Training of explosive-detecting dogs

With a sense of smell much greater than humans, dogs are considered the gold standard for explosive detection in many situations. But that doesn’t mean there isn’t room for improvement.

In a study appearing in the journal Analytical Chemistry, scientists report on a new, more rigorous approach to training dogs and their handlers based on real-time analysis of what canines actually smell when they are exposed to explosive materials.

Vapor analysis training

New training techniques based on real-time analysis of what a dog smells could improve explosives detection. Credit: wellphoto/Shutterstock.com

Explosives are often used in terrorist attacks. Dogs trained to detect odors emanating from TNT, nitroglycerin and other explosives are a crucial part of the first-line defense against these incidents. But delivering low-concentration vapor during training sessions is a challenging task. Cross-contamination of training materials with samples from different explosives can skew results and confuse both dogs and handlers. To address these concerns, Ta-Hsuan Ong and colleagues sought to better understand the components within explosive odors that cue a dog’s reaction.

The researchers developed a real-time vapor analysis mass spectrometer to more accurately measure the vapor plumes from explosives that trigger a canine response.

In field trials, they used the device and found that some mistakes the dogs made were indeed correct identifications. For example, some “blanks” were used that were ostensibly prepared without explosive material, but the dogs indicated an explosive was present. When the researchers used the mass spectrometer on such blanks, they found evidence of explosive vapors, indicating cross contamination occurred or other interferents were present.

Based on these findings, the researchers suggest that use of real-time vapor analysis could help differentiate canine mistakes from cross contamination and other issues during training.

Source:  American Chemical Society

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