Decoding Sulfur-Containing Aroma Compounds in Foods: From Key Odorant Mapping to Structure–Odor Mechanisms and Flavor Design
With extremely low odor thresholds and potent flavor activity, sulfur-containing aroma compounds (SACs) constitute the molecular cornerstone of characteristic flavors in meat, coffee, and fermented foods. Research has advanced from early component identification to the elucidation of structure–activity relationships, olfactory receptor recognition mechanisms, and food-flavor improvement. This review first summarizes the detection and quantification methods for SACs, their distribution in foods, key odor contributions, and major formation pathways. It then highlights progress in understanding molecular structural parameters, olfactory receptor recognition, and computational simulations that decode flavor perception mechanisms. From a translational perspective, we further discuss flavor retention in real food matrices, off-flavor regulation, cross-modal perceptual enhancement, and functional applications. Current challenges include food matrix complexity, high compound reactivity, and nonlinear olfactory combinatorial coding. Future directions involve constructing a multiscale predictive framework integrating neuroscience, developing explainable artificial intelligence to decode olfactory coding, and advancing closed-loop green biomanufacturing for the precise design and sustainable production of SACs.
Authors
- Lulu Ma
- Jiaying Huo (ORCID: https://orcid.org/0000-0003-0763-2361)
- Shugang Li (ORCID: https://orcid.org/0000-0002-0912-6641)
- Jinyuan Sun (ORCID: https://orcid.org/0000-0001-6717-9787)
- Hao Wang (ORCID: https://orcid.org/0009-0008-4494-6793)
- Jinpeng Hu
Institutions
- Anhui University (CN)
- Hefei University of Technology (CN)
- Beijing Technology and Business University (CN)
- Anhui Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Foods
- Published
- 2026-08-26
- DOI
- https://doi.org/10.3390/foods15173003
- Primary Topic
- Olfactory and Sensory Function Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Beijing Technology and Business University