Variables selection: a critical issue for quantitative laser-induced breakdown spectroscopy
BASSEL, Léna
IRAMAT-Centre de recherche en physique appliquée à l’archéologie [IRAMAT-CRP2A]
Centre National de la Recherche Scientifique [CNRS]
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IRAMAT-Centre de recherche en physique appliquée à l’archéologie [IRAMAT-CRP2A]
Centre National de la Recherche Scientifique [CNRS]
BASSEL, Léna
IRAMAT-Centre de recherche en physique appliquée à l’archéologie [IRAMAT-CRP2A]
Centre National de la Recherche Scientifique [CNRS]
IRAMAT-Centre de recherche en physique appliquée à l’archéologie [IRAMAT-CRP2A]
Centre National de la Recherche Scientifique [CNRS]
BOUSQUET, Bruno
Centre d'Etudes Lasers Intenses et Applications [CELIA]
Centre National de la Recherche Scientifique [CNRS]
< Reduce
Centre d'Etudes Lasers Intenses et Applications [CELIA]
Centre National de la Recherche Scientifique [CNRS]
Language
en
Article de revue
This item was published in
Spectrochimica Acta Part B: Atomic Spectroscopy. 2017, vol. 134, p. 6-10
Elsevier
English Abstract
In this paper, we demonstrate the importance of variable selection on the prediction ability of LIBS quantitative partial least squares (PLS) models. The spectral lines of potassium at 766.49 nm and 769.90 nm were considered ...Read more >
In this paper, we demonstrate the importance of variable selection on the prediction ability of LIBS quantitative partial least squares (PLS) models. The spectral lines of potassium at 766.49 nm and 769.90 nm were considered in the framework of an agricultural soils analysis. Univariate models demonstrating very poor correlation between the peak areas of the potassium lines and the related concentration values, a series of PLS models allowed to significantly improve the prediction ability compared to the univariate approach. This improvement was due to advanced variable selection, achieved through the use of two output data provided after PLS calculation, namely the Variable Importance in Projection (VIP) and the Coefficients graph. In this demonstration, the gain was significant because the two spectral lines of potassium at 766.49 nm and 769.90 nm exhibited unusual profiles. Indeed, including in a PLS model only the variables related to the edges of these lines allowed a significant improvement of its predictive ability (Q2 = 0.84, RMSE = 1.49 g/kg) compared to another PLS model only including the variables related to the central parts of these lines (Q2 = 0.78, RMSE = 1.59 g/kg).Read less <
Keywords
potassium
analyse de sol
sol agricole
English Keywords
LIBS
quantitative analysis
variable selection
variable influence on projection
coefficients plot
soil analysis
agricultural soil
Origin
Hal imported