Characterization and decoding of volatile compounds in Rugiboletus extremiorientalis at different drying temperatures: A combined analysis by ATR-FTIR, HS-SPME-GC-MS, E-nose, and molecular docking.

This study used Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, electronic nose, and gas chromatography-mass spectrometry (GC-MS) to investigate the composition of Rugiboletus extremiorientalis changes at different drying temperatures. Molecular docking was employed to elucidate the perception mechanism of aroma, and machine learning was utilized to develop a rapid quality evaluation method. The spectral data reflected compositional change patterns, which were validated by GC-MS. As temperature increased, component concentrations and spectral absorbance gradually decreased. 86 potential key aroma compounds were screened based on differential metabolites and relative odor activity values (ROAVs). Binding energy results for these compounds and 15 olfactory receptors (ORs) revealed strong binding affinities, primarily through hydrogen bonds and hydrophobic interactions. Partial least squares regression (PLSR) models based on derivatives and iterative variable subset optimization (IVSO) effectively predicted the content of 25C-8 compounds. This study established a research framework linking compositional changes, the perception mechanisms of flavor molecules, and rapid quality evaluation. This framework provides a theoretical basis for understanding changes in mushroom flavor and molecular perception. It also provides methodological guidance for drying processes and rapid compositional analysis.