High Mass Resolution fs-LIMS Imaging and Manifold Learning Reveal Insight Into Chemical Diversity of the 1.88 Ga Gunflint Chert

Frontiers in Space Technologies

Space Technologies

David Wacey¹  Anna Neubeck²  Salome Gruchola³  Peter Keresztes Schmidt³  Peter Wurz³  Andreas Riedo³  Coenraad de Koning³  Rustam A. Lukmanov³  Niels F. W. Ligterink³  Marek Tulej³  Valentine Grimaudo³ 

[1] Centre for Microscopy, Characterisation and Analysis, The University of Western Australia, Perth, WA, Australia;Department of Earth Sciences, Uppsala University, Uppsala, Sweden;Space Research and Planetary Sciences (WP), University of Bern, Bern, Switzerland;

关键词: fs-LIMS;    mass-spectrometry;    Umap;    Mapper;    Gunflint;   

DOI  :  10.3389/frspt.2022.718943

 received in 2021-07-30, accepted in 2022-03-29,  发布年份 2022

来源: Frontiers

【 摘 要 】

Extraction of useful information from unstructured, large and complex mass spectrometric signals is a challenge in many application fields of mass spectrometry. Therefore, new data analysis approaches are required to help uncover the complexity of such signals. In this contribution, we examined the chemical composition of the 1.88 Ga Gunflint chert using the newly developed high mass resolution laser ionization mass spectrometer (fs-LIMS-GT). We report results on the following: 1) mass-spectrometric multi-element imaging of the Gunflint chert sample; and 2) identification of multiple chemical entities from spatial mass spectrometric data utilizing nonlinear dimensionality reduction and spectral similarity networks. The analysis of 40′000 mass spectra reveals the presence of chemical heterogeneity (seven minor compounds) and two large clusters of spectra registered from the organic material and inorganic host mineral. Our results show the utility of fs-LIMS imaging in combination with manifold learning methods in studying chemically diverse samples.

【 授权许可】

Unknown   
Copyright © 2022 Lukmanov, de Koning, Schmidt, Wacey, Ligterink, Gruchola, Grimaudo, Neubeck, Riedo, Tulej and Wurz.