Shifted excitation Raman difference spectroscopy for soil component identification and soil carbonate determination in the presence of strong fluorescence interference

被引:3
|
作者
Sowoidnich, Kay [1 ]
Maiwald, Martin [1 ]
Ostermann, Markus [2 ]
Sumpf, Bernd [1 ]
机构
[1] Leibniz Inst Hochstfrequenztech, Ferdinand Braun Inst, Gustav Kirchhoff Str 4, D-12489 Berlin, Germany
[2] BAM Fed Inst Mat Res & Testing, Berlin, Germany
关键词
calcium carbonate; dual-wavelength diode laser; fluorescence rejection; shifted excitation Raman difference spectroscopy; soil minerals; X-RAY-DIFFRACTION; ARCHAEOLOGICAL SITE; FIELD-SCALE; RUTILE TIO2; SPECTRUM; CLASSIFICATION; REJECTION; DIOPSIDE; SAMPLES; ZIRCON;
D O I
10.1002/jrs.6500
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
Detailed knowledge about soil composition is an important prerequisite for many applications, for example precision agriculture. Current standard laboratory methods are complex and time-consuming but could be complemented by non-invasive optical techniques. Its capability to provide a molecular fingerprint of individual soil components makes Raman spectroscopy a very promising candidate. A major challenge is strong fluorescence interference inherent to soil, but this issue can be overcome effectively using shifted excitation Raman difference spectroscopy (SERDS). A customized dual-wavelength diode laser emitting at 785.2 and 784.6 nm was used to investigate 117 soil samples collected from an agricultural field along a distance of 624 m and down to depths of 1 m. To address soil spatial heterogeneity, a raster scan approach comprising 100 measurement spots per sample was applied. Based on the Raman spectroscopic fingerprint extracted from intense fluorescence interference by SERDS, 13 mineral soil constituents were identified, and even closely related molecular species could be discriminated, for example polymorphs of titanium dioxide and calcium carbonate. For the first time, the capability of SERDS is demonstrated to predict the calcium carbonate content as an important soil parameter using partial least squares regression (R-2 = 0.94, root mean square error of cross-validation RMSECV = 2.1%). Our findings demonstrate that SERDS can extract a wealth of spectroscopic information from disturbing backgrounds enabling qualitative and quantitative soil analysis. This highlights the large potential of SERDS for precision agriculture but also in further application areas, for example geology, cultural heritage and planetary exploration.
引用
收藏
页码:1327 / 1340
页数:14
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