A Real-Time Automated Training and Sensing for Gas Odor (RATSGO) System for γ-Butyrolactone Detection

Herein, RATSGO (Real-time Automated Training and Sensing for Gas Odor), a fully automated live-animal olfactory training platform, for the detection of GBL as a sexual assault-facilitating drug is reported. The system integrates four distinct operant conditioning-based training paradigms, all executed without human intervention, to enhance learning speed, consistency, and scalability. Using this fully automated framework, four rats were trained to identify γ-butyrolactone (GBL). Three of the four animals successfully reached the predefined learning completion criterion, whereas one failed to meet the criterion. Across 320 automated trials, the GBL rats achieved a mean detection accuracy of 90%, with sensitivity and specificity values of 97% and 82%, respectively. The corresponding positive and negative predictive values (PPV and NPV) were 85% and 96%. When challenged with GBL diluted in drinking water (180 trials), performance remained high, yielding 88% accuracy, 89% sensitivity, 87% specificity, 85% PPV, and 90% NPV. Similarly, in experiments involving GBL mixed with whisky (200 trials), the rats demonstrated robust recognition capability, achieving 90% overall accuracy, perfect sensitivity (100%), 84% specificity, 79% PPV, and 100% NPV. Importantly, odor discrimination performance was preserved when reassessed four months after the completion of training, indicating strong long-term retention of the learned odor representations. Collectively, these findings confirm that the RATSGO system supports rapid, stable, and precise odor learning, underscoring its promise as a practical and extensible biological sensing platform for chemical detection applications.