DOI: 10.3964/j.issn.1000-0593(2017)11-3600-06

Authors
Han, X. ; Li, Y. C. ; Qui, C. L. ; Tian, D. ; Yang, G.

Keywords

Laser-induced breakdown spectroscopy; Plastic; Partial least squares; Classification;IDENTIFICATION; LIBS; DISCRIMINATION; POLYMERS

Abstract

The traditional ways of waste plastics processing mainly use the burning landfill, which lead to environmental pollution and the waste of resources. Waste plastic recycling is very important on the circulation economy and the sustainable development. The traditional instruments have some shortcomings in plastic classification, such as lower precision, higher cost, the influence of the sample color and a serious threat to operating personnel's health. Laser inducedbreakdown spectroscopy has many advantages, such as simultaneous multielement detection of elements, free from sample preparation, rapid and real-time analysis, slight damages to sample and no impact on the sample color. The method of Chemometrics combined with LIBS technique is applied to the plastic, which improves the accuracy of plastic classification. But at present, the classification has many problems, such as more parameters and the poor universality. Using on a self built LIBS instrument, we can study the laser energy, delay time, integration time and the angle of the optical fiber, which can achieve a better experiment condition. With the experimental platform, we analyze the 2 200 sample points and choose the partial least squares to analyze the spectral data. In order to achieve the correlation between the sample label and the data, we discuss the better ratio of the training set and validation set. The experimental results show that replacing the interference spectra, classification accuracy of all 11 plastic is increased to 100%, while the validation set's accuracy is only 99.8% and the test set is 99.09% without replacing the interference spectra. It can be seen that the laser induced breakdown spectroscopy combined with partial least squares method can be successfully used for the plastic sample classification.

DOI: 10.3964/j.issn.1000-0593(2017)07-2205-05

Authors
Du, M. ; Guo, L. B. ; Hao, Z. Q. ; Li, J. M. ; Li, X. Y. ; Liu, J. G. ; Lu, Y. F. ; Shen, M. ; Yi, R. X. ; Zeng, X. Y.

Keywords

Laser-induced breakdown spectroscopy; Characteristic spectral lines of non-metallic elements; Support vector machine; Plastics identification;ARTIFICIAL NEURAL-NETWORKS; INDUCED PLASMA; EMISSION; POLYMERS

Abstract

Laser-induced breakdown spectroscopy (LIPS) combined with support vector machine (SVM) was adopted to identify 20 kinds of different colored industrial plastics from different manufacturers in open air. The experimental parameters of spectral acquisition were optimized firstly. 100 spectra recorded under optimum conditions were randomly and equally divided into training set and test set. 6 non-metallic characteristic spectral lines were used to avoid the interference with metallic lines. And the training time of SVM model was reduced. The results show that 996 of 1000 test spectra were identified correctly and the average classification accuracy is reached to 99.6%. The classification efficiency is improved with 6 non-metallic characteristic spectral lines. The research demonstrates that, when fewer of major non-metallic characteristic spectral lines are used, laser-induced breakdown spectroscopy technique with support vector machine can identify more kinds of plastics with high accuracy and efficiency.

DOI: 10.1007/s00216-017-0268-z

Authors
Bankapur, A. ; George, S. D. ; Choudhari, K. S. ; Kartha, V. B. ; Kulkarni, S. D. ; Santhosh, C. ; Unnikrishnan, V. K.

Keywords

Laser ablation; Raman spectroscopy; Chemometrics; Complementarity; Polymers;INDUCED BREAKDOWN SPECTROSCOPY; INDUCED PLASMA SPECTROSCOPY; ARTIFICIAL NEURAL-NETWORKS; POLYMER IDENTIFICATION; CLASSIFICATION; MICROSCOPY; PIGMENTS; SPECTRA; COPPER; NIR

Abstract

Classification of plastics is of great importance in the recycling industry as the littering of plastic wastes increases day by day as a result of its extensive use. In this paper, we demonstrate the efficacy of a combined laser-induced breakdown spectroscopy (LIBS)-Raman system for the rapid identification and classification of post-consumer plastics. The atomic information and molecular information of polyethylene terephthalate, polyethylene, polypropylene, and polystyrene were studied using plasma emission spectra and scattered signal obtained in the LIBS and Raman technique, respectively. The collected spectral features of the samples were analyzed using statistical tools (principal component analysis, Mahalanobis distance) to categorize the plastics. The analyses of the data clearly show that elemental information and molecular information obtained from these techniques are efficient for classification of plastics. In addition, the molecular information collected via Raman spectroscopy exhibits clearly distinct features for the transparent plastics (100% discrimination), whereas the LIBS technique shows better spectral feature differences for the colored samples. The study shows that the information obtained from these complementary techniques allows the complete classification of the plastic samples, irrespective of the color or additives. This work further throws some light on the fact that the potential limitations of any of these techniques for sample identification can be overcome by the complementarity of these two techniques.