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

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

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Hybrid Machine Learning Framework for Interpretable Kinetics of α-Tocopherol and Myricetin Synergism.Journal of food science · 2026
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Andrew FulkersonTransport Phenomena Laboratory, Department of Food Science, Purdue University, West Lafayette, IN 47906, USA.

Ipek BayramDepartment of Food Science, University of Massachusetts, Amherst, MA 01003, USA.ORCID 0000-0002-6604-6525

Eric A DeckerDepartment of Food Science, University of Massachusetts, Amherst, MA 01003, USA.

Carlos Parra-EscuderoDepartment of Food Science, University of Massachusetts, Amherst, MA 01003, USA.ORCID 0009-0004-1587-1826

Jiakai LuDepartment of Food Science, University of Massachusetts, Amherst, MA 01003, USA.ORCID 0000-0002-6951-841X

Carlos M CorvalanTransport Phenomena Laboratory, Department of Food Science, Purdue University, West Lafayette, IN 47906, USA.ORCID 0000-0002-8558-0985

Funding

United States Department of Agriculture USDA NIFA 2021-09399

6 · The paper itself

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.

Indexed as

antioxidant degradationfood chemistrymachine learningneural differential equationsshelf life predictionsparse symbolic regression

Identifiers

PMID40565744

DOI10.3390/foods14122135

PMCPMC12192506

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.