Laser Pyrolysis Spectroscopic Detection and Qualitative-Quantitative
Analysis of Organic Compounds in Space Dust
WU Yi-jian1, 4, XU Wei-ming1, 2, 4*, XU Xue-sen1, 4*, LI Lu-ning2, 4, LÜ Wen-hao1, 4, YAN Peng-peng2, SHU Rong1, 2, 3, 4
1. School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
2. Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
3. Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China
4. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：Detecting trace organic compounds in deep-space minor celestial bodies is crucial for understanding the origins of life. However, conventional spectroscopic techniques often struggle to simultaneously excite and release all the organic compounds in the sample for comprehensive detection. This is particularly challenging for organic compounds that are diffusely distributed, as their signals are often difficult to capture effectively, leading to limitations in detection within complex matrices. To address this challenge, this study proposes a novel analytical approach that combines laser pyrolysis-Fourier transform infrared spectroscopy (LP-FTIR) with machine learning, establishing a high-precision method for both qualitative and quantitative detection of organic compounds in space dust. This work aims to provide a new technical solution for identifying potential biosignatures in deep-space exploration. First, simulated space dust samples containing six typical life-related organic molecules glycine, stearic acid, cytidine nucleoside, ribose, deoxyribose, and soybean lecithin were prepared. Infrared spectral data of pyrolysis gases were obtained using a miniaturized LP-FTIR detection platform. For qualitative analysis, a multi-model ensemble classification algorithm was developed, integrating SVM, RF, XGBoost, RNN, and BPNN, with hyper parameters tuned using Bayesian optimization. Predictions were integrated through a majority voting mechanism. For quantitative analysis, a novel regression model was crafted by integrating a one-dimensional CNN with a multi-head attention mechanism, employing segmented pooling to pinpoint critical spectral regions and improve feature extraction efficiency. The study results indicate that the multi-model ensemble classification method achieved a 90% accuracy rate in identifying six types of organic compounds, representing a significant improvement over the best single model (BPNN at 87%). The improved attention-based CNN achieved a coefficient of determination (R2) of 0.979 and a root mean square error (RMSE) of 0.21 mg in predicting glycine content, showing significant performance enhancement over traditional PLSR (R2=0.969) and the basic CNN model (R2=0.891).
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