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Mihalis Papakonstantinou

Agroknow, Greece

Can big data predict food fraud and food contamination?

Can large-scale data analysis reveal patterns related to the way that people modify food products, ingredients or packaging for economic gain? Is it possible to predict whether someone in the supply chain has substituted, misbranded, counterfeited, stolen or enhanced food in an unapproved way? This will probably include, among others: Food fraud incidents that have been officially announced by a governmental agency or inspection authority. Lab test analyses that indicate that there has been a substitution or enhancement of a food for an economic purpose. An interesting question then arises. Can we mine a historical database of international food fraud to successfully predict what will happen in the future? For instance, can we look into product or ingredient categories and try to identify emerging fraud types before they actually occur? What if we add more data in the mix? Can a severe weather phenomenon be linked to an increase of food adulteration incidents in this area? The food supply chain has many critical problems of this kind. People need to make decisions about whether risk exists. They need to know if a preventive action needs to be taken as early as possible. To do so, they rely on a large number and rich variety of data sources.

Mihalis Papakonstantinou

Agroknow, Greece

Mihalis Papakonstantinou

Agroknow, Greece

Biography

Mihalis is a top-tier data engineer and passionate about food safety data. He is the Data Team Leader at Agroknow, He loves open software stacks, crawling that aggregates data from all over the world and he is the person behind our FOODAKAI platform data. He has been involved with FOODAKAI since day#1, implementing its data platform that currently contains more than 65M food safety related data points. Over the past years, he has worked on providing data-powered solutions to sectors varying from academic institutes and media to financial institutions.

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