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Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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Automatic microplastic classification using dual-modality spectral and image data for enhanced accuracy.

Arsanchai Sukkuea1, Jakkaphong Inpun2, Phaothep Cherdsukjai3

  • 1School of Engineering and Technology, Walailak University, 222 Thaiburi, Thasala, Nakhon Si Thammarat 80160, Thailand; Research Center for Intelligent Technology and Integration, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80160, Thailand.

Marine Pollution Bulletin
|February 17, 2025
PubMed
Summary
This summary is machine-generated.

An automated system classifies microplastics (MPs) using spectral data, overcoming manual analysis limitations. A dual-modality approach with machine learning achieved over 99% accuracy, enabling efficient microplastic identification.

Keywords:
Classification systemDual-modality datasetFeature fusionMicroplasticsμFTIR Spectrum

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Data Science

Background:

  • Manual microplastic (MPs) spectral analysis is time-consuming and prone to errors.
  • Accurate and efficient MP identification is critical for understanding and managing plastic pollution.

Purpose of the Study:

  • To develop an automated microplastic classification system using spectral data.
  • To compare the performance of various machine learning models for MP identification.
  • To deploy a user-friendly web application for MP classification.

Main Methods:

  • Utilized a dual-modality dataset from micro-Fourier Transform Infrared Spectroscopy (μFTIR).
  • Extracted spectral features using deep learning models: AlexNet, ResNet18, and Vision Transformer (ViT).
  • Evaluated machine learning classifiers: Decision Tree (DT), Extremely Randomized Trees (ET), Support Vector Classifier (SVC), and Multiclass Logistic Regression (LR).

Main Results:

  • The AlexNet-LR model achieved 99.03% validation and 99.99% test accuracy.
  • ResNet18-LR demonstrated comparable accuracy (99%) with faster training and inference times.
  • The developed web application, MPsSpecClassify, facilitates efficient MP identification.

Conclusions:

  • Deep learning feature extraction combined with machine learning classifiers enables highly accurate automated MP classification.
  • ResNet18-LR offers a practical solution for web deployment due to its efficiency.
  • The MPsSpecClassify application supports improved microplastic pollution management.