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Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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Related Experiment Video

Updated: Jun 25, 2026

Extraction and Purification of Polyphenols from Freeze-dried Berry Powder for the Treatment of Vascular Smooth Muscle Cells In Vitro

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Published on: July 5, 2017

Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves.

Raquel Rodríguez-Fernández¹, Ángela Fernández-Gómez¹, Juan C Mejuto¹

¹Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, Spain.

Foods (Basel, Switzerland)

|December 23, 2023

Summary

Machine learning models, specifically Artificial Neural Networks (ANN), accurately predict olive leaf extract yield and total phenolic content (TPC). These models offer reliable tools for optimizing ultrasound-assisted extraction (UAE) processes.

Keywords:

TPC artificial neural network extract yield machine learning olive leaves random forest support vector machine ultrasound-assisted extraction

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Published on: August 23, 2024

Area of Science:

Agricultural Chemistry
Biotechnology
Computational Chemistry

Background:

Olive leaves (Olea europaea) contain phenolic compounds with significant health benefits.
Optimizing extraction processes for these compounds is crucial for their application.
Ultrasound-assisted extraction (UAE) is a common method for extracting bioactive compounds.

Purpose of the Study:

To develop and evaluate machine learning models for predicting extract yield and total phenolic content (TPC) from olive leaves.
To identify the most effective machine learning algorithm for modeling the ultrasound-assisted extraction (UAE) process.
To assess the performance of different algorithms using temperature, time, and volume as input variables.

Main Methods:

Utilized experimental data from literature on olive leaf extraction.
Developed and compared three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN).
Input variables included temperature, time, and volume; output variables were extract yield and TPC.

Main Results:

The ANN-based model ANN_Z-L showed the best performance for predicting extract yield, with a Root Mean Square Error (RMSE) of 9.44 mg/g DL and Mean Absolute Percentage Error (MAPE) of 3.7% in validation.
The ANN-based model ANN_R was optimal for predicting TPC, achieving an RMSE of 0.89 mg GAE/g DL and MAPE of 2.9% in validation.
Both ANN models demonstrated strong performance in the test phase with low MAPE values (4.9% and 3.5% respectively).

Conclusions:

Artificial Neural Network (ANN) models are effective tools for predicting extract yield and total phenolic content (TPC) in olive leaf UAE.
These models can accurately forecast the outcomes of UAE processes under varying conditions (temperature, time, solvents).
The findings support the use of ANN for optimizing the extraction of valuable compounds from olive leaves.