ArticleApplied spectroscopy2022
Determination of Soil Constituents Using Shifted Excitation Raman Difference Spectroscopy.
Article in Applied spectroscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 35 citations in OpenAlex.
- Quantification of Soil Organic Carbon by Shifted-Excitation Raman Difference Spectroscopy with Machine Learning and Common Mode Rejection.Analytical chemistry · 2026Article
- Calibrated Uncertainty Estimation for Soil Organic Carbon from Raman Spectra.Analytical chemistry · 2026Article
- Advances in surface-enhanced Raman scattering applications for precision agriculture: monitoring plant health and crop quality.RSC advances · 2025Review
- Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic-Mineral Mixtures.Analytical chemistry · 2023Article
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
Soil analysis to estimate soil fertility parameters is of great importance for precision agriculture but nowadays it still relies mainly on complex and time-consuming laboratory methods. Optical measurement techniques can provide a suitable alternative. Raman spectroscopy is of particular interest due to its ability to provide a molecular fingerprint of individual soil components. To overcome the major issue of strong fluorescence interference inherent to soil, we applied shifted excitation Raman difference spectroscopy (SERDS) using an in-house-developed dual-wavelength diode laser emitting at 785.2 and 784.6 nm. To account for the intrinsic heterogeneity of soil components at the millimeter scale, a raster scan with 100 individual measurement positions has been applied. Characteristic Raman signals of inorganic (quartz, feldspar, anatase, and calcite) and organic (amorphous carbon) constituents within the soil could be recovered from intense background interference. For the first time, the molecule-specific information derived by SERDS combined with partial least squares regression was demonstrated for the prediction of the soil organic matter content (coefficient of determination R
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