An interpretable machine learning approach for process optimization and characterization of key model-derived parameter effects in sunflower oil diacylglycerol synthesis.
- DOI
- 10.1016/j.foodchem.2026.151771
- Published
- 2026 Oct 26
- Container
- Food chemistry
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.foodchem.2026.151771,
title = {An interpretable machine learning approach for process optimization and characterization of key model-derived parameter effects in sunflower oil diacylglycerol synthesis.},
author = {Liu H and Dai Y and Yu H and Zhang S and Liao Q and Chen D and Jiang X and Liu H and He D},
year = {2026},
journal = {Food chemistry},
doi = {10.1016/j.foodchem.2026.151771},
url = {https://doi.org/10.1016/j.foodchem.2026.151771}
}RIS
TY - JOUR TI - An interpretable machine learning approach for process optimization and characterization of key model-derived parameter effects in sunflower oil diacylglycerol synthesis. AU - Liu H AU - Dai Y AU - Yu H AU - Zhang S AU - Liao Q AU - Chen D AU - Jiang X AU - Liu H AU - He D PY - 2026 JO - Food chemistry DO - 10.1016/j.foodchem.2026.151771 UR - https://doi.org/10.1016/j.foodchem.2026.151771 ER -
APA
H, L., Y, D., H, Y., S, Z., Q, L., D, C., X, J., H, L., & D, H. (2026). An interpretable machine learning approach for process optimization and characterization of key model-derived parameter effects in sunflower oil diacylglycerol synthesis.. Food chemistry. https://doi.org/10.1016/j.foodchem.2026.151771
Source records
- pubmed · retrieved 2026-11-10T21:08:57.839Z