Quantifying Soil Inorganic Matter: Integrating Shifted Excitation Raman Difference Spectroscopy (SERDS) with Machine Learning for Enhanced Analysis of Carbonates

Soil carbonates are critical players in the global carbon cycle and have a profound influence on soil health and agricultural productivity. Their quantification is also central to carbon sequestration efforts, where accurate measurement of soil carbonates can inform strategies for reducing atmospheric CO₂. However, conventional methods of carbonate analysis in soil---while effective---are often slow, costly, and labor-intensive ⁽¹⁾.

In this study, we introduce Shifted Excitation Raman Difference Spectroscopy (SERDS) as a rapid, non-destructive alternative, further enhanced by advanced preprocessing techniques and machine learning algorithms. Specifically, we employ Asymmetric Least Squares (ALS) for background correction, Standard Normal Variate (SNV) for normalization, and Savitzky–Golay filtering for smoothing. Unlike conventional Raman spectroscopy, SERDS effectively eliminates background fluorescence and reduces overlapping peaks, resulting in clearer spectral signatures ⁽²⁾. 

We employed Partial Least Squares Regression (PLSR) and eXtreme Gradient Boosting (XGBoost) to predict the inorganic carbon content from the carbonate vibrational modes in conventional Raman and SERDS spectra, benchmarked against total inorganic carbon (TIC) measurements from coulometric titration. Our results show that switching to dual-laser SERDS substantially boosted model performance. For PLSR, the coefficient of determination (R²) improved from 0.8 to 0.88 (an increase of about 10.5%), and the root-mean-square error (RMSE) declined from 0.29 to 0.22 (26% decrease). The XGBoost model exhibited an even greater increase, with R² increasing from 0.63 to 0.93 (approximately 49% improvement) and RMSE dropping from 0.39 to 0.16 (59% reduction).

Figure 1: Left: All SERDS data of soil samples showing the main carbonate peak; Right: XGBoost model prediction of soil inorganic carbon using SERDS data.

These findings underscore the potential of SERDS to replace conventional methods for carbonate quantification, offering reduced cost, faster analysis, and essentially no sample preparation. Furthermore, by providing highly accurate carbonate measurements, this methodology can be pivotal for carbon sequestration assessments and large-scale soil management practices, helping to advance both environmental sustainability and agricultural productivity.

References:

1) Barra I, Haefele SM, Sakrabani R, Kebede F. Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review. TrAC Trends in Analytical Chemistry. 2021 Feb 1;135:116166.

2) Orlando A, Franceschini F, Muscas C, Pidkova S, Bartoli M, Rovere M, Tagliaferro A. A comprehensive review on Raman spectroscopy applications. Chemosensors. 2021 Sep 13;9(9):262.