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Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning.

Ingrid A Moraes1, Sylvio Barbon Junior2, Javier E L Villa3

  • 1Department of Food Engineering and Technology, School of Food Engineering, University of Campinas (UNICAMP), Campinas, Brazil.

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|March 14, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a non-invasive spectroscopic method to classify oleogels and quantify their components. This technique accurately identifies oleogelators and their concentrations, ensuring food additive quality control.

Keywords:
Edible oilOil loss and free fatty acid contentPLSRRandom ForestStructuring

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Area of Science:

  • Food Science and Technology
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Oleogels are food additives requiring regulatory approval, making classification by characteristics, cost, and origin essential for consumer choice.
  • Accurate quantification of oleogelator concentration, free fatty acid content, and oil loss is crucial for quality control and regulatory compliance.

Purpose of the Study:

  • To develop and validate a non-invasive, eco-friendly, and rapid method for classifying oleogels based on different oleogelators.
  • To quantify oleogelator concentration, free fatty acid content, and oil loss using spectroscopic techniques.
  • To assess the performance of Principal Component Analysis (PCA), Random Forest (RF), and Partial Least Squares Regression (PLSR) models for oleogel analysis.

Main Methods:

  • Utilized a colorimeter, Raman spectrometer, and two near-infrared (NIR) spectroscopes for spectral data acquisition.
  • Prepared oleogels using sunflower and soybean oils with varying concentrations (1-10%) of beeswax, glycerol monostearate, and ethylcellulose.
  • Applied spectral pretreatment, PCA, RF classification, and PLSR regression for data analysis and model development.

Main Results:

  • Random Forest models achieved 100% accuracy in classifying oil type and oleogelator presence, and 94% accuracy in predicting oleogelator concentration.
  • Partial Least Squares Regression models demonstrated high performance for predicting free fatty acid content and oil loss, with RPD > 3 and RER > 10.
  • Spectroscopic instruments, particularly colorimeter and NIR, proved effective for monitoring additives and predicting key quality parameters.

Conclusions:

  • Spectroscopic methods offer a promising, non-destructive approach for the quality control of oleogels.
  • The developed method enables accurate classification and quantification, supporting regulatory compliance and consumer choice.
  • Colorimetry and NIR spectroscopy are valuable tools for ensuring the quality and consistency of oleogel formulations.