Headspace analysis of ammonium nitrate variants and the effects of differing vapor profiles on canine detection

Odour generalisation and detection dog training

Detection dogs are required to search for and alert to specific odours of interest, such as drugs, cadavers, disease markers and explosives. However, the odour released from different samples of the same target substance will vary for a number of reasons, including the production method, evaporation, degradation, or by being mixed with extraneous odours. Generalisation, the tendency to respond in the same manner to stimuli which are different - but similar to - a conditioned stimulus, is therefore a crucial requirement for working detection dogs. Odour is a complex modality which poses unique challenges in terms of reliably predicting generalisation, when compared with auditory or visual stimuli. The primary aim of this review is to explore recent advances in our understanding of generalisation and the factors that influence it, and to consider these in light of detection dog training methods currently used in the field. We identify potential risks associated with certain training practices, and highlight areas where research is lacking and which warrant further investigation.

Towards maintaining canine training aid integrity: Effects of environmental factors and operational use on the triacetone triperoxide polymer odor capture-and-release system

There are many factors that may affect the longevity of or guide the use of canine training aids. Literature to date has mainly focused on identifying the headspace volatiles associated with training aids or odors and only minimal research exists into how different variables may alter those volatiles. The current study examines several factors affecting canine training aids: humidity, air flow, transportation, and operational deployment, using the triacetone triperoxide polymer odor capture-and-release canine training aid (TATP POCR) as the target. The TATP POCR is an absorption-based canine training aid developed to be used to safely train canines to detect the odor of the explosive TATP in operational settings. Comparisons of the TATP POCR to neat TATP are made throughout the manuscript. First, humidity increased the background components of the POCR matrix, as well as the amount of TATP recovered was above the POCR. Humidity thus affected the amount of TATP detected but did not prevent detection. Second, air flow lessened the lifetime of the TATP POCR. Third, the practice of using primary and secondary containment successfully prevented contamination, cross-contamination, and significant target loss, thereby maintaining kit integrity. Finally, the absorption of background odors from training environments was not observed. TATP headspace concentrations between a Deployed and Control POCR kit were not significantly different at time 0 (i.e., upon opening), which suggests that the operational use does not affect the function of the TATP POCR system. This information provides pivotal evidence for explosives detection canine handlers or trainers who utilize the TATP POCR.

Physical properties of odorants affect behavior of trained detection dogs during close-quarters searches

Trained detection dogs have a unique ability to find the sources of target odors in complex fluid environments. How dogs derive information about the source of an odor from an odor plume comprised of odorants with different physical properties, such as diffusivity, is currently unknown. Two volatile chemicals associated with explosive detection, ammonia (NH3, derived from ammonium nitrate-based explosives) and 2-ethyl-1-hexanol (2E1H, associated with composition C4 plastic explosives) were used to ascertain the effects of the physical properties of odorants on the search behavior and motion of trained dogs. NH3 has a diffusivity 3.6 times that of 2E1H. Fourteen civilian detection dogs were recruited to train on each target odorant using controlled odor mimic permeation systems as training aids over 6 weeks and then tested in a controlled-environment search trial where behavior, motion, and search success were analyzed. Our results indicate the target-odorant influences search motion and time spent in the stages of searching, with dogs spending more time in larger areas while localizing NH3. This aligns with the greater diffusivity of NH3 driving diffusion-dominated odor transport when dogs are close to the odor source in contrast to the advection-driven transport of 2E1H at the same distances.

Dog sniffing biomechanic responses in an odor detection test of odorants with differing physical properties

Dogs are utilized in forensic science for their extensive scent-detection capabilities. They are often considered the "gold standard" in-field detection for targets such as illicit drugs and explosives. Despite their prevalence in the field, relatively little is known about how dogs interact with and transport volatile organic compounds through their olfactory system. In this study, 2 groups of dogs were utilized-Sport detection dogs (n = 19) that participate in the National Association of Canine Scent Work and have achieved advanced standing through training and successful search competitions and law enforcement explosive detection dogs (n = 8) which were included for comparison. Both groups were presented with 2 target odorants having differing molecular properties, 2-ethyl-1-hexanol and ammonia, 2 non-target odorants, 1-bromooctane and methyl benzoate, and a negative control. Canines were tested prior to experience with the target odorants, when all odorants were novel, after some brief training with the target odorants, and after a longer training time with the target odorants. The non-target odorants were never used in training. Sniffing was measured using flow sensors embedded in a wall immediately in front of the odorants held in a closed cylinder. Sensor data were used to calculate sniff flow rate, frequency (sniffs per seconds), and volume. Results indicated no difference in sniffing dynamics between target odorants; however, sniffing frequency increased significantly with increased experience with the target odorants (Wilcoxon rank sum exact test, W = 148, P = 6 × 10-5). Sniff volume and flow rate showed a positive correlation to body mass for all sport detection dogs (slope = 2.71, F(1,17) = 9.48, P = 0.007, R2 = 0.32), though the R2 was low, indicating other factors at play. Law enforcement detection dogs were shown to take in significantly higher mean total sniff volumes (Wilcoxon rank sum exact test: W = 0. 7, P = 10-4) and volume flow rates (Wilcoxon rank sum exact test: W = 5, P = 6 × 10-5) compared to the sport detection dogs, but the sniff frequency remained similar for both groups.

Evaluation of canine training aids containment for homemade explosive and components by headspace analysis and canine testing

While canines are most commonly trained to detect traditional explosives, such as nitroaromatics and smokeless powders, homemade explosives (HMEs), such as fuel-oxidizer mixtures, are arguably a greater threat. As such, it is imperative that canines are sufficiently trained in the detection of such HMEs. The training aid delivery device (TADD) is a primary containment device that has been used to house HMEs and HME components for canine detection training purposes. This research assesses the odor release from HME components, ammonium nitrate (AN), urea nitrate (UN), and potassium chlorate (PC), housed in TADDs. Canine odor recognition tests (ORTs) were used with analytical data to determine the detectability of TADDs containing AN, UN, or PC. Headspace analysis by gas chromatography/mass spectrometry (GC/MS) with solid-phase microextraction (SPME) or online cryotrapping were used to measure ammonia or chlorine, as well as other unwanted odorants, emanating from bulk AN, UN, and PC in TADDs over 28 weeks. The analytical data showed variation in the amount of ammonia and chlorine over time, with ammonia from AN and UN decreasing slowly over time and the abundance of chlorine from PC TADDs dependent on the frequency of exposure to ambient air. Even with these variations in odor abundance, canines previously trained to detect bulk explosive HME components were able to detect all three targets in glass and plastic TADDs for at least 18 months after loading. Detection proficiency ranged from 64% to 100% and was not found to be dependent on either age of material.

Explosive Odor Signature Profiling: A Review of recent advances in technical analysis and detection

With the ever-increasing threat of improvised explosive devices (IEDs) and homemade explosives (HME) both domestically and abroad, detection of explosives and explosive related materials is an area of urgent importance for preventing terrorist activities around the globe. Canines are a common biological detector used in explosive detection due to their enhanced olfactory abilities, high mobility, efficient standoff sampling, and optimal identification of vapor sources. While other sensors based on different principles have emerged, an important concept for the rapid field detection of explosives is understanding key volatile organic compounds (VOCs) associated with these materials. Explosive detection technology needs to be on par with a large number of threats including an array of explosive materials as well as novel chemicals used in the manufacture of IEDs. Within this much needed area of research for law enforcement and homeland security applications, several studies have sought to understand the explosive odor profile from a range of materials. This review aims to provide a foundational overview of these studies to provide a summary of instrumental analysis to date on the various types of explosive odor profiles evaluated focusing on the experimental approaches and laboratory techniques utilized in the chemical characterization of explosive vapors and mixtures. By expanding upon these concepts, a greater understanding of the explosive vapor signature can be achieved, providing for enhanced chemical and biological sensing of explosive threats as well as expanding upon existing laboratory-based models for continued sensor development.