Issue 48, 2013

Analytical predictive capabilities of Laser Induced Breakdown Spectroscopy (LIBS) with Principal Component Analysis (PCA) for plastic classification

Abstract

A Laser Induced Breakdown Spectroscopy (LIBS) technique has been applied for the identification of four widely used plastics, polyethylene terephthalate (PET), high-density polyethylene (PE), polypropylene (PP) and polystyrene (PS), whose recycling is required from commercial and biosafety points of view. The 3rd harmonic (355 nm) nanosecond pulse from an Nd:YAG laser is used to create plasma on the sample surface and identification of the type of the plastic is derived from the plasma emission. Principal Component Analysis (PCA) of the LIBS spectra is employed for the classification of plastics. Distinct methods have been used, apart from principal components of PCA, to further confirm our results. Statistical parameters, viz., Mahalanobis distance (M-distance) and spectral residuals were used for decisive match/no match test which provided successful classification of plastics. Receiver Operating Characteristic (ROC) and Youden's index analyses were carried out to obtain the diagnostic threshold for classification of all four classes of plastics. Sensitivity, specificity, predictive values and discriminative accuracy of the classification tests based on the optimum threshold were calculated. This proves the analytical predictive capabilities of the LIBS technique for plastic identification and classification. The technique of LIBS, in future, can be routinely used in field applications such as plastic waste sorting and recycling.

Graphical abstract: Analytical predictive capabilities of Laser Induced Breakdown Spectroscopy (LIBS) with Principal Component Analysis (PCA) for plastic classification

Article information

Article type
Paper
Submitted
07 Sep 2013
Accepted
17 Oct 2013
First published
18 Oct 2013

RSC Adv., 2013,3, 25872-25880

Analytical predictive capabilities of Laser Induced Breakdown Spectroscopy (LIBS) with Principal Component Analysis (PCA) for plastic classification

V. K. Unnikrishnan, K. S. Choudhari, S. D. Kulkarni, R. Nayak, V. B. Kartha and C. Santhosh, RSC Adv., 2013, 3, 25872 DOI: 10.1039/C3RA44946G

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements