Application of machine learning to predict food processing level using Open Food Facts

TL;DR

This study applies machine learning models to classify food processing levels using a large dataset, revealing health, environmental, and allergenic implications of ultra-processed foods and providing a web tool for NOVA prediction.

Contribution

It introduces the first large-scale machine learning approach to classify food processing levels based on nutrient data from Open Food Facts.

Findings

LightGBM achieved 80-85% accuracy in classification.

Higher NOVA classes are associated with poorer nutritional and environmental scores.

Ultra-processed foods often contain common allergens like gluten and milk.

Abstract

Ultra-processed foods are increasingly linked to health issues like obesity, cardiovascular disease, type 2 diabetes, and mental health disorders due to poor nutritional quality. This first-of-its-kind study at such a scale uses machine learning to classify food processing levels (NOVA) based on the Open Food Facts dataset of over 900,000 products. Models including LightGBM, Random Forest, and CatBoost were trained on nutrient concentration data. LightGBM performed best, achieving 80-85% accuracy across different nutrient panels and effectively distinguishing minimally from ultra-processed foods. Exploratory analysis revealed strong associations between higher NOVA classes and lower Nutri-Scores, indicating poorer nutritional quality. Products in NOVA 3 and 4 also had higher carbon footprints and lower Eco-Scores, suggesting greater environmental impact. Allergen analysis identified gluten and milk as common in ultra-processed items, posing risks to sensitive individuals. Categories like Cakes and Snacks were dominant in higher NOVA classes, which also had more additives, highlighting the role of ingredient modification. This study, leveraging the largest dataset of NOVA-labeled products, emphasizes the health, environmental, and allergenic implications of food processing and showcases machine learning's value in scalable classification. A user-friendly web tool is available for NOVA prediction using nutrient data: https://cosylab.iiitd.edu.in/foodlabel/.