A new machine learning framework can distinguish biological molecules from non-biological ones with over 87 per cent accuracy, potentially solving the challenge of identifying life in samples returned from Mars.
Researchers from NASA and the Georgia Institute of Technology developed “LifeTracer”, a computational tool that analyses mass spectrometry data to identify patterns distinguishing abiotic (non-living) from biotic (living) origins.
The team analysed eight carbonaceous meteorites and ten terrestrial geologic samples using two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry.
“Determining whether organic molecules in planetary samples originate from biological or nonbiological processes is central to the search for life beyond Earth,” the authors wrote in PNAS Nexus.
“Yet, distinguishing these origins is challenging due to overlapping chemical signatures and limited access to pristine extraterrestrial materials.”
Significant differences
The study identified 9,475 peaks in meteorite samples and 9,070 in terrestrial samples, finding statistically significant differences in molecular weight distributions and retention times, the time taken for compounds to move through the chromatograph.
Organic compounds in meteorite samples showed significantly lower retention times, consistent with the higher volatility found in abiotically formed materials.
The framework identified polycyclic aromatic hydrocarbons and their alkylated variants as key predictive features, with naphthalene emerging as the most predictive compound for abiotic samples.
“Our approach offers a significant improvement over traditional targeted methods, which rely on manual compound identification and often neglect the broader chemical context,” the researchers noted.
“It enables scalable, unbiased biosignature detection and offers a powerful tool for interpreting complex organic mixtures returned by current and future planetary missions.”
