Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 23 August 2018 / Approved: 23 August 2018 / Online: 23 August 2018 (04:54:18 CEST)

A peer-reviewed article of this Preprint also exists.

Rzecki, K.; Sośnicki, T.; Baran, M.; Niedźwiecki, M.; Król, M.; Łojewski, T.; Acharya, U.R.; Yildirim, Ö.; Pławiak, P. Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS. Sensors 2018, 18, 3670. Rzecki, K.; Sośnicki, T.; Baran, M.; Niedźwiecki, M.; Król, M.; Łojewski, T.; Acharya, U.R.; Yildirim, Ö.; Pławiak, P. Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS. Sensors 2018, 18, 3670.

Journal reference: Sensors 2018, 18, 3670
DOI: 10.3390/s18113670

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 analysis of collected data. This procedure is essentially based on the comparison of lines present in the spectrum with a literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of non-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 Neighbour, Support Vector Machine, Probabilistic Neural Network, Multi-Layer Perceptron, and Generalized Regression Neural Network), 5-fold stratified cross-validation and test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08% with average classification time of about 0.12 s is 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.

Subject Areas

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

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