Efficient microplastic identification by hyperspectral imaging: A comparative study of spatial resolutions, spectral...

Area of Science:

• Environmental Science
• Analytical Chemistry
• Spectroscopy

Background:

• Microplastic (MP) pollution is a significant global environmental challenge.
• Efficient analytical methods are crucial for monitoring MP contamination and impact.
• Hyperspectral imaging (HSI) offers potential for MP characterization.

Purpose of the Study:

• To establish an optimal analytical protocol for characterizing microplastics using hyperspectral imaging (HSI).
• To compare different HSI configurations, including spatial resolution, spectral range, and classification models.
• To determine the limit of detection (LOD) for various microplastic sizes.

Main Methods:

• Investigated common polymers: polystyrene (PS), polypropylene (PP), and high-density polyethylene (HDPE) in size classes (250-2000 μm).
• Assessed smaller particles (30-250 μm) to determine the limit of detection (LOD).
• Acquired hyperspectral images using two spatial resolutions (150 and 30 μm/pixel) and two spectral ranges (NIR: 1000-1700 nm, SWIR: 1000-2500 nm).
• Tested three classification models: PLS-DA, ECOC-SVM, and NNPR, evaluating performance with prediction maps and statistical parameters.

Main Results:

• For MPs >250 μm, optimal setup: 150 μm/pixel resolution, 1000-1700 nm spectral range, and PLS-DA model (time/cost-efficient).
• For MPs <250 μm, optimal setup: 30 μm/pixel resolution, 1000-2500 nm spectral range, and ECOC-SVM model.
• LOD was 250 μm at 150 μm/pixel resolution and 100-200 μm at 30 μm/pixel resolution.

Conclusions:

• The study provides a guide for selecting optimal HSI parameters for microplastic characterization based on particle size.
• Tailored HSI protocols enhance accuracy and efficiency in microplastic analysis.
• This research supports improved environmental monitoring strategies for microplastic pollution.