Scholarlink

<p>Global food systems must deliver nutritious, sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical and fragmented. Artificial intelligence (AI) offers a transformative path to link molecular composition to functional performance, connect chemical structure to sensory outcomes and accelerate cross-disciplinary innovation across the production pipeline. While it is broadly applicable to food systems, we focus on sustainable proteins—plant-based, fermentation-derived and cultivated—as a high-impact test bed for AI-driven closed-loop design. We review the applications, opportunities and challenges of AI for food as an emerging discipline that integrates ingredient design, formulation development, fermentation and production, texture analysis, sensory science, manufacturing and recipe generation. We identify four priorities: advancing scientific machine learning with embedded domain priors, treating food as a programmable biomaterial, building self-driving laboratories for automated discovery and developing deep reasoning models that integrate nutrition and sustainability. Integrating AI responsibly into the food innovation cycle can accelerate the transition to sustainable food systems and establish a predictive, design-driven science of food for human and planetary health.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial intelligence for food innovation

  • 期刊论文

  • 发表日期：

    2026年7月3日

查看全文
  • Bianca Datta,
  • Markus J. Buehler,
  • Yvonne Chow,
  • Kristina Gligorić,
  • Dan Jurafsky,
  • David L. Kaplan,
  • Rodrigo Ledesma-Amaro,
  • Giorgia Del Missier,
  • Lisa Neidhardt,
  • Karim Pichara,
  • Benjamin Sanchez-Lengeling,
  • Miek Schlangen,
  • Skyler R. St. Pierre,
  • Ilias Tagkopoulos,
  • Anna Thomas,
  • Nik Watson,
  • Ellen Kuhl

摘要

Global food systems must deliver nutritious, sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical and fragmented. Artificial intelligence (AI) offers a transformative path to link molecular composition to functional performance, connect chemical structure to sensory outcomes and accelerate cross-disciplinary innovation across the production pipeline. While it is broadly applicable to food systems, we focus on sustainable proteins—plant-based, fermentation-derived and cultivated—as a high-impact test bed for AI-driven closed-loop design. We review the applications, opportunities and challenges of AI for food as an emerging discipline that integrates ingredient design, formulation development, fermentation and production, texture analysis, sensory science, manufacturing and recipe generation. We identify four priorities: advancing scientific machine learning with embedded domain priors, treating food as a programmable biomaterial, building self-driving laboratories for automated discovery and developing deep reasoning models that integrate nutrition and sustainability. Integrating AI responsibly into the food innovation cycle can accelerate the transition to sustainable food systems and establish a predictive, design-driven science of food for human and planetary health.