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In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer.
The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements
For the first time, µ-X-ray fluorescence (µ-XRF) mapping combined with fluorine K-edge µ-X-ray absorption near-edge structure (µ-XANES) spectroscopy was applied to depict per- and polyfluoroalkyl substances (PFAS) contamination and inorganic fluoride in samples concentrations down to 100 µg/kg fluoride. To demonstrate the matrix tolerance of the method, several PFAS contaminated soil and sludge samples as well as selected consumer product samples (textiles, food contact paper and permanent baking sheet) were investigated. µ-XRF mapping allows for a unique element-specific visualisation at the sample surface and enables localisation of fluorine containing compounds to a depth of 1 µm. Manually selected fluorine rich spots were subsequently analysed via fluorine K-edge µ-XANES spectroscopy. To support spectral interpretation with respect to inorganic and organic chemical distribution and compound class determination, linear combination (LC) fitting was applied to all recorded µ-XANES spectra. Complementarily, solvent extracts of all samples were target-analysed via LC-MS/MS spectrometry. The detected PFAS sum values range from 20 to 1136 µg/kg dry weight (dw). All environmentally exposed samples revealed higher concentration of PFAS with a chain length >C8 (e.g. 580 µg/kg dw PFOS for Soil1), whereas the consumer product samples showed a more uniform distribution with regard to chain lengths from C4 to C8. Independent from quantified PFAS amounts via target analysis, µ-XRF mapping combined with µ-XANES spectroscopy was successfully applied to detect both point-specific concentration maxima and evenly distributed surface coatings of fluorinated organic contaminants in the corresponding samples.
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 (R2 = 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.