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Classification and Quantification of Microplastics (<100 μm) Using a Focal Plane Array-Fourier Transform Infrared
Vitor H da Silva1, Fionn Murphy1, José M Amigo2,3
1Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark.
Analytical Chemistry
|September 18, 2020
Summary
This study introduces an automated method using micro-Fourier transform infrared (μ-FTIR) imaging and machine learning to identify and quantify small microplastics (<100 μm). The developed approach enhances accuracy and efficiency in microplastic analysis for environmental monitoring.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Materials Science
Background:
- Microplastics are pervasive environmental pollutants with potential risks to ecosystems and human health.
- Current analytical methods for microplastic detection are often slow, labor-intensive, and lack standardization.
- Accurate characterization of small microplastics (<100 μm) is crucial but challenging due to limitations in existing techniques.
Purpose of the Study:
- To develop and validate an automated analytical method for the characterization of small microplastics (<100 μm).
- To employ micro-Fourier transform infrared (μ-FTIR) hyperspectral imaging combined with machine learning for polymer identification.
- To enable rapid, routine, and unbiased measurements of microplastic abundance, size, and polymer type.
Main Methods:
- Utilized micro-Fourier transform infrared (μ-FTIR) hyperspectral imaging for data acquisition.
- Applied machine learning algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA), for polymer classification.
- Developed automated routines for quantifying particle abundance and size distribution from hyperspectral data.
- Evaluated different data preprocessing strategies to optimize classification performance.
Main Results:
- The Partial Least Squares Discriminant Analysis (PLS-DA) model demonstrated superior analytical performance over SIMCA, exhibiting higher sensitivity, specificity, and lower misclassification rates.
- The automated method successfully characterized nine common polymers, quantified particle abundance, and determined size distribution.
- PLS-DA showed robustness against spectral variations caused by edge effects and out-of-focus regions.
- The method was successfully applied to a real-world environmental sample from Roskilde Fjord seabed sediment.
Conclusions:
- The proposed automated μ-FTIR hyperspectral imaging and machine learning approach provides an efficient and reliable method for microplastic analysis.
- This technique significantly advances the capability for routine, unbiased characterization of small microplastics in environmental matrices.
- The developed method holds substantial potential for standardizing microplastic analysis and improving environmental monitoring efforts.
