Laser-based classification of olive oils assisted by machine learning

Gazeli, Odhisea; Bellou, Elli; Stefas, Dimitrios; Couris, Stelios

doi:10.1016/j.foodchem.2019.125329

Cited by 50 publications

(20 citation statements)

References 25 publications

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“…Supplementary Material), where the most important spectral features are clearly observed. The assignment of the spectral lines was based on the National Institute of Standards and Technology (NIST) Atomic Spectra Database (ASD) and are also confirmed by other works and from our unpublished data 33,34,41 . As can been seen, both atomic and molecular origin emissions are apparent, resulting from the atomization and/or fragmentation of the molecular constituents of olive oil.…”

Section: Resultssupporting

confidence: 78%

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

Gyftokostas

Stefas

Kokkinos

et al. 2021

Sci Rep

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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.

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Section: Resultssupporting

confidence: 78%

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

Gyftokostas

Stefas

Kokkinos

et al. 2021

Sci Rep

Self Cite

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“…Gazeli et al [ 55 ], suggested, as a proof of concept, the capability of LIBS to classify olive oil samples of different acidities and designation of origin. The major spectral features of olive oil LIBS spectra were thoroughly discussed, and various machine learning algorithms were used to classify these spectra, i.e., Linear Discriminant Analysis, Support Vector Machines, and Random Forests.…”

Section: Libs Applications In Food Analysismentioning

confidence: 99%

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)

et al. 2021

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Laser-Induced Breakdown Spectroscopy (LIBS), having reached a level of maturity during the last few years, is generally considered as a very powerful and efficient analytical tool, and it has been proposed for a broad range of applications, extending from space exploration down to terrestrial applications, from cultural heritage to food science and security. Over the last decade, there has been a rapidly growing sub-field concerning the application of LIBS for food analysis, safety, and security, which along with the implementation of machine learning and chemometric algorithms opens new perspectives and possibilities. The present review intends to provide a short overview of the current state-of-the-art research activities concerning the application of LIBS for the analysis of foodstuffs, with the emphasis given to olive oil, honey, and milk.

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“…Typically, the spectral data of characteristic lines are extracted from the spectra and used as a sample set to establish a suitable classification model. Currently, the most common algorithms used in LIBS analysis are principal component analysis (PCA), [82][83][84][85] linear discriminant analysis (LDA), [86][87][88] support vector machine (SVM), [89][90][91] random forest (RF), [92][93][94] and artificial neural networks (ANN). [95][96][97][98] For the classification of pollutants, these methods work equally well.…”

Section: Atmospheric Pollution Sourcesmentioning

confidence: 99%

Review of In-Situ Online LIBS Detection in the Atmospheric Environment

Zhang¹,

Liu²

2021

At.Spectrosc.

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The aim of this review is to provide a brief introduction to recent research advances in in-situ online detection of atmospheric pollutants based on laser-induced breakdown spectroscopy (LIBS) under atmospheric environments. Atmospheric pollution has drawn much public attention, and there is increasing demand for rapid and accurate evaluation of atmospheric environments. LIBS has the advantages of in-situ online detection, simultaneous multi-element analysis, and noncontact measurement, making it a highly competitive analytical technique in the field of environmental monitoring. In terms of the different target samples, some typical research cases, including atmospheric particulate matter, atmospheric pollution sources, halogens in VOCs, atmospheric sulfur, and stable isotope abundance, are presented to illustrate the current development and problems of LIBS detection in this field.

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Laser-based classification of olive oils assisted by machine learning

Cited by 50 publications

References 25 publications

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

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

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)

Review of In-Situ Online LIBS Detection in the Atmospheric Environment

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