FlavorDiffusion: Predicting Food Pairings and Chemical Interactions Using Diffusion Models
This addresses food science and computational gastronomy by providing scalable, interpretable solutions for ingredient pairing prediction, though it appears incremental as it builds on existing diffusion and graph methods.
The paper tackles the problem of predicting food pairings and chemical interactions without chromatography by developing FlavorDiffusion, a diffusion model framework that integrates graph embeddings and chemical encoding. It achieves state-of-the-art NMI scores and enables discovery of novel ingredient combinations.
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.