Hans Marvin and Yamine Bouzembrak (RIKILT … · The food fraud filter in MedISys, as developed by...
Transcript of Hans Marvin and Yamine Bouzembrak (RIKILT … · The food fraud filter in MedISys, as developed by...
Food Fraud and early warning
Hans Marvin and Yamine Bouzembrak
(RIKILT Wageningen UR)
International symposium on Food Safety, July 16 2015, Mauritius
Outline
Food fraud
Identification and early warning systems/ approaches
Conclusions
Food fraud definition
There is no universally accepted definition of food fraud
Food and Drug Administration (FDA) adopted a working definition of Economically Motivated Adulteration (EMA):
“fraudulent, intentional substitution or addition of a substance in a product for the purpose of increasing the apparent value of the product or reducing the cost of its production, i.e. for economic gain”.
Source: Saskia van Ruth (RIKILT), 2015
Food fraud may result in a food safety risk
Food Fraud
Why?
Mostly economic gain (the scale of all product counterfeiting is estimated at 5–10% of world trade)
How?
Dilution (e.g. juices and olive oil),
Substitution (e.g. “species swapping” with fish),
Origin masking (e.g. Honey, biologic vs conventional),
Addition of an unapproved additive (e.g. antibiotics or dyes).
How to detect and prevent food fraud?
Outline
Food fraud
Detection and early warning systems/ approaches
Conclusions
Detection of fraud
Analytical methods (chemical, physical, microscopy). Is the product what it is claimed to be.
● Targeted approach: single marker
● Untargeted approach: Multiple markers
● Fingerprints
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Early warning of food fraud
Use of databases
Food fraud databases analysis (RASFF, EMA, UPS etc)
Prediction modelling (agent modelling, Bayesian networks)
Internet/ media based systems
Text mining approach (MedISys)
Food fraud databases analysed
Rapid Alert System for Food and Feed
Food fraud notifications in RASFF (2000 to 2013).
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Products related to food fraud in RASFF
Food fraud types in RASFF
Fraud type Description Count of notification type
Percentage
HC Improper or fraudulent or missing or absent of health certificate
370 44%
Illegal importation Illegal or unauthorized import or trade or transit
256 31%
Tampering Adulteration or fraud or tampering 126 15%
CED Improper or expired or fraudulent or missing of common entry document or import declaration
45 5%
Expired Date Expired Date 24 3%
Mislabelling Mislabelling 13 2%
Grand Total 834 100%
Modelling of Fraud in RASFF
Model developed
with RASFF data up
to 2013
Model validated
with RASFF data of
2014
Model performance:
• 82% correct
prediction of
fraud type
• 51% prediction
of new fraud
combinations
• static
Higher performance with dynamic model
approach
Food fraud databases analysed
Economically Motivated Adulteration (EMA)
EMA refers to the intentional adulteration of food for economic gain
Information in this database is collected from the following sources:
LexisNexis, PubMed, Google, FDA Consumer and FDA recall records, state reports, and RASFF
Food fraud incidents in EMA
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Products related to food fraud in EMA
Text mining approach
The Europe Media Monitor (EMM) provides advanced analysis systems for
monitoring of both traditional and social media.
• EMM presents the latest news and classifies it according to subject.
• It is updated every 10 minutes, 24 hours per day.
• It gathers reports from news portals world-wide in 60 languages,
• EMM classifies the articles, analyses the news texts, aggregates the
information and issues alerts.
• EMM applies text mining techniques to screen different types of media
on the world wide web: websites, databases, blogs, ..etc.
• EMM contain 3 portals: NewsBrief, NewsExplorer and MedISys
(http://emm.newsbrief.eu/overview.html)
MedISys: public health related topics
http://medusa.jrc.it/medisys/homeedition/en/home
No collection of
publications on
food fraud
Food Fraud keywords
Generic Key words Specific key words Excluded key word
• Food Fraud• Mislabelled • Counterfeit• Tampering• Misleading• False• Intentional
substitution• Smuggling• Dilution • Substitution• ..etc
• food+fraud• fake+food• mislabeled+food• economic+adulteration
+food• food+adultration• counterfeit+food• olive+oil+dilution• olive+oil+fraud• counterfeit+olive+oil• mislabeled+olive+oil• honey+mislabling• honey+fraud
• Fraud• Artificial Food• Counterfeit Medicine• Goods fraud• Wine• How to make fake
alcohol?• How to make fake
milk?• How to make fake
Oil?
Total of included key words
64 x 8 = 512
Key Words Languages
Arabic
Dutch
Spanish
English
Chinese
Italian
French
German
Food fraud filter: Number of articles
Food fraud filter efficiency
Method improvement
Key words improvement
Key words improvement
Food Fraud filter efficiency:More than 95 Food Fraud articles in last 2 months.Relevance index = 86%.
MedISys adapted to detect food fraud
Food fraud reports in MedISys(period September 2014 to June 2015; N > 600)
Food fraud reports in MedISys(period September 2014 to June 2015; N > 600)
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USA
France
India
Food fraud reports in MedISys(period September 2014 to February 2015)
Conclusions
There is an increasing trend in the number of food fraud incidents, as reported in RASFF and EMA
In both databases meat & meat products and fish & fish products are among the top 3 food categories with the highest number of reports
Dynamic BN models are suitable to predict the food fraud category as reported in RASFF
The food fraud filter in MedISys, as developed by RIKILT, is useful to collect publications in the media on food fraud
The food fraud coverage in the media differ greatly between countries
Thank you