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Updated: Aug 6, 2026

Sampling and Analysis of Animal Scent Signals
Published on: February 13, 2021
Canine scent detection: an analytical chemistry perspective
Dinakaran Thirumalai1, Varvara Bolikava2, Shelley Brady3
1School of Chemical Sciences, Insight Research Ireland Centre for Data Analytics, Dublin City University, D09 V209, Ireland. aoife.morrin@dcu.ie.
Abstract:
Canine olfactory detection has been applied to explosives and narcotics identification for over five decades, yet the mechanistic and methodological basis of canine detection performance remains insufficiently understood from an analytical chemistry perspective. This review systematically examines canine scent detection through an analytical chemistry framework. The canine olfactory system, characterised by high densities of olfactory receptor cells, favourable nasal airflow dynamics, and extensive neurological processing capacity, provides sensitivity at parts per trillion concentrations as well as complex pattern recognition capability. These analytical attributes remain challenging to replicate with instrumentation. We discuss canine detection in longstanding applications of explosives and narcotics in terms of the types of training aids used, odour delivery systems for training and testing, and the conditioning protocols that have been implemented for canine training as best practice. We address the transition from traditional security applications where target volatile profiles are relatively well-characterised, to emerging biomedical diagnostics. In this context, poorly defined target VOCs embedded within complex biological matrices introduce fundamental analytical challenges not encountered in more conventional detection tasks. We identify gaps in canine training, standardisation, cross-study comparability, and VOC biomarker identification, and the value of integrating mass spectral profiling in canine detection studies is emphasised throughout. This review provides a framework for advancing canine detection as a rigorous analytical method and for informing biomimetic translation to novel sensor technologies.
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