Investigating the flavor contributions of sulfur compounds in soy sauce flavor Baijiu using flavoromics and machine learning.

This study systematically investigated the flavor contribution of sulfur compounds to the soy sauce flavor Baijiu ( SSFB) by integrating traditional flavor analysis with machine learning methods. A total of 88 sulfur compounds were identified through the comprehensive two-dimensional gas chromatography-hydrogen sulfide chemiluminescence detector. The significant flavor contribution was confirmed by orthogonal partial least squares-discriminant analysis and odor activity value, and correlation analysis revealed their systematic associations with 8 aroma notes. Eight core sulfur compounds were screened in combination with 7 machine learning methods. The double matrix addition experiment demonstrated these compounds significantly enhanced the jiang and chen aroma. Notably, the study revealed the matrix-dependent flavor regulation of sulfur compounds. In young SSFB, these compounds simultaneously enhanced the jiang and chen aroma along with floral and fruity notes, while in aged SSFB, they acted more specifically to intensify the core jiang and chen aroma, with a reduced contribution to others.