Abstract
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.
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Acknowledgements
This project was supported by the Collaborative Innovation Center of Fragrance Flavor and Cosmetics, the Ministry of Education, Singapore, under the Academic Research Fund Tier 1 (A-8003718-00-00), and the Start-Up Grant of the National University of Singapore (A-0010237-00-00). The authors thank the anonymous reviewers for their valuable comments. The authors acknowledged using an AI tool (ChatGPT) for text polishing and grammar check. The author is fully responsible for the content and conclusions of the manuscript.
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X. K. and D.Z. designed the research. Q.K., J.Z., X. H., X.K., and D.Z. wrote the initial manuscript. J.Z., Q.K., X.K., and D.Z. rechecked the manuscript and participated in manuscript revision. All authors approved the final paper.
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Ke, Q., Zhang, J., Huang, X. et al. Machine learning unveils three layers of food complexity. npj Sci Food (2026). https://doi.org/10.1038/s41538-026-00730-w
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DOI: https://doi.org/10.1038/s41538-026-00730-w
