Quantitative Analysis of Multi-Elements in a Micron-Sized Single Particle Based on Laser-Induced Breakdown...

With rapid, energy-intensive, and coal-fueled economic growth, global air quality is deteriorating, and particulate matter pollution has emerged as one of the major public health problems worldwide. It is extremely urgent to achieve carbon emission reduction and air pollution prevention and control, aiming at the common problem of weak and unstable signals of characteristic elements in the application of laser-induced breakdown spectroscopy (LIBS) technology for trace element detection. In this study, the influence of the optical fiber collimation signal enhancement method on the LIBS signal was explored. Then, the influence of the LIBS signal enhancement system based on an optical fiber collimated system on LIBS spectral signal intensity and signal-to-noise ratio (SNR) was compared, and the influences of different spectral preprocessing methods and different variable selection methods on the prediction performance of the random forest (RF) calibration model were investigated. Finally, the Savitzky-Golay convolution derivative (SG)-variable importance projection (VIP)-mutual information (MI)-RF (Zn), first-order derivative (D1st)-variable importance measurement (VIM)-successive projections algorithm (SPA)-RF (Cu), and D1st-VIM-MI-RF (Ni) optimal models were constructed according to the optimal spectral preprocessing method and the optimal hybrid variable selection method. The prediction performances of their optimal RF model after SG-VIP-MI (Zn), D1st-VIM-SPA (Cu), and D1st-VIM-MI (Ni) spectral preprocessing and hybrid variable selection method are presented as follows: Zn (R_p² = 0.9860; MREP = 0.0590), Cu (R_p² = 0.9817; MREP = 0.0405), and Ni (R_p² = 0.9856; MREP = 0.0875). The above results demonstrate that the RF calibration model based on the optical fiber collimated LIBS signal enhancement method, the optimal spectral preprocessing method, and variable selection strategy overcome the key problems of low SNR and low quantitative accuracy in single particle detection. It is expected to provide a theoretical basis and technical support for in situ online rapid monitoring of particulate matter.