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Updated: May 13, 2025

Dependence of Laser-induced Breakdown Spectroscopy Results on Pulse Energies and Timing Parameters Using Soil Simulants

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Efficient and anti-interference plastic classification method suitable for one-shot learning based on laser induced

Zhiying Xu¹, Xingyu Zhao², Xinying Peng³

¹State Key Laboratory of Nuclear Physics and Technology, and Key Laboratory of HEDP of the Ministry of Education, CAPT, Peking University, Beijing, 100871, China; Guangdong Institute of Laser Plasma Accelerator Technology, 510540, China.

|April 16, 2025

Summary

This summary is machine-generated.

This study introduces an efficient, anti-interference method for plastic classification using laser-induced breakdown spectroscopy (LIBS) and one-shot learning. The new technique significantly improves plastic identification accuracy for recycling applications.

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Area of Science:

Materials Science
Analytical Chemistry
Computer Science

Background:

Efficient plastic recycling is crucial for environmental sustainability.
Accurate and rapid classification of plastics is a key challenge in the recycling industry.
Existing methods often struggle with interference from plastic additives.

Purpose of the Study:

To develop an efficient and anti-interference method for plastic classification.
To leverage one-shot learning and laser-induced breakdown spectroscopy (LIBS) for this purpose.
To enhance the accuracy and speed of plastic identification in recycling.

Main Methods:

Development of a residual neural network with full-spectrum training (ResNet-FST) for one-shot learning classification.
Implementation of a multi-parameter peak search algorithm for spectral feature extraction.
Creation of a linear residual classification model with peak auto-search (LRC-PAS) for improved efficiency and anti-interference capabilities.
Optimization of model parameters, including residual blocks (2) and neurons (80).

Main Results:

The ResNet-FST model achieved 99.65% accuracy in one-shot learning classification.
The LRC-PAS model demonstrated significantly improved classification efficiency compared to ResNet-FST.
The mechanism of spectral interference from plastic additives was elucidated.
High accuracy in anti-interference classification was achieved, effectively handling additive-induced spectral interference.

Conclusions:

The proposed method offers a highly efficient and anti-interference solution for plastic classification.
The developed LRC-PAS model shows great potential for real-time plastic classification in the recycling industry.
This approach contributes to advancing sustainable plastic recycling practices through improved material identification.