Abstract
Protein-flavor binding is a common challenge in food formulation. Prediction models provide a time-, resource-, and cost-efficient way to investigate how the structural and physicochemical properties of flavor compounds affect this binding mechanism. This study presents a Quantitative Structure-Activity Relationship model derived from five commercial plant-based proteins and thirty-three flavor compounds. The results showed that protein-flavor binding is primarily influenced by the structure and physicochemical properties of the flavor compound, with the protein source having a minor contribution. In addition to hydrophobicity, topological, electronic, and geometrical descriptors significantly contribute to the observed protein-flavor binding. The Random Forest model demonstrated a strong correlation between predicted and experimental values (Q2 = 0.93) and a high predictive ability for a validation set of flavors and proteins not previously used (Q2 = 0.88). The prediction model developed holds promise for customizing flavor combinations and streamlining product design, thereby, optimizing efficiency while reducing the risk of flavor overdose.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call