
of  all  economic activities for  firms located in third  countries  could be  used. The two systems  have 
the same structure, but the former is more detailed than the latter304. For example, there are cases 
where  the  two  codes  are  the  same,  e.g.  0111  is  the  class  “Growing  of  cereals  (except  rice), 
leguminous crops and oil seeds” in both the systems, whereas other codes. e.g. the 1012 “Processing 
and  preserving  of  poultry  meat”,  exist  in  NACE  but  is  part  of  1010  “Processing  and  preserving  of 
meat”,  which  is  the  broader  definition  comprehending  three  different  NACE  classes.  A  detailed 
conversion table is available through EUROSTAT304. There are two possibilities: 

—  To use solely the ISIC classification for all the firms regardless of the provenience; 

—  To use the NACE for the European firms and ISIC for firms based in third countries. 

298  

EUROSTAT. (2021). Glossary: Herfindahl Hirschman Index (HHI) . https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Herfindahl_Hirschman_Index_(HHI) 

299  

Roberts, M. T., Viinikainen, T., & Bullon, C. (2022). International and national regulatory strategies to counter food fraud. Food & Agriculture Organization. 

300  

van Ruth, S. M., Luning, P. A., Silvis, I. C., Yang, Y., & Huisman, W. (2018). Differences in fraud vulnerability in various food supply chains and their tiers. Food Control, 84, 375-381. 

301  

Yang, Z., Zhou, Q., Wu, W., Zhang, D., Mo, L., Liu, J., & Yang, X. (2022). Food fraud vulnerability assessment in the edible  vegetable oil supply chain: A perspective of Chinese enterprises. Food 

Control, 138, 109005. 

302  

van Ruth, S. M., & Nillesen, O. (2021). Which company characteristics make a food business at risk for food fraud?. Foods, 10(4), 842. 

303  

Guntzburger, Y., Théolier, J., Barrere, V., Peignier, I., Godefroy, S., & de Marcellis-Warin, N. (2020). Food industry perceptions and actions towards food fraud: Insights from a pan-Canadian study. 

Food Control, 113, 107182. 

304  

EUROSTAT. (2008). NACE rev. 2. Office for Official Publications of the European Communities 

104 

 
                                                        
 
In the latter case, there might be more detailed information for the internal market that is generally 
less  controlled  than  imports  (given  the  absence  of  borders),  hence  balancing  this  weakness  and 
using more granular information. 

In  order  to  allow  any  digital  IT  tool  to  take  into  account  for  the  firm  supply  chain  position,  it  is 
necessary to associate each firm to its primary NACE or ISIC. As for any other firm level data, they 
can be accessed either through interaction with Chambers of Commerce or tax authorities is needed 
or the access to private databases can be available upon payment. 

Checkpoint location 

In  the  previous  section  “Address”  the  report  has  already  analysed  the  influence  of  the  territory  on 
the  quality  of  official  controls.  Similar  reasoning  apply  to  inspections  carried  out  along  the  food 
chains:  inspections  performed  in  some  countries  are  more  vulnerable  to  corruption  and 
inefficiencies compared to others. 

Non-compliance 

The Transaction ID Card should include any inspection carried out by official authorities related to a 
specific  transaction.  Data  should  include:  a  date,  the  results  of  the  inspection,  and  in  case  of  non-
compliances the type of non-compliances and the actions taken. 

There is neither a consensus nor a list legally established of non-compliances at EU level; therefore, 
it is pivotal to establish a standardised list of non-compliances for food fraud and food safety issues 
to be assessed by the ideal digital IT tool. 

E-commerce 

Consumers  are  increasing  their  reliance  on  technology  (e.g.  e-commerce)  for  food  consumption305 
and the growth will be exponential in the future306. The business-to-consumers e-commerce allows 
consumers to acquire favourably groceries via multiple online marketplaces307 but on the other side 
it represents an environment favourable for criminal behaviours to infiltrate the supply chains 308,309. 
As  a  matter  of  fact,  consumers  make  their  purchases  without  face-to-face  contact  with  sellers, 
without the opportunity to inspect food items and usually they have to pay before receiving the good. 
Furthermore,  e-commerce  may  reduce  traceability  compared  to  brick-and-mortar  shops  since  it 
connects  consumers  with  businesses  from  all  around  the  world  through  new  and  emerging 
operators310.  Finally,  even  legitimate  FBOs  cannot  have  full  control  over  the  final  delivery  of  the 
products311.  These  characteristics  have  made  popular  grocery  items,  like  honey  or  olive  oil,  prime 
targets for e-commerce fraudsters307.  

Since  e-commerce  is  now  emerging,  the  literature  is  not  extensive  but  experts  are  calling  for 
further attention to the phenomenon310. Hence, for our purposes it may be reasonable to account for 
this  variable,  marking  as  more  vulnerable  any  kind  of  transaction  coming  from  e-commerce 
marketplaces.  

Current Transaction ID (CTI) 

One unique code to identify a single Transaction ID Card. 

305  

Elferink, M., & Schierhorn, F. (2016). Global demand for food is rising. Can we meet it. Harvard Business Review, 7(04), 2016. 

306  

Bedford, E. (2022). Europe: Grocery sales value by sector 2021-2026. Statista. https://www.statista.com/statistics/979888/value-of-grocery-sales-in-europe-by-sector/ 

307  

Lee 

, B., Fenoff, R., & Spink, J. (2022). Routine activities theory and food fraud victimization. Security Journal, 35(2), 506-530. 

308  

Farrand, B. (2018). Combatting physical threats posed via digital means: the European Commission's developing approach to the sale of counterfeit goods on the Internet. European Politics and 

Society, 19(3), 338-354. 

309  

Hunter, J. & Riefa, C. (2017). The challenge of protecting EU consumers in global online markets. In: The European Consumer Organisation and the Federation of German Consumer Organisations. 

https://www.vzbv.de/sites/default/files/downloads/2017/11/08/17-11-08_brochure-vzbv-beuc-lr3.pdf 

310  

FAO/WHO  CAC  (Codex  Alimentarius  Commission).  (2017).  Discussion  Paper  on  Food  Integrity  and  Food  Authenticity.  CX/FICS  17/23/5.  Prepared  by  Iran  with  assistance  from  Canada  and  the 

Netherlands, Codex Committee on Food Import and Export Inspection and Certification Systems (CCFICS) (23rd Session), Mexico City, Mexico, 1-5 May 2017. 

311  Comans,  C. 

(2019).  eCommerce  of  Food  – 

International  Conference  on 

Trends  and  Official  Control.  Online  Food  Fraud.  PowerPoint  presented 

in  Berlin. 

https://www.bvl.bund.de/SharedDocs/Downloads/10_Veranstaltungen/eCommerce2019/PPT/Comans_PPT.pdf?__blob=publicationFile&v=1 

105 

 
                                                        
 
Backward Transaction IDs (BTIs) 

A list of all CTIs of the Transaction ID Cards covering the ingredients of the food under analysis, even 
if  the  ingredients  are  the  same  and  from  the  same  company,  but  purchased  with  different 
transactions.  Such  information  are  theoretically  already  in  the  hands  of  FBOs,  who  have  the  legal 
obligation  to  store  data  on  their  suppliers.  For  example:  in  case  a  company  produces  a  processed 
product, the CTI will include all the BTIs of the related ingredients. 

Forward Transaction IDs (FTIs) 

Within  this  data  item  there  will  be  listed  all  the  CTIs  of  the  Transaction  ID  Cards  covering  the 
products  that  have  included  as  their  future  ingredient  the  product  covered  by  the  CTI.  Such 
information  are  theoretically  already  in  the  hands  of  FBOs,  who  have  the  legal  obligation  to  store 
data  on  their  buyers.  For  example,  the  data  item  FTIs  will  be  automatically  updated  with  the 
processed product including the food under analysis as an ingredient. 

Current market price   

[Derived data item] 

Product

Country of 
origin

Country of 
destination

Current 
market 
price

Value 
adding 
features

The  product  market  price  is  a  key  factor  to  take  into  account  when  investigating  foodstuff  fraud 
vulnerability  and  it  may  result  from  a  combination  of  other  previous  data  items: country of origin, 
country of destination,  value adding features  and  product.  As  a  matter  of  fact,  the  higher  is  the 
commodity  price,  the  higher  the  probability  of  adulteration312,313  because  of  pressure  on  the  supply 
chain. Since product fraud is economically driven314, high unit profitability (i.e. the difference between 
the product price and its production cost) may incentivize adulteration315. If analysed in the context of 
the Beckerian model, given the  fixed expected sanction in the  case  of discovery by the authorities, 
the higher the margin a fraudster can have for a product, the higher the probability that product will 
be  targeted.  There  are  many  examples  of  relatively  expensive  products  substituted  by  a  cheaper 
one,  e.g.  spices  mixed  with  flour,  honey  substituted  by  glucose  syrup301  or  peanut  shells  used  as 
filler in ground cumin316. 

In conclusion, high prices are clearly important in food fraud vulnerability assessment as they offer 
higher  fraudulent  profits  thanks  to  low  cost  adulteration.  Data  availability  on  this  topic  may  be  an 
issue. To our knowledge different options are: 

—  Monthly market prices317: a database elaborated by DG AGRI providing price data at country level 
for  beef  (6  products),  cereals  (10  products),  dairy  (5  products),  eggs  and  poultry  (4  products), 
fruit and vegetables (46 products), olive oil (3 products), pigmeat (5 products) and sheep & goat 
meat (2 products) for all Member States. Not all foodstuff is covered and the granularity level is 
rather coarse; 

312  

Handford, C. E., Campbell, K., & Elliott, C. T. (2016). Impacts of milk fraud on food safety and nutrition with special emphasis on developing countries. Comprehensive Reviews in Food Science and 

Food Safety, 15(1), 130-142. 

313  

Huck, C. W., Pezzei, C. K., & Huck-Pezzei, V. A. (2016). An industry perspective of food fraud. Current Opinion in Food Science, 10, 32-37. 

314  

Smith, M., Ashraf, M., Austin, C., & Lester, R. (2021). Product fraud: Impacts on Australian agriculture, fisheries and forestry industries (p. 116). 

315  

OECD. (2007). The economic impact of counterfeiting and piracy. 

316  

Cavallaro, E., Date, K., Medus, C., Meyer, S., Miller, B., Kim, C., ... & Behravesh, C. B. (2011). Salmonella typhimurium infections associated with peanut products. New England Journal of Medicine, 

365(7), 601-610. 

317  

https://agridata.ec.europa.eu/extensions/DashboardPrice/DashboardMarketPrices.html 

106 

 
 
                                                        
 
—  FAO Food Price Index318: monthly index also calculated for cereals, vegetable oil, dairy, meat and 
sugar.  The  defined  commodity  groups  cover  only  the  main  markets  and  are  calculated  at 
international level; 

—  Fair price calculated by THESEUS: an algorithm developed by the JRC that can calculate the so-
called  “fair  price”  of  imported  goods,  i.e.  an  estimate  of  what  can  be  the  market  price.  The 
calculation is made at very high level of granularity, i.e. 10-digits CN, for combinations of origin 
and destination country, but an EU mean can be obtained too. Data at country level can be useful 
as they will give more specific information, but not necessarily that product with the associated 
cost  will  remain  in  the  destination  country  instead  of  being  retransferred  within  the  internal 
market; 

—  EUMOFA  database:  developed  by  DG  MARE,  contains  data  on  the  prices  of  fisheries  and 
aquaculture  products  throughout  the  supply  chain.  The  database  takes  into  account  also  the 
derivative products and import-export traffic319.  

The  representativeness  of  imported  goods  prices  compared  to  those  produced  within  the  internal 
market is an issue. For some products, the quality may be the same hence the price will converge. 
On the other hand, if - for the same product code - imported goods and EU products differ in average 
quality,  then  the  average  import  price  for  that  good  may  not  be  representative.  Thus,  using  import 
prices  as  proxies  for  commodity  prices  at  EU  or  national  level  is  not  straightforward  and  must  be 
done with awareness of its downsides.  

Unit price 

[Derived data item] 

Quantity

Transaction 
price

Unit price

The unit price can be automatically calculated by dividing the Transaction price for the Quantity. The 
rationale  of the importance of such data item is quite intuitive: if the price is “too good to  be true”, 
then there is a higher risk of buying a fraudulent product. The THESEUS IT tool is already capable of 
identifying outliers when scanning imported foodstuffs. 

Future market price 

[Derived data item] 

Considering the commodity price in a given moment does not give the full picture of the incentives 
that  food  fraudsters  may  have.  Indeed,  for  a  complete  fraud  risk  assessment  it  is  important  to 
capture also price shifts268. Taking into account this variable is critical since it can capture a possible 
shift in fraud vulnerability, as the 2013 house meat scandal clearly showed. In fact, in that case horse 
meat  was  deceptively  substituted  with  beef.  It  happened  because  in  that  period  the  economic 
recession led to the decrease of many commodity prices, hence putting many companies in financial 
distress. At the same time, beef prices increased 45% arriving at a price of $5 300/ton (compared to 
horse meat - $1 300/ton), hence clearly representing a relevant profit for fraudsters320. 

Price changes may also happen due to natural shocks (e.g. heat waves, droughts, floods) or external 
shocks  (e.g.  war  or  trade  treaties).  Being  capable  of  predicting  the  effects  of  these  phenomena 
would  give  the  possibility  to  redirect  controls  with  a  timely  manner,  serving  as  red  flag.  Together 

318  

https://www.fao.org/worldfoodsituation/foodpricesindex/en/ 

319  

https://www.eumofa.eu/data 

320  

Lawrence, F. (2013). Horsemeat scandal: The essential guide, The Guardian (UK). URL: http://www.theguardian.com/uk/2013/feb/15/horsemeat-scandal-the-essential-guide 

107 

 
 
 
 
 
 
 
                                                        
 
with  exogenous  shocks,  product  demand  may  change  also  due  to  seasonality321,  hence  creating 
higher demand due to cyclical consumption (e.g. festivities, seasonal consumption patterns, etc…). 

Indeed,  a  price  increase  will  certainly  allow  higher  profits  for  fraudsters  since  the  expected  gain 
from fraudulent activity will increase, hence given the same expected costs (e.g. fines, incarceration, 
etc...)  criminal  activity  is  expected  to  increase  according  to  the  Beckerian  model  of  engagement  in 
criminal 
activity.  
To  achieve  the  objective  of  timely  control  redirection,  forecasting  platforms  shall  be  used.  To  our 
knowledge there are multiple alternatives:  

—  Short-term outlook322: issued three times per year by DG AGRI, covers only some key markets, 
i.e. cereals, oilseeds, protein crops, sugar, olive oil, wine, apples, oranges, milk, dairy products, 
beef,  pigmeat,  poultry,  sheep  and  goat  meat.  The  report  also  considers  the  macroeconomic 
outlook and exogenous shocks for the part that will affect the food sector. It provides year-to-
year forecast changes in crop yield, imports and exports but not on the commodity prices. 

—  JRC  MARS  Bulletin323:  issued  monthly  for  the  EU  Member  States  and  some  neighbouring 
countries,  and  twice  per  year  for  Turkey,  Ukraine,  Kazakhstan,  Russia  and  North  Africa.  The 
bulletin  provides  information  on  the  condition  of  crops  and  weather  affecting  crop  growth  and 
development.  It  also  provides  crop  yield  forecasts.  The  analysed  crop  will  depend  on  the 
analysed month since seasonal crops are taken into account.  

—  Medium-term outlook: published once a year, it provides the medium-term outlook for the EU-27 
agricultural markets. In the report many variables are examined: the macroeconomic conditions, 
external  shocks,  commodity  availability,  links  with  the  consumption  of  other  commodities, 
import and exports. One of the simulation output will be the equilibrium price for the following 
commodity  groups:  cereals,  oilseeds,  oilmeals  and  vegetable  oils,  biofuels,  protein  crops  and 
rice,  feed,  sugar,  milk,  dairy  products,  beef  and  veal,  pigmeat,  poultry  meat,  sheep  and  goat 
meat, olive oil, wine, fruit and vegetables. 

Information  are  already  present  in  several  European  databases  and  intelligence  systems.  In 
particular,  the  MARS  Bulletin  can  be  used  to  predict  the  evolution  of  the  main  crops  throughout 
Europe. In this way, a prompt forecast of the evolution of crop yields could be doable, but in order to 
forecast the effect that e.g. natural disasters may have on the commodity price, an adjustment of the 
model developed according to the Medium-term outlook is needed. 

Non-compliance history 

[Derived data item] 

Product

Country of 
destination

Country of 
origin

Non-
compliance

Non-
compliance 
history

Value 
adding 
features

This derived data item should  register how  frequent  a specific food product (with or  without value 
adding features) is  affected by a specific non-compliance, taking into account the country of origin 
and  the country of destination.  Official  controls  should  take  into  account  such  analysis  in  order  to 
understand which are the most common non-compliances affecting the various food supply chains, 
the  reasoning  behind  and  how  to  react  efficiently.  For  example:  it  is  quite  common  to  adulterate 
honey  with  sugar  syrups.  In  the  ideal  digital  IT  tool,  the  product  “honey”  would  be  frequently 

321  

Preedy, V. R. (2016). Electronic noses and tongues in food science. Academic Press. 

322  

https://agriculture.ec.europa.eu/data-and-analysis/markets/outlook/short-term_en 

323  

https://joint-research-centre.ec.europa.eu/monitoring-agricultural-resources-mars/jrc-mars-bulletin_en 

108 

 
 
 
 
 
                                                        
 
associated  with  the  related  non-compliance  “adulteration”,  providing  an  analysis  of  the  food  chain 
and even triggering the development of new methodologies and/or policies to address the problem. 

Ease of detection 

[Derived data item] 

Product

Non-
compliance

Ease of 
detection

Different frauds show different difficulties in being detected, when considering the product and the 
non-compliance.  For  example:  smuggling  cereals  without  traceability  documentation  is  easy  to 
detect (requiring only visual inspection and checking the documents), whereas identifying the origin 
mislabelling  on  a  wine,  or  the  adulteration  of  oregano  with  olive  tree  leaves  powder,  requires 
several  expensive  analytical  (molecular  and/or  biochemical)  techniques  and  staff  expertise  not 
easily available everywhere. 

Every data item may have a value for the ideal digital IT tool only if translated into numerical entities. 
Therefore, a specialised Working Group should be created in order to rank and quantify the difficulty 
of  detection  for  each  combination  of  food  product  and  non-compliance.  It  is  expected  that  frauds 
most difficult to detect are those attracting the attention of fraudsters. 

109 

 
 
 
 
 
 
 
 
Annex 3: The candidate food sector(s) 

The best candidate commodity to test a pilot project should have the following characteristics: 

—  A high market value in terms of EU imports: according to the statistics provided by DG AGRI324 
and EUROSTAT325, in 2020 the most valuable commodities imported into the EU were “Edible fruit 
and nuts; peel of citrus fruits or melons”  (20.1  million  Euros)  and  “Fish and crustaceans, 
molluscs & other aquatic invertebrates” (19.5 million Euros). 

—  A  high  value  for  each  individual  product:  taking  into  account  the  perspective  of  Customs 
controls, expensive products (i.e. €/kg) provide a benefit because they require less efforts (e.g. 
fewer  inspections)  in  order  to  identify  non-compliances  (e.g.  safety  risks,  tax  evasion, 
counterfeit). 

—  Internationally  recognised  as  a  commodity  with  vulnerable  value  chains:  in  2020,  the  EU  Food 
Fraud Network Report326 listed (in decreasing order) “fats and oils”, “fish and fish products” and 
“poultry meat and poultry meat products”  as  the  most  reported  food  categories  in  the  AAC-FF 
System, whereas the food categories mostly covered by AA requests with potential suspicion of 
fraud  were  “fruits and vegetables”,  “bivalve molluscs and products thereof”,  “meat and meat 
products (other than poultry)”,  “dietetic foods, food supplements, fortified foods”,  “fish and fish 
products”. 

In the 2021 Annual Report Alert and Cooperation Network327, the food categories mostly notified 
with  non-compliances  were  (in  decreasing  order)  “fruits and vegetables”,  “dietetic foods, food 
supplements, fortified foods”,  “meat and meat products (other than poultry)”,  “fish and fish 
products”,  whereas  the  food  categories  mostly  notified  with  fraud  notifications  were  “fish and 
fish products”, “fats and oils” and “meat and meat products (other than poultry)”. 

By  analysing  the  food  fraud  cases  covered  within  the  Food  Fraud  Monthly  Reports  from 
September  2016  to  December  2020,  the  JRC328  identified  as  the  most  globally  targeted 
commodities (in decreasing order): seafood, wine and alcoholic beverages, meat products, milk 
and dairy, oils, herbs and spices, and honey. 

Also the scientific literature329 analysed the food fraud cases registered in EU (RASFF database) 
and  USA  (EMA  database)  between  2000  and  2015,  identifying  seafood  as  the  most  reported 
commodity. 

—  Low  level  of  self-sufficiency  ratio  (SSR)  in  the  EU:  by  manually  calculating  the  2018  SSR330  (in 
terms  of  volume,  and  not  economic  value)  of  the  EU  for  each  individual  commodity  from  the 
FAOSTAT  Food  Balance  Sheets331  (Table  5),  the  individual  EU  food  supply  chains  mostly 
dependent  on  imports  were  (in  decreasing  order)  “stimulants”  (e.g.  coffee  and  tea),  “spices”, 
“seafood”, “treenuts” and “vegetable oils”. Similar data were confirmed by other EU sources. The 
lower the SSR, the more dependant the EU is from imports. DG AGRI and DG MARE publish each 
year  their  market  analyses  that  provide  a  clear  overview  of  self-sufficiency  rates  for  each 
commodity  of  commodity  group  (Table  6  and  Table  7).  The  EU  food  supply  chains  mostly 

324  

Page 70 of European Commission (2021) Key figures on the European food chain — 2021 edition. ISBN: 978-92-76-41514-5 

325  

EUROSTAT, EU trade since 1988 by HS2-4-6 and CN8 

326  

European Commission, Directorate-General for Health and Food Safety, (2021). The EU agri-food fraud network and the administrative assistance and cooperation system – 2020 annual report, 

Publications Office 

327  

European Commission, Directorate-General for Health and Food Safety, (2021). Alert and cooperation network – 2021 annual report, Publications Office of the European Union 

328  

Siligato  R.,  and  Ulberth  F. 

(2021)  Four  years  of  monitoring 

food 

fraud  globally.  New  Food  Magazine  –  Food 

Integrity  Supplement.  Volume  24, 

issue  02. 

https://www.newfoodmagazine.com/article/143865/food-integrity-supplement-april-2021/ 

329   Marvin, H. J., Bouzembrak, Y., Janssen, E. M., van der Fels-Klerx, H. V., van Asselt, E. D., & Kleter, G. A. (2016). A holistic approach to food safety risks: Food fraud as an example. Food research 

international, 89, 463-470. 

330  

“The self-sufficiency ratio (SSR) is defined as: SSR = production*100/(production + imports  – exports). The SSR can be calculated for individual commodities, groups of commodities of similar 

nutritional values and, after appropriate conversion of the commodity equations, also for the aggregate of all commodities. In the context of food security, the SSR is often taken to indicate the 

extent to which a country relies on its own production resources, i.e. the higher the ratio the greater the self-sufficiency.While the SSR can be the appropriate tool when assessing the supply 

situation for individual commodities, a certain degree of caution should be observed when looking at the overall food situation. In the case, however, where a large part of a country's production 

of  one  commodity,  e.g.  other  cereals,  is  exported,  the  SSR  may  be  very  high  but  the  country  may  still  have  to  rely  heavily  on  imports  of  food  commodities  to  feed the  population.  The  self-

sufficiency  rate  (as  defined  above)  cannot  be  the  complement  to  100  of  the  import  dependency  rate,  or  vice-versa.”  Source:  FAO.  2001.  Food  balance  sheets.  A  handbook.  Rome. 

https://www.fao.org/3/x9892e/x9892e00.htm 

331  

https://www.fao.org/faostat/en/#data/FBS/report 

110 

 
                                                        
 
dependent on imports were (in decreasing order): oranges (processed), seafood (total), oilseeds, 
oilmeals.  The  EU  is  also  the  global  largest  importer  of  fishery  products  (one  third  of  the  total 
world trade in value)332 and imports 60% of seafood products consumed. 

Table 5: List of FAOSTAT commodities and the related Self-Sufficiency Ratio (SSR) values, from the lowest SSR 
value to the highest, related to the EU. 

Self-sufficiency ratio (%) [quantities]  
for each Macro-category 

FAOSTAT  
Macro-category 

FAOSTAT Micro-categories  
included in the Macro-category 

0 

19,3 

50,1 

52,2 

68,3 

Stimulants 

Spices 

Fish, Seafood 

Coffee and products 
Cocoa Beans and products 
Tea (including mate) 

Pepper 
Pimento 
Cloves 
Spices, Other 

Freshwater Fish 
Demersal Fish 
Pelagic Fish 
Marine Fish, Other 
Crustaceans 
Cephalopods 
Molluscs, Other 

Treenuts 

Nuts and products 

Vegetable Oils 

Soyabean Oil 
Groundnut Oil 
Sunflowerseed Oil 
Rape and Mustard Oil 
Cottonseed Oil 
Palmkernel Oil 
Palm Oil 
Coconut Oil 
Sesameseed Oil 
Olive Oil 
Ricebran Oil 
Maize Germ Oil 
Oilcrops Oil, Other 

Soyabeans 
Groundnuts 
Sunflower seed 
Rape and Mustardseed 
Cottonseed 
Coconuts - Incl Copra 
Sesame seed 
Palm kernels 
Olives (including preserved) 
Oilcrops, Other 

Aquatic Animals, Others 
Aquatic Plants 

Beans 
Peas 
Pulses, Other and products 

Oranges, Mandarines 
Lemons, Limes and products 
Grapefruit and products 
Citrus, Other 
Bananas 
Plantains 
Apples and products 
Pineapples and products 

68,6 

Oilcrops 

77,2 

82,1 

86,7 

Aquatic Products, Other 

Pulses 

Fruits - Excluding Wine 

332  

FAO. (2020). The State of World Fisheries and Aquaculture 2020. Sustainability in action. Rome 

111 

 
                                                        
 
99,9 

102,0 

103,6 

105,9 

106,5 

107,0 

111,7 

112,1 

112,6 

114,2 

271,4 

Dates 
Grapes and products (excl wine) 
Fruits, other 

Sugar Crops 

Sugar cane 
Sugar beet 

Cereals - Excluding Beer  Wheat and products 

Rice and products 
Barley and products 
Maize and products 
Rye and products 
Oats 
Millet and products 
Sorghum and products 
Cereals, Other 

Sugar non-centrifugal 
Sugar (Raw Equivalent) 
Sweeteners, Other 
Honey 

Sugar & Sweeteners 

Milk - Excluding Butter 

Milk - Excluding Butter 

Eggs 

Animal fats 

Starchy Roots 

Alcoholic Beverages 

Vegetables 

Meat 

Eggs 

Butter, Ghee 
Cream 
Fats, Animals, Raw 
Fish, Body Oil 
Fish, Liver Oil 

Cassava and products 
Potatoes and products 
Sweet potatoes 
Yams 
Roots, Other 

Wine 
Beer 
Beverages, Fermented 
Beverages, Alcoholic 
Alcohol, Non-Food 

Tomatoes and products 
Onions 
Vegetables, other 

Bovine Meat 
Mutton & Goat Meat 
Pigmeat 
Poultry Meat 
Meat, Other 

Offals 

Offals, Edible 

Source: 2018 FAOSTAT Balance Sheet of the European Union. https://www.fao.org/faostat/en/#data/FBS/report 

Table 6: Self-sufficiency rates by seafood commodity group in 2019. 

Commodity groups (share of total apparent consumption in 2019) 

Self-sufficiency rates (2019) 

Freshwater fish (5%) 

Crustaceans (7%) 

Groundfish (25%) 

Miscellaneous aquatic products (3%) 

Tuna and tuna-like species (13%) 

Salmonids (12%) 

Cephalopods (6%) 

13% 

20% 

21% 

23% 

29% 

29% 

37% 

112 

 
 
Other marine fish (7%) 

Flatfish (1%) 

Bivalves and other molluscs and aquatic invertebrates (9%) 

Small pelagics (12%) 

Total 

55% 

69% 

79% 

101% 

41,2% 

Source: European Commission (2021) The EU Fish Market 2021 edition. ISBN 978-92-76-28905-0. 

Table 7: EU-27 self-sufficiency rates by agriculture commodity group in 2021. 

Commodity groups 

Oranges (processed) 

Oilseeds 

Oilmeals 

Sorghum 

Vegetable oils 

Protein crops 

Durum 

Maize 

Oranges (fresh) 

Tomatoes (fresh) 

Sheep and goat meat 

Triticale 

Sugar 

Oats 

Peaches and Nectarines (fresh) 

Fresh dairy products 

Rye 

Apples (processed) 

Beef/veal 

Butter 

Cheese 

Poultry meat 

Apples (fresh) 

Wine 

Barley 

Pigmeat 

Tomatoes (processed) 

Peaches and Nectarines (processed) 

Soft wheat 

Olive oil 

Whey 

Whole milk powder (WMP) 

Skim milk powder (SMP) 

EU-27 self-sufficiency rate 2021 

38 

61 

63 

73 

73 

79 

85 

89 

93 

94 

95 

100 

101 

103 

103 

104 

106 

107 

108 

110 

112 

112 

115 

119 

120 

125 

130 

133 

139 

141 

145 

172 

209 

Source: European Commission (2022) Short-term outlook for EU agricultural markets in 2022 (Summer Edition) - Annex. 
ISSN 2600-0873. 

113 

 
The seafood sector seems to be the best candidate, also considering the increasing attention of the 
EU on IUU fishing333. Seafood commodities are subject to fraudulent practices and food safety risks, 
and the EU is heavily dependent on imports in terms of quantities and economic value. According to 
the EUMOFA 2021 Yearly report334,335,336, in 2020 extra-EU imports of fishery and aquaculture products 
totalled  6.15  million  tonnes  worth  24.21  billion  €.  Per  capita  apparent  consumption  is  estimated  at 
almost 24 kg. In 2019, the EU self-sufficiency ratio337 was 41.2% (Figure 31). The trend is worsening, as 
EU catches are diminishing and imports are increasing. 

Figure 31: EU supply balance (2019, live weight equivalent, food use 
only). 

Source: European Commission (2021) The EU Fish Market 2021 edition. ISBN 978-92-76-28905-0 

Notably,  according  to  the  Farm  to  Fork  Strategy338  (Section  2.1)  “The proposed revision of the EU’s 
fisheries control system will contribute to the fight against fraud through an enhanced traceability 
system. The mandatory use of digitalised catch certificates will strengthen measures to prevent 
illegal fish products from entering the EU market”.  The  Opson  IX  Report  also  states:  “The threat 
related to this category of product [seafood] is increasing and becoming more complex, as it might 
involve concurrent illicit deeds, such as illegal fishing, food safety issues but also IPR [Intellectual 
Property  Rights] crimes especially related to products subjected to the designation of origin”339, 
although  seafood  is  not  among  the  food  commodities  mostly  targeted  in  the  Opson  operations. 
Finally  the  European  Commission  has  stated  that  “The EU is largely self-sufficient for key 
agricultural products, being a main wheat and barley exporter and largely able to cover its 
consumption for other staple crops such as maize or sugar. […] However, the EU is a considerable 
net-importer for specific products which may be difficult to (swiftly) substitute, such as feed protein, 
sunflower oil or seafood”  340  and  “The fish sector has a high degree of import-dependency, the EU 
self-sufficiency being at 14% for the top five species consumed”341. 

In addition, the World Wildlife Fund (WWF) in a recent report highlighted that the EU “[…] is also the 
world’s largest seafood importer, importing more than half of what it consumes. Some of this 

333  

European Parliament Research Service (2022) Illegal, unreported and unregulated (IUU) fishing. PE 614.598 

334  

https://www.eumofa.eu/the-eu-fish-market-2021-edition-is-now-online 

335  

https://oceans-and-fisheries.ec.europa.eu/news/eu-fish-market-2021-edition-now-online-2021-11-22_en 

336  

European Commission (2021) The EU Fish Market 2021 edition. ISBN 978-92-76-28905-0 

337  

Ratio between EU production and apparent consumption of the EU market. It measures the capacity of EU Member States to meet demand with their own production. 

338  

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS A Farm 

to Fork Strategy for a fair, healthy and environmentally-friendly food system [COM(2020) 381 final] 

339  

EUROPOL (2021) Opson IX Report. https://www.europol.europa.eu/publications-documents/operation-opson-ix-%E2%80%93-analysis-report 

340  

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE EUROPEAN COUNCIL, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE 

OF THE REGIONS Safeguarding food security and reinforcing the resilience of food systems. COM/2022/133 final 

341  

COMMUNICATION  FROM  THE  COMMISSION  TO  THE  EUROPEAN  PARLIAMENT,  THE  COUNCIL,  THE  EUROPEAN  ECONOMIC  AND  SOCIAL  COMMITTEE  AND  THE  COMMITTEE  OF  THE  REGIONS 

Contingency plan for ensuring food supply and food security in times of crisis. COM/2021/689 final 

114 

 
                                                        
 
seafood comes from tropical regions where local communities rely on these fish stocks for protein, 
but are facing declining catches due to overfishing and climate change”342. 

The  US  FDA  reached  similar  conclusions.  The  U.S.  Government  Accountability  Office  noted 
collaboration  issues  for  the  three  federal  agencies  primarily  responsible  for  detecting  and 
preventing seafood fraud343. As the United States import 94% of its seafood supply, the FDA has run a 
pilot of its Artificial Intelligence Imported Seafood Pilot program344,345,346 in order to react quickly to 
imported seafood potentially posing a threat to public health. The pilot program utilised ML to target 
seafood shipments. 

342  

Ruiz-Mirazo, J. (2022). Europe eats the world. WWF European Policy Office. https://www.wwf.eu/?6642391/Europe-eats-the-world 

343  

Upton, H. F. (2015). Seafood fraud. Congressional Research Service. 

344  

https://www.fda.gov/food/cfsan-constituent-updates/fda-moves-second-phase-ai-imported-seafood-pilot-program?utm_medium=email&utm_source=govdelivery 

345  

https://www.fda.gov/news-events/fda-voices/import-screening-pilot-unleashes-power-data-and-leverages-artificial-intelligence 

346  

https://www.fda.gov/food/cfsan-constituent-updates/fda-moves-third-phase-artificial-intelligence-imported-seafood-pilot-program 

115 

 
 
                                                        
 
Annex 4: The legal bases for risk-based official controls 

The EU legislation and international guidelines already recommend utilising risk-based strategies to 
optimize resources and personnel in the routine official control activities.  

Recital  32  of  the  Official  Controls  Regulation  (OCR)347  states  that  “Competent authorities should 
perform official controls regularly, on a risk basis and with appropriate frequency, on all the sectors 
and in relation to all operators, activities, animals and goods governed by Union agri-food chain 
legislation. The frequency of official controls should be established by the competent authorities 
having regard to the need to adjust the control effort to the risk and to the level of compliance 
expected in the different situations, including the possible violations of the Union agri-food chain 
legislation perpetrated through fraudulent or deceptive practices. Accordingly, the likelihood of non-
compliance with all the areas of the Union agri-food chain legislation which fall within the scope of 
this Regulation should be taken into account where adjusting the control efforts. In some cases, 
however, and in view of the issuance of an official certificate or attestation which is a pre-requisite 
for the placing on the market or for the movements of animals or goods, Union agri-food chain 
legislation requires that official controls be performed irrespective of the level of risk or the 
likelihood of non-compliance. In such cases, the frequency of the official controls is dictated by the 
certification or attestation needs.” Recital 38 continues with “the competent authorities should have 
the power to perform official controls at all stages of production, processing and distribution of 
animals and goods concerned by that legislation” and “should draw up and maintain a list or register 
of the operators to be controlled”.  Recital  39  stresses  the  last  point  by  stating  “Competent 
authorities should also, subject to certain conditions, be entitled to publish or to make available 
information about the rating of individual operators based on the outcome of official controls. The 
use of rating schemes by Member States should be allowed and encouraged as a means to increase 
transparency  along  the  agri-food  chain,  provided  that  appropriate  guarantees  of  fairness, 
consistency, transparency and objectiveness are offered by such schemes”. 

Speaking about imports, Recital 53 of the OCR highlights “Official controls performed on animals and 
goods entering the Union from third countries are of key importance since these controls ensure 
compliance with legislation applicable within the Union and, in particular, with the rules established 
to protect human, animal and plant health, animal welfare and, as regards GMOs and plant 
protection products, also the environment. Such official controls should take place before the 
animals or goods are released for free circulation within the Union. The frequency of official controls 
should adequately address risks to human, animal and plant health, animal welfare and to the 
environment that animals and goods entering the Union might pose, taking into account the 
operator’s history of compliance with the requirements provided for in Union agri-food chain 
legislation, the controls already performed on those animals and goods in the third country 
concerned, and the guarantees given by that third country that animals and goods exported to the 
Union meet the requirements laid down in Union legislation”.  Recital  58  continues  with  “The 
frequency of physical checks should be determined and modified on the basis of risks to human, 
animal or plant health, animal welfare or, as regards GMOs and plant protection products, also to 
the environment. That approach should enable the competent authorities to allocate resources for 
controls where the risk is highest. The frequency of identity checks should also be subject to 
reduction or limited to the verification of a consignment’s official seal where this is justified by a 
reduced risk posed by the consignments entering the Union. The risk-based approach to identity 
checks and physical checks should be pursued by making use of available data sets and information, 
and of computerised data collection and management systems”. 

Recital  86  of  the  OCR,  although  referring  to  the  IMSOC  system,  established  the  very  bases  for  the 
creation  of  a  future  information  system  as  described  in  this  report:  “To support a more efficient 
management of official controls, a computerised information system integrating and upgrading as 
necessary all relevant existing information systems should be set up by the Commission, allowing 
for the use of advanced communication and certification tools, and for the most efficient use of the 
data and information related to official controls. In view of avoiding unnecessary duplications of 

347  

Regulation (EU) 2017/625 of the European Parliament and of the Council of 15 March 2017 on official controls and other official activities performed to ensure the application of food and feed law, 

rules on animal health and welfare, plant health and plant protection products 

116 

 
                                                        
 
information requirements, the design of such computerised system should take into account the 
need to ensure, wherever appropriate, the compatibility and inter-operability of such a computerised 
system with other information systems operated by public authorities and through which relevant 
data is automatically exchanged or made available”. 

The OCR legal text strongly echoes what is framed within the Recitals. Article 9.1 and 9.2 state that: 

“1. Competent authorities shall perform official controls on all operators regularly, on a risk basis 
and with appropriate frequency, taking account of: 

(a) identified risks associated with: 

(i) animals and goods; 

(ii) the activities under the control of operators; 

(iii) the location of the activities or operations of operators; 

(iv) the use of products, processes, materials or substances that may influence food safety, 
integrity and wholesomeness, or feed safety, animal health or animal welfare, plant health 
or, in the case of GMOs and plant protection products, that may also have an adverse impact 
on the environment; 

(b) any information indicating the likelihood that consumers might be misled, in particular as to the 
nature,  identity,  properties,  composition,  quantity,  durability,  country  of  origin  or  place  of 
provenance, method of manufacture or production of food; 

(c) operators’ past record as regards the outcome of official controls performed on them and their 
compliance with the rules referred to in Article 1(2); 

(d) the reliability and results of own controls that have been performed by the operators, or by a 
third party at their request, including, where appropriate, private quality assurance schemes, for the 
purpose of ascertaining compliance with the rules referred to in Article 1(2); and 

(e) any information that might indicate non-compliance with the rules referred to in Article 1(2). 

2. Competent authorities shall perform official controls regularly, with appropriate frequencies 
determined on a risk basis, to identify possible intentional violations of the rules referred to in 
Article 1(2),  perpetrated  through  fraudulent  or  deceptive  practices,  and  taking  into  account 
information regarding such violations shared through the mechanisms of administrative assistance 
[…]. 

Border control posts are addressed in Article 44 of the OCR: 

“1. To ascertain compliance with the rules referred to in Article 1(2), the competent authorities shall 
perform official controls regularly, on a risk basis and with appropriate frequency, on animals and 
goods entering the Union and to which Articles 47 and 48 do not apply. 

2. On animals and goods referred to in paragraph 1 the appropriate frequency of the official controls 
shall be determined, taking into account: 

(a)  the risks to human, animal or plant health, animal welfare or, as regards GMOs and plant 
protection products, also to the environment, associated with different types of animals and goods; 

(b) any information indicating the likelihood that consumers might be misled, in particular as to the 
nature,  identity,  properties,  composition,  quantity,  durability,  country  of  origin  or  place  of 
provenance, method of manufacture or production of goods; 

(c)  the history of compliance with the requirements established by the rules referred to in 
Article 1(2) applicable to the animals or goods concerned: 

(i) of the third country and establishment of origin or place of production, as appropriate; 

(ii) of the exporter; 

(iii) of the operator responsible for the consignment; 

(d) the controls that have already been performed on the animals and goods concerned; and 

117 

 
(e)  the guarantees that the competent authorities of the third country of origin have given with 
regard to compliance of the animals and goods with the requirements established by the rules 
referred to in Article 1(2) or with requirements recognised to be at least equivalent thereto. 

4. Notwithstanding paragraphs 1 and 3, the competent authorities at border control posts and other 
points of entry into the Union shall perform official controls on the following whenever they have 
reason to believe that their entry into the Union may pose a risk to human, animal or plant health, 
animal welfare or, as regards GMOs and plant protection products, also to the environment: 

(a) means of transport, including where empty; and 

(b) packaging, including pallets.” 

Article  54  states  that  the  frequency  rate  of  identity  checks  and  physical  checks  on  animals  and 
goods should be adjusted according to the level of risk, taking into account: 

“(i) information collected by the Commission in accordance with Article 125(1); 

(ii) the outcome of controls performed by Commission experts in accordance with Article 120(1); 

(iii) operators’ past record as regards compliance with the rules referred to in Article 1(2); 

(iv) data and information collected via the information management system for official controls 
(IMSOC) referred to in Article 131; 

(v) available scientific assessments; and 

(vi) any other information regarding the risk associated to the categories of animals and goods;” 

The  Codex  Alimentarius  Commission  has  also  produced  a  Guideline348  document  covering  control 
systems.  Codex  Guidelines  have  no  legal  validity;  however,  it  is  worthy  to  mention  briefly  how  the 
United  Nations  address  the  topic.  Some  paragraphs  especially  focus  on  the  need  to  develop  risk-
based control programs for fraud and deception: 

“17. A competent authority should make decisions within a national food control system based on 
scientific information, evidence and/or risk analysis principles as appropriate.” 

“36. A national food control system should possess three main characteristics which, among other 
things, can be used in self-assessment or other evaluation to determine if the system is fully 
functional and effective: 

i) Characteristic 1 Situational awareness means that a national food control system avails itself of 
accurate and current information on the entire food chain. 

ii) Characteristic 2 Pro-activity means that a national food control system is capable of identifying 
existing or emerging hazards before they materialise as risks in the food production and/or 
processing chain and at the early stages rather than in the end product. Early warning and/or rapid 
alert systems, traceability and contingency planning for managing and preparing for potential food 
safety incidents should be an inherent part of a pro-active control system. 

iii) Characteristic 3 Continuous Improvement means that a national food control system should 
possess the capability to learn through a process of review and reform utilising mechanisms that 
check and evaluate whether the system is able to achieve its objectives.” 

“45. An appropriate system design should consider a range of factors including (but not limited to) 
product risk, current scientific information, industry based controls and system review findings. It 
should also provide for flexibility in the application of control measures to reflect variations in these 
factors.” 

“46. Development of an effective method of data collection across the food chain is important for 
situational awareness, performance measurement and continuous review and system improvement. 
For instance, surveillance and monitoring programs can be used to target priority risks.” 

348  

Codex  Alimentarius  Commission.  (2013).  Principles  and  Guidelines  for  National  Food  Control  Systems;  CAC/GL  82-2013;  Food  and  Agriculture  Organization  of  the  United  Nations:  Rome, Italy. 

World Health Organization: Geneva, Switzerland. 

118 

 
                                                        
 
“47. The competent authority should utilise findings from laboratories to monitor trends in the food 
chain and assist in compliance and enforcement. Laboratory access and capacity should be 
commensurate with the need to address priority food risks.” 

“50. Control programs should be based on risk and designed to take into account a number of 
factors including but not limited to: 

—  Food safety hazards associated with different products and the risk to human health posed by 

the food or food related products; 

—  Risk of unfair practices in the food trade associated with different products, such as potential 

fraud or deception of consumers; 

—  Information that may be available from a range of sources including government, academia, 

scientific institutions and industry data; 

—  Statistical data on production, trade and consumption; 

—  Results of previous controls including analytical results; 

—  The effectiveness and reliability of controls including those of food business operators; 

—  Knowledge of operators at various stages of the food chain typical and atypical use of products, 
raw  materials  and  by-products;  structure  of  production  and  supply  chains;  production 
technologies, processes and practices; relevant product tracing information; and 

—  Epidemiological data on food borne disease.” 

“57. Compliance and enforcement programs should be designed to provide the ability for the 
competent authority to take corrective action to ensure the situation is remedied where the food 
business operators are not meeting their obligations or a product or process is found not to be in 
conformity. Programs should be designed to: 

—  Be proportionate to the degree of public health risk or potential fraud or deception of 

consumers; 

—  Encourage acceptance of responsibility and compliance by all participants; and 

—  Provide for a full range of responses from provision of information or education material, 

imposing of corrective actions, setting of sanctions. 

—  Take into account repeated non-conformity by food business operators.” 

“60. The design of a national food control system should incorporate timely access to adequate 
information relating to the surveillance, investigation and response to food borne illness and food 
related incidents. Such information can identify the risks or issues that need to be addressed and 
also whether or not the controls or measures in place are effective” 

“81. Where a product or process is found not to be in conformity, the competent authority should take 
action to ensure that the operator remedies the situation. The resulting measures should take into 
account any repeated non-conformity of the same product or process to ensure that any action is 
proportionate: to the degree of public health risk, potential fraud or deception of consumers. As an 
example to illustrate this point the specific measures that may be applied in continuous cases of 
non-conformity may include: 

—  Increased intensity of audits and/or inspection and/or monitoring of products and/or processes; 

identified as being not in conformity and/or the undertakings concerned; and 

—  In the most serious or persistent cases, de-registration of the producer and/or processor or 

closure of the relevant establishment.” 

“85. The review of food-related non-compliances and/or incidents is an opportunity to learn which 
can be used as a feedback loop for the planning process by the competent authority. A competent 
authority should use these opportunities to engage in continuous improvement by assessing an 
incident from first signal through response and incorporating lessons learned in the design and 
planning phase.” 

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A  similar  approach  is  echoed  by  the  related  Codex  Alimentarius  Guidelines349,350,  where  risk 
assessment  is  praised  as  the  underlining  principle  to  ensure  food  safety.  Risk  assessment  should 
take  into  account  commodities,  processing  methods,  and  the  country  of  origin.  The  extent  and 
stringency of requirements for imported food should be “proportionate to risk, noting that risk may 
vary from one source to another because of factors such as specific and/or similar situations in the 
region of origin, technology employed, compliance history, etc. and/or examination of relevant 
attributes of a sample of products at import”.  Legislation  should  provide  the  competent  authority 
with “the ability to apply risk-based sampling plans, taking into consideration the compliance history 
of the particular food, the validity of accompanying certification, and other relevant information”. The 
frequency of inspection and testing of imported food should take into account several factors as e.g.: 
the  risk to human health; the likelihood of  non-compliance; history  of conformity of  the food  chain 
actors; adequacy of laws, regulations and policies in the exporting country; history of compliance of 
the food; previous reports and certificates. 

349  

Codex Alimentarius Commission. (1995). Principles for Food import and Export inspection and certification. CAC/GL, 20. [CXG 20-1995] 

350  

Codex Alimentarius Commission. (2003). Guidelines for Food Import Control Systems. Document CAC/GL, 47-2003. [CXG 47-2003] 

120 

 
 
 
                                                        
 
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