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Odor Dilution Assessment for Explosive Detection
- Source :
- Analytica, Vol 5, Iss 3, Pp 402-413 (2024)
- Publication Year :
- 2024
- Publisher :
- MDPI AG, 2024.
-
Abstract
- Canine olfaction is a highly developed sense and is utilized for the benefit of detection applications, ranging from medical diagnostics to homeland security and defense prevention strategies. Instrumental validation of odor delivery methods is key to standardize canine olfaction research to establish baseline data for explosive detection applications. Solid-phase microextraction gas chromatography (SPME/GC-MS) was used to validate the odor delivery of an olfactometer. Three explosive classes were used in this study: composition C-4 (C-4), trinitrotoluene (TNT), and ammonium nitrate (AN). Dynamic airflow sampling yielded the successful detection of previously reported target volatile organic compounds (VOCs): 2,3-dimethyl-2,3-dinitrobutane (DMNB) in C-4 and 2-ethylhexan-1-ol (2E1H) in ammonium nitrate and TNT across odor dilutions of 80%, 50%, 25%, 12%, and 3%. C-4 highlighted the most reliable detection from the olfactometer device, depicting a response decrease as a function of dilution factor of its key odor volatile DMNB across the entire range tested. TNT only portrayed 2-ethylhexan-1-ol as a detected odor volatile with a detection response as a function of dilution from 80% down to 12%. Comparatively, ammonium nitrate also depicted 2-ethylhexan-1-ol as an odor volatile in the dynamic airflow sampling but with detection only within the upper scale of the dilution range (80% and 50%). The results suggest the importance of monitoring odor delivery across different dilution ranges to provide quality control for explosive odor detection using dynamic airflow systems.
Details
- Language :
- English
- ISSN :
- 26734532
- Volume :
- 5
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Analytica
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.50c1234fe604ebf8ff02bafa99a2e35
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/analytica5030025