A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea. (November 2019)

• Record Type: Journal Article
• Authors: Kedzierski, Mikaël; Falcou-Préfol, Mathilde; Kerros, Marie Emmanuelle; Henry, Maryvonne; Pedrotti, Maria Luiza; Bruzaud, Stéphane
• Is Part Of: Chemosphere. Volume 234(2019)
• Page Start: 242
• Page End: 251
• Publication Date: 2019-11
• Subjects: Microplastic -- Tara mediterranean campaign -- FTIR spectra -- Machine learning -- k-nearest neighbor classification; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2019.05.113
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: 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 whenthousands of FTIR spectra have to be studied. This method is the first part of a software dedicated to the study of microplastics: POSEIDON.