Statistical Learning-Assisted Dual-Signal Sensing Arrays Based on Conjugated Molecules for Pathogen Detection and Identification

Pathogenic microbial infections pose a serious threat to human health and safety. Therefore, rapid detection and accurate identification of pathogenic microorganisms are critical for effective diagnosis and prevention. However, clinical testing often faces challenges such as processing large sample volumes and achieving a high detection efficiency. Here, we developed a series of sensing arrays based on cationic conjugated polymer/silver nanoparticle (CCP/Ag) composites, enabling fluorescence and colorimetric dual-signal readouts for microbial detection and identification. Five conjugated polymers with distinct optical and electronic properties were selected to construct a diverse sensor array: fluorenephenylene-based PFP, phenylenevinylene-based PPV, BODIPY-based PBF, thiophene-based PMNT, and phenylenevinylene-based oligomer OPV. These polymers bind to microbial surfaces through hydrophobic and electrostatic interactions, producing polymer-specific signal changes upon target recognition. The incorporation of silver nanoparticles regulates the interaction-induced responses by modulating local plasmonic effects, leading to changes in both the fluorescence and colorimetric signals. The resulting complex signals were then analyzed by using elastic net regression to distinguish different microbial samples and classify unknown ones. This dual-signal system supports rapid and high-throughput analysis, providing a reliable and straightforward strategy for microbial identification and improving the diagnostic efficiency.