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

D’Andrea, Eleonora; Pagnotta, Stefano; Grifoni, Emanuela; Lorenzetti, Giulia; Legnaioli, Stefano; Palleschi, Vincenzo; Lazzerini, Beatrice

doi:10.1016/j.sab.2014.06.012

Cited by 75 publications

(21 citation statements)

References 21 publications

(26 reference statements)

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“…It is thus clear that using the Matlab® defaults for the number of neurons in the hidden layer would only increase the risk of overfitting. In fact, the proper optimization of the network would imply a series of tests varying of the number of hidden neurons [25]. This procedure is time-consuming, and totally empirical.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a ‘black box’ approach and how to avoid them

Safi

Campanella

Grifoni

et al. 2018

Spectrochimica Acta Part B: Atomic Spectroscopy

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The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemicalphysical knowledge that is at the base of any LIBS quantitative analysis.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a ‘black box’ approach and how to avoid them

Safi

Campanella

Grifoni

et al. 2018

Spectrochimica Acta Part B: Atomic Spectroscopy

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show abstract

“…Furthermore, D'Andrea et al 111 developed an approach based on ANN for LIBS quantitative analysis. Nevertheless, strong matrix effects can degrade the precision and accuracy.…”

Section: Libsmentioning

confidence: 99%

Atomic spectrometry update: review of advances in atomic spectrometry and related techniques

Evans

Pisonero

Smith

et al. 2015

J. Anal. At. Spectrom.

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“…Once the calibration curve is obtained, we can easily determine the concentration of target element given the spectral intensity of its emission peak. Due to its simplicity, the Calibration Curve Model gains increasing popularity in LIBS analysis, however, the plasma fluctuation and the problem of self-absorption and matrix effects usually lead to unsatisfactory determination results [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Hackem-LIBS: An Heterogeneous Stacking Ensemble Model for Laser-Induced Breakdown Spectroscopy Elemental Quantitative Analysis

Zeng

Gong

et al. 2020

IEEE Access

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Laser-Induced Breakdown Spectroscopy (LIBS) is a popular technique for elemental quantitative analysis in chemistry community, based on which, various methods are developed to determinate the concentrations of chemical samples. Despite the successful applications of the existing methods, they still struggle to obtain accurate samples analyses, due to their limited prediction capability, the complex compositions of samples and mutual interference of elements. In this paper, we propose a novel heterogeneous stacking ensemble learning model called Heterogeneous stACKing Ensemble Model LIBS (Hackem-LIBS) to achieve LIBS quantitative analysis with higher accuracy. Specifically, we propose a stacking ensemble learning framework consisting two stages. In the first stage, we train different heterogeneous component learners with multiple sub-training sets and pick out the optimal learners. In the second stage, we leverage the enhanced features predicted by the selected learners to train a stronger metalearner, which is used to make the final prediction. In addition, we combine Genetic Algorithm (GA) with Sequential Forward Selection (SFS) to reduce the redundancy of training features, which ensures more effective learning and higher computation efficiency. Extensive experiments on two public benchmarks are conducted and the results show that our approach achieves better accuracy in determinating the concentrations of elements and is practically applicable to the quantitative analysis of complex chemical samples via the LIBS technique.

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

Cited by 75 publications

References 21 publications

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a ‘black box’ approach and how to avoid them

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a ‘black box’ approach and how to avoid them

Atomic spectrometry update: review of advances in atomic spectrometry and related techniques

Hackem-LIBS: An Heterogeneous Stacking Ensemble Model for Laser-Induced Breakdown Spectroscopy Elemental Quantitative Analysis

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