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

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Understanding 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 rare in most abiotic samples and hence point to a potential agnostic molecular biosignature. They also tend to have higher normalized intensity values in biologically derived materials and display different distributions in contemporary biotic samples compared to taphonomically altered biotic samples. We utilize the full resolution of the 3D structure of the py‐GC‐MS data and describe in detail the preprocessing steps and the ML pipeline for analyzing such data, which could be automated for future data collection.