Machine learning-enhanced colorimetric sensor array for rapid detection of nerve agents

Rapid and reliable discrimination of authentic nerve agents in complex environments is a critical, unsolved challenge for security and public safety. Current detection technologies are often limited by bulky instrumentation, prolonged analysis times, or validation restricted to simulants, hindering their real-world threat-monitoring capabilities. Here, we demonstrate a machine learning-enhanced colorimetric sensor array that overcomes these limitations. We employed a systematic data-driven approach using hierarchical clustering, analysis of variance, and correlation analysis to optimally select just six commercially available fluorescent dyes from an initial 29-candidate library. This optimized array was rigorously evaluated against five authentic nerve agents (GA, GB, GD, GF, VX) and a key simulant using a novel dual-mode (Visible/UV) illumination strategy. By quantifying colorimetric responses (RGB-to-ΔE₀₀), our linear discriminant analysis classifier achieved 100% classification accuracy. The sensor provides an instantaneous visual response and maintains discrimination capability down to 10 μM, markedly outperforming the 87.5% accuracy of visible-light-only detection. This study establishes a simple, low-cost, and scalable platform for the rapid, accurate discrimination of multiple authentic nerve agents. By integrating data-driven sensor design with robust visual analysis, our system provides a clear pathway toward next-generation portable technologies for real-time chemical threat surveillance.