Bulk Analysis of Nonconducting Materials

Noll, Reinhard

doi:10.1007/978-3-642-20668-9_14

Cited by 1 publication

(2 citation statements)

References 89 publications

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“…As can be seen, they were all found exhibiting a similar structure, despite the different types of additives they contain, due to their similar composition. Specifically, they were all found dominated by the molecular bands of C 2 and CN, which had led us and other research groups to use these spectral features for qualitative and quantitative analysis for plastic/polymer sorting [32][33][34][35][36].…”

Section: Investigation Of the Acquired Spectramentioning

confidence: 99%

“…So, in the first case, the high-resolution spectra of the C 2 Swan band system (∆v = 0) were used as inputs for the PCA algorithm. This choice was based on the results of several studies where this molecular emission is directly correlated with the polymeric structure of the samples [23,30,33,34]. The explained variance of the first two Principal Components (PCs) was found to be 85.8%.…”

Section: Plastics Identification Using the Principal Component Analysmentioning

confidence: 99%

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Laser-Induced Breakdown Spectroscopy Assisted by Machine Learning for Plastics/Polymers Identification

Stefas

Gyftokostas

Bellou

et al. 2019

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In the present work, Laser-Induced Breakdown Spectroscopy (LIBS) is used for the discrimination/identification of different plastic/polymeric samples having the same polymeric matrix but containing different additives (as e.g., fillers, flame retardants, etc.). For the classification of the different plastic samples, some machine learning algorithms were employed for the analysis of the LIBS spectroscopic data, such as the Principal Component Analysis (PCA) and the Linear Discriminant Analysis (LDA). The combination of LIBS technique with these machine learning algorithmic approaches, in particular the latter, provided excellent classification results, achieving identification accuracies as high as 100%. It seems that machine learning paves the way towards the application of LIBS technique for identification/discrimination issues of plastics and polymers and eventually of other classes of organic materials. Machine learning assisted LIBS can be a simple to use, efficient and powerful tool for sorting and recycling purposes.

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