LIBS Info: Element Analysis
Title | Authors | Material | Detector | Spectrometer | Software |
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Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS Spectra | Jin Yu, Nicole Delepine-Gilon, Zengqi Yue, Yuqing Zhang, Hua Li, Tianlong Zhang, Yishuai Niu, Liang Gao, Ye Tian, Chen Sun | Soil | ICCD | Mechelle 5000 | SciKit-Learn |
Laser: | Nd:YAG 1064.0000nm 60.0000mJ NoneHz |
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Gate Delay: | 1.000us | ||||
Gate Width: | 2.000us | ||||
In this paper, the authors use machine learning to improve results for analysis of Ag in Soils - the base material are NIST reference soils, which are then spiked with Ag standard reference solutions before compressing into pellets for analysis. The paper undertakes univariate analysis using the Ag I 328.1nm peak, forming models specific to samples based on a Reference soil and a generalised models across all soil types. The univariate results are compared with output of a Back-propogation Neural Network (BPNN). Input data to the BPNN consists of 150 pixels most correlated to the analyte concentrations. The fully trained BPNN shows a significant improvement in the LOD for the samples. |
Element | Detection Limit (ppm) | Wavelength (nm) | Other Wavelengths (nm) | Calibration Method | Calibration Range (ppm) | Notes |
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Ag | 4.9600 (None) | -10.0000 | N/A | BPNN | 20.0000-840.0000 | Generalised Back Propagation Neural Network model for generalised soil. |
Ag | 1.4200 (None) | -10.0000 | N/A | BPNN | 20.0000-840.0000 | Soil specific Back Propagation Neural Network analysis of soil pellets |
Ag | 23.8300 (Calibration Curve Slope) | 328.1000 | N/A | Univariate | 20.0000-840.0000 | Generalised Univariate calibration over 4 different soil types. |
Ag | 18.4700 (Calibration Curve Slope) | 328.1000 | N/A | Univariate | 20.0000-840.0000 | Soil type specific calibration using NIST Soil as background, spiked with Ag solutions. |
Element | RMSE (ppm) | Wavelength (nm) | Calibration Method | Notes |
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