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Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach
Geochemical variations of sedimentary records contain vital information for understanding paleoenvironment and paleoclimate. However, to obtain quantitative data in the laboratory is laborious, which ultimately restricts the temporal and spatial resolution. Quantification based on fast-acquisition a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718834/ https://www.ncbi.nlm.nih.gov/pubmed/36460746 http://dx.doi.org/10.1038/s41598-022-25377-x |
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author | Lee, An-Sheng Chao, Weng-Si Liou, Sofia Ya Hsuan Tiedemann, Ralf Zolitschka, Bernd Lembke-Jene, Lester |
author_facet | Lee, An-Sheng Chao, Weng-Si Liou, Sofia Ya Hsuan Tiedemann, Ralf Zolitschka, Bernd Lembke-Jene, Lester |
author_sort | Lee, An-Sheng |
collection | PubMed |
description | Geochemical variations of sedimentary records contain vital information for understanding paleoenvironment and paleoclimate. However, to obtain quantitative data in the laboratory is laborious, which ultimately restricts the temporal and spatial resolution. Quantification based on fast-acquisition and high-resolution provides a potential solution but is restricted to qualitative X-ray fluorescence (XRF) core scanning data. Here, we apply machine learning (ML) to advance the quantification progress and target calcium carbonate (CaCO(3)) and total organic carbon (TOC) for quantification to test the potential of such an XRF-ML approach. Raw XRF spectra are used as input data instead of software-based extraction of elemental intensities to avoid bias and increase information. Our dataset comprises Pacific and Southern Ocean marine sediment cores from high- to mid-latitudes to extend the applicability of quantification models from a site-specific to a multi-regional scale. ML-built models are carefully evaluated with a training set, a test set and a case study. The acquired ML-models provide better results with R(2) of 0.96 for CaCO(3) and 0.78 for TOC than conventional methods. In our case study, the ML-performance for TOC is comparably lower but still provides potential for future optimization. Altogether, this study allows to conveniently generate high-resolution bulk chemistry records without losing accuracy. |
format | Online Article Text |
id | pubmed-9718834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97188342022-12-04 Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach Lee, An-Sheng Chao, Weng-Si Liou, Sofia Ya Hsuan Tiedemann, Ralf Zolitschka, Bernd Lembke-Jene, Lester Sci Rep Article Geochemical variations of sedimentary records contain vital information for understanding paleoenvironment and paleoclimate. However, to obtain quantitative data in the laboratory is laborious, which ultimately restricts the temporal and spatial resolution. Quantification based on fast-acquisition and high-resolution provides a potential solution but is restricted to qualitative X-ray fluorescence (XRF) core scanning data. Here, we apply machine learning (ML) to advance the quantification progress and target calcium carbonate (CaCO(3)) and total organic carbon (TOC) for quantification to test the potential of such an XRF-ML approach. Raw XRF spectra are used as input data instead of software-based extraction of elemental intensities to avoid bias and increase information. Our dataset comprises Pacific and Southern Ocean marine sediment cores from high- to mid-latitudes to extend the applicability of quantification models from a site-specific to a multi-regional scale. ML-built models are carefully evaluated with a training set, a test set and a case study. The acquired ML-models provide better results with R(2) of 0.96 for CaCO(3) and 0.78 for TOC than conventional methods. In our case study, the ML-performance for TOC is comparably lower but still provides potential for future optimization. Altogether, this study allows to conveniently generate high-resolution bulk chemistry records without losing accuracy. Nature Publishing Group UK 2022-12-02 /pmc/articles/PMC9718834/ /pubmed/36460746 http://dx.doi.org/10.1038/s41598-022-25377-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lee, An-Sheng Chao, Weng-Si Liou, Sofia Ya Hsuan Tiedemann, Ralf Zolitschka, Bernd Lembke-Jene, Lester Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title | Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_full | Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_fullStr | Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_full_unstemmed | Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_short | Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach |
title_sort | quantifying calcium carbonate and organic carbon content in marine sediments from xrf-scanning spectra with a machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718834/ https://www.ncbi.nlm.nih.gov/pubmed/36460746 http://dx.doi.org/10.1038/s41598-022-25377-x |
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