Identifying laser-induced plasma emission spectra of particles in a gas–solid flow based on the standard deviation of intensity across an emission line

Abstract

A new conditional data processing scheme named the standard deviation (SD) method is presented and evaluated for identifying the spectral data of a gas–solid flow based on laser-induced breakdown spectroscopy. The SD method is compared with two conditional data processing methods called the signal-to-noise ratio (SNR) method and the absolute peak intensity method. First, the performance of the three methods for identifying the spectral data of the same fly ash sample was compared. Then, the stability of the three methods was checked by identifying the spectral data of a set of 12 coal samples under different experimental conditions. The rejection rate, false rejection rate and false acceptance rate under various conditional analysis threshold values were used to evaluate these three different methods. The characteristic peaks at Si 288.16 nm and C 247.86 nm were selected for the analysis of fly ash and coal samples, respectively. The results show that true data and spurious data could be completely and accurately identified by the SD method. Moreover, it has been proved that the threshold values of the absolute peak intensity method and the SNR method fluctuate dramatically while the threshold value of the SD method remains stable under different experimental conditions. Compared with the other two methods, the SD method has better applicability and reliability when faced with variable detection conditions. So it has a greater advantage in identifying spurious laser-induced plasma emission spectra of particles in a gas–solid flow.