A robust, agnostic molecular biosignature based on py-GC-MS and machine learning

Humanity is embarking on a potential golden age of astrobiology, with numerous current and future space missions being tasked with the explicit goal of searching for evidence of habitability and life within our Solar System. However, the science of biosignatures—the detection and interpretation of signs of extant or extinct life—remains incomplete. Here, we report a flight-ready, robust, agnostic molecular biosignature detection technique based on pyrolysis–gas chromatography–mass spectrometry (py–GC–MS) combined with machine learning. To develop this technique, we used py–GC–MS to collect rich chemical spectra from hundreds of carbon-rich samples, including but not limited to: biological specimens from all three domains of life, taphonomically altered/fossilized life forms, carbonaceous meteorites, and laboratory organic synthesis experiments. We trained a machine learning algorithm on the presence, absence, and magnitude of tens of thousands of unique chemical features in the dataset. The final algorithm can: (1) predict the biogenicity of an unknown sample to >90%…