A spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning for improving...

Poor spectral stability seriously hinders the wide application of laser-induced breakdown spectroscopy (LIBS), so how to improve its stability is the focus, hotspot, and difficulty of current research. In this study, to achieve high precision quantitative analysis under complex detection conditions, utilizing the fusion of multi-dimensional plasma information and the integration of physical models and algorithmic models, a spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning (SBESC-PISF) was proposed. In this method, based on the statistical properties of LIBS spectra, the actual obtained spectra were decomposed into three parts: the ideal spectral intensity related only to the element concentration, and the spectral bias and spectral error caused by the fluctuation of complex high-dimensional plasma parameters. Further, the deep learning methods were used to fully excavate all the effective features in the plasma images and spectra to invert the complex high-dimensional plasma parameters according to the physical models. Finally, the estimation models of spectral bias and spectral error were established based on these features, to realize the high-precision correction of spectral intensity. To verify the feasibility of SBESC-PISF, the spectra of aluminum alloy samples obtained under three complex detection conditions were used for analysis. Under the experimental condition of laser energy fluctuation, after correction by SBESC-PISF, R² of the three calibration curves was all increased to 0.999, RMSE and STD of the validation set (RMSE_V, STD_V) were reduced by 55.246 % and 50.167 %, respectively. Under the experimental condition of defocusing amount fluctuation, R² was also all increased to 0.999, RMSE_V and STD_V were decreased by 58.201 % and 51.006 %, respectively. When the laser energy and defocusing amount fluctuate simultaneously, R² was increased to 0.999, 0.996 and 0.988, RMSE_V and STD_V were reduced by 58.776 % and 54.397 %, respectively. These experimental results demonstrate that the spectral fluctuation correction of SBESC-PISF under complex detection conditions is effective and has wide applicability.