Towards quantitative laser-induced breakdown spectroscopy analysis of soil samples

Bousquet, Bruno; Sirven, Jean-Baptiste; Canioni, Lionel

doi:10.1016/j.sab.2007.10.018

Cited by 117 publications

(81 citation statements)

References 18 publications

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“…In the present study, similarly to the papers [20][21][22][23][24], the role of laser pulse energy and plasma fluctuations was diminished by normalising the line intensity with the total intensity recorded between 220 and 850 nm. Figure 3(a) based on Table 1 shows a high correlation coefficient R 2 = 0 9846 between the mass fraction of total carbon and the heating value.…”

Section: Selection Of Spectral Linessupporting

confidence: 67%

Determination of Heating Value of Estonian Oil Shale by Laser-Induced Breakdown Spectroscopy

Aints¹,

Paris²,

Laan³

et al. 2018

Journal of Spectroscopy

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The laser-induced breakdown spectroscopy (LIBS) combined with multivariate regression analysis of measured data were utilised for determination of the heating value and the chemical composition of pellets made from Estonian oil shale samples with different heating values. The study is the first where the oil shale heating value is determined on the basis of LIBS spectra. The method for selecting the optimal number of spectral lines for ordinary multivariate least squares regression model is presented. The correlation coefficient between the heating value predicted by the regression model, and that measured by calorimetric bomb, was R 2 = 0 98. The standard deviation of prediction was 0.24 MJ/kg. Concentrations of oil shale components predicted by the regression model were compared with those measured by ordinary methods.

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Section: Selection Of Spectral Linessupporting

confidence: 67%

Determination of Heating Value of Estonian Oil Shale by Laser-Induced Breakdown Spectroscopy

Aints¹,

Paris²,

Laan³

et al. 2018

Journal of Spectroscopy

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“…Indeed, a mixture of sand containing graphite was found to absorb four times and to provide a LIBS signal for the analyte twice the value of a cellulose sand mixture [6]. In ad dition, the plasma temperature T e and the electron density N e are ideal parameters to probe the differences between the two soil sam ples due to matrix effects since they are very sensitive to the soil ma trix composition [7]. This problem of matrix effect can be usually overcome by the use of matrix matched samples for the calibration step but unfortunately, this approach can't be applied to soils because of the huge diversity of natural soils.…”

Section: Introductionmentioning

confidence: 99%

“…However, the use of an internal standard is not possible in the case of soil samples because there is no chemical element in the soil matrices with constant concentration or even with known different values of concentration [9,10]. Other ways of normalization have been proposed such as normalization by the whole spectrum [7] or by the acoustic signal monitored during the plasma ablation and supposed to be proportional to the ablated mass [11]. Finally, these different solutions of normalization efficient ly allow reducing the experimental fluctuations due to physical pa rameters such as laser energy, focusing conditions, or sample moisture but are not sufficient to remove matrix effects due to the chemical composition of the samples.…”

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

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109

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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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“…Plotting a calibration curve between known concentrations against the LIBS signal intensities yields the relative composition of the respective elements [19][20][21][22]. To calculate the percentage composition of toxic and other elements present in textile dyes, we used three calibration approaches.…”

Section: Quantitative Measurementsmentioning

confidence: 99%

Determination of Metals Present in Textile Dyes Using Laser-Induced Breakdown Spectroscopy and Cross-Validation Using Inductively Coupled Plasma/Atomic Emission Spectroscopy

Rehan

Sultana

et al. 2017

International Journal of Spectroscopy

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Laser-induced breakdown spectroscopy (LIBS) was used for the quantitative analysis of elements present in textile dyes at ambient pressure via the fundamental mode (1064 nm) of a Nd:YAG pulsed laser. Three samples were collected for this purpose. Spectra of textile dyes were acquired using an HR spectrometer (LIBS2000+, Ocean Optics, Inc.) having an optical resolution of 0.06 nm in the spectral range of 200 to 720 nm. Toxic metals like Cr, Cu, Fe, Ni, and Zn along with other elements like Al, Mg, Ca, and Na were revealed to exist in the samples. The %-age concentrations of the detected elements were measured by means of standard calibration curve method, intensities of every emission from every species, and calibration-free (CF) LIBS approach. Only Sample 3 was found to contain heavy metals like Cr, Cu, and Ni above the prescribed limit. The results using LIBS were found to be in good agreement when compared to outcomes of inductively coupled plasma/atomic emission spectroscopy (ICP/AES).

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Towards quantitative laser-induced breakdown spectroscopy analysis of soil samples

Cited by 117 publications

References 18 publications

Determination of Heating Value of Estonian Oil Shale by Laser-Induced Breakdown Spectroscopy

Determination of Heating Value of Estonian Oil Shale by Laser-Induced Breakdown Spectroscopy

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

Determination of Metals Present in Textile Dyes Using Laser-Induced Breakdown Spectroscopy and Cross-Validation Using Inductively Coupled Plasma/Atomic Emission Spectroscopy

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