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| Kedzierski, Mikaël; Falcou-préfol, Mathilde; Kerros, Marie Emmanuelle; Henry, Maryvonne; Pedrotti, Maria Luiza; Bruzaud, Stéphane. |
| The development of methods to automatically determine the chemical nature of microplastics by FTIR-ATR spectra is an important challenge. A machine learning method, named k-nearest neighbors classification, has been applied on spectra of microplastics collected during Tara Expedition in the Mediterranean Sea (2014). To realize these tests, a learning database composed of 969 microplastic spectra has been created. Results show that the machine learning process is very efficient to identify spectra of classical polymers such as poly(ethylene), but also that the learning database must be enhanced with less common microplastic spectra. Finally, this method has been applied on more than 4000 spectra of unidentified microplastics. The verification protocol... |
| Tipo: Text |
Palavras-chave: Microplastic; Tara mediterranean campaign; FTIR spectra; Machine learning; K-nearest neighbor classification. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00501/61247/64825.pdf |
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| Galgani, Francois; Bruzaud, Stéphane; Duflos, Guillaume; Fabre, Pascale; Casdaldi, Emmanuelle; Ghiglione, Jeff; Grimaud, Régis; George, Matthieu; Huvet, Arnaud; Lagarde, Fabienne; Paul-pont, Ika; Ter Halle, Alexandra. |
| Human behavior and the intensive use of plastic, combined with poor performance of management systems have led to a massive accumulation of plastic debris in the marine environment, accounting for 50 to 80 % of all marine litter. Their distribution, their fate, their degradation and their impacts stem directly from their composition and use properties. This article reviews knowledge and describes the main scientific, environmental and socio-economic issues, as well as the possible solutions needed to manage an environmental problem that has become global. |
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| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00663/77471/79429.pdf |
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