Academic Journal
A Hybrid Machine Learning Framework for Interpretable Kinetics of α-Tocopherol and Myricetin Synergism.
| Title: | A Hybrid Machine Learning Framework for Interpretable Kinetics of α-Tocopherol and Myricetin Synergism. |
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| Authors: | Lu J; Department of Food Science, University of Massachusetts, Amherst, Massachusetts, USA., Iyer S; Transport Phenomena Laboratory, Department of Food Science, Purdue University, West Lafayette, Indiana, USA., Bayram I; Department of Food Science, University of Massachusetts, Amherst, Massachusetts, USA.; Department of Food Engineering, Faculty of Engineering, Middle East Technical University, Ankara, Türkiye., Decker EA; Department of Food Science, University of Massachusetts, Amherst, Massachusetts, USA., Singh SP; Transport Phenomena Laboratory, Department of Food Science, Purdue University, West Lafayette, Indiana, USA., Corvalan CM; Transport Phenomena Laboratory, Department of Food Science, Purdue University, West Lafayette, Indiana, USA. |
| Source: | Journal of food science [J Food Sci] 2026 Jul; Vol. 91 (7), pp. e71291. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Wiley on behalf of the Institute of Food Technologists Country of Publication: United States NLM ID: 0014052 Publication Model: Print Cited Medium: Internet ISSN: 1750-3841 (Electronic) Linking ISSN: 00221147 NLM ISO Abbreviation: J Food Sci Subsets: MEDLINE |
| Imprint Name(s): | Publication: Malden, Mass. : Wiley on behalf of the Institute of Food Technologists Original Publication: Champaign, Ill. Institute of Food Technologists |
| MeSH Terms: | Flavonoids*/chemistry , alpha-Tocopherol*/chemistry , Machine Learning*, Antioxidants/chemistry ; Emulsions/chemistry ; Kinetics ; Neural Networks, Computer |
| Abstract: | Predicting the stabilizing efficacy of antioxidant mixtures in food oil emulsions is highly complex due to synergistic or antagonistic interactions between individual antioxidants. To address this challenge, we present an innovative hybrid machine learning framework, known as universal differential equations (UDEs), which integrates the expressive power of deep learning with the mechanistic boundaries of traditional kinetic models. We demonstrate the utility of this data-efficient approach by characterizing the coupled degradation dynamics of α-tocopherol in the presence of myricetin in oil. By embedding compact artificial neural networks directly into a system of ordinary differential equations, the hybrid UDE model successfully learned the hidden interactions from a small dataset, quantitatively revealing their mutualistic protection. Furthermore, we translated these machine-learned interactions into an interpretable, fully analytical model based on Hill-type saturation kinetics. Critically, this transparent analytical model not only accurately reproduced the training data (R2 = 0.998) but successfully extrapolated antioxidant dynamics to previously unseen experimental formulations (R2 = 0.978) with a fivefold increase in myricetin concentration. This work provides a powerful, interpretable AI tool for understanding complex kinetic interactions in food systems, with broad applications for accelerating product development, optimizing preservation strategies, and extending food shelf life. (© 2026 The Author(s). Journal of Food Science published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.) |
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| Substance Nomenclature: | 76XC01FTOJ (myricetin) 0 (Flavonoids) H4N855PNZ1 (alpha-Tocopherol) 0 (Antioxidants) 0 (Emulsions) |
| Entry Date(s): | Date Created: 20260714 Date Completed: 20260714 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13367022 |
| DOI: | 10.1111/1750-3841.71291 |
| PMID: | 42446086 |
| Database: | MEDLINE |
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