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Laser-induced breakdown spectroscopy with neural network approach for plastic identification and classification in waste management
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Researchers applied laser-induced breakdown spectroscopy combined with neural network algorithms to identify and classify different plastic types, addressing the need for rapid and accurate plastic sorting in recycling chains. The system demonstrated high classification accuracy for common polymer types based on their elemental emission spectra.
The threats to the environment and humans are increasing every day due to the use of modern plastics and their improper disposal approaches. Researchers pay more attention to reducing plastic waste through recycling so that it can be used as a raw material. In the recycling chain, grading or identifying different types of plastic is essential. For this, Lase Induced Breakdown Spectroscopy (LIBS) has been established. LIBS is an effective investigation tool that analyzes plastics in a qualitative and quantitative manner. Spectral analysis of different kinds of plastics is performed from the plasma emission obtained from LIBS. In this research work different types of plastic samples are identified using LIBS and classified using back propagation neural network algorithm (BPNN). The research aimed to attain a simple application to detect plastic polymers compared to existing approaches. To validate the better results proposed model performances are compared with existing kNN, SIMCA and ANN based classification models.
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