  The world is becoming more globalized every day and people can buy products
from almost every country in the world in their local stores. Given the
different food and feed safety laws from country to country, the European Union
began to register in 1977 all irregularities related to traded products to
ensure cross-border monitoring of information and a quick reaction when risks
to public health are detected in the food chain. This information has also an
enormous potential as a preventive tool, in order to warn actors involved in
food safety and optimize their resources. In this paper, a set of data related
to food issues was scraped and analysed with Machine Learning techniques to
predict some features of future notifications, so that pre-emptive measures can
be taken. The novelty of the work relies on two points: the use of categorical
embeddings with Deep Learning models (Multilayer Perceptron and 1-Dimension
Convolutional Neural Networks) and its application to solve the problem of
predicting food issues in the European Union. The models allow several features
to be predicted: product category, hazard category and finally the proper
action to be taken. Results show that the system can predict these features
with an accuracy ranging from 74.08% to 93.06%.
