Deep learning regression for quantitative LIBS analysis

Eynde, Simon Van den; Díaz‐Romero, Dillam Jossue; Zaplana, Isiah; Peeters, Jef

doi:10.1016/j.sab.2023.106634

Cited by 10 publications

(1 citation statement)

References 57 publications

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“…Choi et al [84] used LIBS to monitor laser cleaning of painted stainless steel and proposed a deep learning method based on CNN for quantitatively analyzing whether the base elemental material appeared in LIBS spectra and achieved satisfied results in terms of real time and accuracy. Eynde et al [85] used the CNN-architected GHOSTNET network for trace elements such as Fe, Cu, Mn, Mg, and Zn, which are found in very low levels in fertilizers, and compared it with the BPNN method. The results show that the average Recent Advances in Machine Learning Methodologies for LIBS Quantitative Analysis DOI: http://dx.doi.org/10.5772/intechopen.1004414 RMSE of the deep learning method does not exceed up to 0.01%.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

recent advances in machine learning methodologies for LIBS quantitative analysis

Liu,

Han,

Yang

et al. 2024

Pulsed Laser Processing of Materials

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The mapping between LIBS spectral data to the quantitative results can become highly complicated and nonlinear due to experimental conditions, sample surface state, matrix effect, self-absorption, etc. Therefore, the accurate quantitative analysis is the longstanding dream of the LIBS community. The advantages of machine learning in dealing with high-dimensional and nonlinear problems have made it a cutting-edge hot topic in quantitative LIBS in recent years. This chapter introduces the current bottlenecks in quantitative LIBS, sorts out the data processing methods, and reviews the research status and progress of conventional machine learning methods such as PLS, SVM, LSSVM, Lasso, and artificial neural network-based methods. By comparing the results of different methods, the perspective of future developments on learning-based methods is discussed. This chapter aims to review the applications of the combination of quantitative LIBS and machine learning methods and demonstrate the performance of different machine learning methods based on experimental results.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%