Increasing the Accessibility for Characterizing Microplastics: Introducing New Application-Based and Spectral Libraries of Plastic Particles (SLoPP and SLoPP-E)

Abstract

As smaller particle sizes are increasingly included in microplastic research, it is critical to chemically characterize microparticles to identify whether particles are indeed microplastics. To increase the accessibility of methods for characterizing microparticles via Raman spectroscopy, we created an application-based library of Raman spectroscopy parameters specific to microplastics based on color, morphology, and size. We also created two spectral libraries that are representative of microplastics found in environmental samples. Here, we present SLoPP, a spectral library of plastic particles, consisting of 148 reference spectra, including a diversity of polymer types, colors, and morphologies. To account for the effects of aging on microplastics and associated changes to Raman spectra, we present a spectral library of plastic particles aged in the environment (SLoPP-E). SLoPP-E includes 113 spectra, including a diversity of types, colors, and morphologies. The microplastics used to make SLoPP-E include environmental samples obtained across a range of matrices, geographies, and time. Our libraries increase the likelihood of spectral matching for a broad range of microplastics because our libraries include plastics containing a range of additives and pigments that are not generally included in commercial libraries. When used in combination with commercial libraries of over 24 000 spectra, 63% of the top 5 matches across all particles tested (product and environmental) are from SLoPP and SLoPP-E. These tools were developed to improve the accessibility of microplastics research in response to a growing and multidisciplinary field, as well as to enhance data quality and consistency.

Is It or Isn't It: The Importance of Visual Classification in Microplastic Characterization

“…Parameters for spectral acquisition were determined using the application-based library. 53 Manual baseline correction and smoothing of Raman spectra was performed in LabSpec6 if necessary. Raman spectra were matched using Bio-Rad KnowItAll software with associated reference libraries and the SLoPP/SLoPP-E Raman libraries created at the University of Toronto.…”

Towards Raman Automation for Microplastics: Developing Strategies for Particle Adhesion and Filter Subsampling

“…Spectra were sent to Bio-Rad KnowItAll software to assign spectral matches with reference spectra using KnowItAll Raman Spectral Library, as well as databases consisting of reference plastic polymers and environmentally aged MPs created in our lab. 22 The Bio-Rad KnowItAll and ID Expert spectral matching software applies corrections to the spectra automatically (baseline, vertical clipping, intensity distortion, horizontal offset, vertical offset, Raman intensity distortion). Spectral matches are assigned based on visual confirmation for similar peak alignment and intensity, as well as Hit Quality Index (HQI) score as calculated by the Bio-Rad KnowItAll and ID Expert matching algorithm.…”

Plastic ingestion, retention, and transport in animals from the eastern Canadian Arctic

“…Additionally, in this study we chemically identified all particles from the faecal precursor samples using micro-Raman spectroscopy, which allowed us to exclude potentially natural particles from our analyses. Forty-four percent of the initial particles found on the filters were natural or unknown, highlighting the importance of particle identification to reduce overestimations of anthropogenic particles, especially when dealing with small particle sizes (Lenz et al, 2015;Munno et al, 2020).…”

“…Chemically identifying and characterizing particles is becoming an increasingly crucial step in order to ensure particles are in fact plastic or of anthropogenic origin, and to avoid overestimating counts of anthropogenic particles (Lenz et al, 2015;Munno et al, 2020). This is particularly relevant as more studies are now reporting on very small particles that are impossible to positively confirm visually (Lenz et al, 2015;Munno et al, 2020). Particle identification and characterization is most commonly done through Fourier-transform infrared spectroscopy (FTIR; e.g.…”

“…Catarino et al, 2018) have also been published as methods of identifying polymers, however, these methods seem to be less commonly employed in studies assessing microplastics ingested by animals. Recently, spectral libraries have been developed specifically for identifying microplastic particles and various additives commonly used in the production of plastics (Munno et al, 2020;Primpke et al, 2018), which greatly improves the ability and accessibility of positively identifying particles.…”