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Multi-task deep learning meets hyperspectral imaging: A unified modeling framework for WEEE plastic identification

Huihuang Zou1, Pinjing He2, Fan Lü2

  • 1Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

Journal of Hazardous Materials
|December 17, 2025
PubMed
Summary

A new multitask deep learning framework enables simultaneous plastic identification and flame retardant quantification for efficient waste electrical and electronic equipment (WEEE) recycling using hyperspectral imaging.

Keywords:
Flame retardantsHyperspectral imagingMultitask learningWaste electrical and electronic equipmentWaste recycling

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Area of Science:

  • Materials Science
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Efficient recycling of Waste Electrical and Electronic Equipment (WEEE) requires rapid plastic identification and additive quantification.
  • Current single-task methods struggle with high-dimensional spectral data analysis for WEEE plastics.

Purpose of the Study:

  • To develop an integrated framework for simultaneous polymer classification, flame retardant identification, and loading quantification.
  • To enhance the analysis of hyperspectral imaging data for WEEE recycling applications.

Main Methods:

  • A multitask convolutional neural network (CNN) integrating shared and task-specific learning with cross-task fusion and attention.
  • Utilized near-infrared (NIR) and mid-wave infrared (MWIR) hyperspectral imaging data of WEEE plastics (ABS, HIPS, PP) with four flame retardants at varying loadings (1-30%).

Main Results:

  • The proposed multitask model significantly improved polymer substrate and flame retardant type classification accuracy by 7-10% compared to single-task models.
  • Achieved a notable increase in the R² score for flame retardant loading regression (0.04-0.071).
  • Comparable performance gains were observed for both NIR and MWIR hyperspectral data.

Conclusions:

  • The multitask framework offers a robust solution for intelligent WEEE plastic recognition and sorting.
  • The developed model provides a strong theoretical and technical foundation for advanced spectral sensing applications in environmental monitoring and waste management.