Onboard Chemically Specific Monitoring and Detection of Microorganisms

This proposal seeks to develop a simple and robust technique for detecting and identifying microorganisms onboard spacecraft and plans construct a prototype of the proposed system. Microbes produce volatile organic compounds (VOC) through metabolic activity. These compounds have been used previously to study and identify different types of organisms. Three species of bacteria have been selected to serve as example species in this study. Mass spectrometry (MS), a chemical analysis technique, will be used to collect data on the VOCs emitted by samples of bacteria. Researchers will then identify patterns in the MS data, referred to as chemical signatures, that correspond to specific bacterial species. Datasets depicting these chemical signatures will be prepared and processed using a random forest (RF) model. An RF is a machine learning technique for classifying data. The RF model will be used to determine if VOCs produced by bacteria were detected in the MS data and which of the studied species of bacteria produced the detected VOCs. Data will be collected for the RF model using a laboratory MS analysis system. A prototype microbial monitoring instrument will also be constructed using miniaturized commercial-off-the-shelf components. The proposed system will be capable of providing information about the microorganisms onboard the spacecraft without reliance on ground-based analysis. This work can help to enable microbial monitoring on extended duration missions and contribute to improving spacecraft health and safety. The project described in this proposal will specifically contribute to Technology Area 6.4 by supporting efforts to advance spacecraft environmental monitoring and safety.

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