Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3×) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. New methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

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Publication Details

Journal
Analytical Chemistry
Published
2025-08-11
DOI
https://doi.org/10.1021/acs.analchem.5c00962
Citations
10
Primary Topic
Microplastics and Plastic Pollution
Type
article
Field-Weighted Citation Impact
5.04

Funders

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article

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

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10 citations
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Microplastics and Plastic Pollution
5.04
article

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Ali Chamas, Monica M. Arienzo, Clarissa Lincoln, Win Cowger, Andrew B. Gray, Benjamin Maurer, Katherine S. Lasdin, Nicholas Leong, Rachel Kozloski, Christine M. Knauss, Clare Murphy-Hagan, Sebastian Primpke, Zacharias Steinmetz, Vesna Teofilović, Gwen Lattin, Aleksandra Karapetrova, Lindsay E. Darjany, Shelly Moore, Jeremiah Bryksa, Hannah Sherrod, Charles J. Moore
article en
10 citations

Abstract

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3×) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. New methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

Analytical ChemistryVol. 97(32)
University of California, Riverside (US), Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (DE), National Laboratory of the Rockies (US), University of Maryland Center for Environmental Science (US), University of Kaiserslautern (DE), Moore College of Art and Design (US), University of Novi Sad (RS), Northern Alberta Institute of Technology (CA), University of Washington (US), University of California System (US), Southern California Institute for Research and Education (US), Renewable Energy Systems (United States) (US), University of Washington Applied Physics Laboratory (US), Desert Research Institute (US), University of Applied Sciences Kaiserslautern (DE)
U.S. Department of Agriculture, Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, University of Southern California Sea Grant, University of Southern California, National Institute of Food and Agriculture, Division of Agriculture and Natural Resources, University of California, Water Power Technologies Office, National Renewable Energy Laboratory
Openalex Percentile: Top 4%
Microplastics and Plastic Pollution
5.04
DOISourceOpenAlex
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