Spectral fingerprinting of soil organic matter composition

Large scale environmental monitoring schemes would benefit from accurate information on the composition of soil organic matter (SOM), but so far routine procedures for describing SOM composition remain a chimera. Here, we present the initial assessment of a two step strategy for expeditious determination of SOM composition that involves: (i) building infrared fingerprints from near and mid infrared spectroscopies, two rapid and cheap yet reliable technologies; and (ii) calibrating such infrared fingerprints with multivariate chemometrics from a molecular mixing model based on the more expensive and time consuming 13C nuclear magnetic resonance technique, which discriminates five biochemical components: carbohydrate, protein, lignin, lipid and black carbon. We show fair to excellent predictive ability of the calibrated infrared fingerprints for four out of these five biochemical components, with cross-validated ratios of performance to inter-quartile distance from 3.2 to 8.3, on a small set of 23 soil samples with a wide range of organic carbon content (12–500 g/kg). Multivariate calibration models were highly selective (<2% of infrared data were used for all models). However, the specificity to one particular biochemical component of the infrared wavebands automatically selected by each model was relatively low, except for lipid. Achieving direct predictions of SOM composition on unknown soil samples with infrared spectroscopy alone will require further independent validation and a larger number of samples. Overall, the implementation of our strategy at a broader scale, based on available 13C nuclear magnetic resonance soil libraries, could provide a cost effective solution for the routine assessment of SOM composition.