Automatic focusing system for laser-induced breakdown spectroscopy based on the combination of images and spectra

In Laser-Induced Breakdown Spectroscopy (LIBS) detection, the different surface morphologies of the sample can change the focusing distance between the focusing lens and the sample surface, resulting in instability of LIBS spectra and low signal-to-noise ratio, which affects the accuracy and reliability of detection. The LIBS auto-focusing system is designed to search the optimal focusing distance and thus obtain stable and reliable spectra. By adding a visible light source, the combination of focal spot image of the visible light on the sample surface and LIBS spectra are applied to search the focus position. The focusing process is divided into coarse-step and fine-step focusing. The four-neighborhood weighted gradient operator is adopted as the evaluation index of the clarity of the visible spot image. The hill-climbing method is used to focus by coarse-step. The Self-Organizing feature Mapping (SOM) artificial neural network is established by training and learning the fused data of visible spot images and LIBS spectra, which is to achieve fine step focusing. The experimental results show that, compared with the other two focusing methods, the proposed method has the best spectral performance. Its LIBS spectral intensity is the highest and the Relative Standard Deviation (RSD) of characteristic spectral line is the lowest, average value reduced from 14.71% to 6.31%. The Signal-to-Noise Ratios (SNRs) of characteristic spectral lines of the main elements, Fe I 385.9955 nm and Cr I 513.8852 nm, are increased from 19.18 and 14.28 to 24.71 and 23.62, respectively. The spectral intensity, stability, and sensitivity are improved effectively.