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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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| Goydaragh,Maryam Ghebleh; Jafarzadeh,Ali Asghar; Shahbazi,Farzin; Oustan,Shahin; Taghizadeh-Mehrjardi,Ruhollah; Lado,Marcos. |
| ABSTRACT Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: FTIR spectra; Random forest; Weathering. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662019000600460 |
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