Machine Learning Identifies a Parsimonious Differential Equation for Myricetin Degradation from Scarce Data

Fulkerson A., BAYRAM İ., Decker E. A., Parra-Escudero C., Lu J., Corvalan C. M.

FOODS, cilt.14, sa.12, 2025 (SCI-Expanded, Scopus) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 12
  • Basım Tarihi: 2025
  • Doi Numarası: 10.3390/foods14122135
  • Dergi Adı: FOODS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Directory of Open Access Journals
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Accurately modeling the degradation of food antioxidants in oils is essential for understanding oxidative stability and improving food shelf life. This study presents an innovative machine learning approach integrating neural differential equations and sparse symbolic regression to derive a parsimonious differential equation for myricetin degradation in stripped soybean oil. Despite being trained on a small experimental dataset, the model successfully predicts degradation trends across a wide range of initial concentrations and extrapolates beyond the learning data. This capability demonstrates the robustness of machine learning for uncovering governing equations in complex food systems, particularly when experimental data is scarce. Our findings provide a framework for improving antioxidant efficiency in food formulations.