LIBS Info: Element Analysis
| Title | Authors | Material | Detector | Spectrometer | Software |
|---|---|---|---|---|---|
| Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS Spectra | J. Yu, Nicole Delepine-Gilon, Zengqi Yue, Yuqing Zhang, Hua Li, Tianlong Zhang, Yishuai Niu, Liang Gao, Ye Tian, Chen Sun | Soil | ICCD | Mechelle 5000 | |
| Laser: | Nd:YAG 1064.0000nm 60.0000mJ NoneHz |
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| Gate Delay: | 1.000us | ||||
| Gate Width: | 2.000us | ||||
| A very thorough paper where the Authors utilise both Standard Univariate analysis, and a Neural Network [Back Propagation Neural Network BPNN] to analyse Ag spiked soil samples. Significant improvement is obtained using the BPNN. | |||||
| Element | Detection Limit (ppm) | Wavelength (nm) | Other Wavelengths (nm) | Calibration Method | Calibration Range (ppm) | Notes |
|---|---|---|---|---|---|---|
| Ag | 4.9620 (Calibration Curve Slope) | -10.0000 | N/A | Univariate | 20.0000-800.0000 | Authors use an Artificial Neural Network to improve the performance of calibration using Ag spiked soil samples |
| Ag | 0.7100 (Calibration Curve Slope) | -10.0000 | N/A | Univariate | 20.0000-800.0000 | Method utilises an Artificial Neural Network to improve performance on a single (Ag spiked) soil sample |
| Ag | 23.8300 (Calibration Curve Slope) | 328.1000 | N/A | Univariate | 20.0000-800.0000 | LOD for calibration curve based on combination of Ag spiked soil samples - this provides worse performance than the calibration curves based on a single (spiked) soil sample. |
| Ag | 18.4700 (Calibration Curve Slope) | 328.1000 | N/A | Univariate | 20.0000-800.0000 | Best result for a calibration curve utilising additions to the one soil sample. |
| Element | RMSE (ppm) | Wavelength (nm) | Calibration Method | Notes |
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