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%.

中文翻译：

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使用欧盟数据分类嵌入的深度学习模型进行食品安全风险预测

世界每天都在变得越来越全球化，人们可以在他们的本地商店从世界上几乎每个国家购买产品。鉴于各国之间不同的食品和饲料安全法律，欧盟于1977年开始注册与贸易产品有关的所有违规行为，以确保对食品信息中的公共卫生风险进行跨境监控和快速反应。 。该信息还具有巨大的潜力，可作为预防工具，以警告参与食品安全的行为者并优化其资源。在本文中，我们收集了一组与食品问题相关的数据，并使用机器学习技术对其进行了分析，以预测未来通知的某些功能，从而可以采取先发制人的措施。作品的新颖性取决于两点：将类别嵌入与深度学习模型（多层感知器和一维卷积神经网络）配合使用，并将其用于解决欧盟预测食物问题的问题。这些模型可以预测几个特征：产品类别，危害类别以及最终要采取的适当措施。结果表明，该系统可以以74.08％至93.06％的精度预测这些特征。