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

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

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 be- cause 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 recon- struct 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 char- acterized by a 0.95 r prediction, with 0.77 wt% ofmodel uncertainty based on 27 relevant wavelengths. The concen- tration 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 pre- diction with a r of 0.65.

Article

https://doi.org/10.1016/j.scitotenv.2019.01.320

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