Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Digital and Green Technological Drivers of Transformation in the Agri-Food Sector.

Foods (Basel, Switzerland)·2026
Same author

Ferrocenoylation of Uracil Derivatives: Study of Reaction Regioselectivity and Biological Activity.

Molecules (Basel, Switzerland)·2026
Same author

Changes in the mobility of oxyanions from coal combustion residues after 50 years of natural weathering.

Waste management (New York, N.Y.)·2026
Same author

Basil as a Green Alternative to Synthetic Additives in Clean Label Gilthead Sea Bream Patties.

Foods (Basel, Switzerland)·2026
Same author

Integrated Targeted and Suspect Screening Workflow for Identifying PFAS of Concern in Urban-Impacted Serbian Rivers.

Toxics·2026
Same author

Linking Atmospheric and Soil Contamination: A Comparative Study of PAHs and Metals in PM<sub>10</sub> and Surface Soil near Urban Monitoring Stations.

Toxics·2025

Related Experiment Video

Updated: Mar 29, 2026

Investigation of Xenobiotics Metabolism In Salix alba Leaves via Mass Spectrometry Imaging
09:21

Investigation of Xenobiotics Metabolism In Salix alba Leaves via Mass Spectrometry Imaging

Published on: June 15, 2020

3.7K

Predicting Bioactive Compounds in Arbutus unedo L. Leaves Using Machine Learning: Influence of Extraction Technique,

Jasmina Lapić1, Anica Bebek Markovinović1, Nikolina Račić1

  • 1Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.

Foods (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

Green extraction of Arbutus unedo (strawberry tree) leaves using 70% ethanol yielded the most bioactive compounds. Geographical origin and extraction method also influenced results, with machine learning models predicting compound recovery.

Keywords:
extractionextraction solventgeographical locationmachine learningpolyphenolsstrawberry tree leaves

More Related Videos

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B
11:44

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

Published on: January 19, 2022

3.1K
Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis
07:50

Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis

Published on: December 8, 2023

1.3K

Related Experiment Videos

Last Updated: Mar 29, 2026

Investigation of Xenobiotics Metabolism In Salix alba Leaves via Mass Spectrometry Imaging
09:21

Investigation of Xenobiotics Metabolism In Salix alba Leaves via Mass Spectrometry Imaging

Published on: June 15, 2020

3.7K
Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B
11:44

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

Published on: January 19, 2022

3.1K
Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis
07:50

Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis

Published on: December 8, 2023

1.3K

Area of Science:

  • Phytochemistry and Natural Product Chemistry
  • Green Chemistry and Sustainable Extraction Technologies
  • Plant Biotechnology and Pharmacognosy

Background:

  • Arbutus unedo L. (strawberry tree) is a plant rich in bioactive compounds with potential health benefits.
  • Optimizing extraction methods is crucial for maximizing the recovery of these valuable compounds.
  • Understanding the influence of geographical origin and extraction parameters is key for consistent product quality.

Purpose of the Study:

  • To investigate the impact of extraction technique, solvent, and geographical origin on bioactive compound recovery from Arbutus unedo leaves.
  • To evaluate the efficiency of green extraction methods, including ultrasound-assisted extraction (UAE).
  • To explore the application of machine learning for predicting bioactive compound yields.

Main Methods:

  • Extraction of Arbutus unedo leaves using conventional, Soxhlet, and ultrasound-assisted extraction (UAE) with green solvents (water, 70% ethanol, ethyl acetate).
  • Purification and characterization using chromatography (TLC, column) and FTIR spectroscopy.
  • Spectrophotometric quantification of total phenols, hydroxycinnamic acids, flavonols, condensed tannins, and antioxidant capacity.
  • Application of Decision Tree and Gradient Boosting machine learning models.

Main Results:

  • Solvent type significantly influenced recovery, with 70% ethanol yielding the highest levels of bioactives and antioxidant capacity.
  • Geographical origin affected total phenolics and condensed tannins, with Vis island samples showing higher concentrations.
  • UAE demonstrated slightly higher efficiency for thermolabile phenolics compared to conventional and Soxhlet methods.
  • Machine learning models (Decision Tree, Gradient Boosting) achieved high predictive accuracy (R² > 0.91) for bioactive compound estimation.

Conclusions:

  • Green extraction strategies, particularly using 70% ethanol, are effective for maximizing bioactive compound recovery from Arbutus unedo leaves.
  • Geographical origin plays a significant role in the phytochemical profile of the plant.
  • Data-driven approaches using machine learning can aid in optimizing and predicting extraction outcomes for sustainable valorization.