Laser induced fluorescence and machine learning: a novel approach to microplastic identification

Identifying the types of materials such as plastics, microplastics, and oil pollutants is essential for understanding their effects on marine life. We propose a new methodology for the real-time detection and identification of microplastics in aquatic environments. Our experiments are based on a com...

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Bibliographic Details
Published inApplied physics. B, Lasers and optics Vol. 130; no. 9
Main Authors Merlemis, Nikolaos, Drakaki, Eleni, Zekou, Evangelini, Ninos, Georgios, Kesidis, Anastasios L.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024
Springer Nature B.V
Subjects
Algorithms
Aquatic environment
Engineering
Excitation spectra
Laser induced fluorescence
Lasers
Lubricating oils
Machine learning
Marine environment
Optical Devices
Optics
Organic materials
Photonics
Physical Chemistry
Physics
Physics and Astronomy
Plastic pollution
Pollutants
Polyethylene terephthalate
Polystyrene resins
Quantum Optics
Real time
Seawater
Visible spectrum
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Summary:Identifying the types of materials such as plastics, microplastics, and oil pollutants is essential for understanding their effects on marine life. We propose a new methodology for the real-time detection and identification of microplastics in aquatic environments. Our experiments are based on a compact Laser Induced Fluorescence (LIF) device, with machine learning techniques applied to classify the materials. A 405 nm CW laser excitation source effectively induces fluorescence spectra in the visible spectrum from material samples that are either floating or submerged in water. We examine known plastic pollutants in seawater, including polyethylene (PE), polypropylene (PP), polystyrene (PS) and polyethylene terephthalate (PET), as well as maritime fuels, lubricating oils, and other organic substances that are abundant in the marine environment. Our two-step identification process first employs machine learning algorithms to distinguish microplastics from other organic materials with a high degree of accuracy (97.6%). Subsequently, the type of plastic is determined with an accuracy of 88.3% in a second application of machine learning techniques.
ISSN:0946-2171
1432-0649
DOI:10.1007/s00340-024-08308-8