A Mass Spectrometry-Machine Learning Approach for Detecting Volatile Organic Compound Emissions for Early Fire Detection-Reference-Cited by-同舟云学术

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A Mass Spectrometry-Machine Learning Approach for Detecting Volatile Organic Compound Emissions for Early Fire Detection

Published:2023-04-20 Issue:5 Volume:34 Page:826-835
ISSN:1044-0305
Container-title:Journal of the American Society for Mass Spectrometry
language:en
Short-container-title:J. Am. Soc. Mass Spectrom.

Author:

Kingsley Sarah¹^ORCID,Xu Zhaoyi²^ORCID,Jones Brant¹^ORCID,Saleh Joseph²,Orlando Thomas M.¹^ORCID

Affiliation:

1. School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Dr, Atlanta, Georgia 30318, United States

2. Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, 270 Ferst Dr, Atlanta, Georgia 30313, United States

Funder

National Aeronautics and Space Administration

Publisher

American Chemical Society (ACS)

Subject

Spectroscopy,Structural Biology

Link

https://pubs.acs.org/doi/pdf/10.1021/jasms.2c00304

Reference51 articles.

1. Detection of Mycobacterium avium ssp. paratuberculosis in Cultures From Fecal and Tissue Samples Using VOC Analysis and Machine Learning Tools

2. Real time monitoring of slow pyrolysis of polyethylene terephthalate (PET) by different mass spectrometric techniques

3. Comprehensive VOC profiling of an apple germplasm collection by PTR-ToF-MS

4. Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

5. Fast Detection of Volatile Organic Compounds from Bacterial Cultures by Secondary Electrospray Ionization-Mass Spectrometry

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