OSA's Digital Library

Applied Spectroscopy

Applied Spectroscopy

| PUBLISHED BY SAS — AVAILABLE FROM SAS AND OSA

  • Vol. 65, Iss. 3 — Mar. 1, 2011
  • pp: 307–314

Identification of Polymer Materials Using Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Networks

Myriam Boueri, Vincent Motto-Ros, Wen-Qi Lei, Qain-LiMa, Li-Juan Zheng, He-Ping Zeng, and JinYu

Applied Spectroscopy, Vol. 65, Issue 3, pp. 307-314 (2011)


View Full Text Article

Acrobat PDF (2244 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations
  • Export Citation/Save Click for help

Abstract

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.

Virtual Issues
Vol. 6, Iss. 4 Virtual Journal for Biomedical Optics

Citation
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)
http://www.opticsinfobase.org/as/abstract.cfm?URI=as-65-3-307

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited