Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research

Author:

Cowger Win1ORCID,Gray Andrew1,Christiansen Silke H.234,DeFrond Hannah5,Deshpande Ashok D.6,Hemabessiere Ludovic5ORCID,Lee Eunah7,Mill Leonid8,Munno Keenan5,Ossmann Barbara E.910,Pittroff Marco11ORCID,Rochman Chelsea5,Sarau George23ORCID,Tarby Shannon1,Primpke Sebastian12ORCID

Affiliation:

1. Department of Environmental Science, University of California, Riverside, USA

2. Research Group Christiansen, Helmholtz-Zentrum Berlin für Materialien und Energie, Berlin, Germany

3. Max Planck Institute for the Science of Light, Erlangen, Germany

4. Physics Department, Freie Universität Berlin, Berlin, Germany

5. Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada

6. NOAA Fisheries, James J. Howard Marine Sciences Laboratory at Sandy Hook, Highlands, USA

7. HORIBA Scientific, Sunnyvale, USA

8. Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany

9. Bavarian Health and Food Safety Authority, Erlangen, Germany

10. Food Chemistry Unit, Department of Chemistry and Pharmacy–Emil Fischer Center, University of Erlangen-Nuremberg, Erlangen, Germany

11. TZW: DVGW-Technologiezentrum Wasser (German Water Centre), Karlsruhe, Germany

12. Alfred-Wegener-Institute Helmholtz Centre for Polar and Marine Research, Helgoland, Germany

Abstract

Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas–chromatography mass–spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability.

Publisher

SAGE Publications

Subject

Spectroscopy,Instrumentation

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