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

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/J.SCITOTENV.2019.01.320.

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Authors

Kévin Jacq , Yves Perrette, Fanget Bernard, Pierre Sabatier , Coquin Didier, Martinez-Lamas Ruth, Debret Maxime, Arnaud Fabien

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.

DOI

https://doi.org/10.31223/osf.io/s35kd

Subjects

Analytical Chemistry, Chemistry, Earth Sciences, Environmental Chemistry, Environmental Monitoring, Environmental Sciences, Optics, Physical Sciences and Mathematics, Physics, Sedimentology, Statistical Models, Statistics and Probability

Keywords

Hyperspectral Imaging, organic matter, Chemometrics, High resolution analysis, Near-infrared spectroscopy

Dates

Published: 2019-02-21 10:11

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License

GNU Lesser General Public License (LGPL) 2.1

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