Quantitative Analysis of Meteorite Elements Based on the Multidimensional Scaling–Back Propagation Neural Network Algorithm Combined with Raman Mapping-Assisted Micro-Laser Induced Breakdown Spectroscopy

Abstract

Meteorites are an essential reference for human exploration of the universe and its cosmic evolution and an essential research object for searching for extraterrestrial life. Ways to quickly identify and screen suspected meteorite samples have become the foundation and prerequisite for research on high-value meteorite samples. Therefore, this paper proposes a Raman mapping-assisted micro-laser induced breakdown spectroscopy (micro-LIBS) technology for field detection of suspected meteorite material composition without sample pre-processing, with a high detection speed and cost-effectiveness, to realize the detection of element composition and molecular structure. Raman mapping carries out multispectral imaging with high spectral resolution of the region of interest. The fusion of Raman mapping and optical microscopy images can provide mineral categories and spatial distribution characteristics in regions of interest. A quantitative analysis model for Fe, Mg, and Na elements was constructed based on the multidimensional scaling–back propagation neural network (MDS-BPNN) algorithm. The determination coefficient of the model test set was better than 0.997, and the root mean square error was better than 0.65. The content of Fe, Mg, and Na elements in the meteorite was preliminarily evaluated, providing a reference for further analysis of element information in spectral image fusion data. The Raman–LIBS combined technology has significant application potential in rapidly evaluating suspected meteorite samples. Without high-end precision instruments or field research, this technology can provide scientists with significant reference value atomic and molecular spectral information. At the same time, this technology can be extended to other petrology research. We offer a fast, efficient, cost-effective, and reliable analysis scheme for reference.