This paper presents an application of artificial intelligence on mass
spectrometry data for detecting habitability potential of ancient Mars.
Although data was collected for planet Mars the same approach can be replicated
for any terrestrial object of our solar system. Furthermore, proposed
methodology can be adapted to any domain that uses mass spectrometry. This
research is focused in data analysis of two mass spectrometry techniques,
evolved gas analysis (EGA-MS) and gas chromatography (GC-MS), which are used to
identify specific chemical compounds in geological material samples. The study
demonstrates the applicability of EGA-MS and GC-MS data to extra-terrestrial
material analysis. Most important features of proposed methodology includes
square root transformation of mass spectrometry values, conversion of raw data
to 2D sprectrograms and utilization of specific machine learning models and
techniques to avoid overfitting on relative small datasets. Both EGA-MS and
GC-MS datasets come from NASA and two machine learning competitions that the
author participated and exploited. Complete running code for the GC-MS
dataset/competition is available at GitHub.1 Raw training mass spectrometry
data include [0, 1] labels of specific chemical compounds, selected to provide
valuable insights and contribute to our understanding of the potential past
habitability of Mars.

这篇研究论文应用了人工智能技术分析质谱数据，用于检测古代火星的适居潜力，并探讨了该方法在质谱学和其他领域的适用性。

利用人工智能分析质谱数据，辅助理解火星过去的生存条件并为未来任务提供思路

Analyze Mass Spectrometry data with Artificial Intelligence to assist  the understanding of past habitability of Mars and provide insights for  future missions