Food safety risk prediction with Deep Learning models using categorical embeddings on European Union data

• URL: http://arxiv.org/abs/2009.06704v1
• Date: Mon, 14 Sep 2020 19:36:58 GMT
• Title: Food safety risk prediction with Deep Learning models using categorical embeddings on European Union data
• Authors: Alberto Nogales, Rodrigo D\'iaz Mor\'on, \'Alvaro J. Garc\'ia-Tejedor
• Abstract summary: The European Union began to register in 1977 all irregularities related to traded products to ensure cross-border monitoring. Data related to food issues was scraped and analysed with Machine Learning techniques to predict some features of future notifications. Results show that the system can predict these features with an accuracy ranging from 74.08% to 93.06%.
• Score: 1.4502611532302039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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