A systematic review of data and models for predicting food flavor and texture

Abstract

This review systematically examines the current landscape of data resources and computational models for predicting food flavor and texture. Taste is the most well-defined sensory component, and molecular classification is aligned with the five basic tastes: sweet, sour, bitter, salty, and umami. Odor prediction, while similar in premise, faces greater challenges due to the vast and diverse range of detectable odors and a lack of standardized olfactory metrics. Machine learning models, including graph neural networks and deep learning methods, have shown promise in identifying taste and odor compounds. Texture prediction has seen comparatively less research interest but may prove to be impactful in food quality control pipelines, although more work is needed in creating robust food texture datasets. The review highlights the growing availability of specialized databases which support the development and benchmarking of predictive models. Despite recent advancements, gaps remain in mapping sensory spaces and incorporating receptor-level data. Future directions include creating more extensive and high-quality datasets, improving model explainability, and exploring innovative applications in food design, fragrance, pharmaceuticals, and environmental monitoring. This work provides a comprehensive resource for researchers aiming to advance the field of flavor and texture prediction.