Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning.
Saved in:
| Title: | Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning. |
|---|---|
| Authors: | Moraes IA; Department of Food Engineering and Technology, School of Food Engineering, University of Campinas (UNICAMP), Campinas, Brazil., Barbon Junior S; Department of Engineering and Architecture, University of Trieste, Trieste, Italy., Villa JEL; Institute of Chemistry, University of Campinas, Campinas, Brazil., Cunha RL; Department of Food Engineering and Technology, School of Food Engineering, University of Campinas (UNICAMP), Campinas, Brazil., Barbin DF; Department of Food Engineering and Technology, School of Food Engineering, University of Campinas (UNICAMP), Campinas, Brazil. Electronic address: dfbarbin@unicamp.br. |
| Source: | Food research international (Ottawa, Ont.) [Food Res Int] 2025 Apr; Vol. 207, pp. 116044. Date of Electronic Publication: 2025 Feb 26. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Published on behalf of the Canadian Institute of Food Science and Technology by Elsevier Applied Science Country of Publication: Canada NLM ID: 9210143 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7145 (Electronic) Linking ISSN: 09639969 NLM ISO Abbreviation: Food Res Int Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
Be the first to leave a comment!
