Work

Informatics for Food Processing.

Year: 2025

Type: preprint

Abstract: , 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 foods at scale, offering a new paradigm for food processing assessment in public health and research.

Source: PubMed

Authors: Gordana Ispirova, Michael Sebek, Giulia Menichetti

Institutions: Brigham and Women's Hospital, Harvard University, Northeastern University

Language: English

Topic: Consumer Attitudes and Food Labeling

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Zero hunger

Open Access status: green

Funder: National Heart, Lung, and Blood Institute

Award: Methods for Unraveling the Impact of Dietary Xenobiotics on COPD Exacerbations with Multi-Dimensional Networks

OpenAlex ID: W4416624958

Cites:

Cited by:

Related to: