Search

Food fraud is receiving considerable attention with the growing body of literature that recognises its importance. No system exists that collects media reports on food fraud. In this study, we used the infrastructure provided by the European Media Monitor (EMM), in particular it's MedISys portal for this purpose. We developed a food fraud tool (MedISys-FF) that collects, processes and presents food fraud reports published world-wide in the media. MedISys-FF is updated every 10 min 24/7. Food fraud reports were collected with MedISys-FF for 16 months (September 2014 to December 2015) and benchmarked against food fraud reports published in Rapid Alert for Food and feed (RASFF), Economically Motivated Adulteration Database (EMA) and HorizonScan. The results showed that MedISys-FF collects food fraud publications with high relevance >75% and the top 4 most reported fraudulent commodities in the media were i) meat, ii) seafood, iii) milk and iv) alcohol. These top stories align with those found in RASFF and EMA but differences in frequency are apparent. Analysis of the collected articles can help understanding food fraud issues in the origin countries and can facilitate the development of control measures and to detect food fraud in the food supply chain. [ABSTRACT FROM AUTHOR]

Abstract: We evaluated the Medical Information System (MedISys) as an early-warning system for the detection of food- and feed-borne hazards. Nine hazards were selected in the period from January 2007 to March 2009 from the Rapid Alert System for Food and Feed (RASFF) and traced back on MedISys and ProMED-mail. In addition, from January to March 2009, food- and feed-borne (re-)emerging hazards were monitored on MedISys and traced back on ProMED-mail and RASFF. MedISys has demonstrated to be an effective early-warning system for food- and feed-borne hazards. However, further customization is required to improve its sensitivity, in particular by increasing the number of multi-lingual categories related to food and feed items. MedISys tended to detect food- and feed-borne hazards earlier and more frequently than ProMED-mail, but the information from both systems was often complementary. [Copyright &y& Elsevier]

The European Food Safety Authority (EFSA) develops methodologies and tools for the detection of emerging risks in food and feed. This includes the identification of drivers of emerging risks, such as food frauds, which requires innovative approaches. In this study, an unsupervised machine learning technique called the Latent Dirichlet Allocation (LDA) topic model, was applied on a media corpus in the view of detecting rapidly specific food fraud incidents in the media, i.e. on the Europe Media Monitor Medical Information System (EMM/MEDISYS). LDA topic model can explore large collection of documents discovering the themes associated with the corpus and organize and summarize text documents identifying topics comprised in them, where a topic is defined as a pattern of words with their probability to belong to it. As a specific food fraud incident, beeswax adulteration was taken as an example. Beeswax can be adulterated for financial gain, and, although it is a product from apiculture, it might enter the food chain when it is introduced as honeycomb in honey pots. With the beeswax example, a total of 2276 news articles were retrieved on EMM/MEDISYS and classified into 10 topics showing different levels of relatedness to beeswax adulteration. A manual screening of all articles allowed to validate the classification made by the topic model. The topics that were found the most relevant contained indeed articles on beeswax adulteration incidents reported from official sources. In addition, those topics contained signals of potential emerging risks in the cosmetic and food wrapping sectors. The remaining topics highlighted the emergence of new beeswax market opportunities which supported the identified signals. It is concluded that the LDA topic model can be used to process rapidly information in the media, support the definition of more specific food fraud filters on EMM/MEDISYS and be of direct use for all stakeholders involved in the monitoring, assessment and management of food frauds. • Adulteration of beeswax used in apiculture may pose emerging food safety concerns. • MedISys monitors efficiently beeswax adulteration events in the media. • Topic models for rapid identification of emerging risks on beeswax adulteration. [ABSTRACT FROM AUTHOR]