A hybrid chemometric and deep learning model for monitoring quality loss in thermally processed edible oils.
- DOI
- 10.1016/j.foodres.2026.119497
- Published
- 2026 Aug 31
- Container
- Food research international (Ottawa, Ont.)
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.foodres.2026.119497,
title = {A hybrid chemometric and deep learning model for monitoring quality loss in thermally processed edible oils.},
author = {Varma SO and Vishwakarma AL and Sonawane MR and Garad NP and Kumbharkhane AC},
year = {2026},
journal = {Food research international (Ottawa, Ont.)},
doi = {10.1016/j.foodres.2026.119497},
url = {https://doi.org/10.1016/j.foodres.2026.119497}
}RIS
TY - JOUR TI - A hybrid chemometric and deep learning model for monitoring quality loss in thermally processed edible oils. AU - Varma SO AU - Vishwakarma AL AU - Sonawane MR AU - Garad NP AU - Kumbharkhane AC PY - 2026 JO - Food research international (Ottawa, Ont.) DO - 10.1016/j.foodres.2026.119497 UR - https://doi.org/10.1016/j.foodres.2026.119497 ER -
APA
SO, V., AL, V., MR, S., NP, G., & AC, K. (2026). A hybrid chemometric and deep learning model for monitoring quality loss in thermally processed edible oils.. Food research international (Ottawa, Ont.). https://doi.org/10.1016/j.foodres.2026.119497
Source records
- pubmed · retrieved 2026-09-27T08:21:37.044Z