Machine learning in laser-induced breakdown spectroscopy: A review

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.

Graphical abstract

Keywords

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

References

[1] Z. Wang, M. S. Afgan, W. Gu, Y. Song, Y. Wang, Z. Hou, W. Song, and Z. Li, Recent advances in laser-induced breakdown spectroscopy quantification: From fundamental understanding to data processing, Trends Analyt. Chem. 143, 116385 (2021)

[2] Y. T. Fu, W. L. Gu, Z. Y. Hou, S. A. Muhammed, T. Q. Li, Y. Wang, and Z. Wang, Mechanism of signal uncertainty generation for laser-induced breakdown spectroscopy, Front. Phys. 16(2), 22502 (2021)

[3] S. Sheta, M. S. Afgan, Z. Y. Hou, S. C. Yao, L. Zhang, Z. Li, and Z. Wang, Coal analysis by laser-induced breakdown spectroscopy: A tutorial review, J. Anal. At. Spectrom. 34(6), 1047 (2019)

[4] N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. K. Gundawar, 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)

[5] T. L. Zhang, W. U. Shan, H. S. Tang, K. Wang, Y. X. Duan, and L. I. Hua, Progress of chemometrics in laser-induced breakdown spectroscopy analysis, Chin. J. Anal. Chem. 43(6), 939 (2015)

[6] T. Zhang, H. Tang, and H. Li, Chemometrics in laser‐induced breakdown spectroscopy, J. Chemometr. 32(11), e2983 (2018)

[7] D.ZhangH. ZhangY.ZhaoY.ChenC.Ke T.XuY.He, A brief review of new data analysis methods of laser-induced breakdown spectroscopy: Machine learning, Appl. Spectrosc. Rev. 57(2), 89 (2022)

[8] M. R. Wójcik, R. Zdunek, and A. J. Antończak, Unsupervised verification of laser-induced breakdown spectroscopy dataset clustering, Spectrochim. Acta B 126, 84 (2016)

[9] Y. Tang, Y. Guo, Q. Sun, S. Tang, J. Li, L. Guo, and J. Duan, Industrial polymers classification using laser-induced breakdown spectroscopy combined with self-organizing maps and K-means algorithm, Optik (Stuttg.) 165, 179 (2018)

[10] Y. Guo, Y. Tang, Y. Du, S. Tang, L. Guo, X. Li, Y. Lu, and X. Zeng, Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means, Plasma Sci. Technol. 20(6), 065505 (2018)

[11] P. Pořízka, J. Klus, E. Kepes, D. Prochazka, D. W. Hahn, and J. Kaiser, On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review, Spectrochim. Acta B 148, 65 (2018)

[12] L. A. He, X. Q. Wang, Y. Zhao, L. Liu, and Z. Peng, Study on cluster analysis used with laser-induced breakdown spectroscopy, Plasma Sci. Technol. 18(6), 647 (2016)

[13] T. Chen, L. Huang, M. Yao, H. Hu, C. Wang, and M. Liu, Quantitative analysis of chromium in potatoes by laser-induced breakdown spectroscopy coupled with linear multivariate calibration, Appl. Opt. 54(25), 7807 (2015)

[14] W. Sha, J. T. Li, C. P. Lu, and C. H. Zhen, Quantitative analysis of P in compound fertilizer by laser-induced breakdown spectroscopy coupled with linear multivariate calibration, Spectroscopy & Spectral Anal. 39(6), 1958 (2019)

[15] H. Y. Li, L. Mazzei, C. D. Wallis, and A. S. Wexler, Improving quantitative analysis of spark-induced breakdown spectroscopy: Multivariate calibration of metal particles using machine learning, J. Aerosol Sci. 159, 105874 (2022)

[16] S. N. Zhu, Y. Ding, Y. J. Chen, F. Deng, F. F. Chen, and F. Yan, 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)

[17] Jr De Lucia J. L. Gottfried., Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification, Spectrochim. Acta B 66(2), 122 (2011)

[18] Y. Lee, S. H. Han, and S. H. Nam, 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)

[19] Z. Cao, J. Cheng, X. Han, L. Li, J. Wang, Q. Fan, and Q. Lin, Rapid classification of coal by laser-induced breakdown spectroscopy (LIBS) with K-nearest neighbor (KNN) chemometrics, Instrum. Sci. Technol. 51(1), 59 (2023)

[20] J. ShangGuan, Y. Tong, A. Yuan, X. Ren, J. Liu, H. Duan, Z. Lian, X. Hu, J. Ma, Z. Yang, and D. Wang, 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)

[21] X. Yan, X. Peng, Y. Qin, Z. Xu, B. Xu, C. Li, N. Zhao, J. Li, Q. Ma, and Q. Zhang, Classification of plastics using laser-induced breakdown spectroscopy combined with principal component analysis and K nearest neighbor algorithm, Results in Optics 4, 100093 (2021)

[22] L. Liang, T. Zhang, K. Wang, H. Tang, X. Yang, X. Zhu, Y. Duan, and H. Li, Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines, Appl. Opt. 53(4), 544 (2014)

[23] M. V. Dastjerdi, S. J. Mousavi, M. Soltanolkotabi, and A. N. Zadeh, 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)

[24] P. Yang, H. T. Liu, Z. L. Nie, and X. N. Qu, 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)

[25] J. Jia, H. Fu, Z. Hou, H. Wang, Z. Ni, and F. Dong, 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)

[26] J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, Support vector machine classification of suspect powders using laser‐induced breakdown spectroscopy (LIBS) spectral data, J. Chemometr. 26(5), 143 (2012)

[27] E.D’AndreaS.PagnottaE.Grifoni S.LegnaioliG. LorenzettiV.PalleschiB.Lazzerini, A hybrid calibration-free/artificial neural networks approach to the quantitative analysis of LIBS spectra, Appl. Phys. B 118(3), 353 (2015)

[28] F. G. Rendon-Sauz, T. Flores-Reyes, and A. Ponce-Flores, Rapid classification of bacteria using libs in multi-pulse laser regime and neural networks processing, Revista Cubana De Fisica 35(1), 10 (2018)

[29] X. Cui, Q. Wang, Y. Zhao, X. Qiao, and G. Teng, Laser-induced breakdown spectroscopy (LIBS) for classification of wood species integrated with artificial neural network (ANN), Appl. Phys. B 125(4), 56 (2019)

[30] J.El HaddadM. Villot-KadriA.IsmaelG.GallouK.Michel D.BruyereV. LapercheL.CanioniB.Bousquet, Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy, Spectrochim. Acta B 79–80, 51 (2013)

[31] Q. Q. Wang, Z. W. Huang, K. Liu, W. J. Li, and J. X. Yan, 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)

[32] N. Li, J. Qi, P. Wang, X. Zhang, T. Zhang, and H. Li, 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)

[33] L. Sheng, T. Zhang, G. Niu, K. Wang, H. Tang, Y. Duan, and H. Li, Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF), J. Anal. At. Spectrom. 30(2), 453 (2015)

[34] L.ZhanX. MaW.FangR.WangZ.Liu Y.SongH. Zhao, 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] T. Feng, X. Zhang, M. Li, T. Chen, L. Jiao, Y. Xu, H. Tang, T. Zhang, and H. Li, 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)

[36] K. Liu, D. Tian, H. Xu, H. Wang, and G. Yang, 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)

[37] J. Liang, C. Yan, Y. Zhang, T. Zhang, X. Zheng, and H. Li, 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)

[38] Y. Mei, S. Cheng, Z. Hao, L. Guo, X. Li, X. Zeng, and J. Ge, Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM, Plasma Sci. Technol. 21(3), 034020 (2019)

[39] C.YanT. ZhangY.SunH.TangH.Li, A hybrid variable selection method based on wavelet transform and mean impact value for calorific value determination of coal using laser-induced breakdown spectroscopy and kernel extreme learning machine, Spectrochim. Acta B 154, 75 (2019)

[40] G. Vítková, K. Novotny, L. Prokes, A. Hrdlicka, J. Kaiser, J. Novotny, R. Malina, and D. Prochazka, Fast identification of biominerals by means of stand-off laser-induced breakdown spectroscopy using linear discriminant analysis and artificial neural networks, Spectrochim. Acta B 73, 1 (2012)

[41] P. Yang, R. Zhou, W. Zhang, S. S. Tang, Z. Q. Hao, X. Y. Li, Y. F. Lu, and X. Y. Zeng, Laser-induced breakdown spectroscopy assisted chemometric methods for rice geographic origin classification, Appl. Opt. 57(28), 8297 (2018)

[42] Z. F. Zhao, L. Chen, F. Liu, F. Zhou, J. Y. Peng, and M. H. Sun, Fast classification of geographical origins of honey based on laser-induced breakdown spectroscopy and multivariate analysis, Sensors (Basel) 20(7), 1878 (2020)

[43] X. M. Li, H. L. Lu, J. H. Yang, and F. Chang, Semi-supervised LIBS quantitative analysis method based on co-training regression model with selection of effective unlabeled samples, Plasma Sci. Technol. 21(3), 034015 (2018)

[44] Q. Wang, G. Teng, C. Li, Y. Zhao, and Z. Peng, Identification and classification of explosives using semi-supervised learning and laser-induced breakdown spectroscopy, J. Hazard. Mater. 369, 423 (2019)

[45] S. Xie, T. Xu, X. Han, Q. Lin, and Y. Duan, Accuracy improvement of quantitative LIBS analysis using wavelet threshold de-noising, J. Anal. At. Spectrom. 32(3), 629 (2017)

[46] R. Wang, X. Ma, Q. Yu, Y. Song, H. Zhao, M. Zhang, and Y. Liao, Methods of data processing for trace elements analysis using laser induced breakdown spectroscopy, Plasma Sci. Technol. 17(11), 944 (2015)

[47] H. Yang, L. Huang, T. B. Chen, G. F. Rao, M. H. Liu, J. Y. Chen, and M. Y. Yao, Spectral filtering method for improvement of detection accuracy of lead in vegetables by laser induced breakdown spectroscopy, Chin. J. Anal. Chem. 45(8), 1123 (2017)

[48] N.AliZ. HuangJ.ZongY.MaY.Xiao L.WangP. ZhangD.Chen, Real-time analysis of mineral elements in oat using laser-induced breakdown spectroscopy, J. Food Safety & Food Quality 72(4), 131 (2021)

[49] H. Guo, M. Cui, Z. Feng, D. Zhang, and D. Zhang, Classification of aviation alloys using laser-induced breakdown spectroscopy based on a WT-PSO-LSSVM model, Chemosensors (Basel) 10(6), 220 (2022)

[50] T.YuanZ. WangZ.LiW.NiJ.Liu, A partial least squares and wavelet-transform hybrid model to analyze carbon content in coal using laser-induced breakdown spectroscopy, Anal. Chim. Acta 807, 29 (2014)

[51] B.ZhangL. SunH.YuY.XinZ.Cong, A method for improving wavelet threshold denoising in laser-induced breakdown spectroscopy, Spectrochim. Acta B 107, 32 (2015)

[52] J. Wei, J. Dong, T. Zhang, Z. Wang, and H. Li, Quantitative analysis of the major components of coal ash using laser induced breakdown spectroscopy coupled with a wavelet neural network (WNN), Anal. Methods 8(7), 1674 (2016)

[53] L. Yang, Y. Zhang, J. Liu, Z. Zhang, M. Xu, F. Ji, J. Chen, T. Zhang, and R. Lu, Spectral preprocessing to improve accuracy of quantitative detection of elemental Cr in austenitic stainless steel by laser-induced breakdown spectroscopy, Rev. Sci. Instrum. 93(3), 033002 (2022)

[54] H.DuanS. MaL.HanG.Huang, A novel denoising method for laser-induced breakdown spectroscopy: Improved wavelet dual threshold function method and its application to quantitative modeling of Cu and Zn in Chinese animal manure composts, Microchem. J. 134, 262 (2017)

[55] J. Chappell, M. Martinez, and M. Baudelet, Statistical evaluation of spectral interferences in laser-induced breakdown spectroscopy, Spectrochim. Acta B 149, 167 (2018)

[56] K. Liu, R. Zhou, W. Zhang, Z. Tang, J. Yan, M. Lv, X. Li, Y. Lu, and X. Zeng, Interference correction for laser-induced breakdown spectroscopy using a deconvolution algorithm, Anal. At. Spectrom. 35, 762 (2020)

[57] B. Tan, M. Huang, Q. Zhu, Y. Guo, and J. Qin, Decomposition and correction overlapping peaks of LIBS using an error compensation method combined with curve fitting, Appl. Opt. 56(25), 7116 (2017)

[58] Y.WangY. BuF.WuY.CaoY.Yu X.Wang, Research on LIBS quantitative analysis of heavy metal concentration in polluted water-based on Fourier self-deconvolution method, in: AOPC 2019: Optical Spectroscopy and Imaging, SPIE, 2019, pp 167–172

[59] J. Guezenoc, A. Gallet-Budynek, and B. Bousquet, Critical review and advices on spectral-based normalization methods for LIBS quantitative analysis, Spectrochim. Acta B 160, 105688 (2019)

[60] R. Wang and X. Ma, Study on LIBS Standard Method via Key Parameter Monitoring and Backpropagation Neural Network, Chemosensors (Basel) 10(8), 312 (2022)

[61] P.LuZ.Zhuo W.H. ZhangJ. TangY.WangH.L. ZhouX.L. Huang T.F. SunJ. Q. Lu, A hybrid feature selection combining wavelet transform for quantitative analysis of heat value of coal using laser-induced breakdown spectroscopy, Appl. Phys. B 127(2), 19 (2021)

[62] S. Xie, T. Xu, G. Niu, W. Liao, Q. Lin, and Y. Duan, Quantitative analysis of steel samples by laser-induced-breakdown spectroscopy with wavelet-packet-based relevance vector machines, J. Anal. At. Spectrom. 33(6), 975 (2018)

[63] T. Chen, L. Sun, H. Yu, L. Qi, D. Shang, and Y. Xie, Efficient weakly supervised LIBS feature selection method in quantitative analysis of iron ore slurry, Appl. Opt. 61(7), D22 (2022)

[64] E. Harefa and W. Zhou, Performing sequential forward selection and variational autoencoder techniques in soil classification based on laser-induced breakdown spectroscopy, Anal. Methods 13(41), 4926 (2021)

[65] Y. Chu, F. Chen, Z. Sheng, D. Zhang, S. Zhang, W. Wang, H. Jin, J. Qi, and L. Guo, Blood cancer diagnosis using ensemble learning based on a random subspace method in laser-induced breakdown spectroscopy, Biomed. Opt. Express 11(8), 4191 (2020)

[66] Y. Jiang, Z. Lu, X. Chen, Z. Yu, H. Qin, J. Chen, J. Lu, and S. Yao, Optimizing the quantitative analysis of solid biomass fuel properties using laser induced breakdown spectroscopy (LIBS) coupled with a kernel partial least squares (KPLS) model, Anal. Methods 13(45), 5467 (2021)

[67] H. Y. Kong, L. X. Sun, J. T. Hu, and P. Zhang, Automatic method for selecting characteristic lines based on genetic algorithm to quantify laser-induced breakdown spectroscopy, Spectroscopy & Spectral Anal. 36(5), 1451 (2016)

[68] L. P. Gan, T. Sun, J. Liu, and M. H. Liu, Double pulse LIBS combined with variable screening to detect procymidone content, Spectroscopy & Spectral Anal. 39(02), 584 (2019)

[69] S. Ma, Q. Ma, L. Han, and G. Huang, Modelling of calcuim content in manure using laser-induced breakdown spectroscopy and genetic algorithm combined with partial least squares, Spectroscopy & Spectral Anal. 37(5), 1532 (2017)

[70] T. He, J. Liang, H. Tang, T. Zhang, C. Yan, and H. Li, Quantitative analysis of coal quality by mutual information-particle swarm optimization (MI-PSO) hybrid variable selection method coupled with spectral fusion strategy of laser-induced breakdown spectroscopy (LIBS) and fourier transform infrared spectroscopy (FTIR), Spectrochim. Acta B 178, 106112 (2021)

[71] F. Duan, X. Fu, J. Jiang, T. Huang, L. Ma, and C. Zhang, Automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy, Spectrochim. Acta B 143, 12 (2018)

[72] X.FuF.J. Duan T.T. HuangL. MaJ.J. JiangY.C. Li, A fast variable selection method for quantitative analysis of soils using laser-induced breakdown spectroscopy, J. Anal. At. Spectrom. 32(6), 1166 (2017)

[73] P. Lu, Z. Zhuo, W. Zhang, J. Tang, H. Tang, and J. Lu, Accuracy improvement of quantitative LIBS analysis of coal properties using a hybrid model based on a wavelet threshold de-noising and feature selection method, Appl. Opt. 59(22), 6443 (2020)

[74] G.WangL. SunW.WangT.ChenM.Guo P.Zhang, A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy, Plasma Sci. Technol. 22(7), 074002 (2020)

[75] F. Ruan, J. Qi, C. Yan, H. Tang, T. Zhang, and H. Li, Quantitative detection of harmful elements in alloy steel by LIBS technique and sequential backward selection-random forest (SBS-RF), J. Anal. At. Spectrom. 32(11), 2194 (2017)

[76] F.RuanL. HouT.ZhangH.Li, A modified backward elimination approach for the rapid classification of Chinese ceramics using laser-induced breakdown spectroscopy and chemometrics, J. Anal. At. Spectrom. 35(3), 518 (2020)

[77] Y. Ding, Y. Shu, A. Hu, M. Zhao, J. Chen, L. Yang, W. Chen, and Y. Wang, Determination of soil source using laser induced breakdown spectroscopy combined with feature selection, J. Anal. At. Spectrom. 38(11), 2499 (2023)

[78] W. You, Y. P. Xia, Y. T. Huang, J. J. Lin, and X. M. Lin, Research on selection method of LIBS feature variables based on CART regression tree, Spectroscopy & Spectral Anal. 41(10), 3240 (2021)

[79] Z. Lv, H. Yu, L. Sun, and P. Zhang, Composition analysis of ceramic raw materials using laser-induced breakdown spectroscopy and autoencoder neural network, Anal. Methods 14(13), 1320 (2022)

[80] Y.WuT.Sun J.LiuL. GanM.Liu, Detection of chromium content in edible vegetable oil with DP-LIBS combined with LSSVM and CARS methods, Laser & Optoelectron. Prog. 55(1), 013005–1 (2018) (in Chinese)

[81] D. H. Zhu, M. C. Wang, L. J. Xu, X. J. Chen, B. T. Sun, J. Zhang, W. W. Liu, Y. Cao, L. M. Yuan, and Y. Cai, Detection of Pb element composition in irregular copper alloy samples based on multi-line internal standard method, Spectroscopy & Spectral Anal. 39(10), 3159 (2019)

[82] J.LongW. SongZ.HouZ.Wang, A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy, Plasma Sci. Technol. 25(7), 075501 (2023)

[83] J. a. Liu, J. m. Li, N. Zhao, Q. x. Ma, L. Guo, and Q. m. Zhang, Rapid classification and identification of plastic using laser-induced breakdown spectroscopy with principal component analysis and support vector machine, Spectroscopy & Spectral Anal. 41(6), 1955 (2021)

[84] P. Pořízka, J. Klus, E. Képeš, D. Prochazka, D. W. Hahn, and J. Kaiser, On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis ‒ A review, Spectrochim. Acta B 148, 65 (2018)

[85] J. B. Sirven, B. Salle, P. Mauchien, J. L. Lacour, S. Maurice, and G. Manhes, Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods, J. Anal. At. Spectrom. 22(12), 1471 (2007)

[86] Z. A. Abdel-Salam, V. Palleschi, and M. A. Harith, Study of the feeding effect on recent and ancient bovine bones by nanoparticle-enhanced laser-induced breakdown spectroscopy and chemometrics, J. Adv. Res. 17, 65 (2019)

[87] A. H. Farhadian, M. K. Tehrani, M. H. Keshavarz, and S. M. R. Darbani, Energetic materials identification by laser-induced breakdown spectroscopy combined with artificial neural network, Appl. Opt. 56(12), 3372 (2017)

[88] M. Yuan, Q. Zeng, J. Wang, W. Li, G. Chen, Z. Li, Y. Liu, L. Guo, X. Li, and H. Yu, Rapid classification of steel via a modified support vector machine algorithm based on portable fiber-optic laser-induced breakdown spectroscopy, Opt. Eng. 60(12), 124114 (2021)

[89] K. Li, L. Guo, J. Li, X. Yang, R. Yi, X. Li, Y. Lu, and X. Zeng, Quantitative analysis of steel samples using laser-induced breakdown spectroscopy with an artificial neural network incorporating a genetic algorithm, Appl. Opt. 56(4), 935 (2017)

[90] Q. X. Zhong, T. Z. Zhao, X. Li, F. Q. Lian, H. Xiao, S. Z. Nie, S. N. Sun, and Z. W. Fan, Standardized cross-validation and its optimization for multi-element LIBS analysis, Spectroscopy & Spectral Anal. 40(2), 622 (2020)

[91] H.DongL. SunL.QiH.YuP.Zeng, A lightweight convolutional neural network model for quantitative analysis of phosphate ore slurry based on laser-induced breakdown spectroscopy, J. Anal. At. Spectrom. 36(11), 2528 (2021)

[92] D. Prochazka, P. Porízka, J. Hruska, K. Novotny, A. Hrdlicka, and J. Kaiser, Machine learning in laser-induced breakdown spectroscopy as a novel approach towards experimental parameter optimization, J. Anal. At. Spectrom. 37(3), 603 (2022)

[93] H. Tang, T. Zhang, X. Yang, and H. Li, Classification of different types of slag samples by laser-induced breakdown spectroscopy (LIBS) coupled with random forest based on variable importance (VIRF), Anal. Methods 7(21), 9171 (2015)

[94] P. Yang, R. Zhou, W. Zhang, S. Tang, Z. Hao, X. Li, Y. Lu, and X. Zeng, Laser-induced breakdown spectroscopy assisted chemometric methods for rice geographic origin classification, Appl. Opt. 57(28), 8297 (2018)

[95] F. I. Alarsan and M. Younes, Analysis and classification of heart diseases using heartbeat features and machine learning algorithms, J. Big Data 6(1), 81 (2019)

[96] J. Yu, Z. Hou, S. Sheta, J. Dong, W. Han, T. Lu, and Z. Wang, Provenance classification of nephrite jades using multivariate LIBS: A comparative study, Anal. Methods 10(3), 281 (2018)

[97] N. Gyftokostas, D. Stefas, V. Kokkinos, C. Bouras, and S. Couris, Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination, Sci. Rep. 11(1), 5360 (2021)

[98] Z. Zhao, L. Chen, F. Liu, F. Zhou, J. Peng, and M. Sun, Fast classification of geographical origins of honey based on laser-induced breakdown spectroscopy and multivariate analysis, Sensors (Basel) 20(7), 1878 (2020)

[99] Z. Luo, L. Zhang, T. Chen, M. Liu, J. Chen, H. Zhou, and M. Yao, Rapid identification of rice species by laser-induced breakdown spectroscopy combined with pattern recognition, Appl. Opt. 58(7), 1631 (2019)

[100] W. Huang, L. Guo, W. Kou, D. Zhang, Z. Hu, F. Chen, Y. Chu, and W. Cheng, Identification of adulterated milk powder based on convolutional neural network and laser-induced breakdown spectroscopy, Microchem. J. 176, 107190 (2022)

[101] K. Kiss, A. Sindelárová, L. Krbal, V. Stejskal, K. Mrázová, J. Vrábel, M. Kaska, P. Modlitbová, P. Porízka, and J. Kaiser, Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning, J. Anal. At. Spectrom. 36(5), 909 (2021)

[102] J. Ding, D. C. Zhang, B. W. Wang, Z. Q. Feng, X. Y. Liu, and J. F. Zhu, The classification of plant leaves by applying chemometrics methods on laser-induced breakdown spectroscopy, Spectroscopy & Spectral Anal. 41(2), 606 (2021)

[103] X. Li, S. Yang, R. Fan, X. Yu, and D. Chen, Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers, Opt. Laser Technol. 102, 233 (2018)

[104] M. S. Babu, T. Imai, and R. Sarathi, Classification of aged epoxy micro-nanocomposites through PCA- and ANN-adopted LIBS analysis, IEEE Trans. Plasma Sci. 49(3), 1088 (2021)

[105] M. Singh and A. Sarkar, Comparative study of the plsr and pcr methods in laser-induced breakdown spectroscopic analysis, J. Appl. Spectrosc. 85(5), 962 (2018)

[106] Y. Liu, S. Zhao, X. Gao, S. Fu, Chao Song, Y. Dou, Shaozhong Song, C. Qi, and J. Lin, Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin, RSC Adv. 12(53), 34520 (2022)

[107] B. Campanella, E. Grifoni, S. Legnaioli, G. Lorenzetti, S. Pagnotta, F. Sorrentino, and V. Palleschi, Classification of wrought aluminum alloys by artificial neural networks evaluation of laser induced breakdown spectroscopy spectra from aluminum scrap samples, Spectrochim. Acta B 134, 52 (2017)

[108] S.ParkJ. LeeE.KwonD.KimS.Shin S.JeongK. Park, 3D sensing system for laser-induced breakdown spectroscopy-based metal scrap identification, Int. J. Precis. Eng. & Manuf. -Green Tech. 9, 695 (2022)

[109] A.DemirD. K. ÜrkK.AkbenM.DoğanE.Pehlivan Ö.YalçınM.A. KıştanG.GökçeA. Obalı, Elemental Analysis and Classification of Molten Aluminum Alloys by LIBS, Springer Nature Switzerland, Cham, 2024, pp. 984-990.

[110] J. Yan, S. Li, K. Liu, R. Zhou, W. Zhang, Z. Hao, X. Li, D. Wang, Q. Li, and X. Zeng, An image features assisted line selection method in laser-induced breakdown spectroscopy, Anal. Chim. Acta 1111, 139 (2020)

[111] N. Gyftokostas, E. Nanou, D. Stefas, V. Kokkinos, C. Bouras, and S. Couris, Classification of greek olive oils from different regions by machine learning-aided laser-induced breakdown spectroscopy and absorption spectroscopy, Molecules 26(5), 1241 (2021)

[112] T. Zhang, C. Yan, J. Qi, H. Tang, and H. Li, Classification and discrimination of coal ash by laser-induced breakdown spectroscopy (LIBS) coupled with advanced chemometric methods, J. Anal. At. Spectrom. 32(10), 1960 (2017)

[113] G. Rao, L. Huang, M. Liu, T. Chen, J. Chen, Z. Luo, F. Xu, X. Xu, and M. Yao, Identification of Huanglongbing-infected navel oranges based on laser-induced breakdown spectroscopy combined with different chemometric methods, Appl. Opt. 57(29), 8738 (2018)

[114] S. Lu, M. Dong, J. Huang, W. Li, J. Lu, and J. Li, Estimation of the aging grade of T91 steel by laser-induced breakdown spectroscopy coupled with support vector machines, Spectrochim. Acta B 140, 35 (2018)

[115] S. Müller and J. A. Meima, Mineral classification of lithium-bearing pegmatites based on laser-induced breakdown spectroscopy: Application of semi-supervised learning to detect known minerals and unknown material, Spectrochim. Acta B 189, 106370 (2022)

[116] L. M. Narla and S. V. Rao, Identification of metals and alloys using color CCD images of laser-induced breakdown emissions coupled with machine learning, Appl. Phys. B 126(6), 113 (2020)

[117] M. Yelameli, B. Thornton, T. Takahashi, T. Weerakoon, and K. Ishii, Classification and statistical analysis of hydrothermal seafloor rocks measured underwater using laser-induced breakdown spectroscopy, J. Chemometr. 33(2), e3092 (2019)

[118] Q. Zeng, G. Chen, W. Li, Z. Li, J. Tong, M. Yuan, B. Wang, H. Ma, Y. Liu, L. Guo, and H. Yu, Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine, Plasma Sci. Technol. 24(8), 084009 (2022)

[119] T. Chen, L. Sun, H. Yu, W. Wang, L. Qi, P. Zhang, and P. Zeng, Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging, Appl. Geochem. 136, 105135 (2022)

[120] W. Hao, X. Hao, Y. Yang, X. Liu, Y. Liu, P. Sun, and R. Sun, Rapid classification of soils from different mining areas by laser-induced breakdown spectroscopy (LIBS) coupled with a PCA-based convolutional neural network, J. Anal. At. Spectrom. 36(11), 2509 (2021)

[121] W. H. Yan, X. Y. Yang, X. Geng, L. S. Wang, L. Lu, Y. Tian, Y. Li, and H. Lin, Rapid identification of fish products using handheld laser induced breakdown spectroscopy combined with random forest, Spectroscopy & Spectral Anal. 42(12), 3714 (2022)

[122] Y. W. Chu, S. S. Tang, S. X. Ma, Y. Y. Ma, Z. Q. Hao, Y. M. Guo, L. B. Guo, Y. F. Lu, and X. Y. Zeng, Accuracy and stability improvement for meat species identification using multiplicative scatter correction and laser-induced breakdown spectroscopy, Opt. Express 26(8), 10119 (2018)

[123] M. Guo, R. Zhu, L. Zhang, R. Zhang, G. Huang, and H. Duan, Quantitative detection of chromium pollution in biochar based on matrix effect classification regression model, Molecules 26(7), 2069 (2021)

[124] V. C. Costa, F. W. B. Aquino, C. M. Paranhos, and E. R. Pereira-Filho, Identification and classification of polymer e-waste using laser-induced breakdown spectroscopy (LIBS) and chemometric tools, Polym. Test. 59, 390 (2017)

[125] Y. Chen, Y. Liu, B. Han, W. Yu, and E. Wan, Identification of writing marks from pencil lead through machine learning based on laser-induced breakdown spectroscopy, Optik (Stuttg.) 259, 169008 (2022)

[126] K. Liu, D. Tian, X. Deng, H. Wang, and G. Yang, Rapid classification of plastic bottles by laser-induced breakdown spectroscopy (LIBS) coupled with partial least squares discrimination analysis based on spectral windows (SW-PLS-DA), J. Anal. At. Spectrom. 34(8), 1665 (2019)

[127] Z. A. Abdel-Salam, S. A. M. Abdel-Salam, I. I. Abdel-Mageed, and M. A. Harith, Evaluation of proteins in sheep colostrum via laser-induced breakdown spectroscopy and multivariate analysis, J. Adv. Res. 15, 19 (2019)

[128] W. Yu, Z. Sun, and Y. Liu, Rapid detection and identification of objects using a self-designed methodology based on LIBS and PCA-DVSM – taking rosewood for example, Optik (Stuttg.) 248, 168069 (2021)

[129] A. K. Pathak, A. Singh, R. Kumar, and A. K. Rai, Laser-induced breakdown spectroscopy coupled with PCA study of human tooth, Natl. Acad. Sci. Lett. 42(1), 87 (2019)

[130] H.SongL. MaE.ZhuY.WangY.Liu W.SunP. PengC.Li, Plastic classification and recognition by laser-induced breakdown spectroscopy and GA-BP neural network, Laser & Optoelectron. Prog. 57(15), 153002 (2020) (in Chinese)

[131] P. Dong, S. Zhao, K. Zheng, J. Wang, X. Gao, Z. Hao, and J. Lin, Rapid identification of ginseng origin by laser induced breakdown spectroscopy combined with neural network and support vector machine algorithm, Acta Phys. Sin. 70(4), 040201 (2021)

[132] X. Liu, X. Che, K. Li, X. Wang, Z. Lin, Z. Wu, and Q. Zheng, Geographical authenticity evaluation of Mentha haplocalyx by LIBS coupled with multivariate analyses, Plasma Sci. Technol. 22(7), 074006 (2020)

[133] Q.GodoiF. O. LemeL.C. TrevizanE.R. Pereira FilhoI. A. RufiniJrSantosF.J. Krug, Laser-induced breakdown spectroscopy and chemometrics for classification of toys relying on toxic elements, Spectrochim. Acta B 66(2), 138 (2011)

[134] P. M. Mukhono, K. H. Angeyo, A. Dehayem-Kamadjeu, and K. A. Kaduki, Laser induced breakdown spectroscopy and characterization of environmental matrices utilizing multivariate chemometrics, Spectrochim. Acta B 87, 81 (2013)

[135] T. F. Akhmetzhanov and A. M. Popov, Direct determination of lanthanides by LIBS in REE-rich ores: Comparison between univariate and DoE based multivariate calibrations with respect to spectral resolution, J. Anal. At. Spectrom. 37(11), 2330 (2022)

[136] T. F. Akhmetzhanov, T. A. Labutin, D. M. Korshunov, A. A. Samsonov, and A. M. Popov, Determination of Ce and La in REE-rich ores using handheld LIBS and PLS regression, J. Anal. At. Spectrom. 38(10), 2134 (2023)

[137] A. P. Rao, P. R. Jenkins, D. M. Vu, J. D. Auxier Ii, A. K. Patnaik, and M. B. Shattan, Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning, Anal. Methods 13(30), 3368 (2021)

[138] A. P. Rao, P. R. Jenkins, J. D. II Auxier, and M. B. Shattan, Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device, J. Anal. At. Spectrom. 36(2), 399 (2021)

[139] Y. H. Gu, Y. Li, Y. Tian, and Y. Lu, Study on the multivariate quantitative analysis method for steel alloy elements using LIBS, Spectroscopy & Spectral Anal. 34(8), 2244 (2014)

[140] P. Yaroshchyk, D. L. Death, and S. J. Spencer, Comparison of principal components regression, partial least squares regression, multi-block partial least squares regression, and serial partial least squares regression algorithms for the analysis of Fe in iron ore using LIBS, J. Anal. At. Spectrom. 27(1), 92 (2012)

[141] A. Erler, D. Riebe, T. Beitz, H. G. Löhmannsröben, R. Gebbers, Soil nutrient detection for precision agriculture using handheld laser-induced breakdown spectroscopy (LIBS), and multivariate regression methods (PLSR, Lasso and GPR), Sensors (Basel) 20(2), 418 (2020)

[142] A. P. Rao, P. R. Jenkins, M. B. Auxier, and Shattan K. Patnaik, Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra, Appl. Opt. 61(7), D30 (2022)

[143] R. J. Yuan, X. Wan, Q. He, and H. P. Wang, Research on olivine component analysis using LIBS combined with back-propagation algorithm, Spectroscopy & Spectral Anal. 39(12), 3861 (2019)

[144] Q. Shi, G. Niu, Q. Lin, T. Xu, F. Li, and Y. Duan, Quantitative analysis of sedimentary rocks using laser-induced breakdown spectroscopy: Comparison of support vector regression and partial least squares regression chemometric methods, J. Anal. At. Spectrom. 30(12), 2384 (2015)

[145] Y. Ding, F. Yan, G. Yang, H. Chen, and Z. Song, Quantitative analysis of sinters using laser-induced breakdown spectroscopy (LIBS) coupled with kernel-based extreme learning machine (K-ELM), Anal. Methods 10(9), 1074 (2018)

[146] S. Wu, T. Zhang, H. Tang, K. Wang, X. Yang, and H. Li, Quantitative analysis of nonmetal elements in steel using laser-induced breakdown spectroscopy combined with random forest, Anal. Methods 7(6), 2425 (2015)

[147] L. R. Xiang, Z. H. Ma, X. Y. Zhao, F. Liu, Y. He, and L. Feng, Comparative analysis of chemometrics method on heavy metal detection in soil with laser-induced breakdown spectroscopy, Spectroscopy & Spectral Anal. 37(12), 3871 (2017)

[148] S. Ye, X. Chen, D. Dong, J. Wang, X. Wang, and F. Wang, Rapid determination of water COD using laser-induced breakdown spectroscopy coupled with partial least-squares and random forest, Anal. Methods 10(40), 4879 (2018)

[149] T. A. Labutin, S. M. Zaytsev, A. M. Popov, and N. B. Zorov, Carbon determination in carbon-manganese steels under atmospheric conditions by laser-induced breakdown spectroscopy, Opt. Express 22(19), 22382 (2014)

[150] L.SunL. LiuM.ZhuM.WangQ.Wang X.PengJ. Qu, Quantitative analysis of laser-induced breakdown spectroscopy of Pb in water using particle swarm optimization algorithm, in: 2015 Optoelectronics Global Conference (OGC), Shenzhen, China, 2015, pp 29–31

[151] Z. Q. Hao, C. M. Li, M. Shen, X. Y. Yang, K. H. Li, L. B. Guo, X. Y. Li, Y. F. Lu, and X. Y. Zeng, Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression, Opt. Express 23(6), 7795 (2015)

[152] P. Wang, N. Li, C. Yan, Y. Feng, Y. Ding, T. Zhang, and H. Li, Rapid quantitative analysis of the acidity of iron ore by the laser-induced breakdown spectroscopy (LIBS) technique coupled with variable importance measures-random forests (VIM-RF), Anal. Methods 11(27), 3419 (2019)

[153] G. Yang, X. Han, C. Wang, Y. Ding, K. Liu, D. Tian, and L. Yao, The basicity analysis of sintered ore using laserinduced breakdown spectroscopy (LIBS) combined with random forest regression (RFR), Anal. Methods 9(36), 5365 (2017)

[154] C. Lu, G. Lv, C. Shi, D. Qiu, F. Jin, M. Gu, and W. Sha, Quantitative analysis of pH value in soil using laser-induced breakdown spectroscopy coupled with a multivariate regression method, Appl. Opt. 59(28), 8582 (2020)

[155] Y. Zhang, Z. Xiong, Y. Ma, C. Zhu, R. Zhou, X. Li, Q. Li, and Q. Zeng, Quantitative analysis of coal quality by laser-induced breakdown spectroscopy assisted with different chemometric methods, Anal. Methods 12(27), 3530 (2020)

[156] E. Képeš, H. Saeidfirozeh, V. Laitl, J. Vrábel, P. Kubelík, P. Pořízka, M. Ferus, and J. Kaiser, Interpreting neural networks trained to predict plasma temperature from optical emission spectra, J. Anal. At. Spectrom. 39(4), 1160 (2024)

[157] H.SaeidfirozehA.K. MyakalwarP.KubelíkA.GhaderiV.LaitlL.Petera P.B. RimmerO. ShorttleA.N. HeaysA.KrivkováM. KrusS.CivisJ.YáñezE. KépesP.PorízkaM.Ferus, ANN-LIBS analysis of mixture plasmas: Detection of Xenon, J. Anal. At. Spectrom. 37(9), 1815 (2022)

[158] N. Ahmed, J. A. Awan, K. Fatima, S. M. Z. Iqbal, M. Rafique, S. A. Abbasi, and M. A. Baig, Machine learning-based calibration LIBS analysis of aluminium-based alloys, Eur. Phys. J. Plus 137(6), 671 (2022)

[159] K. H. Li, L. B. Guo, C. M. Li, X. Y. Li, M. Shen, Z. Zheng, Y. Yu, R. F. Hao, Z. Q. Hao, Q. D. Zeng, Y. F. Lu, and X. Y. Zeng, Analytical-performance improvement of laserinduced breakdown spectroscopy for steel using multi-spectral-line calibration with an artificial neural network, J. Anal. At. Spectrom. 30(7), 1623 (2015)

[160] P. Yang, X. Li, and Z. Nie, Determination of the nutrient profile in plant materials using laser-induced breakdown spectroscopy with partial least squares-artificial neural network hybrid models, Opt. Express 28(15), 23037 (2020)

[161] S. Shabbir, W. Xu, Y. Zhang, C. Sun, Z. Yue, L. Zou, F. Chen, and J. Yu, Machine learning and transfer learning for correction of the chemical and physical matrix effects in the determination of alkali and alkaline earth metals with LIBS in rocks, Spectrochim. Acta B 194, 106478 (2022)

[162] H. Zhang, S. Wang, D. Li, Y. Zhang, J. Hu, and L. Wang, Edible gelatin diagnosis using laser-induced breakdown spectroscopy and partial least square assisted support vector machine, Sensors (Basel) 19(19), 4225 (2019)

[163] J. Huang, M. Dong, S. Lu, W. Li, J. Lu, C. Liu, and J. H. Yoo, Estimation of the mechanical properties of steel via LIBS combined with canonical correlation analysis (CCA) and support vector regression (SVR), J. Anal. At. Spectrom. 33(5), 720 (2018)

[164] F. P. Yu, J. J. Lin, X. M. Lin, and L. Li, Detection of C element in alloy steel by double pulse laser induced breakdown spectroscopy with a multivariable GA-BP-ANN, Spectroscopy & Spectral Anal. 42(1), 197 (2022)

[165] W. Song, Z. Hou, W. Gu, M. S. Afgan, J. Cui, H. Wang, Y. Wang, and Z. Wang, Incorporating domain knowledge into machine learning for laser-induced breakdown spectroscopy quantification, Spectrochim. Acta B 195, 106490 (2022)

[166] Z.WangJ. FengL.LiW.NiZ.Li, A multivariate model based on dominant factor for laser-induced breakdown spectroscopy measurements, J. Anal. At. Spectrom. 26(11), 2289 (2011)

[167] J.FengZ. WangL.WestZ.LiW.Ni, A PLS model based on dominant factor for coal analysis using laser-induced breakdown spectroscopy, Anal. Bioanal. Chem. 400(10), 3261 (2011)

[168] Z.WangJ. FengL.LiW.NiZ.Li, A multivariate model based on dominant factor for laser-induced breakdown spectroscopy measurements, J. Anal. At. Spectrom. 26(11), 2289 (2011)

[169] X.LiZ.Wang Y.FuZ.Li W.Ni, A model combining spectrum standardization and dominant factor based partial least square method for carbon analysis in coal using laser-induced breakdown spectroscopy, Spectrochim. Acta B 99, 82 (2014)

[170] Z. Hou, Z. Wang, L. Li, X. Yu, T. Li, H. Yao, G. Yan, Q. Ye, Z. Liu, and H. Zheng, Fast measurement of coking properties of coal using laser induced breakdown spectroscopy, Spectrochim. Acta B 191, 106406 (2022)

[171] Z.WangJ. FengL.LiW.NiZ.Li, A non-linearized PLS model based on multivariate dominant factor for laser-induced breakdown spectroscopy measurements, J. Anal. At. Spectrom. 26(11), 2175 (2011)

[172] Y. Zhang, Y. Lu, Y. Tian, Y. Li, W. Ye, J. Guo, and R. Zheng, Quantitation improvement of underwater laser induced breakdown spectroscopy by using self-absorption correction based on plasma images, Anal. Chim. Acta 1195, 339423 (2022)

[173] Z.HouZ. WangT.YuanJ.LiuZ.Li W.Ni, A hybrid quantification model and its application for coal analysis using laser induced breakdown spectroscopy, J. Anal. At. Spectrom. 31(3), 722 (2016)

[174] J.FengZ. WangL.LiZ.LiW.Ni, A nonlinearized multivariate dominant factor-based partial least squares (PLS) model for coal analysis by using laser-induced breakdown spectroscopy, Appl. Spectrosc. 67(3), 291 (2013)

[175] M.DongL. WeiJ.LuW.LiS.Lu S.LiC.Liu J.H. Yoo, A comparative model combining carbon atomic and molecular emissions based on partial least squares and support vector regression correction for carbon analysis in coal using LIBS, J. Anal. At. Spectrom. 34(3), 480 (2019)

[176] W. Song, M. S. Afgan, Y. H. Yun, H. Wang, J. Cui, W. Gu, Z. Hou, and Z. Wang, Spectral knowledge-based regression for laser-induced breakdown spectroscopy quantitative analysis, Expert Syst. Appl. 205, 117756 (2022)

[177] E. Kepes, J. Vrábel, T. Brázdil, P. Holub, P. Porízka, and J. Kaiser, Interpreting convolutional neural network classifiers applied to laser-induced breakdown optical emission spectra, Talanta 266, 124946 (2024)

[178] A. P. Rao, P. R. Jenkins, J. D. Auxier, M. B. Shattan, and A. K. Patnaik, Analytical comparisons of handheld LIBS and XRF devices for rapid quantification of gallium in a plutonium surrogate matrix, J. Anal. At. Spectrom. 37(5), 1090 (2022)

[179] J. Amador-Hernández, L. E. García-Ayuso, J. M. Fernández-Romeroa, and M. D. Luque de Castro, Partial least squares regression for problem solving in precious metal analysis by laser induced breakdown spectrometry, J. Anal. At. Spectrom. 15, 587 (2000)

[180] D. L. Death, A. P. Cunningham, and L. J. Pollard, Multi-element analysis of iron ore pellets by laser-induced breakdown spectroscopy and principal components regression, Spectrochim. Acta Part B 63(7), 763 (2008)

[181] S. M. Zaytsev, A. M. Popov, E. V. Chernykh, R. D. Voronina, N. B. Zorov, T. A. Labutin, Comparison of single-, and multivariate calibration for determination of Si, Cr and Ni in high-alloyed stainless steels by laser-induced breakdown spectrometry, J. Anal. At. Spectrom. 29(8), 1417 (2014)

[182] S. M. Zaytsev, I. N. Krylov, A. M. Popov, N. B. Zorov, and T. A. Labutin, Accuracy enhancement of a multivariate calibration for lead determination in soils by laser induced breakdown spectroscopy, Spectrochim. Acta B 140, 65 (2018)

[183] Y. C. Huang, S. S. Harilal, A. Bais, and A. E. Hussein, Progress toward machine learning methodologies for laser-induced breakdown spectroscopy with an emphasis on soil analysis, IEEE Trans. Plasma Sci. 51(7), 1729 (2023)

[184] N. Rethfeldt, P. Brinkmann, D. Riebe, T. Beitz, N. Köllner, U. Altenberger, and H. G. Löhmannsröben, Detection of rare earth elements in minerals and soils by laser-induced breakdown spectroscopy (LIBS) using interval PLS, Minerals (Basel) 11(12), 1379 (2021)

[185] C. R. Bhatt, F. Y. Yueh, and J. P. Singh, Univariate and multivariate analyses of rare earth elements by laser-induced breakdown spectroscopy, Appl. Opt. 56(8), 2280 (2017)

[186] E. H. Kwapis, J. Borrero, K. S. Latty, H. B. Andrews, S. Phongikaroon, and K. C. Hartig, Laser ablation plasmas and spectroscopy for nuclear applications, Appl. Spectrosc. 78(1), 9 (2024)

[187] C. Sun, W. J. Xu, Y. Q. Tan, Y. Q. Zhang, Z. Q. Yue, L. Zou, S. Shabbir, M. T. Wu, F. Y. Chen, and J. Yu, From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration, Sci. Rep. 11(1), 21379 (2021)

[188] V. Motto-Ros, A. S. Koujelev, G. R. Osinski, and A. E. Dudelzak, Quantitative multi-elemental laser-induced breakdown spectroscopy using artificial neural networks, J. Eur. Opt. Soc. Rapid Publ. 3, 08011 (2008)

[189] J. B. Sirven, B. Bousquet, L. Canioni, and L. Sarger, Laser-induced breakdown spectroscopy of composite SampIes: Comparison of advanced chemometrics methods, Anal. Chem. 78(5), 1462 (2006)

[190] X. B. Xu, F. Ma, J. M. Zhou, and C. W. Du, Applying convolutional neural networks (CNN) for end-to-end soil analysis based on laser-induced breakdown spectroscopy (LIBS) with less spectral preprocessing, Comput. Electron. Agric. 199, 107171 (2022)

[191] J.H. YangC. C. YiJ.W. XuX.H. Ma, A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model, J. Anal. At. Spectrom. 30(7), 1541 (2015)

[192] Z. Yang, B. Tang, and Y. Qiu, Measurement of transient temperature using laser induced breakdown spectroscopy (LIBS) with the surface temperature effect, J. Anal. At. Spectrom. (10), 38 (2023)

[193] L. Sun, H. Yu, Z. Cong, Y. Xin, Y. Li, and L. Qi, In situ analysis of steel melt by double-pulse laser-induced breakdown spectroscopy with a Cassegrain telescope, Spectrochim. Acta B 112, 40 (2015)

[194] L.X. SunY. XinZ.B. CongY.LiL.F. Qi, Online compositional analysis of molten steel by laser-induced breakdown spectroscopy, Adv. Mater. Res. 694–697, 1260 (2013)

[195] Y. Lee, R. I. Foster, H. Kim, and S. Choi, Machine learning-assisted laser-induced breakdown spectroscopy for monitoring molten salt compositions of small modular reactor fuel under varying laser focus positions, Anal. Chim. Acta 1241, 340804 (2023)

[196] P. J. Gasda, R. B. Anderson, A. Cousin, O. Forni, S. M. Clegg, A. Ollila, N. Lanza, J. Frydenvang, S. Lamm, R. C. Wiens, S. Maurice, O. Gasnault, R. Beal, A. Reyes-Newell, and D. Delapp, Quantification of manganese for ChemCam Mars and laboratory spectra using a multivariate model, Spectrochim. Acta B 181, 106223 (2021)

[197] F. Yang, L. N. Li, W. M. Xu, X. F. Liu, Z. C. Cui, L. C. Jia, Y. Liu, J. H. Xu, Y. W. Chen, X. S. Xu, J. Y. Wang, H. Qi, and R. Shu, Laser-induced breakdown spectroscopy combined with a convolutional neural network: A promising methodology for geochemical sample identification in Tianwen-1 Mars mission, Spectrochim. Acta B 192, 106417 (2022)

[198] T. Takahashi, B. Thornton, T. Sato, T. Ohki, K. Ohki, and T. Sakka, Temperature based segmentation for spectral data of laser-induced plasmas for quantitative compositional analysis of brass alloys submerged in water, Spectrochim. Acta B 124, 87 (2016)

[199] T. Takahashi, B. Thornton, T. Sato, T. Ohki, K. Ohki, and T. Sakka, Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy, Appl. Opt. 57(20), 5872 (2018)

[200] C. Liu, J. Guo, Y. Tian, C. Zhang, K. Cheng, W. Ye, and R. Zheng, Development and field tests of a deep-sea laser-induced breakdown spectroscopy (LIBS) system for solid sample analysis in seawater, Sensors (Basel) 20(24), 7341 (2020)

[201] A. Li, X. Zhang, X. Liu, Y. He, Y. Shan, H. Sun, W. Yi, and R. Liu, Real time and high-precision online determination of main components in iron ore using spectral refinement algorithm based LIBS, Opt. Express 31(23), 38728 (2023)

[202] P.LuZ.Zhuo W.ZhangJ. TangY.WangH.ZhouX.Huang T.SunJ. Lu, A hybrid feature selection combining wavelet transform for quantitative analysis of heat value of coal using laser-induced breakdown spectroscopy, Appl. Phys. B 127(2), 19 (2021)

[203] Y.YangP. WangC.Ma, Quantitative analysis of Mn element in liquid steel by LIBS based on particle swarm optimized support vector machine, Laser & Optoelectron. Prog. 52(7), 073004–1 (2015) (in Chinese)

[204] Y. Huang, J. Lin, X. Lin, and W. Zheng, Quantitative analysis of Cr in soil based on variable selection coupled with multivariate regression using laser-induced breakdown spectroscopy, J. Anal. At. Spectrom. 36(11), 2553 (2021)

[205] Y. M. Guo, L. B. Guo, Z. Q. Hao, Y. Tang, S. X. Ma, Q. D. Zeng, S. S. Tang, X. Y. Li, Y. F. Lu, and X. Y. Zeng, Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model, J. Anal. At. Spectrom. 33(8), 1330 (2018)

[206] Z.WangL. LiL.WestZ.LiW.Ni, A spectrum standardization approach for laser-induced breakdown spectroscopy measurements, Spectrochim. Acta B 68, 58 (2012)

[207] H. Li, M. Huang, and H. Xu, High accuracy determination of copper in copper concentrate with double genetic algorithm and partial least square in laser-induced breakdown spectroscopy, Opt. Express 28(2), 2142 (2020)

[208] F. Chang, J. Yang, H. Lu, and H. Li, Repeatability enhancing method for one-shot LIBS analysis via spectral intensity correction based on probability distribution, J. Anal. At. Spectrom. 36(8), 1712 (2021)

[209] T. B. Chen, M. H. Liu, L. Huang, H. M. Zhou, C. H. Wang, H. Yang, H. Q. Hu, and M. Y. Yao, Effects of different pretreatment method on laser-induced breakdown spectroscopy measurement of Pb in pork, Chin. J. Anal. Chem. 44(7), 1029 (2016)

[210] H. Q. Hu, X. H. Xu, M. H. Liu, J. P. Tu, L. Huang, L. Huang, M. Y. Yao, T. B. Chen, and P. Yang, Determination of Cu in shell of preserved egg by LIBS coupled with PLS, Spectroscopy & Spectral Anal. 35(12), 3500 (2015)

[211] W. B. Li, L. T. Yao, M. H. Liu, L. Huang, M. Y. Yao, T. B. Chen, X. W. He, P. Yang, H. Q. Hu, and J. H. Nie, Influence of spectral pre-processing on PLS quantitative model of detecting Cu in navel orange by LIBS, Spectroscopy & Spectral Anal. 35(5), 1392 (2015)

[212] H. Yang, L. Huang, M. Liu, T. Chen, C. Wang, H. Hu, and M. Yao, Detection of Pb in navel orange by peel laser induced breakdown spectroscopy coupled with PLS, Chin. J. Anal. Lab. 35(7), 760 (2016)

[213] K. Ke, Y. Lu, and C. c. Yi, Improvement of convex optimization baseline correction in laser-induced breakdown spectral quantitative analysis, Spectroscopy & Spectral Anal. 38(7), 2256 (2018)

[214] C.YiY.Lv H.XiaoK. KeX.Yu, A novel baseline correction method using convex optimization framework in laser-induced breakdown spectroscopy quantitative analysis, Spectrochim. Acta B 138, 72 (2017)

[215] P. Yaroshchyk and J. E. Eberhardt, Automatic correction of continuum background in Laser-induced Breakdown Spectroscopy using a model-free algorithm, Spectrochim. Acta B 99, 138 (2014)

[216] S. Yoon, J. Choi, S. J. Moon, and J. H. Choi, Determination and quantification of heavy metals in sediments through laser-induced breakdown spectroscopy and partial least squares regression, Appl. Sci. (Basel) 11(15), 7154 (2021)

[217] C. Ma and J. Cui, Quantitative analysis of composition in molten steel by LIBS based on improved partial least squares, Laser Technology 40(6), 876 (2016)

[218] Z. Zhu, J. Li, Y. Guo, X. Cheng, Y. Tang, L. Guo, X. Li, Y. Lu, and X. Zeng, Accuracy improvement of boron by molecular emission with a genetic algorithm and partial least squares regression model in laser-induced breakdown spectroscopy, J. Anal. At. Spectrom. 33(2), 205 (2018)

[219] S. Ye, Y. H. Gu, X. F. Du, W. T. Zhang, J. J. Wang, X. Q. Wang, and D. M. Dong, Chemometrics method for real-time measurement of water COD based on laser-induced breakdown spectroscopy, Spectroscopy & Spectral Anal. 37(11), 3585 (2017)

[220] M. Li, H. Fu, Y. Du, X. Huang, T. Zhang, H. Tang, and H. Li, Laser induced breakdown spectroscopy combined with hybrid variable selection for the prediction of the environmental risk Nemerow index of heavy metals in oily sludge, J. Anal. At. Spectrom. 37(5), 1099 (2022)

[221] J. He, C. Pan, Y. Liu, and X. Du, Quantitative analysis of carbon with laser-induced breakdown spectroscopy (LIBS) using genetic algorithm and back propagation neural network models, Appl. Spectrosc. 73(6), 678 (2019)

[222] J. Chen, Q. Li, K. Liu, X. Li, B. Lu, and G. Li, Correction of moisture interference in laser-induced breakdown spectroscopy detection of coal by combining neural networks and random spectral attenuation, J. Anal. At. Spectrom. 37(8), 1658 (2022)

[223] D. Luarte, A. K. Myakalwar, M. Velásquez, J. Álvarez, C. Sandoval, R. Fuentes, J. Yañez, and D. Sbarbaro, Combining prior knowledge with input selection algorithms for quantitative analysis using neural networks in laser induced breakdown spectroscopy, Anal. Methods 13(9), 1181 (2021)

[224] L.N. LiX. F. LiuW.M. XuJ.Y. WangR.Shu, A laser-induced breakdown spectroscopy multi-component quantitative analytical method based on a deep convolutional neural network, Spectrochim. Acta B 169, 105850 (2020)

[225] C. Yan, J. Qi, J. Ma, H. Tang, T. Zhang, and H. Li, Determination of carbon and sulfur content in coal by laser induced breakdown spectroscopy combined with kernel-based extreme learning machine, Chemom. Intell. Lab. Syst. 167, 226 (2017)

Declarations

The authors declare that they have no competing interests and there are no conflicts.

Acknowledgements

This research was financially supported by the project funded by the National Natural Science Foundation of China (Nos. 62305123, 32301694, and 12064029) and the National Key R&D Program of China (No. 2023YFE0204600).

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