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Developing and testing a workflow to identify microplastics using near infrared hyperspectral imaging.

Andrea Faltynkova1, Martin Wagner1

  • 1Department of Biology, Norwegian University of Science and Technology, Høgskoleringen 5, 7491 Trondheim, Norway.

Chemosphere
|June 24, 2023
PubMed
Summary
This summary is machine-generated.

Near-infrared hyperspectral imaging (NIR-HSI) rapidly identifies common microplastics (MP). This method accurately detects larger plastic particles and is unaffected by photooxidation, aiding pollution monitoring.

Keywords:
ChemometricsFourier transform infrared spectroscopyImaging spectroscopyMacroplasticMethod developmentRaman spectroscopyWeathering

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Materials Science

Background:

  • Microplastic (MP) analysis is crucial for understanding plastic pollution but is often time-consuming.
  • Near-infrared hyperspectral imaging (NIR-HSI) offers rapid, large-area analysis compared to other spectroscopic methods.
  • Existing barriers include a lack of open databases and standardized analysis pipelines for MP detection.

Purpose of the Study:

  • To develop and validate a spectral database for microplastic identification using NIR-HSI.
  • To assess the accuracy and limitations of NIR-HSI for detecting and quantifying microplastics.
  • To evaluate the impact of photooxidation on the detection and classification of microplastics.

Main Methods:

  • A spectral database was created using a Hyspex SWIR-320me imager, including preproduction pellets, consumer products, and marine debris.
  • A SIMCA model was developed to identify four polymer types: polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and polystyrene (PS).
  • Accuracy of size estimates for PS MP was determined using fluorescence microscopy; the impact of photooxidation was tested on PE, PP, PS, and PET.

Main Results:

  • The SIMCA model achieved high accuracy (>88% internal, >80% external validation) for identifying the four polymer types.
  • NIR-HSI consistently detected PS MP >1000 μm, with accurate size estimations, but struggled with particles <500 μm.
  • Photooxidation did not significantly alter spectra or decrease model precision, though recall varied across polymer types and oxidation stages.

Conclusions:

  • NIR-HSI is a rapid and accurate method for identifying the four most prevalent microplastic polymer types.
  • The technique overcomes the limitations of single-particle analysis, enabling efficient environmental monitoring.
  • NIR-HSI is a valuable tool for rapid, large-scale assessment of plastic debris in environmental monitoring.