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Related Experiment Videos

Artificial intelligence for food innovation.

Bianca Datta1, Markus J Buehler2,3, Yvonne Chow4

  • 1Good Food Institute, Washington, DC, USA.

Nature Food
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can accelerate sustainable food innovation by linking molecular data to food performance. This approach is crucial for developing nutritious, eco-friendly foods and transforming global food systems.

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Area of Science:

  • Food Science and Technology
  • Computational Biology
  • Sustainable Agriculture

Background:

  • Global food systems face pressure to provide nutritious, sustainable food while minimizing environmental impact.
  • Current food innovation is often slow, empirical, and fragmented, hindering progress.
  • Artificial intelligence (AI) presents an opportunity to revolutionize food systems by connecting molecular composition to function and sensory attributes.

Purpose of the Study:

  • To review the applications, opportunities, and challenges of AI in food innovation.
  • To focus on AI-driven design for sustainable proteins (plant-based, fermentation-derived, cultivated) as a key area.
  • To identify priorities for advancing AI in the food sector.

Main Methods:

  • Review of existing literature on AI applications in food science.
  • Analysis of AI's role in ingredient design, formulation, fermentation, texture analysis, sensory science, and manufacturing.
  • Identification of key research and development priorities for AI in food.

Main Results:

  • AI can link molecular composition to functional performance and chemical structure to sensory outcomes.
  • AI facilitates cross-disciplinary innovation across the entire food production pipeline.
  • Sustainable proteins serve as a high-impact test bed for AI-driven closed-loop design.

Conclusions:

  • Integrating AI responsibly can accelerate the transition to sustainable food systems.
  • AI enables a predictive, design-driven science of food for improved human and planetary health.
  • Key priorities include advancing scientific machine learning, treating food as a programmable biomaterial, developing self-driving laboratories, and creating deep reasoning models for nutrition and sustainability.