Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods

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Size and concentration are two important parameters for the analysis of microplastics (MPs) in water. The analytical tools reported so far extract this information in a single-particle analysis mode, dramatically increasing the analysis time. Here, we present a combination of multi-angle static light scattering technique, called "Goniophotometry", with chemometric multivariate data processing for the batch analysis of size and concentration of MPs in water. Nine different sizes of polystyrene (PS) MPs with diameters between 500 nm and 20 mu m are investigated in two different scenarios with uniform (monodisperse) and non-uniform (polydisperse) size distribution of MPs, respectively. It is shown that Principal Component Analysis (PCA) can reveal the existing relationship between the scattering data of mono- and polydisperse samples according to the size distribution of MPs in mixtures. Therefore, a Linear Discriminant Analysis (LDA) model is constructed based on the PCA of scattering data of PS monodisperse samples and is subsequently employed to classify the size of MPs not only in unknown mono- and polydisperse PS samples, but also for other types of MPs such as Polyethylene (PE) and Polymethylmethacrylate (PMMA). When the size of MPs is classified, their concentration is measured using a simple linear fit. Finally, a Linear Least Square (LLS) model is used to evaluate the reproducibility of the measurements.
Original languageEnglish
Pages (from-to)3840-3849
Number of pages10
JournalAnalytical Methods
Issue number39
Publication statusPublished - 13 Oct 2022

Bibliographical note

Funding Information:
This work was supported by the “MONPLAS” European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860775. This work was also supported in part by the Methusalem and Hercules foundations and the OZR of the Vrije Universiteit Brussel (VUB).

Publisher Copyright:
© 2022 The Royal Society of Chemistry.

Copyright 2022 Elsevier B.V., All rights reserved.




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