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.