Trained Scent Dog Detection and GC-MS Analysis of Volatile Organic Compounds from Murine Coronavirus-Infected Cell...

Volatile organic compounds (VOCs) are increasingly recognized as metabolic byproducts of viral infection and may serve as olfactory cues detectable by trained scent dogs. This study examined whether dogs could distinguish cell culture samples infected with murine hepatitis virus strain 1 (MHV-1), a biosafety level 2 coronavirus model, from uninfected controls. Parallel chemical analysis using gas chromatography-mass spectrometry (GC-MS) identified 14 VOCs in infected and 12 in control samples. Notably, 3-heptanone and 1-nonanol were unique to infected samples, while others such as acetophenone, nonanal, decanal, and benzaldehyde were significantly elevated-often by 1.5 to 3 times-in infected cultures. Two trained dogs demonstrated high detection sensitivity (0.95) for infected samples compared to a previously trained odor cinnamon group (0.88) and responded with shorter latency (p = 0.04), suggesting perceptual salience of infection-related VOCs. Reliable detection required pooled volumes (~600 µL), suggesting a threshold effect related to VOC concentration. Additionally, a Random Forest-based machine learning classifier trained on the GC-MS-obtained VOC profiles achieved a cross-validated accuracy of 0.82 (SD = 0.25). These findings suggest that dogs use quantitative VOC differences, rather than unique compounds, for detection. The study provides a validated experimental framework for olfactory diagnostics of viral infections and highlights the potential of scent dogs as non-invasive biosensors in both veterinary and public health contexts.