| Summary: | A growing body of research has developed over the last 30 years exploring disease scent detection using animals. Research project methodologies and results in this field vary significantly, including some which are quite promising. Critiques of aspects of current and past practice in disease scent detection using animals inform recommendations for the development of scientifically robust standard operating procedures in this field. Greater standardisation and transparency of practice has the potential to strengthen and clarify disease scent detection results. My primary aim was to contribute to this standardisation by using a theoretical understanding of concept formation and learning acquisition to inform three experiments in which pet dogs (Canis lupus familiaris) were trained to detect the presence of lung cancer in human breath and saliva samples. Operant conditioning processes and an automated apparatus were used to train and test the dogs. Data for all three experiments were collected concurrently. The experiments involved: designing a process for evaluating sample comparisons; evaluating the efficacy of sample re-use for training purposes; and developing a mathematical model to support decision-making about the transition from training to testing of detector organisms. Firstly, we evaluated the comparative utility of human breath and saliva samples to train and test dogs for lung cancer detection. Signal detection measures were used to gauge the dogs’ target acquisition and concept formation during the training process. The dogs acquired the lung cancer target concept more quickly from breath samples, but also demonstrated higher-than-chance recognition using saliva samples. Secondly, we systematically evaluated the effect of breath sample re-use on dogs’ performance during lung cancer scent detection training. There were no significant changes associated with the detectability of the target across samples re-used up to four times, and observed changes in performance were small. Finally, we explored ...
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