Elucidating the Material Basis and Receptor Mechanism of Bitterness in Castanopsis fissa Honey Using Machine Learning, Metabolomics, and Molecular Docking.

The distinctive bitter profile of Castanopsis fissa honey (LSZH) has not yet been clearly characterized at the chemical and molecular levels. Based on the LSZH samples (n = 6), this study investigated bitterness-associated compounds and their potential receptor interactions by integrating sensory evaluation, machine learning, untargeted metabolomics, electronic tongue analysis, targeted UPLC-QQQ-MS/MS quantification, and molecular docking. A Random Forest model combined with untargeted metabolomics screened 71 candidate bitter compounds, among which alkaloid-related metabolites were prominently represented. Electronic tongue analysis showed that several compounds exhibited higher bitterness-related sensor responses than quinine under the tested conditions. Targeted UPLC-QQQ-MS/MS analysis identified and quantified five key compounds, among which kynurenic acid was the most abundant, reaching approximately 4500 ppm (mg/kg). Molecular docking suggested that these compounds could favorably interact with the human bitter taste receptor TAS2R46, with binding affinities ranging from -5.4 to -6.5 kcal/mol, mainly through hydrogen bonding, hydrophobic interactions, and π-related interactions. Overall, this study provides chemical evidence and mechanistic clues for understanding the bitterness of LSZH and offers an integrated analytical framework for screening bitterness-associated compounds in complex food systems.