Authentication and quality control of foods by different spectroscopic techniques (laser-induced breakdown spectroscopy (LIBS), UV-VIS absorption spectroscopy and fluorescence spectroscopy, assisted by machine learning techniques
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Date
2026-02-13
Authors
Γαρίδας, Αθανάσιος
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Abstract
In the present study, the results of a comparative study concerning the application of three spectroscopic techniques, assisted by machine learning methods, for the authentication of Greek olive oils are presented. In this context, Laser-Induced Breakdown Spectroscopy (LIBS), fluorescence spectroscopy, and UV–Vis absorption spectroscopy is applied and compared. For the analysis and classification of the spectroscopic data, various machine learning algorithms were employed, including Principal Component Analysis (PCA), Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and decision tree–based algorithms, specifically Random Forest Classifier (RFC), Extra Trees Classifier (ETC), and eXtreme Gradient Boosting (XGB). The present study focuses on the evaluation of the application of the three spectroscopic techniques to classify olive oils based on the harvesting year. The results highlight both the advantages and the limitations of each experimental approach. This comparative study provides a clear and well-documented framework for evaluating the performance and suitability of the three analytical techniques, contributing to quality control and authenticity verification of Greek olive oils.
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Keywords
Laser-Induced breakdown spectroscopy, Steady-state fluorescence spectroscopy, UV-VIS absorption spectroscopy, Olive oil, Machine learning