Repeatability enhancing method for one-shot LIBS analysis via spectral intensity correction based on probability distribution. Issue 8 (7th July 2021)

• Record Type: Journal Article
• Authors: Chang, Fu; Yang, Jianhong; Lu, Huili; Li, Haixia
• Is Part Of: Journal of analytical atomic spectrometry. Volume 36:Issue 8(2021)
• Page Start: 1712
• Page End: 1723
• Publication Date: 2021-07-07
• Subjects: Atomic spectra -- Periodicals; Atomic absorption spectroscopy -- Periodicals; 543.0858
• DOI: 10.1039/d1ja00040c
• Languages: English
• ISSNs: 0267-9477

Abstract

Abstract : A correction model is established between the spectrum of one-shot measurement and the averaged spectrum of multiple measurements based on spectral intensity probability distribution. Only one measured spectrum is needed during the test stage. Abstract : It is important to improve the repeatability of quantitative analysis of laser-induced breakdown spectroscopy (LIBS) in a one-shot measurement, where averaging is not valid because multiple measurements are not available or allowable. Therefore, a new spectral intensity correction method based on probability distribution is proposed, in which a weighted correction model is built by training samples to obtain the relationship between the averaged spectral intensity of multiple measurements and five typical spectral intensities based on probability distribution. Before being sent to the elemental analysis model, each one-shot spectrum of testing samples is corrected via the weighted correction model. Experiments on the Cr elemental analysis of 7 standard samples and one gradient material sample were conducted. The results show that the relative standard deviation (RSD) of 40 spectra is reduced from 19.10% to 3.95% after correction based on normal distribution, while RSD after correction based on Poisson distribution is reduced to 11.91%. The average relative error (MRE) of the LIBS quantitative analysis results is reduced from 8.68% to 3.32%, while MRE based on Poisson distribution is reduced to 5.98%. The proposed method provides a simple and feasible method to enhance the repeatability of one-shot LIBS quantitative analysis, which will be helpful for expanding the application of LIBS in cases like the elemental analysis of anisotropic materials, archaeological analysis, or the online analysis of samples with rapidly varying compositions.