Laser-induced breakdown spectroscopy with artificial neural network processing for material identification

Koujelev, A.; Sabsabi, Mohamad; Motto-Ros, Vincent; Laville, S.; Lui, Siu-Lung

doi:10.1016/j.pss.2009.06.022

Cited by 66 publications

(36 citation statements)

References 14 publications

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“…They also noticed that the analytically viable methods of analysis are de fined by an average relative error of prediction below 20%. Moreover, artificial neural network (here after called ANN) was applied to LIBS for qualitative purposes like classification of polymers [16], or identifi cation of soils [17] and also for quantification [18]. Indeed, Sirven et al [15] quantified chromium in soil samples by ANN applied to LIBS data with interesting discussion on the selection of input data for the ANN, and Ferreira et al [19] reported quantitative LIBS analysis of copper in soils by ANN.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy

Haddad

Villot-Kadri

Ismaël

et al. 2013

Spectrochimica Acta Part B: Atomic Spectroscopy

110

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network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy. Spectrochimica Acta Part B: Atomic Spectroscopy, Elsevier, 2013Elsevier, , 78-79, pp.51-57. 10.1016Elsevier, /j.sab.2012 Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy Nowadays, due to environmental concerns, fast on site quantitative analyses of soils are required. Laser in duced breakdown spectroscopy is a serious candidate to address this challenge and is especially well suited for multi elemental analysis of heavy metals. However, saturation and matrix effects prevent from a simple treatment of the LIBS data, namely through a regular calibration curve. This paper details the limits of this ap proach and consequently emphasizes the advantage of using artificial neural networks well suited for non linear and multi variate calibration. This advanced method of data analysis is evaluated in the case of real soil samples and on site LIBS measurements. The selection of the LIBS data as input data of the network is particularly detailed and finally, resulting errors of prediction lower than 20% for aluminum, calcium, cop per and iron demonstrate the good efficiency of the artificial neural networks for on site quantitative LIBS of soils.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy

Haddad

Villot-Kadri

Ismaël

et al. 2013

Spectrochimica Acta Part B: Atomic Spectroscopy

110

View full text Add to dashboard Cite

show abstract

“…Multivariate analysis algorithms are most frequently applied to identify the LIBS spectra [77,78]. A different approach to spectrum identification is the application of an artificial neural network for spectral signal processing and classification [79].…”

Section: Laser Induced Breakdown Spectroscopy (Libs)mentioning

confidence: 99%

Sensors and Systems for the Detection of Explosive Devices - An Overview

Bielecki¹,

Janucki²,

Kawalec³

et al. 2012

Metrology and Measurement Systems

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The paper presents analyses of current research projects connected with explosive material sensors. Sensors are described assigned to X and γ radiation, optical radiation sensors, as well as detectors applied in gas chromatography, electrochemical and chemical sensors. Furthermore, neutron techniques and magnetic resonance devices were analyzed. Special attention was drawn to optoelectronic sensors of explosive devices.

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“…Material identification has been demonstrated recently with a conventional three-layer feedforward ANN (Koujelev et al, 2010). High success rate of the identification algorithm has been demonstrated with using standard samples made of powders (Fig.…”

Section: Materials Identificationmentioning

confidence: 99%

“…Additional, soft threshold is introduced at 45 % (orange dashed line) such that if the maximum CL falls between 45 % and 70 %, the sample is regarded as a similar class. An improved design of ANN structure incorporating a sequential learning approach has been proposed and demonstrated (Lui & Koujelev, 2010). Here we review those improvements and provide a comparative analysis of the conventional and the constructive leaning network.…”

Section: Materials Identificationmentioning

confidence: 99%

Artificial Neural Networks for Material Identification, Mineralogy and Analytical Geochemistry Based on Laser-Induced Breakdown Spectroscopy

Koujelev¹,

Lui²

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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Laser-induced breakdown spectroscopy with artificial neural network processing for material identification

Cited by 66 publications

References 14 publications

Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy

Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy

Sensors and Systems for the Detection of Explosive Devices - An Overview

Artificial Neural Networks for Material Identification, Mineralogy and Analytical Geochemistry Based on Laser-Induced Breakdown Spectroscopy

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