[2505.17087] Informatics for Food Processing

Computer Science > Computation and Language arXiv:2505.17087 (cs) [Submitted on 20 May 2025 (v1), last revised 3 Apr 2026 (this version, v2)] Title:Informatics for Food Processing Authors:Gordana Ispirova, Michael Sebek, Giulia Menichetti View a PDF of the paper titled Informatics for Food Processing, by Gordana Ispirova and 2 other authors View PDF Abstract:This chapter explores the evolution, classification, and health implications of food processing, while emphasizing the transformative role of machine learning, artificial intelligence (AI), and data science in advancing food informatics. It begins with a historical overview and a critical review of traditional classification frameworks such as NOVA, Nutri-Score, and SIGA, highlighting their strengths and limitations, particularly the subjectivity and reproducibility challenges that hinder epidemiological research and public policy. To address these issues, the chapter presents novel computational approaches, including FoodProX, a random forest model trained on nutrient composition data to infer processing levels and generate a continuous FPro score. It also explores how large language models like BERT and BioBERT can semantically embed food descriptions and ingredient lists for predictive tasks, even in the presence of missing data. A key contribution of the chapter is a novel case study using the Open Food Facts database, showcasing how multimodal AI models can integrate structured and unstructured data to classify fo...