Multianalyzer Spectroscopic Data Fusion for Soil Characterization

Applied Sciences

J. Bruce Harrison¹  Russell S. Harmon²  John R. Plumer²  Karen A. Harmon²  Richard R. Hark²  Chandra S. Throckmorton³  Jan M. H. Hendrickx⁴  Jay L. Clausen⁵ 

[1] Department of Earth and Environmental Sciences, New Mexico Institute of Technology, 801 Leroy Place, Socorro, NM 87801, USA;JRPlumer Associates, LLC, 36 Country Club Road, Suite 926, Gilford, NH 03249, USA;Signal Analysis Solutions, LLC, Bahama, NC 27503, USA;SoilHydrology Associates, LLC, 1113 Valley View Drive SW, Los Lunas, NM 87031, USA;US Army Engineer Cold Regions Research and Engineering Laboratory, 72 Lyme Road, Hanover, NH 03755, USA;

关键词: laser-induced breakdown spectroscopy;    LIBS;    Raman spectroscopy;    RS;    X-ray fluorescence spectroscopy;    XRFS;   

DOI  :  10.3390/app10238723

来源: DOAJ

【 摘 要 】

The ability to rapidly conduct in-situ chemical analysis of multiple samples of soil and other geological materials in the field offers many advantages over a traditional approach that involves collecting samples for subsequent examination in the laboratory. This study explores the application of complementary spectroscopic analyzers and a data fusion methodology for the classification/discrimination of >100 soil samples from sites across the United States. Commercially available, handheld analyzers for X-ray fluorescence spectroscopy (XRFS), Raman spectroscopy (RS), and laser-induced breakdown spectroscopy (LIBS) were used to collect data both in the laboratory and in the field. Following a common data pre-processing protocol, principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to build classification models. The features generated by PLSDA were then used in a hierarchical classification approach to assess the relative advantage of information fusion, which increased classification accuracy over any of the individual sensors from 80-91% to 94% and 64-93% to 98% for the two largest sample suites. The results show that additional testing with data sets for which classification with individual analyzers is modest might provide greater insight into the limits of data fusion for improving classification accuracy.

【 授权许可】

Unknown