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Membrane filter removal in FTIR spectra through dictionary learning for exploring explainable environmental

Suphachok Buaruk1, Pattara Somnuake1, Sarun Gulyanon2

  • 1Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12120, Thailand.

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We developed a new method using dictionary learning to remove membrane filter signals from Fourier transform infrared (FTIR) spectroscopy, improving microplastic analysis in environmental samples. This technique enhances accuracy for identifying plastic contamination sources.

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Microplastic contamination is a significant environmental issue requiring accurate identification of plastic types and sources.
  • Fourier transform infrared (FTIR) spectroscopy is a key tool for microplastic analysis, but membrane filters used in sample collection can interfere with spectral data.
  • Small plastic particles can be obscured by the membrane filter's spectral signature, complicating analysis.

Purpose of the Study:

  • To develop a novel preprocessing method for FTIR spectroscopy to effectively remove membrane filter interference in microplastic analysis.
  • To improve the accuracy and explainability of microplastic identification from environmental water samples.
  • To enhance the capability of FTIR spectroscopy for analyzing microplastics, especially in low signal-to-noise ratio scenarios.

Main Methods:

  • A dictionary learning technique was employed to decompose FTIR spectra and isolate membrane filter characteristic bands.
  • The analysis was divided into two subtasks: membrane filter removal and plastic classification for enhanced explainability.
  • The method was tested on generated spectra with varying noise levels (SNR 0 to -30dB) and real-world lab samples.

Main Results:

  • The proposed method demonstrated a 1.5-fold improvement over baseline methods in microplastic analysis.
  • Comparable results were achieved against state-of-the-art methods like UNet, particularly for noisy spectra.
  • The technique provides crucial explainability, a feature lacking in other advanced methods.

Conclusions:

  • The dictionary learning-based preprocessing method effectively removes membrane filter signals from FTIR spectra, enabling more accurate microplastic analysis.
  • The approach offers significant advantages in explainability and performance, especially for challenging samples with low signal-to-noise ratios.
  • This method represents a practical advancement for identifying environmental contamination sources and controlling plastic pollution.