Simultaneous measurement of carbon emission and gas temperature via laser-induced breakdown spectroscopy coupled with machine learning.

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A method, which can accurately measure carbon emission and gas temperature simultaneously in real-time from a laser-induced breakdown spectrum (LIBS) via machine learning, is proposed in this study. In typical, peak intensity ratios had been used to map species concentrations prior to plasma formation, after removing the broadband continuum of the spectrum; however, the dependence of these peak intensity ratios on the concentration changes with the change in gas density. Therefore, considering the fact that the strength and shape of this broadband continuum is a function of the gas density for a given optical setup, we attempted to collect a spectrum by shortening the time delay after the laser fire, such that the spectrum can contain some of the broadband continuum. Since the analytical quantification of this broadband continuum is not trivial, we employed a machine learning approach to acquire a model that simultaneously predicts the gas temperature and CO2 concentration. The predictive performance of the model trained with spectra that contain the broadband continuum was much better than that without it; the gradient-weighted regression activation mapping (Grad-RAM) analysis revealed that the model utilizes the broadband spectrum for temperature prediction and correction of changes in peak intensity due to temperature changes in the concentration prediction process.