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The labelling of food products in the EU is regulated by the Food Informationof Customers (FIC). Companies are required to provide the correspondinginformation regarding nutrients and allergens among others. With the rise ofe-commerce more and more food products are sold online. There are often errorsin the online product descriptions regarding the FIC-relevant information dueto low data quality in the vendors' product data base. In this paper we proposea hybrid approach of both rule-based and machine learning to verify nutrientdeclaration and allergen labelling according to FIC requirements. Special focusis given to the problem of false negatives in allergen prediction since thisposes a significant health risk to customers. Results show that a neural nettrained on a subset of the ingredients of a product is capable of predictingthe allergens contained with a high reliability.

Hybrid consistency and plausibility verification of product data according to FIC