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

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

Automated Particle Analysis in Image J

This is a tutorial development for automating the particle size, shape, and color quantification with a use case of microplastic particles

Particle Shape and Surface Topology Images

Images of plastic particle shapes and surface topology

Particle Dimensions

Particle dimensions and shapes

Plastic Spectra

spectral classification and raw data of particles

Plastic Particle Data

Images, spectra, and dimensions of plastic particles

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

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.S. Primpke was supported by the German Federal Ministry of Education and Research (Project BASEMAN (JPI-Oceans)– Defining the baselines and standards for microplastics analyses in European waters; BMBF grant 03F0734A).W. Cowger was supported by the National Science Foundation Graduate Research Fellowship. A. Gray was supported in part by the USDA National Institute of Food and Agriculture, Hatch program (project number CA-R-ENS-5120-H), USDA Multistate Project W4170 funds, and UCANR AES Mission funds. G. Sarau, L. Mill, and S. Christiansen acknowledge the financial support from the German Research Foundation (DFG), German Federal Ministry for Education and Research (BMBF), and European Union within the research projects FOR 1616, HIOS, CC-Sens, and npSCOPE. B. Ossmann thanks the Bavarian State Ministry of the Environment and Consumer Protection for financial support. European Commission Horizon 202