A combination of laser-induced breakdown spectroscopy (LIBS) and artificial neural networks (ANNs) has been used for the identification of polymer materials, including polypropylene (PP), polyvinyl chloride (PVC), polytetrafluoroethylene (PTFE), polyoxymethylene (POM), polyethylene (PE), polyamide or nylon (PA), polycarbonate (PC) and poly(methyl methacrylate) (PMMA). After optimization of the experimental setup and the spectrum acquisition protocol, successful identification rates between 81 and 100% were achieved using spectral features gathered from single spectra without averaging (1 second acquisition time) over a wide spectral range (240–820 nm). Furthermore, ten different materials based on PVC were tested using the identification procedure. Correct identifications were obtained as well. Sorting of the materials into sub-categories of PVC materials according to their charges (concentration in trace elements such as Ca) was performed. The demonstrated capacities fit, in practice, the needs of plastic-waste sorting and of producing high-grade recycled plastic materials.
Vol. 6, Iss. 4 Virtual Journal for Biomedical Optics
Myriam Boueri, Vincent Motto-Ros, Wen-Qi Lei, Qain-LiMa, Li-Juan Zheng, He-Ping Zeng, and JinYu, "Identification of Polymer Materials Using Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Networks," Appl. Spectrosc. 65, 307-314 (2011)
References are not available for this paper.
OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.