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Sensors (Basel). 2018 Oct 29;18(11). pii: E3670. doi: 10.3390/s18113670.

Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS.

Author information

1
Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland. krz@pk.edu.pl.
2
Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland. tsosnicki@pk.edu.pl.
3
Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland. mbaran@pk.edu.pl.
4
Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland. nkg@pk.edu.pl.
5
Laboratory for Forensic Chemistry, Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387 Krakow, Poland. krolm@chemia.uj.edu.pl.
6
Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30 Av., 30-059 Krakow, Poland. lojewski@agh.edu.pl.
7
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, 599489 Singapore, Singapore. aru@np.edu.sg.
8
Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, 599494 Singapore, Singapore. aru@np.edu.sg.
9
School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia. aru@np.edu.sg.
10
Department of Computer Engineering, Munzur University, 62000 Tunceli, Turkey. oyildirim@munzur.edu.tr.
11
Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland. plawiak@pk.edu.pl.

Abstract

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

KEYWORDS:

LIBS; classification; computational intelligence methods; discrimination power; machine learning; paper-ink analysis

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