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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10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

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).

Year

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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

Year

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In

cid

en

ts

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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UK

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