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Plastic Classification with X-ray Absorption Spectroscopy Based on Back Propagation Neural Network.

Qian Wang¹, Xiaomei Wu¹, Lingcong Chen¹

¹12466 Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China.

Applied Spectroscopy

|May 10, 2017

Summary

X-ray absorption spectroscopy (XAS) offers a stable and effective method for classifying plastics, overcoming limitations of traditional spectral analysis. This technique achieves high accuracy, showing potential for plastic waste sorting and recycling applications.

Keywords:

X-ray absorption spectroscopy material recognition neural networks plastic classification principle component analysis

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Area of Science:

Materials Science
Analytical Chemistry
Spectroscopy

Background:

Traditional spectral analysis methods struggle with opaque plastics and experimental stability.
Limitations in current plastic classification hinder efficient recycling and waste management.

Purpose of the Study:

To investigate the efficacy of X-ray absorption spectroscopy (XAS) for classifying various plastic types.
To address the limitations of existing methods for opaque plastics and improve result stability.

Main Methods:

Fifteen types of plastics were analyzed using X-ray absorption spectroscopy (XAS) with X-rays excited at 60 kV.
Spectral data were processed using principal component analysis (PCA) and other data analysis techniques.
Classification was performed using a back propagation neural network (BPNN) algorithm.

Main Results:

The XAS method achieved an average plastic recognition rate of 96.95%.
Detailed analysis confirmed accurate classification across all tested plastic types.
The study demonstrated the strong penetrability and stability of XAS for plastic analysis.

Conclusions:

X-ray absorption spectroscopy (XAS) shows significant potential for accurate plastic classification.
XAS is a promising technology for applications in plastic waste sorting and recycling.
The methodology can potentially be extended to classify a wider range of substances in the future.