A Novel and Effective Multivariate Method for Compositional Analysis using Laser Induced Breakdown Spectroscopy

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Wang, W.^1 ; Ayhan, B.^2 ; Kwan, C.^2 ; Qi, H.^1 ; Vance, S.^3

University of Tennessee, Knoxville, TN, United States^1

Signal Processing Inc., Rockville, MD, United States^2

Jet Propulsion Laboratory, Pasadena, CA, United States^3

关键词: Compositional analysis;    Elemental compositions;    Elemental concentrations;    Laserinduced breakdown spectroscopy (LIBS);    Multi variate analysis;    Multivariate analysis methods;    Multivariate methods;    Partial least square (PLS);   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012208/pdf
DOI  :  10.1088/1755-1315/17/1/012208

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Compositional analysis is important to interrogate spectral samples for direct analysis of materials in agriculture, environment and archaeology, etc. In this paper, multi-variate analysis (MVA) techniques are coupled with laser induced breakdown spectroscopy (LIBS) to estimate quantitative elemental compositions and determine the type of the sample. In particular, we present a new multivariate analysis method for composition analysis, referred to as "spectral unmixing". The LIBS spectrum of a testing sample is considered as a linear mixture with more than one constituent signatures that correspond to various chemical elements. The signature library is derived from regression analysis using training samples or is manually set up with the information from an elemental LIBS spectral database. A calibration step is used to make all the signatures in library to be homogeneous with the testing sample so as to avoid inhomogeneous signatures that might be caused by different sampling conditions. To demonstrate the feasibility of the proposed method, we compare it with the traditional partial least squares (PLS) method and the univariate method using a standard soil data set with elemental concentration measured a priori. The experimental results show that the proposed method holds great potential for reliable and effective elemental concentration estimation.