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Informatics for Food Processing.

Gordana Ispirova1, Michael Sebek2, Giulia Menichetti1,2,3

  • 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

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|November 24, 2025
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Summary
This summary is machine-generated.

This study introduces AI and machine learning for food processing classification, addressing limitations of existing systems. Novel computational methods like FoodProX and multimodal AI offer scalable, data-driven solutions for public health research.

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Area of Science:

  • Food Science and Technology
  • Computational Biology
  • Public Health Informatics

Background:

  • Traditional food processing classification systems (e.g., NOVA, Nutri-Score) face challenges in subjectivity and reproducibility, impacting research and policy.
  • Existing frameworks struggle to consistently assess the health implications of diverse food processing levels.
  • There is a need for objective, scalable, and data-driven methods to classify food processing.

Purpose of the Study:

  • To critically evaluate existing food processing classification systems and their limitations.
  • To introduce and explore novel computational approaches, including machine learning and AI, for food processing assessment.
  • To demonstrate the application of these advanced methods using real-world food composition and description data.

Main Methods:

  • Review of traditional classification frameworks (NOVA, Nutri-Score, SIGA).
  • Development and application of a random forest model (FoodProX) using nutrient data for a continuous processing score.
  • Utilizing large language models (BERT, BioBERT) for semantic embedding of food descriptions and ingredients.
  • Employing multimodal AI models to integrate structured and unstructured data from the Open Food Facts database.

Main Results:

  • FoodProX model successfully infers processing levels and generates a continuous FPro score from nutrient data.
  • Large language models effectively handle missing data and semantically represent food information for predictive tasks.
  • Multimodal AI models demonstrate scalability in classifying foods using integrated data, establishing a new paradigm.

Conclusions:

  • Computational approaches, particularly AI and machine learning, offer significant advantages over traditional methods for food processing classification.
  • Novel methods like FoodProX and multimodal AI provide more objective, reproducible, and scalable tools for food informatics.
  • These advancements hold promise for improving public health research, policy-making, and consumer guidance regarding food processing.