Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology
Youssef Ezzaky, Elgin Ee Lin Yap, Wei Ning Chen · 2026
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Description
Machine tools can guess how germs in food will grow, yet a sharp guess on a test set is not proof a tool is safe to trust. Youssef Ezzaky, Elgin Ee Lin Yap, and Wei Ning Chen make that case in a 2026 piece on food germ work. They back tools that wed learned maps with the rules of germ life.
Such wed tools can stay more true to life, learn from less data, and show their work. The team sets out four wed shapes. They then give a check plan built for food: outside checks in true food scenes, firm doubt bars on each guess, and grades tied to the call at hand. Wide trust, they warn, still needs care.
Catalog record
| CREATOR | Youssef Ezzaky, Elgin Ee Lin Yap, Wei Ning Chen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |