@article{WANG2026, 
author = {Bei WANG and Yunhan DUAN},
title = {Application and Integration Prospects of Sensomics and Flavoromics in Food Flavor Research},
year = {2026},
journal = {Journal of Food Science and Technology},
volume = {44},
number = {3},
pages = {32-43},
keywords = {artificial intelligence, flavor compounds, food flavor, flavoromics, sensomics},
url = {https://www.sciopen.com/article/10.12301/spxb202600113},
doi = {10.12301/spxb202600113},
abstract = {Flavor is a core element determining food quality. To solve the challenge of establishing a precise causal relationship between “chemical compositions” and “human perceptions” in complex food systems, sensomics and flavoromics have became two major paradigms in food flavor research in recent years. The core workflows, advantages, disadvantages, and future integration trends of these two research paradigms were systematically reviewed. Guided by human senses, sensomics established a causal chain between “key aroma-active compounds” and sensory attributes through gas chromatography-olfactometry (GC-O), precise quantification, and aroma recombination and omission experiments. It possessed strong mechanistic explanatory power but faced limitations such as low throughput, high quantification costs, and subjective evaluation. Flavoromics relied on high-throughput instruments to obtain untargeted “chemical fingerprints” and combined multivariate statistics to mine differential compounds. It had broad coverage and was commonly used for quality monitoring and traceability. However, its static analysis struggled to reflect the dynamic release in the oral cavity, and its correlation mining did not directly equate to causal verification. Given the complementarity between the “top-down causal verification” of sensomics and the “bottom-up correlation mining” of flavoromics, this study proposed that flavor science evolved toward an intelligent integration paradigm, which deeply merged the mechanistic verification logic of sensomics with the big data mining capabilities of flavoromics. It was suggested that future research relied on artificial intelligence and multi-omics technologies to construct standardized multimodal flavor databases and deepen the mechanistic analysis of spatiotemporal dynamic perception. This integrated paradigm was expected to complete the digital closed-loop between chemical compositions and sensory experiences, providing scientific support for the precise regulation and targeted creation of food flavors.}
}