Detecting Biosignatures in Complex Molecular Mixtures From Pyrolysis‐Gas Chromatography‐Mass Spectrometry Data Using Machine Learning

AbstractUnderstanding how measured molecular signals can distinguish the chemistry of life from the chemistry of the nonliving world is a central focus of astrobiology and paleobiology. We train and compare several machine learning (ML) classification models on data from pyrolysis‐gas chromatography‐mass spectrometry (py‐GC‐MS)—a widely available analytical method that has been employed in space missions. We analyzed various organic carbon‐bearing geomaterials to consider relationships among suites of molecules that can help identify their biogenicity and potentially be used to analyze data from various solar system exploration missions. These supervised classification models can discriminate between abiotic and biotic samples with ∼86–89% accuracy. We use and compare 4 different ML models, coupled with range of statistical and visualization methods, to investigate the patterns and distribution of diagnostic features— specific combinations of chromatographic retention time and mass‐to‐charge ratio, which contribute to the classification of the samples into biologically derived versus abiologically derived materials. These diagnostic discriminators are common in biotic samples and r