  The labelling of food products in the EU is regulated by the Food Information
of Customers (FIC). Companies are required to provide the corresponding
information regarding nutrients and allergens among others. With the rise of
e-commerce more and more food products are sold online. There are often errors
in the online product descriptions regarding the FIC-relevant information due
to low data quality in the vendors' product data base. In this paper we propose
a hybrid approach of both rule-based and machine learning to verify nutrient
declaration and allergen labelling according to FIC requirements. Special focus
is given to the problem of false negatives in allergen prediction since this
poses a significant health risk to customers. Results show that a neural net
trained on a subset of the ingredients of a product is capable of predicting
the allergens contained with a high reliability.
