Issue |
EPJ Web Conf.
Volume 266, 2022
EOS Annual Meeting (EOSAM 2022)
|
|
---|---|---|
Article Number | 12004 | |
Number of page(s) | 2 | |
Section | Topical Meeting (TOM) 12- Optofluidics | |
DOI | https://doi.org/10.1051/epjconf/202226612004 | |
Published online | 13 October 2022 |
https://doi.org/10.1051/epjconf/202226612004
Analysis of size and concentration of microplastics in water using static light scattering combined with PCA and LDA
1 Vrije Universiteit Brussel, Department of Applied Physics and Photonics, Brussel Photonics, Pleinlaan 2, 1050 Brussels, Belgium
2 CNR-Istituto di Fisica Applicata "Nello Carrara", Via Madonna del Piano 10 - 50019, Sesto Fiorentino (FI) -Italy
3 Vrije Universiteit Brussel and Flanders Make, Department of Applied Physics and Photonics, Brussel Photonics, Pleinlaan 2, 1050 Brussels, Belgium
* Corresponding author: Mehrdad.Lotfi.Choobbari@vub.be
Published online: 13 October 2022
Quantitative analysis of size and concentration of microplastics is a crucial step for having a better understanding of plastic pollution in the environment. Such information is typically obtained in a single particle mode that significantly increases the analysis time and can be a cumbersome task. Therefore, we demonstrate here a measurement technique based on Static Light Scattering (SLS) combined with chemometric methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for resolving the size and concentration of multiple microplastic particles in water. Two sets of samples with uniform and non-uniform size distribution of microplastics, called “monodisperse” and “polydisperse”, respectively, are fully investigated. It is shown that a relationship exists between the scattering signals of mono- and polydisperse samples on the PCA space. Hence, a PCA-LDA model that is constructed on the PCA space of monodisperse samples is used to discriminate the size of the microplastics in polydisperse samples. By specifying the size of the particles, their concentration is determined using a simple linear fit.
© The Authors, published by EDP Sciences
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