Microbial Deodorization of Gastrodia elata: Aroma Profile Improvement and Gastrodin Enrichment via ANN-GA-Guided Fermentation

The industrial potential of Gastrodia elata is constrained by its distinct sensory characteristics. This work employed a computational optimization approach to refine the solid-state biotransformation using Aspergillus cristatus, aiming to boost the yield of the target metabolite while addressing undesirable volatiles. Through this strategy, the content of the principal bioactive compound reached 0.3887 ± 0.05 mg/g, marking a 1.5-fold increase compared to untreated samples (p = 0.023). Volatile profiling via HS-SPME-GC/MS revealed significant reductions in three major off-flavour contributors: phenethyl alcohol (90.9% decrease, p < 0.01), 3-mercapto-2-pentanone (85.6% decrease, p < 0.01), and 4-aminopyridine (82.8% decrease, p < 0.01). Metabolic analysis elucidated two underlying mechanisms: the suppression of sulphur-containing volatiles through the downregulation of the glutathione and glucosinolate pathways, and the generation of favourable notes via the augmented synthesis of (E, Z)-2,6-nonadienal (7.4-fold) and 2,4-undecadienal (3.3-fold). This study demonstrates how machine learning-driven microbial processing can simultaneously enhance functional constituents and mitigate sensory limitations in herbal materials, offering a viable route for value-added utilization.