The high-accuracy prediction of carbon content in semi-coke by laser-induced breakdown spectroscopy

Xu, Xiaojun; Li, Angze; Wang, Xianshuang; Ding, Chunjie; Qiu, Suling; He, Yafeng; Lu, Tianqi; He, Feng; Zou, B. S.

doi:10.1039/c9ja00443b

Cited by 26 publications

(12 citation statements)

References 30 publications

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“…Several predefined statistical indicators 33 were applied to evaluate the accuracy of the calibration modeling, including the goodness of fit of model ( R 2 ), maximum relative error of the training set (MRETr), average relative error of the training set (ARETr), room mean square error of the training set (RMSETr), maximum relative error of the test set (MRETe), average relative error of the test set (ARETe), and room mean square error of the test set (RMSETe). Furthermore, to achieve better accuracy, these model assessment parameters obtained from the mean results of five repeatable experiments are listed in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Fast explosive performance prediction via small-dose energetic materials based on time-resolved imaging combined with machine learning

Wang

Cao

et al. 2022

J. Mater. Chem. A

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Section: Resultsmentioning

confidence: 99%

Fast explosive performance prediction via small-dose energetic materials based on time-resolved imaging combined with machine learning

Wang

Cao

et al. 2022

J. Mater. Chem. A

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“…RSqCOD stands for coefficient of determination, equivalent to squared correlation coefficient (R Squared, R 2 ). According to the indicator, Langmuir-Freundlich models had the highest accuracy [ 34 ]. Its R 2 of Soil 1 , Soil 2 and Soil 3 was 0.970, 0.994 and 0.986, respectively (three significant digits reserved).…”

Section: Resultsmentioning

confidence: 99%

Rapid Detection of Available Nitrogen in Soil by Surface-Enhanced Raman Spectroscopy

Qin

Zhang

Ren

et al. 2022

IJMS

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Soil-available nitrogen is the main nitrogen source that plants can directly absorb for assimilation. It is of great significance to detect the concentration of soil-available nitrogen in a simple, rapid and reliable method, which is beneficial to guiding agricultural production activities. This study confirmed that Raman spectroscopy is one such approach, especially after surface enhancement; its spectral response is more sensitive. Here, we collected three types of soils (chernozem, loess and laterite) and purchased two kinds of nitrogen fertilizers (ammonium sulfate and sodium nitrate) to determine ammonium nitrogen (NH4-N) and nitrate nitrogen (NO3-N) in the soil. The spectral data were acquired using a portable Raman spectrometer. Unique Raman characteristic peaks of NH4-N and NO3-N in different soils were found at 978 cm−1 and 1044 cm−1, respectively. Meanwhile, it was found that the enhancement of the Raman spectra by silver nanoparticles (AgNPs) was greater than that of gold nanoparticles (AuNPs). Combined with soil characteristics and nitrogen concentrations, Raman peak data were analyzed by multiple linear regression. The coefficient of determination for the validation (R2 p) of multiple linear regression prediction models for NH4-N and NO3-N were 0.976 and 0.937, respectively, which deeply interpreted the quantitative relationship among related physical quantities. Furthermore, all spectral data in the range of 400–2000 cm−1 were used to establish the partial least squares (PLS), back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) models for quantification. After cross-validation and comparative analysis, the results showed that LSSVM optimized by particle swarm methodology had the highest accuracy and stability from an overall perspective. For all datasets of particle swarm optimization LSSVM (PSO-LSSVM), the R2 p was above 0.99, the root mean square errors of prediction (RMSEP) were below 0.15, and the relative prediction deviation (RPD) was above 10. The ultra-portable Raman spectrometer, in combination with scatter-enhanced materials and machine learning algorithms, could be a promising solution for high-efficiency and real-time field detection of soil-available nitrogen.

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“…The spectral background is corrected by a simple window-moving method, which segments and fits the spectrum according to the different weights of each pixel, as mentioned in our previous work. 19…”

Section: Resultsmentioning

confidence: 99%

High-accuracy quantitative analysis of coal by small sample modelling algorithm based laser induced breakdown spectroscopy

Liu

Zhang

Wang

et al. 2022

J. Anal. At. Spectrom.

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The high-accuracy prediction of carbon content in semi-coke by laser-induced breakdown spectroscopy

Abstract: LIBS technology is used in the realization process of semi-coke detection.

Cited by 26 publications

References 30 publications

Fast explosive performance prediction via small-dose energetic materials based on time-resolved imaging combined with machine learning

Fast explosive performance prediction via small-dose energetic materials based on time-resolved imaging combined with machine learning

Rapid Detection of Available Nitrogen in Soil by Surface-Enhanced Raman Spectroscopy

High-accuracy quantitative analysis of coal by small sample modelling algorithm based laser induced breakdown spectroscopy

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