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Open AccessArticle

Laser-Induced Breakdown Spectroscopy Assisted by Machine Learning for Plastics/Polymers Identification

1
Department of Physics, University of Patras, 26 504 Patras, Greece
2
Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), Stadiou Str., 26 504 Patras, Greece
*
Author to whom correspondence should be addressed.
Atoms 2019, 7(3), 79; https://doi.org/10.3390/atoms7030079
Received: 26 July 2019 / Revised: 12 August 2019 / Accepted: 13 August 2019 / Published: 19 August 2019
(This article belongs to the Special Issue Laser Plasma Spectroscopy Applications)
In the present work, Laser-Induced Breakdown Spectroscopy (LIBS) is used for the discrimination/identification of different plastic/polymeric samples having the same polymeric matrix but containing different additives (as e.g., fillers, flame retardants, etc.). For the classification of the different plastic samples, some machine learning algorithms were employed for the analysis of the LIBS spectroscopic data, such as the Principal Component Analysis (PCA) and the Linear Discriminant Analysis (LDA). The combination of LIBS technique with these machine learning algorithmic approaches, in particular the latter, provided excellent classification results, achieving identification accuracies as high as 100%. It seems that machine learning paves the way towards the application of LIBS technique for identification/discrimination issues of plastics and polymers and eventually of other classes of organic materials. Machine learning assisted LIBS can be a simple to use, efficient and powerful tool for sorting and recycling purposes. View Full-Text
Keywords: laser produced plasmas; laser-induced breakdown; LIBS; polymers; machine learning; identification of polymers laser produced plasmas; laser-induced breakdown; LIBS; polymers; machine learning; identification of polymers
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Stefas, D.; Gyftokostas, N.; Bellou, E.; Couris, S. Laser-Induced Breakdown Spectroscopy Assisted by Machine Learning for Plastics/Polymers Identification. Atoms 2019, 7, 79.

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