Multiplexed bacterial recognition based on “All-in-One” semiconducting polymer dots sensor and machine learning

The accurate discrimination of bacterial infection is imperative for precise clinical diagnosis and treatment. Here, this work presents a simplified sensor array utilizing “All-in-One” Pdots for efficient discrimination of diverse bacterial samples. The “All-in-One” Pdots sensor (AOPS) were synthesized using three components that exhibit fluorescence resonance energy transfer (FRET) effect, facilitating the efficient integration of multiple discrimination channels to generate specific fluorescence response patterns through a single detection under single-wavelength excitation. Additionally, machine learning techniques were employed to visually represent the fluorescence response patterns of AOPS upon exposure to bacterial metabolites derived from diverse bacterial species. The as-prepared sensor platform demonstrated excellent performance in analyzing eight common bacteria, drug-resistant strains, mixed bacterial samples, bacterial biofilms and real samples, presenting significant potential in the identification of complex samples for bacterial analysis. A simplified sensor array was developed utilizing “All-in-One” Pdots for efficient discrimination of diverse bacterial samples. [Display omitted] •A simplified sensor array based on “All-in-One” Pdots was successfully constructed.•The “All-in-One” Pdots integrates array sensors with a spectral coding strategy, thereby streamlining the architecture of the sensor array.•The “All-in-One” sensor can be excited at a single wavelength as a singular nanoparticle, thereby streamlining the procedure.•The sensor platform is capable of accurately identifying multiple bacterial samples with ultra-high accuracy.•Bacterial species can be visualized through the utilization of machine learning algorithms.