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Deep learning-powered efficient characterization and quantification of microplastics.

Pengwei Guo1, Yuhuan Wang1, Shenghua Wu2

  • 1Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA.

Journal of Hazardous Materials
|October 25, 2024
PubMed
Summary

This study introduces an AI framework to automate microplastic (MP) identification and quantification using computer vision and deep learning. The novel approach significantly improves accuracy and accessibility in MP analysis.

Keywords:
Artificial intelligenceAutomatic assessmentImage segmentationSpectroscopy analysis

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Computer Science

Background:

  • Traditional microplastic (MP) characterization and quantification are laborious and time-consuming.
  • Developing automated methods is crucial for efficient MP analysis.

Purpose of the Study:

  • To present an artificial intelligence (AI) framework for automating microplastic identification and quantification.
  • To integrate computer vision and deep learning for enhanced MP analysis.

Main Methods:

  • Developed an AI framework integrating data processing, analytics, visualization, and human-computer interaction.
  • Transformed Fourier Transform Infrared (FTIR) data into contour images and employed data augmentation.
  • Utilized deep learning models for MP identification and computer vision for quantification.

Main Results:

  • Achieved high accuracy in classification (98%), segmentation (99%), and quantification (96%) of microplastics.
  • Demonstrated the framework's efficacy across various polymers including polyethylene, polypropylene, and polystyrene.
  • Developed an engineer-friendly graphic user interface (GUI) for improved data accessibility.

Conclusions:

  • The AI framework successfully automates microplastic characterization and quantification.
  • This research offers a significant advancement in the automatic assessment of microplastics.
  • The developed system enhances efficiency and accuracy in environmental monitoring and research.