development of early warning systems to detect, predict
TRANSCRIPT
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Ensuring the Integrity of the European food chain
Development of early warning systems to detect, predict and assess
food fraud
Hans Marvin, Bram Steen & Yamine BouzembrakRIKILT Wageningen UR, Wageningen, the Netherlands
Rabin NesloUniversity Medical Center Utrecht , the Netherlands
Partner(s) logo(s)
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Outline
Data sources of food fraud: development of European Media Monitor (EMM)
Prediction of food fraud: Bayesian Network (BN) modelling approach
3rd FoodIntegrity Conference, Prague, 6-7 April 2016
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WP8 objectiveDevelop a structured approach for collecting and analysing information regarding potential drivers of the EU food chain fraud events and frequency of fraud incidents for commodities
Existing: RASFF, UPS, EMA
Develop new: EMM
Data sources
Application of Bayesians Network modelling
Prediction models
ENDUSER (industry, authorities)
3rd FoodIntegrity Conference, Prague, 6-7 April 2016
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The Europe Media Monitor (EMM) provides advanced analysis
systems for monitoring of both traditional and social media.
EMM applies text mining techniques to screen different types of
media on the world wide web: websites, databases, blogs, ..etc.
EMM is updated every 10 minutes, 24 hours per day.
EMM gathers reports from news portals world-wide in 60
languages.
EMM contain 3 portals: NewsBrief, NewsExplorer and MedISys
(http://emm.newsbrief.eu/overview.html)
EMM; characteristics
3rd FoodIntegrity Conference, Prague, 6-7 April 2016
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MedISys: public health related topics
http://medusa.jrc.it/medisys/homeedition/en/home
No collection of publications on food fraud
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EMM Food Fraud Filter Design steps
Step 4: Evaluation and improvement of the filter.
Analyse the articles Relevance evaluation Key words improvement
Step 3: The design of the food fraud filter in EMM
EMM system
Step 2: Validation of the keys words by Food fraud experts
Prof. Saskia Van Ruth (RIKILT) Dr. Hans Marvin (RIKILT) Dr. Karen Everstine (USA)
Step 1: Definition of food fraud key words
Scientific articles Food Fraudarticles USP Database EMA Database RASFF
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News papers Blogs Databases Websites
Text mining tool
- Updated every 10 minutes.- 24/7- 60 languages
- Automatic retrieval of reports- Automatic data storage- Automatic data processing
- Data visualisation using
EMM Food Fraud Filter Design steps
3 4
1
2
- 6000 websites- ...etc
- 600 keywords.- 8 languages.- ...etc
3rd FoodIntegrity Conference, Prague, 6-7 April 2016
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Food fraud reports (overall) in MedISys (period September 2014 to December 2015; N = 1114)
Data visualisation using ArcGIS 4
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Food fraud reports (milk) in MedISys (period September 2014 to December 2015)
Data visualisation using ArcGIS 4
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EMM results
Developed in September 2014 Tested in the period September 2014 to
December 2015 Number of articles collected 1144 Number of relevant articles ca. 75% Will be public available by end of April 2016
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Food fraud alerts/ reports: a comparison
NEW
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Outline
Data sources of food fraud: development of European Media Monitor (EMM)
Prediction of food fraud: Bayesian Network (BN) modelling approach
3rd FoodIntegrity Conference, Prague, 6-7 April 2016
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3rd FoodIntegrity Conference, Prague, 6-7 April 2016
Can we help the customs controller to decide what type of
fraud should be checked?
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Food fraud is reported in Rapid Alert System for Food and Feed (RASFF)
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Prediction of food fraud type using BN
• All notifications reported in the RASFF database under the hazard category “adulteration/fraud” from the period 01/01/2000 to 31/12/2013 (N = 749).
• Machine learning technique: expectation-maximization-algorithm.
1. Variable identification
3. BN model validation
Variable name Node name StatesFraud type Fraud HC, Illegal-importation, Tampering, CED, Expiration Date,
LabellingNotification type Notification information, border-rejection, alertProduct category Product alcoholic, molluscs,..., wildNotification year Year 2000, 2001,..., 2013Notifying country Notified United kingdom, Portugal,...,Austria (i.e. member countries to
RASFF)Country of origin Origin United States, Japan,...,Brazil (i.e. countries from which the
product was imported)
2. Learning the BN model
• RASFF food fraud notifications reported in 2014 were used to validate the BN model (N=88).
Predicted 80% of cases correctly
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Modelling of Fraud in RASFF=> statistic relationships between all parameters
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Applications of such BN models
Provides understanding of relationships between all parameters
Supports the evaluation of the effects of mitigations measures (scenario’s)
Allows forecasting/ prediction
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Extended BN model that connects many drivers and parameters relevant to food fraud
Some examples
Economic drivers:• Prices of the fraudulent product at the time of detection• Price spike around the period of detection• Trade volumes of the product between the country of
detection and country of origin• Complexity of the food chain
Parameters of the country of origin & detection• Indices: corruption index, food safety index, governance
index, legal system index, press index, human development index and technology index.
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BNs in holistic approach of food safety linking 36 data sources (18 databases and 8 expert judgements)
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Extended BN model to assess food fraud
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The type of food fraud may depend on country of origin
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Conclusions
An EMM filter has been created that collects media reports on food fraud worldwide and will be online end of April 2016
Automatic retrieval of these reports from the EMM filter has been realised
Models based on Bayesians network can be used to predict the type of food fraud as reported in RASFF
BN models can be used to evaluate relationships between food fraud parameters
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The project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement No. 613688.
www.foodintegrity.eu
3rd FoodIntegrity Conference, Prague, 6-7 April 2016