Hybrid consistency and plausibility verification of product data according to FIC
This work is significant for e-commerce food vendors and consumers, as it aims to improve the accuracy of FIC-relevant information, particularly allergen labeling, thereby mitigating health risks for customers.
This paper addresses errors in online food product descriptions, specifically regarding nutrient declarations and allergen labeling, which are regulated by the Food Information of Customers (FIC) in the EU. The authors propose a hybrid rule-based and machine learning approach, demonstrating that a neural network trained on a subset of ingredients can reliably predict allergens.
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.