Literature DB >> 14584511

Electronic nose for space program applications.

Rebecca C Young1, William J Buttner, Bruce R Linnell, Rajeshuni Ramesham.   

Abstract

The ability to monitor air contaminants in the shuttle and the International Space Station is important to ensure the health and safety of astronauts, and equipment integrity. Three specific space applications have been identified that would benefit from a chemical monitor: (a) organic contaminants in space cabin air; (b) hypergolic propellant contaminants in the shuttle airlock; (c) pre-combustion signature vapors from electrical fires. NASA at Kennedy Space Center (KSC) is assessing several commercial and developing electronic noses (E-noses) for these applications. A short series of tests identified those E-noses that exhibited sufficient sensitivity to the vapors of interest. Only two E-noses exhibited sufficient sensitivity for hypergolic fuels at the required levels, while several commercial E-noses showed sufficient sensitivity of common organic vapors. These E-noses were subjected to further tests to assess their ability to identify vapors. Development and testing of E-nose models using vendor supplied software packages correctly identified vapors with an accuracy of 70-90%. In-house software improvements increased the identification rates between 90 and 100%. Further software enhancements are under development. Details on the experimental setup, test protocols, and results on E-nose performance are presented in this paper along with special emphasis on specific software enhancements. c2003 Elsevier Science B.V. All rights reserved.

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Year:  2003        PMID: 14584511     DOI: 10.1016/s0925-4005(03)00338-1

Source DB:  PubMed          Journal:  Sens Actuators B Chem        ISSN: 0925-4005            Impact factor:   7.460


  8 in total

1.  Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications.

Authors:  Han Fan; Erik Schaffernicht; Achim J Lilienthal
Journal:  Front Chem       Date:  2022-04-28       Impact factor: 5.545

2.  A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training.

Authors:  Pengfei Jia; Tailai Huang; Shukai Duan; Lingpu Ge; Jia Yan; Lidan Wang
Journal:  Sensors (Basel)       Date:  2016-03-14       Impact factor: 3.576

3.  A Set of Platforms with Combinatorial and High-Throughput Technique for Gas Sensing, from Material to Device and to System.

Authors:  Zhenghao Mao; Jianchao Wang; Youjin Gong; Heng Yang; Shunping Zhang
Journal:  Micromachines (Basel)       Date:  2018-11-19       Impact factor: 2.891

Review 4.  Electronic Nose Feature Extraction Methods: A Review.

Authors:  Jia Yan; Xiuzhen Guo; Shukai Duan; Pengfei Jia; Lidan Wang; Chao Peng; Songlin Zhang
Journal:  Sensors (Basel)       Date:  2015-11-02       Impact factor: 3.576

5.  A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs.

Authors:  Tailai Huang; Pengfei Jia; Peilin He; Shukai Duan; Jia Yan; Lidan Wang
Journal:  Sensors (Basel)       Date:  2016-09-10       Impact factor: 3.576

6.  A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm.

Authors:  Li Wang; Pengfei Jia; Tailai Huang; Shukai Duan; Jia Yan; Lidan Wang
Journal:  Sensors (Basel)       Date:  2016-08-12       Impact factor: 3.576

7.  Rapid and Non-Destructive Detection of Compression Damage of Yellow Peach Using an Electronic Nose and Chemometrics.

Authors:  Xiangzheng Yang; Jiahui Chen; Lianwen Jia; Wangqing Yu; Da Wang; Wenwen Wei; Shaojia Li; Shiyi Tian; Di Wu
Journal:  Sensors (Basel)       Date:  2020-03-27       Impact factor: 3.576

8.  Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach.

Authors:  Sigfredo Fuentes; Vasiliki Summerson; Claudia Gonzalez Viejo; Eden Tongson; Nir Lipovetzky; Kerry L Wilkinson; Colleen Szeto; Ranjith R Unnithan
Journal:  Sensors (Basel)       Date:  2020-09-08       Impact factor: 3.576

  8 in total

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