Machine learning in laser-induced breakdown spectroscopy: A review

doi:10.1007/s11467-024-1427-2

Front. Phys.

2024, Vol. 19

Issue (6) : 62501 https://doi.org/10.1007/s11467-024-1427-2

Machine learning in laser-induced breakdown spectroscopy: A review

Zhongqi Hao¹, Ke Liu^2,⁸, Qianlin Lian^2,⁶, Weiran Song³, Zongyu Hou³, Rui Zhang^2,⁷, Qianqian Wang⁴, Chen Sun⁵, Xiangyou Li²(

), Zhe Wang³(

)

¹. Key Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hangkong University, Nanchang 330063, China
². Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan 430074, China
³. State Key Lab of Power System, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Centre, Tsinghua University, Beijing 100084, China
⁴. School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
⁵. School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
⁶. School of Public Health, Xinjiang Medical University, Urumqi 830054, China
⁷. The Second Medical College of Binzhou Medical University, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264100, China
⁸. School of Electronic Engineering, Naval University of Engineering, Wuhan 430014, China

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Abstract

Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic analytic technique with great application potential because of its unique advantages for online/in-situ detection. However, due to the spatially inhomogeneity and drastically temporal varying nature of its emission source, the laser-induced plasma, it is difficult to find or hard to generate an appropriate spatiotemporal window for high repeatable signal collection with lower matrix effects. The quantification results of traditional physical principle based calibration model are unsatisfactory since these models were not able to compensate for complicate matrix effects as well as signal fluctuation. Machine learning is an emerging approach, which can intelligently correlated the complex LIBS spectral data with its qualitative or/and quantitative composition by establishing multivariate regression models with greater potential to reduce the impacts of signal fluctuation and matrix effects, therefore achieving relatively better qualitative and quantitative performance. In this review, the progress of machine learning application in LIBS is summarized from two main aspects: i) Pre-processing data for machine learning model, including spectral selection, variable reconstruction, and denoising to improve qualitative/quantitative performance; ii) Machine learning methods for better quantification performance with reduction of the impact of matrix effect as well as LIBS spectra fluctuations. The review also points out the issues that researchers need to address in their future research on improving the performance of LIBS analysis using machine learning algorithms, such as restrictions on training data, the disconnect between physical principles and algorithms, the low generalization ability and massive data processing ability of the model.

Keywords laser-induced breakdown spectroscopy machine learning repeatability matrix effects qualitative and quantitative analysis

Corresponding Author(s): Xiangyou Li,Zhe Wang

Issue Date: 16 July 2024

Cite this article:

Zhongqi Hao,Ke Liu,Qianlin Lian, et al. Machine learning in laser-induced breakdown spectroscopy: A review[J]. Front. Phys. , 2024, 19(6): 62501.

URL:

https://academic.hep.com.cn/fop/EN/10.1007/s11467-024-1427-2
https://academic.hep.com.cn/fop/EN/Y2024/V19/I6/62501

Tab.1 Comparison of supervised, unsupervised, and semi-supervised.

Fig.1 The role of machine learning for data preprocessing in LIBS.

Fig.2 Comparison of original and reconstructed spectra. Reproduced from Ref. [50].

Fig.3 The original spectrum and the reconstruction spectrum by the hybrid feature selection method. Reproduced from Ref. [61].

Fig.4 PCA analysis for the LIBS spectra of ancient and recent bovine bone. Reproduced from Ref. [86].

Fig.5 The role of machine learning for qualitative and quantitative analysis in LIBS.

Methods	Improvement	Comparison	Materials	Best results	Ref.

LDA	IFALS	MLS-LDA, ASPI-LDA	Rocks	Accuracy: 98.54%	[110]
	−	PCA, DT, RF, PLS-DA, SVM	Rice	Accuracy: 99.20%	[94]
	RSM	KNN, LDA	Blood	Accuracy: 98.34%	[65]
	PCA	PCA-SVM	Olive oil	Accuracy: 100%	[111]
RF	−	−	Aluminium alloy	Accuracy: 98.45%	[112]
	SBS	RF, VIRF	Ceramics	Sensitivity: 0.9526 Specificity: 0.9910 Accuracy: 97.82%	[76]
	CWT	PCA-RF	Huanglongbing-infected navel oranges	Accuracy: 97.89%	[113]
	VI	PLS-DA, SVM, RF	Slag	Sensitivity: 0.9889Specificity: 0.9944Accuracy: 0.9926%	[93]
SVM	−	−	Steel	Accuracy: 95.3%	[114]
	LDA	−	Mineral	Recall: 0.53Precision: 0.73	[115]
	−	KNN	Fat, skin and muscle tissues	Accuracy: 99.83%Sensitivity: 0.995Specificity: 0.998	[103]
	−	PCA, PLS-DA, LDA	Nephrite	Accuracy: 99.3%	[96]
	PCA	−	Metal	Accuracy: 100%	[116]
	PCA	LDA	Plant leaves	Accuracy: 98.89%	[102]
	−	LDA	Honey	Accuracy: 99.7%	[98]
	SFS	SFS-RF, SVM	Soil	Accuracy: 97.88%	[64]
	PCA	−	Steel	Accuracy: 100%	[88]
	PCA	PCA-KNN, CA-ANN	Rocks	Accuracy: 98%	[117]
	Restricted Boltzmann machines	PCA-SVM	Steel	Accuracy: 100%	[118]
CNN	−	SVM	Rocks	Accuracy: 100%	[119]
	PCA	BP-ANN, SVM, KNN, RF	Soil	Accuracy: 99.60%	[120]
	−	SVM	Fish	Accuracy: 98.2%	[121]
KNN	−	−	Paint	Accuracy: 100%	[20]
	Multiplicative scatter correction (MSC)	−	Meat	Accuracy: 100%	[122]
	−	−	Explosive	Accuracy: 99.58%	[44]
	−	PLSR	Biochar	ARSDP: 8.13%	[123]
	−	SIMCA	E-waste	Accuracy: 98%	[124]
	PCA	BP-ANN	Pencil writing marks	Accuracy: 98.33%	[125]
PLS-DA	VI	PLS-DA, RF, VI-RF	Plastic	Accuracy: 99.55%	[83]
	SW	PLS-DA, SVM, RF	Plastic bottles	Accuracy: 93.93%	[126]
	−	−	Organic materials	Accuracy: 95%	[17]
ANN	WT	−	Copper and steel	Accuracy: 100%	[22]
	−	PCA	Aged epoxy micro-nanocomposites	Accuracy: 100%	[104]
	PCA	−	Nergetic materials	Accuracy: 100%	[87]
	PCA	PCA-LDA	Mineralized tissues and bio-mineral	Accuracy: 87.5%	[40]
PCA	−	−	Milk	Accuracy: 95.8%	[127]
	−	−	Rosewood	Accuracy: 100%	[128]
	−	−	Tooth	Accuracy: 100%	[129]
BP	PCA-GA	−	Plastic	Accuracy: 99.72%	[130]
BP	PCA	PCA-SVM	Ginseng	Accuracy: 99.5%	[131]
KELM	PSO	PSO-LSSVM, PSO-RF	Salvia miltiorrhiza	Accuracy: 94.87%	[37]
LSSVM	PSO	PSO–SVM, SVM, LSSVM	Aviation alloy	Accuracy: 99.56%	[49]
LSSVM	−	PCA-LS-SVM	Mentha haplocalyx	Accuracy: 99.2%	[132]

Tab.2 A summary of sample classification using LIBS combined with machine learning algorithms.

Fig.6 Schematic description of combining spectral knowledge and machine learning for LIBS data analysis. Reproduced from Ref. [165].

Methods	Improvement	Comparison	Materials	Target	Best results	Ref.
SVR	WPT-DC-RFECV	WPT-RFECV	Coal	C, Si, Al, Ca, Na, H, N, O, Mg, K, Li, Fe	RMSEP: 0.5786ARE: 2.27%	[202]
	WTD-RFECV	SVR, RFECV-SVR	Coal	Fe, Cu, Si, K	RMSEP: 0.010281%ARE: 2.16%R²: 0.9911	[73]
	−	−	Coal	C, H, N	RSD: 4.16%ARE: 3.28%	[66]
	−	GKR-ANN	Plutonium alloys	Ga	RMSEP: 0.33%LOD:0.015%	[142]
	−	PLSR	Sedimentary rock	Si, Ca, Mg, Fe, Al	RMSE: 0.40wt.%RSD: 1.86%	[144]
	CCA	PCA-SVRPCA-PLSRCCA-PLSR	T91 steel	Fe, Cr, Mo, Mn, V	MRE: 2.47%RSD: 2.94%RMSEP: 6.14%	[163]
	RBF	−	Liquid steel	Mn	MSE: 0.599%RSD: 8.26%R²: 0.997	[203]
	ALASSO	LASSO-SVR	Soil	Cr	R²: 0.998RMSEV: 0.033wt.%RSD: 2.343%	[204]
	PLSR	RF-SVR, PCA-SVR, CARS-SVR, MC-UVE-SVR	Edible gelatin	C, H, O, N, Na, K, Ca, Mg, Cr	R_P²: 0.9708RMSEP: 5.69wt.%	[162]
	−	PLSR	Soil	C, Ca, Na, O, H, Mg, Al, Fe	R²: 0.987RMSE: 0.079%	[154]
	−	MLR, PLSR, BP-ANN	Soil	Pb, Cd	R²: 0.9877RMSEP: 0.1521	[147]
	SPLSR	PLSR, LS-SVR	Iron ore	Fe, Si, Al_, Ca and Mg, Ti	RMSEP: 0.6242%AREP: 0.85%	[205]
	CARS	LS-SVR	Edible vegetable oil	Cr	R²: 0.9926RMSEP: 0.000586%	[80]
PLSR	SDVS	PLSR, iPLSR, NrVS-PLSR	Iron	Fe, Si, Ca, Na, H, O	RMSEP: 0.75%R²: 0.76	[63]
	Dominant Factor	PLSR	Brass	Cu, Pb, Zn, Fe, P, Sn, Sb	RMSEP: 5.25%R²: 0.999	[206]
	Dominant Factor	PLSR	water	Mn, Sr, Li	R²: 0.999	[172]
	Dominant Factor	PLSR	Coal	C, H, N, O, Si, Al, Fe, Ca, Mg, K, etc.	RSD: 0.3%	[173]
	SVR, MLR	−	Coal	C	R²: 0.99RMSEP: 1.43%	[176]
	Backward interval	Interval-PLSR, PLSR	Soil	Cu, Ni	R²: 0.9449RMSEP: 0.0363%	[16]
	UVE-CARS	PLSR	Agricultural fungicide	CI	RMSE: 0.66% R²: 0.9905	[68]
	GA	−	Manure	Ca	R²: 0.90RPD: 3.04	[69]
	Double GA	−	Copper	Cu, Fe, S, Si, Ca, Mg, Al, As, Zn, etc.	RMSECV: 0.29%RMSEP: 0.21%	[207]
	FSC-mIPW	GA, SPA	Soil	Cu, Ba, Cr	RMSEP: 0.2747%	[71]
	mIPW	Interval-PLS	Soil	Cu, Ba, Cr, Mg, Ca	RMSEP: 0.4232wt.%R_p²: 0.9746	[72]
	Ridge-RFE	PLSR, GA-PLSR,	Aluminum alloy	Fe, Si, Mg, Cu, Zn, Mn	R_cv²: 0.9566RMSECV: 0.0601wt.%RMSEP: 0.0476wt.%	[74]
	−	−	Alloy	Cu, Zn, Fe, Pb	RSD: 1.80%R²: 0.992RMSEP: 1.30%	[208]
	MSC	−	Pork	Pb	R²: 0.9908RMSEP: 0.282ARPE: 7.8%	[209]
	MSC	−	Egg	Cu	R²: 0.9789RMSEP: 50ARPE: 7.14%	[210]
	−	−	Navel orange	Cu	R²: 0.9928ARE: 5.55%	[211]
PLSR	−	−	Navel orange	Pb	R²: 0.9633 RMSECV: 1.56ARPE: 6.9%	[212]
	−	SVR	Alloy steel	Cr, Ni, Mn, Fe	R²: 0.9896 RMSE: 0.8230%	[213]
	−	PLSR, SVR	Alloy steel	Cr, Ni, Mn, Fe	R²: 0.9617 RMSE: 1.519	[214]
	−	−	Mineral	Fe, Si, Al, P	RMSE: 3.4wt.% R²: 0.95	[215]
	Savitzky-Golay	PCA	River	Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, etc.	R²: 0.9836 RMSEC: 0.7120RMSECV: 0.9430	[216]
	−	−	Liquid steel	Mn, Si	ARE: 6.125R²: 0.997	[217]
	GA	−	Boron	B	R²: 0.9888 RMSEP: 0.8667wt.%MPE: 10.9685%	[218]
	−	PCA, ANN	Plutonium metal	Fe, Ni	RMSE: 0.00132%R²: 0.997	[137]
	−	Univariate Analysis, MLR	Steel alloys	Cr, Ni	R²: 0.995	[139]
	Savitzky-Golay	−	River	Ca, C	R²: 0.9753RMSEP: 9.7339%	[219]
	Multi-block	PCR, PLSR S-PLSR	Iron ore	Fe	R²: 0.94RMSEP: 3.1%	[140]
RFR	−	PLSR	River	C, Na, H	R²: 0.9248RMSE: 25.1215%	[148]
	−	PLSR	Steel	S, P	R²: 0.9981	[146]
	−	PLSR	Sinter ore	Al, Fe, Si, Ca, Mg	RSD: 0.59%	[153]
	VI	PLSR, LS-SVM	Iron ore	Ca, Mg, Si, Al	RMSE: 0.0554wt.%R²: 0.9103	[152]
	MI-VIM	−	Oily sludge	Cu, Cr, Pb and Zn	R_p²: 0.9681RMSEP: 0.6009	[220]
	SBS	PLSR, RFR	Steel	S, P	R²: 0.9991	[75]
ANN	LR-SUAC	PLSR	Ceramic	Si, Al, Mg, Fe, Ti	R²: 0.93	[79]
	GA	−	Steel	C	RMSE: 0.0114%	[221]
	feature selection BPNN	−	Rock	Li, Rb, Sr, Ba	RMSEP: 0.0162wt.%	[161]
	PLSR	PLSR, ANN	Plant	Mg, Fe, N, Al, B, Ca, K, Mn, P	RMSEC: 0.0028wt.%MPE: 4.22%	[160]
	−	PLSR, SVR, PCR	Coal	C, H, O, N, Fe, Mg, Al, Ca, Si, Li, etc.	AAE: 0.69%	[155]
	GA-BP	BP-ANN	Alloy steel	C, Fe, Cr, Mn, Si	R²: 0.9893	[164]
	MSLC	−	Alloy	Cu, Zn, Sn, Pb, Si	R²: 0.99	[158]
	MSLC	−	Steel	Cr, Ni	RMSE: 0.023wt.%RSD: 12.9%	[159]
	GA	−	Steel	Cu, V	RMSEP: 0.0040wt.%	[89]
	Rectified linear units	−	Coal	C, H, N, O, Li, Na, Mg, Al, K, Ca, etc.	R²: 0.7102 RMSE: 0.4786%	[222]
	LASSO	PCA-ANN, CARS-ANN, KBest-ANN	Rocks and mineral	Cu, Fe, As	RMSEP: 0.18%R²: 0.97	[223]
CNN	Lightweight	PLSR, SVR	Phosphate ore	P	RMSEP: 0.89%R²: 0.9874	[91]
CNN	−	BPNN, PLSR	Mineral	Si, Al, Fe, Ca, Mg, K, Na	RMSE: 0.022	[224]
KELM	−	SVR, LS-SVM, BP-ANN	Coal	C, S	RMSE: 0.7704%R²: 0.9832	[225]
	−	PLSR	Sinter ore	Al, Fe, Si, Ca, Mg	R²: 0.9	[145]
	MI-PSO	KELM	Coal	C, Si, Ca, Mg, Al, Fe, Mn, Na, Ti	RMSECV: 1.2886R_CV²: 0.9868	[70]
	Dominant factor	MLR, PLSR, DF-PLSR, SVR, DF-SVR and KELM	Coal	C, H, O, I, N, Mg, Si, Al, Mn, Ca, Na,	RMSE: 1.075%R²: 0.965	[165]

Tab.3 A summary of works dealing with quantitative analysis using LIBS combined with machine learning algorithms.

1	Wang Z., S. Afgan M., Gu W., Song Y., Wang Y., Hou Z., Song W., and Li Z., Recent advances in laser-induced breakdown spectroscopy quantification: From fundamental understanding to data processing, Trends Analyt. Chem. 143, 116385 (2021) https://doi.org/10.1016/j.trac.2021.116385
2	T. Fu Y., L. Gu W., Y. Hou Z., A. Muhammed S., Q. Li T., Wang Y., and Wang Z., Mechanism of signal uncertainty generation for laser-induced breakdown spectroscopy, Front. Phys. 16(2), 22502 (2021) https://doi.org/10.1007/s11467-020-1006-0
3	Sheta S., S. Afgan M., Y. Hou Z., C. Yao S., Zhang L., Li Z., and Wang Z., Coal analysis by laser-induced breakdown spectroscopy: A tutorial review, J. Anal. At. Spectrom. 34(6), 1047 (2019) https://doi.org/10.1039/C9JA00016J
4	C. Dingari N., Barman I., K. Myakalwar A., P. Tewari S., and K. Gundawar M., Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability, Anal. Chem. 84(6), 2686 (2012) https://doi.org/10.1021/ac202755e
5	L. Zhang T., U. Shan W., S. Tang H., Wang K., X. Duan Y., and I. Hua L., Progress of chemometrics in laser-induced breakdown spectroscopy analysis, Chin. J. Anal. Chem. 43(6), 939 (2015) https://doi.org/10.1016/S1872-2040(15)60832-5
6	Zhang T., Tang H., and Li H., Chemometrics in laser‐induced breakdown spectroscopy, J. Chemometr. 32(11), e2983 (2018) https://doi.org/10.1002/cem.2983
7	Zhang D.Zhang H.Zhao Y.Chen Y.Ke C. Xu T.He Y., A brief review of new data analysis methods of laser-induced breakdown spectroscopy: Machine learning, Appl. Spectrosc. Rev. 57(2), 89 (2022)
8	R. Wójcik M., Zdunek R., and J. Antończak A., Unsupervised verification of laser-induced breakdown spectroscopy dataset clustering, Spectrochim. Acta B 126, 84 (2016) https://doi.org/10.1016/j.sab.2016.10.009
9	Tang Y., Guo Y., Sun Q., Tang S., Li J., Guo L., and Duan J., Industrial polymers classification using laser-induced breakdown spectroscopy combined with self-organizing maps and K-means algorithm, Optik (Stuttg.) 165, 179 (2018) https://doi.org/10.1016/j.ijleo.2018.03.121
10	Guo Y., Tang Y., Du Y., Tang S., Guo L., Li X., Lu Y., and Zeng X., Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means, Plasma Sci. Technol. 20(6), 065505 (2018) https://doi.org/10.1088/2058-6272/aaaade
11	Pořízka P., Klus J., Kepes E., Prochazka D., W. Hahn D., and Kaiser J., On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review, Spectrochim. Acta B 148, 65 (2018) https://doi.org/10.1016/j.sab.2018.05.030
12	A. He L., Q. Wang X., Zhao Y., Liu L., and Peng Z., Study on cluster analysis used with laser-induced breakdown spectroscopy, Plasma Sci. Technol. 18(6), 647 (2016) https://doi.org/10.1088/1009-0630/18/6/11
13	Chen T., Huang L., Yao M., Hu H., Wang C., and Liu M., Quantitative analysis of chromium in potatoes by laser-induced breakdown spectroscopy coupled with linear multivariate calibration, Appl. Opt. 54(25), 7807 (2015) https://doi.org/10.1364/AO.54.007807
14	Sha W., T. Li J., P. Lu C., and H. Zhen C., Quantitative analysis of P in compound fertilizer by laser-induced breakdown spectroscopy coupled with linear multivariate calibration, Spectroscopy & Spectral Anal. 39(6), 1958 (2019) https://doi.org/10.3964/j.issn.1000-0593(2019)06-1958-07
15	Y. Li H., Mazzei L., D. Wallis C., and S. Wexler A., Improving quantitative analysis of spark-induced breakdown spectroscopy: Multivariate calibration of metal particles using machine learning, J. Aerosol Sci. 159, 105874 (2022) https://doi.org/10.1016/j.jaerosci.2021.105874
16	N. Zhu S., Ding Y., J. Chen Y., Deng F., F. Chen F., and Yan F., Quantitative analysis of Cu and Ni in oil-contaminated soil by LIBS combined with variable selection method and PLS, Spectroscopy & Spectral Anal. 40(12), 3812 (2020) https://doi.org/10.3964/j.issn.1000-0593(2020)12-3812-06
17	J. L. Gottfried. Jr De Lucia, Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification, Spectrochim. Acta B 66(2), 122 (2011) https://doi.org/10.1016/j.sab.2010.12.007
18	Lee Y., H. Han S., and H. Nam S., Soft independent modeling of class analogy (SIMCA) modeling of laser-induced plasma emission spectra of edible salts for accurate classification, Appl. Spectrosc. 71(9), 2199 (2017) https://doi.org/10.1177/0003702817697337
19	Cao Z., Cheng J., Han X., Li L., Wang J., Fan Q., and Lin Q., Rapid classification of coal by laser-induced breakdown spectroscopy (LIBS) with K-nearest neighbor (KNN) chemometrics, Instrum. Sci. Technol. 51(1), 59 (2023) https://doi.org/10.1080/10739149.2022.2087185
20	ShangGuan J., Tong Y., Yuan A., Ren X., Liu J., Duan H., Lian Z., Hu X., Ma J., Yang Z., and Wang D., Online detection of laser paint removal based on laser-induced breakdown spectroscopy and the K-nearest neighbor method, J. Laser Appl. 34(2), 022009 (2022) https://doi.org/10.2351/7.0000597
21	Yan X., Peng X., Qin Y., Xu Z., Xu B., Li C., Zhao N., Li J., Ma Q., and Zhang Q., Classification of plastics using laser-induced breakdown spectroscopy combined with principal component analysis and K nearest neighbor algorithm, Results in Optics 4, 100093 (2021) https://doi.org/10.1016/j.rio.2021.100093
22	Liang L., Zhang T., Wang K., Tang H., Yang X., Zhu X., Duan Y., and Li H., Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines, Appl. Opt. 53(4), 544 (2014) https://doi.org/10.1364/AO.53.000544
23	V. Dastjerdi M., J. Mousavi S., Soltanolkotabi M., and N. Zadeh A., Identification and sorting of PVC polymer in recycling process by laser-induced breakdown spectroscopy (LIBS) combined with support vector machine (SVM) model, Iranian J. Sci. Technol. A 42(2), 959 (2018) https://doi.org/10.1007/s40995-016-0084-x
24	Yang P., T. Liu H., L. Nie Z., and N. Qu X., Accuracy improvement of geographical indication of rice by laser-induced breakdown spectroscopy using support vector machine with multi-spectral line, J. Appl. Spectrosc. 89(3), 579 (2022) https://doi.org/10.1007/s10812-022-01397-3
25	Jia J., Fu H., Hou Z., Wang H., Ni Z., and Dong F., Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy, Plasma Sci. Technol. 21(3), 034003 (2019) https://doi.org/10.1088/2058-6272/aae3e1
26	Cisewski J., Snyder E., Hannig J., and Oudejans L., Support vector machine classification of suspect powders using laser‐induced breakdown spectroscopy (LIBS) spectral data, J. Chemometr. 26(5), 143 (2012) https://doi.org/10.1002/cem.2422
27	D’Andrea E.Pagnotta S.Grifoni E. Legnaioli S.Lorenzetti G.Palleschi V.Lazzerini B., A hybrid calibration-free/artificial neural networks approach to the quantitative analysis of LIBS spectra, Appl. Phys. B 118(3), 353 (2015)
28	G. Rendon-Sauz F., Flores-Reyes T., and Ponce-Flores A., Rapid classification of bacteria using libs in multi-pulse laser regime and neural networks processing, Revista Cubana De Fisica 35(1), 10 (2018)
29	Cui X., Wang Q., Zhao Y., Qiao X., and Teng G., Laser-induced breakdown spectroscopy (LIBS) for classification of wood species integrated with artificial neural network (ANN), Appl. Phys. B 125(4), 56 (2019) https://doi.org/10.1007/s00340-019-7166-3
30	El Haddad J.Villot-Kadri M.Ismael A.Gallou G.Michel K. Bruyere D.Laperche V.Canioni L.Bousquet B., Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy, Spectrochim. Acta B 79–80, 51 (2013)
31	Q. Wang Q., W. Huang Z., Liu K., J. Li W., and X. Yan J., Classification of plastics with laser-induced breakdown spectroscopy based on principal component analysis and artificial neural network model, Spectroscopy & Spectral Anal. 32(12), 3179 (2012) https://doi.org/10.3964/j.issn.1000-0593(2012)12-3179-04
32	Li N., Qi J., Wang P., Zhang X., Zhang T., and Li H., Quantitative structure-activity relationship (QSAR) study of carcinogenicity of polycyclic aromatic hydrocarbons (PAHs) in atmospheric particulate matter by random forest (RF), Anal. Methods 11(13), 1816 (2019) https://doi.org/10.1039/C8AY02720J
33	Sheng L., Zhang T., Niu G., Wang K., Tang H., Duan Y., and Li H., Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF), J. Anal. At. Spectrom. 30(2), 453 (2015) https://doi.org/10.1039/C4JA00352G
34	Zhan L.Ma X.Fang W.Wang R.Liu Z. Song Y.Zhao H., A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm, Plasma Sci. Technol. 21(3), 034018 (2019)
35	Feng T., Zhang X., Li M., Chen T., Jiao L., Xu Y., Tang H., Zhang T., and Li H., Pollution risk estimation of the Cu element in atmospheric sedimentation samples by laser induced breakdown spectroscopy (LIBS) combined with random forest (RF), Anal. Methods 13(30), 3424 (2021) https://doi.org/10.1039/D1AY00879J
36	Liu K., Tian D., Xu H., Wang H., and Yang G., Quantitative analysis of toxic elements in polypropylene (PP) via laser-induced breakdown spectroscopy (LIBS) coupled with random forest regression based on variable importance (VI-RFR), Anal. Methods 11(37), 4769 (2019) https://doi.org/10.1039/C9AY01796H
37	Liang J., Yan C., Zhang Y., Zhang T., Zheng X., and Li H., Rapid discrimination of Salvia miltiorrhiza according to their geographical regions by laser induced breakdown spectroscopy (LIBS) and particle swarm optimization-kernel extreme learning machine (PSO-KELM), Chemom. Intell. Lab. Syst. 197, 103930 (2020) https://doi.org/10.1016/j.chemolab.2020.103930
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