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Updated: Apr 29, 2026

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions
Published on: January 10, 2019
3D front face solid-phase fluorescence spectroscopy combined with Independent Components Analysis to characterize
Faten Ammari1, Ryad Bendoula2, Delphine Jouan-Rimbaud Bouveresse3
1IRSTEA UMR ITAP, 361 rue J.F. Breton, B.P. 5095, 34196 Montpellier Cedex 5, France; INRA, UMR1145 Ingénierie Procédés Aliments, F-75005 Paris, France; AgroParisTech, UMR1145 Ingénierie Procédés Aliments, F-75005 Paris, France; CNAM, UMR1145 Ingénierie Procédés Aliments, F-75005 Paris, France.
Abstract:
Soil organic matter (SOM) is a very complex and heterogeneous system which complicates its characterization. In fact, the methods classically used to characterize SOM are time- and solvent-consuming and insufficiently informative. The aim of this work is to study the potential of 3D solid-phase front face fluorescence (3D-SPFFF) spectroscopy to quickly provide a relevant and objective characterization of SOM as an alternative to the existing methods. Different soil models were prepared to simulate natural soil composition and were analyzed by 3D front-face fluorescence spectroscopy without prior preparation. The spectra were then treated using Independent Components Analysis. In this way, different organic molecules such as cellulose, proteins and amino acids used in the soil models were identified. The results of this study clearly indicate that 3D-SPFFF spectroscopy could be an easy, reliable and practical analytical method that could be an alternative to the classical methods in order to study SOM. The use of solid samples revealed some interactions that may occur in natural soils (self-quenching in the case of cellulose) and gave more accurate fluorescence signals for different components of the analyzed soil models. Independent Components Analysis (ICA) has demonstrated its power to extract the most informative signals and thus facilitate the interpretation of the complex 3D fluorescence data.
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