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

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.

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

“…An overview of industrial applications of LIBS and especially on development from 2014–2018 has been published ( Noll et al, 2018 ) addressing recent R&D activities in the field of automated LIBS: (a) combined use of in-line measured three-dimensional (3D) geometry information and LIBS analyses for high-speed sorting tasks of piece goods; (b) sorting of refractories; (c) identification of steel blooms in a rolling mill, and, as we will see later for the plastics case; and (d) inverse production application for the recovery of valuable materials from EoL electronic equipment. Also, other works on automated methods have been extensively reported ( Connemann et al, 2016 ; Rosenwasser et al, 2001 ; Rzecki et al, 2018 ; Ukwatta et al, 2012 ). Finally, LIBS-based imaging has promising applications in biology, medicine and geology ( Jolivet et al, 2019 ).…”

“…the plastics case; and (d) inverse production application for the recovery of valuable materials from EoL electronic equipment. Also, other works on automated methods have been extensively reported (Connemann et al, 2016;Rosenwasser et al, 2001;Rzecki et al, 2018;Ukwatta et al, 2012). Finally, LIBS-based imaging has promising applications in biology, medicine and geology (Jolivet et al, 2019).…”

Interpol review of questioned documents 2016–2019

“…Several studies [ [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] ] have been published concerning the analysis and discrimination of writing inks with several kind of instruments. Some papers focused on only one analytical method while others compared a method to another, or combined them to find a methodology to discriminate inks.…”