Choy, M. J. Y., Theng, A. H. P., Wang, L., Foo, C. S., Khoo, E., & Chiang, J. H. (2026). Enhancing nutritional yeast-derived meaty flavours with multi-task Bayesian optimisation. Food Chemistry, 517, 149493. https://doi.org/10.1016/j.foodchem.2026.149493
Abstract:
This study applied data-driven machine learning to optimise the generation of meaty flavour from Maillardreaction-derived nutritional yeast hydrolysates. Conventional flavour development relies heavily on empirical expertise, limiting efficiency and generalisability. In this study, a Multi-Task Bayesian Optimisation (MTBO) framework integrating gas chromatography–mass spectrometry and descriptive sensory data was developed to accelerate flavour optimisation. A Multi-Task Gaussian Process model was used to fit relationships between input parameters (enzyme-to-substrate and protein-to-sugar ratios) and experimental outputs (volatile compound concentrations and sensory attribute scores). Bayesian Optimisation iteratively recommended new parameters for experimental validation. Model-guided optimisation resulted in a 3.0-fold increase in total pyrazine concentration and a 16.58% reduction in 2-furanmethanol in glucose-derived MRPs, alongside a 2.7-fold increase in total pyrazine concentration for xylose-derived MRPs. Sensory validation demonstrated a 55%–79% reduction in perceived bitterness and up to a 28% increase in perceived meaty flavour intensity, indicating MTBO's potential to accelerate flavour optimisation.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR Biomedical Research Council - SINGAPORE FOOD STORY R&D PROGRAMME INDUSTRY ALIGNMENT FUND-PRE POSITIONING (IAF-PP) ON THEME 2 – FUTURE FOODS: ALTERNATIVE PROTEINS (HBMS DOMAIN)
Grant Reference no. : H20H8a0002