In this work, calibration-free laser-induced breakdown spectroscopy (CF-LIBS) is used to analyze a certified stainless steel sample. Due to self-absorption of the spectral lines from the major element Fe and the sparse lines of trace elements, it is usually not easy to construct the Boltzmann plots of all species. A standard reference line method is proposed here to solve this difficulty under the assumption of local thermodynamic equilibrium so that the same temperature value for all elements present into the plasma can be considered. Based on the concentration and rich spectral lines of Fe, the Stark broadening of Fe(I) 381.584 nm and Saha-Boltzmann plots of this element are used to calculate the electron density and the plasma temperature, respectively. In order to determine the plasma temperature accurately, which is seriously affected by self-absorption, a pre-selection procedure for eliminating those spectral lines with strong self-absorption is employed. Then, one spectral line of each element is selected to calculate its corresponding concentration. The results from the standard reference lines with and without self-absorption of Fe are compared. This method allows us to measure trace element content and effectively avoid the adverse effects due to self-absorption.
Laser-induced breakdown spectroscopy (LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve (CC) and support vector regression (SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error (ARE), relative standard deviation (RSD) and root mean squared error of prediction (RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%, RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.
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