[1N08-11] Analytical Technique 2

3:00 PM - 3:15 PM JST(6:00 AM - 6:15 AM UTC)

[1N09] Analysis of Laser-Induced Breakdown Spectroscopy of Three-Component Samples Using Linear Regression Machine Learning

*Katsuaki Akaoka¹, Takahiro Karino¹, Hironori Ohba¹, Ikuo Wakaida¹ (1. JAEA)

Keywords:

fuel debris,machine learning,LIBS,laser,quantitative analysis,uranium,zirconium,iron

We have been evaluating a quantitative analysis method by machine learning as an analytical method for laser-induced breakdown spectroscopy (LIBS) in the in-situ analysis of fuel debris and other materials generated by the Fukushima Daiichi Nuclear Power Plant accident. In previous presentations, we have evaluated the effects of interpolation/extrapolation, noise, spectral wavelength shift, and spectral width on the predicted straight line using spectra of U/Pu mixture samples obtained by LIBS. This presentation describes the results of quantitative analysis by linear regression machine learning using spectra of U/Zr/Fe mixtures.