Advanced chemometric methodologies on single shot hyphenated LIBS data for rapid and reliable characterization of plastic classes.

Autor: Adarsh UK; Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India., Bankapur A; Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India., Pai AK; Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India., Kartha VB; Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India., Unnikrishnan VK; Department of Atomic and Molecular Physics, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India. Electronic address: unnikrishnan.vk@manipal.edu.
Jazyk: angličtina
Zdroj: Talanta [Talanta] 2024 Sep 01; Vol. 277, pp. 126393. Date of Electronic Publication: 2024 Jun 08.
DOI: 10.1016/j.talanta.2024.126393
Abstrakt: Background: Plastic Solid Waste (PSW) sorting is a procedure of paramount importance in the mechanical recycling process of plastics waste. The major limitation of the techniques relying on physical properties of plastics is the time taken for analysis and poor accuracy. Spectroscopy has been shown to be a suitable method in plastic sorting due to its atomic and molecular characterization capabilities, and ability to give results in very short time scales. However, for practical applications it is essential to translate this technique into an automatic technology, by combining it with advanced chemometric tools which can give observer independent judgement.
Results: The indigenously developed bi-model Laser Induced Breakdown Spectroscopy (LIBS)-Raman system with single source and single detector can record the LIBS-Raman spectral signals in single-shot mode in a total time frame of 20 ms. Out of the combinations of Principal Component Analysis (PCA) and Partial Least Squares (PLS) with Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Partial Least Squares-Discriminant Analysis (PLS-DA) based classifiers, the PLS-DA based model showed the maximum classification accuracy with 95 % based on LIBS data and 100 % based on Raman data. The reliability of the model was assessed using 4-fold cross-validation which showed a sensitivity of 90.28 % and specificity of 98.29 % for predictions based on LIBS data, and 99 % sensitivity and 99.82 % specificity for predictions relying on Raman data.
Significance: The results show how the combination of multimodal spectroscopy with chemometric analysis enhances the applicability of spectroscopic techniques for plastic sorting. The classification model successfully classified seven types of post-consumer plastic samples based on combined LIBS and Raman data. With the home-built software for automated prediction, the system takes less than a second to predict the plastic type illustrating the potential of the method for translation to regular routine industrial applications.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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