Currently, spectral analysis methods used in the classification of plastics have limitations that do not apply to opaque plastics or the stability of experimental results is not strong. In this paper, X-ray absorption spectroscopy (XAS) has been applied to classify plastics due to its strong penetrability and stability. Fifteen kinds of plastics are selected as specimens. X-ray, which is excited by a voltage of 60 kV, penetrated these specimens. The spectral data acquired by CdTe X-ray detector are processed by principal component analysis (PCA) and other data analysis methods. Then the back propagation neural networks (BPNN) algorithm is used to classify the processed data. The average recognition rate reached 96.95% and classification results of all types of plastic results were analyzed in detail. It indicates that XAS has the potential to classify plastics and that XAS can be used in some fields such as plastic waste sorting and recycling. At the same time, the technology of XAS, in the future, can also be used to classify more substances.
HoslerD.BurkettS.L.TarkanianM.J.“Prehistoric Polymers: Rubber Processing in Ancient Mesoamerica”. Science. 1999. 284(5422): 1988–1991.
2.
AndradyA.L.NealM.A.“Applications and Societal Benefits of Plastics”. Philos. T. R. Soc. B. 2009. 364(1526): 1977–1984.
3.
LazarevicD.AoustinE.BucletN.BrandtN.“Plastic Waste Management in the Context of a European Recycling Society: Comparing Results and Uncertainties in a Life Cycle Perspective”. Resour., Conserv. Recycl. 2010. 55(2): 246–259.
4.
HopewellJ.DvorakR.KosiorE.“Plastics Recycling: Challenges and Opportunities”. Philos. T. R. Soc. B. 2009. 364(1526): 2115–2126.
5.
CarvalhoM.T.FerreiraC.SantosL.R.PaivaM.C.“Optimization of Froth Flotation Procedure for Poly(Ethylene Terephthalate) Recycling Industry”. Polym. Eng. Sci. 2012. 52(1): 157–164.
6.
WangC.Q.WangH.LiuY.N.“Separation of Polyethylene Terephthalate from Municipal Waste Plastics by Froth Flotation for Recycling Industry”. Waste Manage. 2015. 35: 42–47.
7.
NecemerM.KumpP.SketP.PlavecJ.GrdadolnikJ.et al.“A Novel Analytical Technique Suitable for the Identification of Plastics”. Acta Chim. Slov. 2013. 60(4): 701–705.
8.
AnzanoJ.BonillaB.Montull-IborB.Casas-GonzalezJ.“Plastic Identification and Comparison by Multivariate Techniques with Laser-Induced Breakdown Spectroscopy”. J. Appl. Polym. Sci. 2011. 21(5): 2710–2716.
9.
LiuK.WangQ.Q.ZhaoH.XiaoY.L.“Differentiation of Plastic with Laser Induced Breakdown Spectroscopy”. Guang Pu Xue Yu Guang Pu Fen Xi. 2011. 31(5): 1171–1174.
10.
UnnikrishnanV.K.ChoudhariK.S.KulkarniS.D.NayakR.KarthaV.B.et al.“Analytical Predictive Capabilities of Laser Induced Breakdown Spectroscopy (LIBS) with Principal Component Analysis (PCA) for Plastic Classification”. RSC Adv. 2013. 3(48): 25872–25880.
11.
AnzanoJ.BonillaB.Montull-IborB.LasherasR.JCasas-GonzalezJ.“Classifications of Plastic Polymers Based on Spectral Data Analysis with Laser Induced Breakdown Spectroscopy”. J. Polym. Eng. 2010. 30(3–4): 177–188.
12.
TognoniE.CristoforettiG.“Signal and Noise in Laser Induced Breakdown Spectroscopy: An Introductory Review”. Opt. Laser Technol. 2016. 79: 164–172.
13.
SavinS.L.P.BerkoA.BlacklocksA.N.EdwardsW.ChadwickA.V.“The Applications of X-Ray Absorption Spectroscopy in the Study of Nanocrystalline Materials and Electrochemical Systems”. C. R. Chim. 2009. 40(47): 948–963.
Q. Wang, X.M. Wu, W. Zhang, H.F. Feng, S.T. He, et al. “Preliminary Research on Organics Recognition by X-ray Absorption Spectroscopy Detection and Classification”. Proc. SPIE. 2017. 10244. 102440L-1-7.
17.
JolliffeI.T.CadimaJ.“Principal Component Analysis: A Review and Recent Developments”. Philos. T.R. Soc. A. 2016. 374(2065): 1–16.
18.
MajdiA.BeikiM.“Evolving Neural Network Using a Genetic Algorithm for Predicting the Deformation Modulus of Rock Masses”. Int. J. Rock Mech. Min. 2010. 47(2): 246–253.
19.
TanabeK.TamuraT.UesakaH.“Neural Network System for the Identification of Infrared Spectra”. Appl. Spectrosc. 1992. 46(5): 807–810.
20.
N.M. Nawi, R.S. Ransing, M.N.S. Salleh, R. Ghazali, N.A. Hamid. “An Improved Back Propagation Neural Network Algorithm on Classification Problems”. In: Proceedings of Database Theory and Application, Bio-Science and Bio-Technology. Jeju Island, South Korea: December 13–15, 2010. Pp. 177–188.
21.
HuB.ZhangX.L.OuyangQ.N.WuX.M.FangZ.“A Prototype System to Measure X-ray Absorption Spectra for Diagnosis In Vivo”. Measurement. 2016. 93: 252–257.
22.
XiangS.M.NieF.P.ZhangC.S.“Learning a Mahalanobis Distance Metric for Data Clustering and Classification”. Pattern Recognition. 2008. 41(12): 3600–3612.
FawcettT.“An Introduction to ROC Analysis”. Pattern Recognit. Lett. 2006. 27(8): 861–874.
25.
ZhangZ.YeJ.ChenB.PerkinsA.E.RoseS.et al.“Investigation of Optimization-Based Reconstruction with an Image-Total-Variation Constraint in PET”. Phys. Med. Biol. 2016. 61(16): 6055–6084.
