A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

Boucher, Thomas; Ozanne, Marie V.; Carmosino, Marco; Dyar, M. D.; Mahadevan, Sridhar; Breves, E. A.; Lepore, Kate; Clegg, S. M.

doi:10.1016/j.sab.2015.02.003

Cited by 129 publications

(53 citation statements)

References 28 publications

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“…Relying on the unique capability, LIBS technique has witnessed tremendous growth and been widely applied in variety of fields, such as environmental monitoring1415, archaeological investigations16, geological applications17, biomedical detection1819, industrial analysis20, agriculture212223, food242526, and space exploration2728.…”

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confidence: 99%

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

Zhao

Liu

et al. 2016

Sci Rep

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The aim of this work was to analyze the variety of soil by laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics methods. 6 certified reference materials (CRMs) of soil samples were selected and their LIBS spectra were captured. Characteristic emission lines of main elements were identified based on the LIBS curves and corresponding contents. From the identified emission lines, LIBS spectra in 7 lines with high signal-to-noise ratio (SNR) were chosen for further analysis. Principal component analysis (PCA) was carried out using the LIBS spectra at 7 selected lines and an obvious cluster of 6 soils was observed. Soft independent modeling of class analogy (SIMCA) and least-squares support vector machine (LS-SVM) were introduced to establish discriminant models for classifying the 6 types of soils, and they offered the correct discrimination rates of 90% and 100%, respectively. Receiver operating characteristic (ROC) curve was used to evaluate the performance of models and the results demonstrated that the LS-SVM model was promising. Lastly, 8 types of soils from different places were gathered to conduct the same experiments for verifying the selected 7 emission lines and LS-SVM model. The research revealed that LIBS technology coupled with chemometrics could conduct the variety discrimination of soil.

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confidence: 99%

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

Zhao

Liu

et al. 2016

Sci Rep

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“…It has been applied to many fields of analysis, such as soil [55,65,66], steel [67][68][69], glass [50], rock [70], iron ore [63] and coal [71,72]. The rest of the analysis methods are PCR [50,73,74], LASSO [75,76], kNN [77], ANN [78][79][80][81], SVM [82,83] and so on. PLS has been implemented either to calculate the concentrations of a single element (PLS-1) or to simultaneously calculate the concentrations of more than one element (PLS-2).…”

Section: The Comparison Of Calibration Methodsmentioning

confidence: 99%

“…PLS has been implemented either to calculate the concentrations of a single element (PLS-1) or to simultaneously calculate the concentrations of more than one element (PLS-2). In addition to PLS, other linear (MLR [84,85], PCR [50,73] and LASSO [75][76][77]) and nonlinear regression methods (ANN [86,87]) have been applied to LIBS quantitative analysis. In order to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples, Boucher et al [77] had studied nine kinds of linear and nonlinear regression methods.…”

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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“…In quantitation by LIBS, chemometric methods are used to cope with nonlinearities between the emission signal and the concentration and with matrix effects. Hence, partial least‐squares regression is currently used to determine the elemental composition of Martian rocks measured by the LIBS instrument onboard the Curiosity rover, and more advanced techniques are being developed . Artificial neural networks were used for on‐site quantitative analysis of soils, for calibration of chrome and nickel in steel, and to determine the concentration of nickel and vanadium in vacuum residues of crude oils .…”

Section: Introductionmentioning

confidence: 99%

“…Hence, partial least-squares regression is currently used to determine the elemental composition of Martian rocks measured by the LIBS instrument onboard the Curiosity rover, and more advanced techniques are being developed. 24 Artificial neural networks were used for on-site quantitative analysis of soils, 25 for calibration of chrome and nickel in steel, 26 and to determine the concentration of nickel and vanadium in vacuum residues of crude oils. 27 More recent techniques were also tested by different authors, such as support vector machines regression for slag samples analysis 9 or random forest regression for steel analysis.…”

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confidence: 99%

Analysis of time‐resolved laser‐induced breakdown spectra by mean field‐independent components analysis (MFICA) and multivariate curve resolution–alternating least squares (MCR‐ALS)

Rakwe

Rutledge

Moutiers

et al. 2017

Journal of Chemometrics

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Laser‐induced breakdown spectroscopy (LIBS) is an analytical technique allowing the determination of elemental concentrations in a variety of matrices in the solid, liquid, and gaseous phases. Because of the inherent complexity of the signal and to the high dimensionality of experimental data, chemometrics has been more and more applied in LIBS to perform samples identification or quantitative measurements. But multivariate methods can also be used for the description and physical interpretation of the plasma, particularly to exploit the temporal dimension of the LIBS signal, which is usually neglected in spectrochemical measurements. In this work, time‐resolved spectra of a pure aluminum sample were treated with 2 methods, mean field‐independent components analysis and multivariate curve resolution–alternating least squares, applying non‐negativity constraints for scores and components in both cases. Results obtained were compared with reference univariate measurements of the emission of the species of interest (ions, neutral atoms, and molecules). The interpretation of scores and components provided a physical description of phenomena that take place between species in the plasma, like ionic recombination and molecules formation. Overall, mean field‐independent components analysis and multivariate curve resolution–alternating least squares yield equivalent solutions with our dataset. This new approach is very promising for the treatment of time‐resolved data obtained by LIBS.

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A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

Cited by 129 publications

References 28 publications

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

Calibration Methods of Laser-Induced Breakdown Spectroscopy

Analysis of time‐resolved laser‐induced breakdown spectra by mean field‐independent components analysis (MFICA) and multivariate curve resolution–alternating least squares (MCR‐ALS)

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