Automated identification of soil functional components based on NanoSIMS data

NanoSIMS technique allows to investigate the micro-spatial organization in complex structures in multiple scientific fields such as material science, cosmochemistry, and biogeochemistry. In soil biogeochemistry applications, NanoSIMS-based approaches aim to disentangle the interactions of organic ma...

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Main Authors: Yahan Hu, Johann Maximilian Zollner, Carmen Höschen, Martin Werner, Steffen A. Schweizer
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Ecological Informatics
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Online Access:http://www.sciencedirect.com/science/article/pii/S1574954124004333
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author Yahan Hu
Johann Maximilian Zollner
Carmen Höschen
Martin Werner
Steffen A. Schweizer
author_facet Yahan Hu
Johann Maximilian Zollner
Carmen Höschen
Martin Werner
Steffen A. Schweizer
author_sort Yahan Hu
collection DOAJ
description NanoSIMS technique allows to investigate the micro-spatial organization in complex structures in multiple scientific fields such as material science, cosmochemistry, and biogeochemistry. In soil biogeochemistry applications, NanoSIMS-based approaches aim to disentangle the interactions of organic matter (OM) and mineral phases in the heterogeneous soil microstructure. Investigating the spatial arrangement of distinct organic and mineral functional components is necessary to understand how these components interact and contribute to biogeochemical processes in soil systems. Identifying soil functional components within NanoSIMS measurements necessitates advanced and efficient data processing tools capable of accessibility and automation. We have developed a pre-processing tool to streamline NanoSIMS data preparation and handling. The tool is provided as an open-source software toolbox (NanoT). In addition, a two-step unsupervised segmentation method was developed to identify soil functional components based on NanoSIMS analyses. To illustrate the segmentation method, here we describe its application to two exemplary NanoSIMS measurements. This allows to distinguish mineral- and OM-dominated regions, as well as different mineral phases. To improve the detection of iron oxides and aluminosilicates, the 56Fe16O− channel was separately processed. The presented NanoSIMS-based processing workflow helps to disentangle functional components within a biogeochemically-diverse microstructure in soils and further warrants applications to a wide range of complex environmental samples.
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spelling doaj-art-663c7429b1b94421ab46eb3543b402922024-12-17T04:59:17ZengElsevierEcological Informatics1574-95412024-12-0184102891Automated identification of soil functional components based on NanoSIMS dataYahan Hu0Johann Maximilian Zollner1Carmen Höschen2Martin Werner3Steffen A. Schweizer4TUM School of Life Sciences, Technical University of Munich, Emil-Ramann- Straße 2, 85354 Freising-Weihenstephan, Germany; Corresponding author.TUM School of Engineering and Design, Technical University of Munich, Lise-Meitner-Straße 9, 85521 Ottobrunn, GermanyTUM School of Life Sciences, Technical University of Munich, Emil-Ramann- Straße 2, 85354 Freising-Weihenstephan, GermanyTUM School of Engineering and Design, Technical University of Munich, Lise-Meitner-Straße 9, 85521 Ottobrunn, GermanyTUM School of Life Sciences, Technical University of Munich, Emil-Ramann- Straße 2, 85354 Freising-Weihenstephan, GermanyNanoSIMS technique allows to investigate the micro-spatial organization in complex structures in multiple scientific fields such as material science, cosmochemistry, and biogeochemistry. In soil biogeochemistry applications, NanoSIMS-based approaches aim to disentangle the interactions of organic matter (OM) and mineral phases in the heterogeneous soil microstructure. Investigating the spatial arrangement of distinct organic and mineral functional components is necessary to understand how these components interact and contribute to biogeochemical processes in soil systems. Identifying soil functional components within NanoSIMS measurements necessitates advanced and efficient data processing tools capable of accessibility and automation. We have developed a pre-processing tool to streamline NanoSIMS data preparation and handling. The tool is provided as an open-source software toolbox (NanoT). In addition, a two-step unsupervised segmentation method was developed to identify soil functional components based on NanoSIMS analyses. To illustrate the segmentation method, here we describe its application to two exemplary NanoSIMS measurements. This allows to distinguish mineral- and OM-dominated regions, as well as different mineral phases. To improve the detection of iron oxides and aluminosilicates, the 56Fe16O− channel was separately processed. The presented NanoSIMS-based processing workflow helps to disentangle functional components within a biogeochemically-diverse microstructure in soils and further warrants applications to a wide range of complex environmental samples.http://www.sciencedirect.com/science/article/pii/S1574954124004333NanoSIMSSoil spatial arrangementPre-processing toolUnsupervised segmentationOrgano-mineral interactions
spellingShingle Yahan Hu
Johann Maximilian Zollner
Carmen Höschen
Martin Werner
Steffen A. Schweizer
Automated identification of soil functional components based on NanoSIMS data
Ecological Informatics
NanoSIMS
Soil spatial arrangement
Pre-processing tool
Unsupervised segmentation
Organo-mineral interactions
title Automated identification of soil functional components based on NanoSIMS data
title_full Automated identification of soil functional components based on NanoSIMS data
title_fullStr Automated identification of soil functional components based on NanoSIMS data
title_full_unstemmed Automated identification of soil functional components based on NanoSIMS data
title_short Automated identification of soil functional components based on NanoSIMS data
title_sort automated identification of soil functional components based on nanosims data
topic NanoSIMS
Soil spatial arrangement
Pre-processing tool
Unsupervised segmentation
Organo-mineral interactions
url http://www.sciencedirect.com/science/article/pii/S1574954124004333
work_keys_str_mv AT yahanhu automatedidentificationofsoilfunctionalcomponentsbasedonnanosimsdata
AT johannmaximilianzollner automatedidentificationofsoilfunctionalcomponentsbasedonnanosimsdata
AT carmenhoschen automatedidentificationofsoilfunctionalcomponentsbasedonnanosimsdata
AT martinwerner automatedidentificationofsoilfunctionalcomponentsbasedonnanosimsdata
AT steffenaschweizer automatedidentificationofsoilfunctionalcomponentsbasedonnanosimsdata