用扩散模型预测食材化学互作，无需实验即可发现新搭配
FlavorDiffusion: Predicting Food Pairings and Chemical Interactions Using Diffusion Models
- 基于图嵌入与扩散过程建模食材-化学关系
- 在Recipe1M和FlavorDB上实现最优NMI得分
- 适合食品科学与智能烹饪研发人员
食品搭配研究已从主观经验转向机器学习驱动。本文提出FlavorDiffusion框架，利用扩散模型预测食材-化学相互作用及配料搭配，无需依赖色谱分析。通过整合基于图的嵌入、扩散过程与化学性质编码，该模型缓解数据不平衡问题并提升聚类质量。基于Recipe1M和FlavorDB构建的异质图数据集上，模型在重建配料-配料关系方面表现优异。新增的化学结构预测（CSP）层进一步优化嵌入空间，达到当前最优的NMI分数，支持发现具有意义的新颖食材组合。该框架为计算烹饪学提供可扩展、可解释且基于化学信息的解决方案。
原文摘要 · Abstract (English)
The study of food pairing has evolved beyond subjective expertise with the advent of machine learning. This paper presents FlavorDiffusion, a novel framework leveraging diffusion models to predict food-chemical interactions and ingredient pairings without relying on chromatography. By integrating graph-based embeddings, diffusion processes, and chemical property encoding, FlavorDiffusion addresses data imbalances and enhances clustering quality. Using a heterogeneous graph derived from datasets like Recipe1M and FlavorDB, our model demonstrates superior performance in reconstructing ingredient-ingredient relationships. The addition of a Chemical Structure Prediction (CSP) layer further refines the embedding space, achieving state-of-the-art NMI scores and enabling meaningful discovery of novel ingredient combinations. The proposed framework represents a significant step forward in computational gastronomy, offering scalable, interpretable, and chemically informed solutions for food science.
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