Analytical-performance improvement of laser-induced breakdown spectroscopy for steel using multi-spectral-line calibration with an artificial neural network

2015 ◽  
Vol 30 (7) ◽  
pp. 1623-1628 ◽  
Author(s):  
Kuohu Li ◽  
Lianbo Guo ◽  
Changmao Li ◽  
Xiangyou Li ◽  
Meng Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spectral Line ◽  
Performance Improvement ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Steel Analysis ◽  
Artificial Neural ◽  
Calibration Approach

A multi-spectral-line calibration approach based on an artificial neural network is presented for steel analysis in LIBS.

scholarly journals Correction: Analytical-performance improvement of laser-induced breakdown spectroscopy for steel using multi-spectral-line calibration with an artificial neural network

2020 ◽  
Vol 35 (7) ◽  
pp. 1497-1497
Author(s):  
Kuohu Li ◽  
Lianbo Guo ◽  
Changmao Li ◽  
Xiangyou Li ◽  
Meng Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spectral Line ◽  
Performance Improvement ◽  
Analytical Performance ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Artificial Neural

Correction for ‘Analytical-performance improvement of laser-induced breakdown spectroscopy for steel using multi-spectral-line calibration with an artificial neural network’ by Kuohu Li et al., J. Anal. At. Spectrom., 2015, 30, 1623–1628, DOI: 10.1039/C5JA00089K.

Energetic materials identification by laser-induced breakdown spectroscopy combined with artificial neural network

2017 ◽  
Vol 56 (12) ◽  
pp. 3372 ◽  
Author(s):  
Amir Hossein Farhadian ◽  
Masoud Kavosh Tehrani ◽  
Mohammad Hossein Keshavarz ◽  
Seyyed Mohammad Reza Darbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energetic Materials ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Artificial Neural

scholarly journals Quantitative Analysis of Major Metals in Agricultural Biochar Using Laser-Induced Breakdown Spectroscopy with an Adaboost Artificial Neural Network Algorithm

Molecules ◽  
2019 ◽  
Vol 24 (20) ◽  
pp. 3753 ◽  
Author(s):  
Hongwei Duan ◽  
Lujia Han ◽  
Guangqun Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Univariate Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Relative Standard ◽  
Significant Difference ◽  
Laser Induced Breakdown ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network

To promote the green development of agriculture by returning biochar to farmland, it is of great significance to simultaneously detect heavy and nutritional metals in agricultural biochar. This work aimed first to apply laser-induced breakdown spectroscopy (LIBS) for the determination of heavy (Pb, Cr) and nutritional (K, Na, Ca, Mg, Cu, and Zn) metals in agricultural biochar. Each batch of collected biochar was prepared to a standardized sample using the separating and milling method. Two types of univariate analysis model were developed using peak intensity and integration area of the sensitive emission lines, but the performance did not satisfy the requirements of practical application because of the poor correlations between the measured values and predicted values, as well as large relative standard deviation of the prediction (RSDP) values. An ensemble learning algorithm, adaboost backpropagation artificial neural network (BP-Adaboost), was then used to develop the multivariate analysis models, which had a more robust performance than traditional univariate analysis, partial least squares regression (PLSR), and backpropagation artificial neural network (BP-ANN). The optimized RSDP values for K, Ca, Mg, and Cu were less than 10%, while the RSDP values for Pb, Cr, Zn, and Na were in the range of 10–20%. Moreover, the pairwise t-test of its prediction set showed that there was no significant difference between the measurements of LIBS and ICP-MS. The promising results indicate that rapid and simultaneous detection of major heavy and nutritional metals in agricultural biochar can be achieved using LIBS and reasonable chemometric algorithms.

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