1. A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea
- Author
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Mathilde Falcou-Préfol, Maryvonne Henry, Stéphane Bruzaud, Marie Emmanuelle Kerros, Maria Luiza Pedrotti, Mikaël Kedzierski, Institut de Recherche Dupuy de Lôme (IRDL), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Observatoire océanologique de Villefranche-sur-mer (OOVM), and Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Microplastics ,Environmental Engineering ,Computer science ,Health, Toxicology and Mutagenesis ,[SDE.MCG]Environmental Sciences/Global Changes ,0208 environmental biotechnology ,02 engineering and technology ,010501 environmental sciences ,Machine learning ,computer.software_genre ,01 natural sciences ,Ftir spectra ,Mediterranean sea ,k-nearest neighbor classification ,FTIR spectra ,Mediterranean Sea ,Environmental Chemistry ,Humans ,Tara Mediterranean campaign ,14. Life underwater ,Throughput (business) ,0105 earth and related environmental sciences ,business.industry ,Microplastic ,Public Health, Environmental and Occupational Health ,General Medicine ,General Chemistry ,Pollution ,020801 environmental engineering ,Identification (information) ,Tara mediterranean campaign ,Artificial intelligence ,business ,computer ,Plastics ,Algorithms ,Water Pollutants, Chemical ,Automated method ,Environmental Monitoring - Abstract
International audience; 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 showed less than 10% difference in the results between the proposed automated method and a human expertise, 75% of which can be very easily corrected. Highlights ► A machine learning algorithm was developed to determine the chemical nature of microplastics. ► This method allows a fast and reliable automated identification even when several thousand of FTIR spectra have to be studied.► This method is the first part of a software dedicated to the study of microplastics: POSEIDON.
- Published
- 2019
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