Journal
CHEMOSPHERE
Volume 234, Issue -, Pages 242-251Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2019.05.113
Keywords
Microplastic; Tara mediterranean campaign; FTIR spectra; Machine learning; k-nearest neighbor classification
Categories
Funding
- CNRS, UPMC
- Tara Expeditions Foundation
- Veolia Environment Foundation
- Foundation Prince Albert II de Monaco
- French Ministry of Foreign Affairs
Ask authors/readers for more resources
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. (C) 2019 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available