ArticleMolecules (Basel, Switzerland)2021
Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Laser-Induced Breakdown Spectroscopy vs. Fluorescence Spectroscopy for Olive Oil Authentication.Foods (Basel, Switzerland) · 2025Article
- Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative Study.Foods (Basel, Switzerland) · 2025Article
- Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning inFoods (Basel, Switzerland) · 2023Article
- Rapid Authentication and Detection of Olive Oil Adulteration Using Laser-Induced Breakdown Spectroscopy.Molecules (Basel, Switzerland) · 2023Article
- Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics.Sensors (Basel, Switzerland) · 2022Article
- Laser-Induced Breakdown Spectroscopy: An Efficient Tool for Food Science and Technology (from the Analysis of Martian Rocks to the Analysis of Olive Oil, Honey, Milk, and Other Natural Earth Products).Molecules (Basel, Switzerland) · 2021Review
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Authors and funding
6 authors.
Funding
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Abstract
In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both "k-fold" cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.
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Registered trials
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