Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Foods

DOI: 10.3390/foods14122135

Authors: Andrew Fulkerson, Ipek Bayram, Eric A. Decker, Carlos Parra-Escudero, Jiakai Lu, Carlos M. Corvalan

Institutions: Purdue University West Lafayette, University of Massachusetts Amherst

Language: English

Topic: Edible Oils Quality and Analysis

Subfield: Organic Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,499