Back to Search Start Over

Odor Dilution Assessment for Explosive Detection

Authors :
Dillon E. Huff
Ariela Cantu
Sarah A. Kane
Lauren S. Fernandez
Jaclyn E. Cañas-Carrell
Nathaniel J. Hall
Paola A. Prada-Tiedemann
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