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<p>Food is a more complex system than commonly perceived, comprising tens of thousands of molecules whose compositions and interactions ultimately shape human perception. To conceptualize this multifaceted nature, we frame food complexity across three interconnected layers: the molecular composition that defines its chemical foundation, the component interactions that shape food properties, and the perceptual responses that arise from human sensory systems. This review discusses how machine learning is advancing our ability to decode each of these layers, together with multimodal and data-fusion frameworks. Understanding these three layers may enable more accurate prediction of food properties, guide food product innovation, and deepen our scientific understanding of food.</p>

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Machine learning unveils three layers of food complexity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月6日

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  • Qinfei Ke,
  • Jingzhi Zhang,
  • Xin Huang,
  • Xingran Kou,
  • Dachuan Zhang

摘要

Food is a more complex system than commonly perceived, comprising tens of thousands of molecules whose compositions and interactions ultimately shape human perception. To conceptualize this multifaceted nature, we frame food complexity across three interconnected layers: the molecular composition that defines its chemical foundation, the component interactions that shape food properties, and the perceptual responses that arise from human sensory systems. This review discusses how machine learning is advancing our ability to decode each of these layers, together with multimodal and data-fusion frameworks. Understanding these three layers may enable more accurate prediction of food properties, guide food product innovation, and deepen our scientific understanding of food.