Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative Study
The fast detection of Extra Virgin Olive Oil (EVOO) adulteration with poorer quality and lower price vegetable oils is important for the protection of consumers and the market of olive oil from fraudulent activities, the latter exhibiting an increasing trend worldwide during the last few years. In t...
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2025-01-01
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| author | Eleni Nanou Marios Bekogianni Theodoros Stamatoukos Stelios Couris |
| author_facet | Eleni Nanou Marios Bekogianni Theodoros Stamatoukos Stelios Couris |
| author_sort | Eleni Nanou |
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| description | The fast detection of Extra Virgin Olive Oil (EVOO) adulteration with poorer quality and lower price vegetable oils is important for the protection of consumers and the market of olive oil from fraudulent activities, the latter exhibiting an increasing trend worldwide during the last few years. In this work, two optical spectroscopic techniques, namely, Laser-Induced Breakdown Spectroscopy (LIBS) and UV-Vis-NIR absorption spectroscopy, are employed and are assessed for EVOO adulteration detection, using the same set of olive oil samples. In total, 184 samples were studied, including 40 EVOOs and 144 binary mixtures with pomace, soybean, corn, and sunflower oils, at various concentrations (ranging from 10 to 90% <i>w</i>/<i>w</i>). The emission data from LIBS, related to the elemental composition of the samples, and the UV-Vis-NIR absorption spectra, related to the organic ingredients content, are analyzed, both separately and combined (i.e., fused), by Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs), and Logistic Regression (LR). In all cases, very highly predictive accuracies were achieved, attaining, in some cases, 100%. The present results demonstrate the potential of both techniques for efficient and accurate olive oil authentication issues, with the LIBS technique being better suited as it can operate much faster. |
| format | Article |
| id | doaj-art-6097df05b5cd440b9c50cda66998a4d0 |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-6097df05b5cd440b9c50cda66998a4d02025-01-24T13:33:16ZengMDPI AGFoods2304-81582025-01-0114232110.3390/foods14020321Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative StudyEleni Nanou0Marios Bekogianni1Theodoros Stamatoukos2Stelios Couris3Department of Physics, University of Patras, 26504 Patras, GreeceDepartment of Physics, University of Patras, 26504 Patras, GreeceDepartment of Physics, University of Patras, 26504 Patras, GreeceDepartment of Physics, University of Patras, 26504 Patras, GreeceThe fast detection of Extra Virgin Olive Oil (EVOO) adulteration with poorer quality and lower price vegetable oils is important for the protection of consumers and the market of olive oil from fraudulent activities, the latter exhibiting an increasing trend worldwide during the last few years. In this work, two optical spectroscopic techniques, namely, Laser-Induced Breakdown Spectroscopy (LIBS) and UV-Vis-NIR absorption spectroscopy, are employed and are assessed for EVOO adulteration detection, using the same set of olive oil samples. In total, 184 samples were studied, including 40 EVOOs and 144 binary mixtures with pomace, soybean, corn, and sunflower oils, at various concentrations (ranging from 10 to 90% <i>w</i>/<i>w</i>). The emission data from LIBS, related to the elemental composition of the samples, and the UV-Vis-NIR absorption spectra, related to the organic ingredients content, are analyzed, both separately and combined (i.e., fused), by Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs), and Logistic Regression (LR). In all cases, very highly predictive accuracies were achieved, attaining, in some cases, 100%. The present results demonstrate the potential of both techniques for efficient and accurate olive oil authentication issues, with the LIBS technique being better suited as it can operate much faster.https://www.mdpi.com/2304-8158/14/2/321Laser-Induced Breakdown Spectroscopy—LIBSUV-Vis-NIR absorption spectroscopyextra virgin olive oiledible oilsadulterationmachine learning |
| spellingShingle | Eleni Nanou Marios Bekogianni Theodoros Stamatoukos Stelios Couris Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative Study Foods Laser-Induced Breakdown Spectroscopy—LIBS UV-Vis-NIR absorption spectroscopy extra virgin olive oil edible oils adulteration machine learning |
| title | Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative Study |
| title_full | Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative Study |
| title_fullStr | Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative Study |
| title_full_unstemmed | Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative Study |
| title_short | Detection of Adulteration of Extra Virgin Olive Oil via Laser-Induced Breakdown Spectroscopy and Ultraviolet-Visible-Near-Infrared Absorption Spectroscopy: A Comparative Study |
| title_sort | detection of adulteration of extra virgin olive oil via laser induced breakdown spectroscopy and ultraviolet visible near infrared absorption spectroscopy a comparative study |
| topic | Laser-Induced Breakdown Spectroscopy—LIBS UV-Vis-NIR absorption spectroscopy extra virgin olive oil edible oils adulteration machine learning |
| url | https://www.mdpi.com/2304-8158/14/2/321 |
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