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

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