High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core

Abstract : In the case of environmental samples, the use of a chemometrics-based prediction model is highly challenging because of the difficulty in experimentally creating a well-ranged reference sample set. In this study, we present a methodology using short wave infrared hyperspectral imaging to create a partial least squares regression model on a cored sediment sample. It was applied to a sediment core of the well-known Lake Bourget (Western Alps, France) to develop and validate a model for downcore high resolution LOI550 measurements used as a proxy of the organic matter. In lake and marine sediment, the organic matter content is widely used, for example, to reconstruct carbon flux variations through time. Organic matter analysis through routine analysis methods is time- and material-consuming, as well as not spatially resolved. A new instrument based on hyperspectral imaging allows high spatial and spectral resolutions to be acquired all along a sediment core. In this study, we obtain a model characterized by a 0.95 r prediction, with 0.77 wt% of model uncertainty based on 27 relevant wavelengths. The concentration map shows the variation inside each laminae and flood deposit. LOI550 reference values obtained with the loss on ignition are highly correlated to the inc/coh ratio used as a proxy of the organic matter in X-ray fluorescence with a correlation coefficient of 0.81. This ratio is also correlated with the averaged subsampled hyperspectral prediction with a r of 0.65.
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https://hal-sde.archives-ouvertes.fr/hal-01995416
Contributor : Kévin Jacq <>
Submitted on : Saturday, January 26, 2019 - 6:40:27 PM
Last modification on : Friday, April 5, 2019 - 8:08:48 PM

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Kevin Jacq, Yves Perrette, Bernard Fanget, Pierre Sabatier, Didier Coquin, et al.. High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core. Science of the Total Environment, Elsevier, 2019, 663, pp.236-244. ⟨10.1016/j.scitotenv.2019.01.320⟩. ⟨hal-01995416⟩

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