One-point calibration for calibration-free laser-induced breakdown spectroscopy quantitative analysis

Cavalcanti, G. H.; Teixeira, Douglas Vidal; Legnaioli, Stefano; Lorenzetti, Giulia; Pardini, L.; Palleschi, Vincenzo

doi:10.1016/j.sab.2013.05.016

Cited by 93 publications

(52 citation statements)

References 38 publications

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“…The composition (percent in weight) of the samples is shown in Table 1. The same samples were used in a recent work by our group, presenting a variation of the Calibration-Free LIBS method [15]. In this way, the results of the present paper would be immediately comparable with a different analytical approach which in principle does not rely on the use of calibration curves or reference samples.…”

Section: Methodsmentioning

confidence: 72%

“…Based on heuristic considerations, we consider the following six meaningful spectral windows (wavelength ranges in [15]. This means that there are 480 spectral points (obtained by concatenating the six spectral windows above) that could be used to represent each spectrum.…”

Section: Feature Selectionmentioning

confidence: 99%

“…[15]. The data for sample S165 (sample S161 cannot be compared because it was used as a reference in [15]) are reported in Table 8.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

See 2 more Smart Citations

An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis

D’Andrea¹,

Pagnotta

Grifoni

et al. 2014

Spectrochimica Acta Part B: Atomic Spectroscopy

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The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a 'black box' system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples

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Section: Methodsmentioning

confidence: 72%

Section: Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis

D’Andrea¹,

Pagnotta

Grifoni

et al. 2014

Spectrochimica Acta Part B: Atomic Spectroscopy

View full text Add to dashboard Cite

show abstract

“…A number of researchers have made some modifications to the CF-LIBS algorithm, such as self-absorption [89,90]. In recent years, several research groups [91][92][93][94] began using standard samples to improve the accuracy of the nonstandard analysis. Cavalcanti et al [92] presented and used one-point-calibration CF-LIBS to analyze a set of copper-based samples.…”

Section: The Comparison Of Calibration Methodsmentioning

confidence: 99%

“…In recent years, several research groups [91][92][93][94] began using standard samples to improve the accuracy of the nonstandard analysis. Cavalcanti et al [92] presented and used one-point-calibration CF-LIBS to analyze a set of copper-based samples. The results show that the new method achieves similar or even higher accuracy than the calibration curve.…”

Section: The Comparison Of Calibration Methodsmentioning

confidence: 99%

Calibration Methods of Laser-Induced Breakdown Spectroscopy

Fu¹,

Jia²,

Wang³

et al. 2018

Calibration and Validation of Analytical Methods - A Sampling of Current Approaches

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Laser-induced breakdown spectroscopy (LIBS) has gained great attention over the past two decades due to its many advantages, such as needless sample preparation, capability of remote measurement and fast multielement simultaneous analysis. However, because of its inherent uncertainty features of plasma, it is still a big challenge for LIBS community worldwide to realize high sensitivity and accurate quantitative analysis. Currently, many chemometric analytical methods have been applied to LIBS calibration analysis, including univariate regression, multivariate regression, principal component regression (PCR), partial least squares regression (PLSR) and so on. In addition, appropriate sample and spectral pretreatment can effectively improve the analytical performance (i.e., limit of detection (LOD), accuracy and repeatability) of LIBS. In this chapter, we briefly summarize the progress of these calibration methods and their applications on LIBS and provide our recommendations.

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