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

## TL;DR

This paper uses machine learning to model how myricetin, a food antioxidant, degrades in oil, even with limited data, helping improve food shelf life.

## Contribution

The study introduces a novel machine learning method combining neural differential equations and symbolic regression for scarce data scenarios.

## Key findings

- The model accurately predicts myricetin degradation trends across various initial concentrations.
- It successfully extrapolates beyond the training data, showing robustness in complex food systems.
- The approach provides a framework for enhancing antioxidant efficiency in food formulations.

## 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.