Ion yields measured from the two-entity inclusion extracted from the chemical depth profile. Color groupings identifies elements with high affinity (i.e., CH-rich kerogen and silicate chert. Molecular ions loosely connected on the sides identifies the plasma chemistry byproducts originated on the way to the detector. Second image - decision borderlines on PCA reduced spectra.

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In this paper we present:

  • weighted mass correlation networks (WMCN) identified for inclusions in the bulk of the analyte material
  • sliding window centrality measure and a divergence of the modularity score on spots with more that one chemical entities present
  • kernel density estimation on ion-intensity regions for specific compounds
  • Van-Krevelen metric for organic vs. inorganic materials
  • PCA scores and loadings for chemical compounds identified in the sample
  • We performed a competitive test of 25 supervised machine learning models to achieve a 99% accuracy rates for identification of investigated materials (bio-organic Precambrian kerogen vs. inorganic host mineral)