Correction of moisture interference in laser-induced breakdown spectroscopy detection of coal by combining neural networks and random spectral attenuation

Abstract

Increased humidity causes terrible accuracy in laser-induced breakdown spectroscopy analysis of coal. The moisture interference was reduced using artificial neural networks (ANN) combined with random spectral attenuation in this study.

Application and Research Progress of Laser-Induced Breakdown Spectroscopy in Agricultural Product Inspection

“…Both matrix effects and self-absorption phenomena can lead to underestimated analytical results. To avoid these issues, current practices often involve error correction through methods such as standard calibration curves, partial least-squares (PLS), discriminant analysis (DA), and principal component analysis (PCA) within the realm of chemometrics. − …”

“…Researchers have integrated LIBS technology with statistical algorithms for spectral signal analysis. Techniques such as Principal Component Analysis (PCA), Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Partial Least Squares (PLS), Linear Discriminant Analysis (LDA), and Decision Trees (DT), − have been employed in qualitative and quantitative analysis procedures. These algorithms facilitate the extraction of relevant information, dimensionality reduction, and accurate elemental analysis in samples.…”

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

“…This is known as bandpass mode. To demonstrate the approach the 109 Ag : 107 Ag isotope ratio of Ag NPs was measured in suspension in high purity water and increasing amounts of Zr, which could form 91 Zr 16 O, using O 2 as the collision gas. For all of these measurements the relative error from the true value was stated to be less than 3%.…”

“…The last paper in this section was by Chen et al who looked into the correction of moisture interference during the LIBS analysis of coal by combining neural networks and random spectral attenuation . 91 In this study regression models of volatile content were established based on low humidity samples to then analyse samples with higher moisture content. The random spectral attenuation method was used to reduce moisture content interference.…”

Determination of ash content, volatile matter, and calorific value in coal by OLS combined with laser-induced breakdown spectroscopy based on PC recombination

“…35 Ji Chen et al proposed a random spectral attenuation correction method combined with a neural network to eliminate the interference of water in the accuracy of LIBS analysis, and the volatile matter content root mean square error (RMSE) was reduced from 1.8709% to 0.4786%. 36 Dong Xiao et al proposed a fast approximate coal analysis method combined with a convolutional neural network (CNN) and a two-layer hidden-layer extreme learning machine (DR_TELM). The comparative experiments were based on improved models of ELM, TELM, and random forest (RF) combined with principal component analysis (PCA).…”