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Practical guide for food scientists to build AI: data, algorithms, and applications.

Dachuan Zhang1

  • 1Department of Food Science and Technology, Faculty of Science, National University of Singapore, 2 Science Drive 2, Singapore 117542, Singapore; National University of Singapore (Suzhou) Research Institute, 377 Lin Quan Street, Suzhou Industrial Park, Jiangsu 215123, China.

Food Chemistry
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Artificial intelligence (AI) adoption in food science needs high-quality data, tailored algorithms, and impactful applications. This guide helps food scientists build effective AI models for innovation.

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Food chemistryFood informaticsLarge language modelsMachine learningMultimodal fusionPhysics-informed neural networks

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Area of Science:

  • Food Science
  • Artificial Intelligence
  • Data Science

Background:

  • AI adoption in food science is fragmented.
  • Current AI applications are often limited in scope.
  • Sustainable progress requires a multi-pillar approach.

Purpose of the Study:

  • Provide a practical guide for food scientists to build effective AI models.
  • Outline three foundational pillars for AI in food science: data, algorithms, and applications.
  • Propose a checklist for AI model development and deployment.

Main Methods:

  • Discuss state-of-the-art AI methods relevant to food science.
  • Highlight the importance of high-quality datasets.
  • Emphasize tailored algorithms and impactful applications.

Main Results:

  • AI can drive new scientific insights and industrial value in food science.
  • Specific AI methods like LLMs, high-throughput platforms, PINNs, and multimodal fusion are applicable.
  • A structured approach to AI development is crucial.

Conclusions:

  • Advancing data, algorithms, and applications in tandem is key for AI in food science.
  • The proposed checklist can guide AI model planning, development, evaluation, and deployment.
  • Aligning future work with these pillars will accelerate AI-driven discovery and innovation in the food sector.