As space agencies gear up to retrieve Martian samples, the prospect of extraterrestrial life seems imminent. NASA’s Mars Sample Return and Japan’s Martian Moons eXploration missions aim to collect rock and dust that may contain signs of past life. However, detecting organic molecules’ origins—whether biological or chemical—remains challenging. Traditional methods rely on specific biomarkers, often overlooking unfamiliar signatures.
Researchers have developed LifeTracer, a machine learning framework that analyzes entire chemical inventories rather than focusing on select compounds. By examining thousands of organic fragments, LifeTracer achieves over 87% accuracy in distinguishing between biotic and abiotic chemistry. This innovative approach utilizes two-dimensional gas chromatography and mass spectrometry to create detailed profiles of samples from Earth and meteorites.
As Mars missions prepare to return samples, LifeTracer offers a promising method for identifying potential signs of life, emphasizing holistic data analysis rather than isolated markers. These findings were published in PNAS Nexus.
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