Volatile Chemistry and Aroma Differences in Commercial Soy Sauces: Sensory Links and Odorant–Receptor Interactions

Volatile and sensory differences among 28 commercial soy sauces were investigated using HS–GC–IMS, check-all-that-apply (CATA) evaluation, aroma addition experiments, and molecular docking. Ninety-nine volatile compounds from 14 chemical classes were detected. Total semiquantitative volatile concentrations ranged from 2024.56 to 6279.59 μg/mL, with esters, alcohols, and aldehydes accounting for 70.68% of the total. Of the 24 CATA descriptors, 13 were retained for product-level correlation analysis. Exploratory Pearson analysis indicated that alcoholic, savory, and malty attributes were associated with a group dominated by fatty acids, branched-chain alcohols, and esters, whereas woody and chocolate-like attributes showed an opposing pattern involving several aldehydes, ketones, and alkylpyrazines. Fifteen candidate compounds were evaluated by single-compound addition. Benzaldehyde and ethyl propanoate enhanced savory perception; 2-ethyl-3,5-dimethylpyrazine enhanced the alcoholic attribute; methyl 2-methylbutyrate enhanced smoky, meaty, and alcoholic attributes; and ethyl acetate enhanced malty, meaty, and alcoholic attributes. Several other additions produced opposing or nonsignificant effects, indicating matrix- and concentration-dependent responses. Among the binary docking models, guaiacol–2-ethyl-3,5-dimethylpyrazine and homofuraneol–benzaldehyde produced the lowest scores with OR10G4 (−10.10 kcal/mol) and OR5M3 (−13.32 kcal/mol), respectively. In combination, these results show that product-level correlations can guide candidate selection but require matrix-based sensory verification, while docking provides structural hypotheses for odorant co-binding.

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Publication Details

Journal
Foods
Published
2026-10-04
DOI
https://doi.org/10.3390/foods15193545
Primary Topic
Sensory Analysis and Statistical Methods
Type
article
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article

Volatile Chemistry and Aroma Differences in Commercial Soy Sauces: Sensory Links and Odorant–Receptor Interactions

Ping Wang, Dandan Pu, Qinguo Quan, Siqi Hu et al.
Foods
Sensory Analysis and Statistical Methods
article

Volatile Chemistry and Aroma Differences in Commercial Soy Sauces: Sensory Links and Odorant–Receptor Interactions

Ping Wang, Dandan Pu, Qinguo Quan, Siqi Hu, Kun Zhao, Baoguo Sun
article en

Abstract

Volatile and sensory differences among 28 commercial soy sauces were investigated using HS–GC–IMS, check-all-that-apply (CATA) evaluation, aroma addition experiments, and molecular docking. Ninety-nine volatile compounds from 14 chemical classes were detected. Total semiquantitative volatile concentrations ranged from 2024.56 to 6279.59 μg/mL, with esters, alcohols, and aldehydes accounting for 70.68% of the total. Of the 24 CATA descriptors, 13 were retained for product-level correlation analysis. Exploratory Pearson analysis indicated that alcoholic, savory, and malty attributes were associated with a group dominated by fatty acids, branched-chain alcohols, and esters, whereas woody and chocolate-like attributes showed an opposing pattern involving several aldehydes, ketones, and alkylpyrazines. Fifteen candidate compounds were evaluated by single-compound addition. Benzaldehyde and ethyl propanoate enhanced savory perception; 2-ethyl-3,5-dimethylpyrazine enhanced the alcoholic attribute; methyl 2-methylbutyrate enhanced smoky, meaty, and alcoholic attributes; and ethyl acetate enhanced malty, meaty, and alcoholic attributes. Several other additions produced opposing or nonsignificant effects, indicating matrix- and concentration-dependent responses. Among the binary docking models, guaiacol–2-ethyl-3,5-dimethylpyrazine and homofuraneol–benzaldehyde produced the lowest scores with OR10G4 (−10.10 kcal/mol) and OR5M3 (−13.32 kcal/mol), respectively. In combination, these results show that product-level correlations can guide candidate selection but require matrix-based sensory verification, while docking provides structural hypotheses for odorant co-binding.

FoodsVol. 15(19)
Beijing Technology and Business University (CN), China Academy of Chinese Medical Sciences (CN)
Openalex Percentile: Top 15%
Sensory Analysis and Statistical Methods
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