Hybrid consistency and plausibility verification of product data according to FIC

TL;DR

This paper presents a hybrid rule-based and machine learning approach to verify food product data online, focusing on nutrient and allergen labeling accuracy according to EU regulations, with special attention to false negatives in allergen detection.

Contribution

It introduces a novel hybrid verification method combining rule-based and neural network techniques for FIC compliance in online food product data.

Findings

Neural network accurately predicts allergens with high reliability.

Hybrid approach effectively detects errors in nutrient and allergen labeling.

Focus on reducing false negatives enhances customer safety.

Abstract

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.