PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 14, 2026Food ChemistryOpen Access

Machine learning and COSMO-RS integration for predicting anthocyanin extraction from berries using eutectic solvents

View Full Paper
Ask AI
Bookmark
Share

Authors

LMLeonardo M.de Souza MesquitaJAJoão A.P.CoutinhoFHFilipe H.B.Sosa

Discussion

Loading...

Member takes

Overview

Randomized trial predicts anthocyanin extraction from berry matrices, indicating efficient solvent use for sustainability.

Key Points

  • This research aims to develop a computational framework to predict the extraction of anthocyanins from berries using eco-friendly solvents.
  • Integrated COSMO-RS with machine learning to assess anthocyanin yields.
  • Trained seven ML algorithms with a dataset of 299 experimental points across 15 biomass types.
  • Validated models with independent literature and new experimental data.
  • Gradient Boosting achieved the highest accuracy (R² = 0.92) in predicting yields.
  • Predicted values closely matched experimental yields across various DES and berry types.
  • Framework optimizes extraction processes while minimizing experimental efforts.

Cite This Study

Mesquita et al. (2026) studied this question.

synapsesocial.com/papers/6a2e45adb1cc60ccdea8aa19https://doi.org/10.1016/j.foodchem.2026.150033
View Full Paper
Ask AI
Bookmark
Share