LIBS Info: Element Analysis
| Title | Authors | Material | Detector | Spectrometer | Software |
|---|---|---|---|---|---|
| A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods | Prasanthi Inakollu, Thomas Philip, Fang Y. Yueh, Jagdish P. Singh | Aluminium Alloy | Princeton Instruments ICCD Max | Jobin Yvon HR460 | |
| Laser: | Nd:YAG 532.0000nm 20.0000mJ 10.000Hz |
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| Gate Delay: | Noneus | ||||
| Gate Width: | Noneus | ||||
| As the title describes, an Artificial Neural Network (ANN) is used to calculate concentrations of minor metals in Aluminium Alloys. Input vector to the model includes +/-20 channels from the element peaks, laser power, neutral density filter settings etc. Peak intensities were ratioed to the maximum intensity in the spectra (to give an input data set in the range -1 to 1, which is favoured by ANNs). | |||||
| Element | Detection Limit (ppm) | Wavelength (nm) | Other Wavelengths (nm) | Calibration Method | Calibration Range (ppm) | Notes |
|---|
| Element | RMSE (ppm) | Wavelength (nm) | Calibration Method | Notes |
|---|---|---|---|---|
| Cr | 52.3211 ppm | 359.3500 | Artificial Neural Network | Input vector uses +/- pixels around element peak (359.59nm) as well as data on laser power, neutral density filter pos. |
| Mn | 883.2812 ppm | 404.1600 | Artificial Neural Network | [Values estimated from Figure in paper] |
| Mg | 348.8075 ppm | 383.8290 | Artificial Neural Network | [Values measured from chart in paper] |
| Cu | 1879.9668 ppm | 324.7500 | Artificial Neural Network | Values estimated from Figure in Paper |