Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Abstract Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown...

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Autores principales:	Nikolaos Gyftokostas, Dimitrios Stefas, Vasileios Kokkinos, Christos Bouras, Stelios Couris
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6b5813d124a34393a78fc6086f0144dc
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spelling	oai:doaj.org-article:6b5813d124a34393a78fc6086f0144dc2021-12-02T11:37:18ZLaser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination10.1038/s41598-021-84941-z2045-2322https://doaj.org/article/6b5813d124a34393a78fc6086f0144dc2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84941-zhttps://doaj.org/toc/2045-2322Abstract Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.Nikolaos GyftokostasDimitrios StefasVasileios KokkinosChristos BourasStelios CourisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nikolaos Gyftokostas Dimitrios Stefas Vasileios Kokkinos Christos Bouras Stelios Couris Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination
description	Abstract Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.
format	article
author	Nikolaos Gyftokostas Dimitrios Stefas Vasileios Kokkinos Christos Bouras Stelios Couris
author_facet	Nikolaos Gyftokostas Dimitrios Stefas Vasileios Kokkinos Christos Bouras Stelios Couris
author_sort	Nikolaos Gyftokostas
title	Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination
title_short	Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination
title_full	Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination
title_fullStr	Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination
title_full_unstemmed	Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination
title_sort	laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6b5813d124a34393a78fc6086f0144dc
work_keys_str_mv	AT nikolaosgyftokostas laserinducedbreakdownspectroscopycoupledwithmachinelearningasatoolforoliveoilauthenticityandgeographicdiscrimination AT dimitriosstefas laserinducedbreakdownspectroscopycoupledwithmachinelearningasatoolforoliveoilauthenticityandgeographicdiscrimination AT vasileioskokkinos laserinducedbreakdownspectroscopycoupledwithmachinelearningasatoolforoliveoilauthenticityandgeographicdiscrimination AT christosbouras laserinducedbreakdownspectroscopycoupledwithmachinelearningasatoolforoliveoilauthenticityandgeographicdiscrimination AT stelioscouris laserinducedbreakdownspectroscopycoupledwithmachinelearningasatoolforoliveoilauthenticityandgeographicdiscrimination
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Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

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