Bacterial species differentiation via real-time detection of microbial volatile organic compounds using a wavelength multiplexed photoionization detector and AI image-based analysis.

Healthcare-associated infections (HCAIs) contribute significantly to global mortality, driven by the increasing antimicrobial resistance. Rapid, high-throughput bacterial detection is crucial for infection control and patient care. We report a real-time, multiplex lamp-based Photoionization Detector (PID) assisted by AI-image-based analysis for bacterial identification. Using four lamps with varying ionization energies, the sensor selectively ionizes VOCs emitted by bacteria, producing four distinct current curves for each target species (Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae). These curves were transformed into image representations, capturing their spectral patterns for bacterial differentiation. A pre-trained ResNet-18 Convolutional Neural Network (CNN) within a Few-Shot Learning (FSL) framework extracted key features, enabling accurate (> 88%) bacterial differentiation even with limited labeled data. This sensor detected bacterial concentrations as low as 10² CFU and distinguished contamination levels. The synergistic integration of PID sensing with AI-driven analysis offers a powerful approach to rapid bacterial diagnostics, demonstrating strong potential for clinical implementation and improved patient care. This study marks an early step toward AI-based VOC sensing, where FSL acts as a proof-of-concept under data scarcity.