Smartphone-based fluorescence imaging combined with a convolutional neural network-assisted intelligent multichannel immunosensor for simultaneous identification of four Salmonella serotypes based on multicolor luminescent iridium complexes.

BACKGROUND

Salmonella Typhi, Salmonella Paratyphi A, Salmonella Paratyphi B, and Salmonella Paratyphi C are known to cause enteric fever and often present similar clinical symptoms, which may lead to misdiagnosis and pose a significant threat to public health. Therefore, there is an urgent need for a portable, cost-effective, and time-saving method for the simultaneous identification and detection of these four Salmonella serotypes.

RESULTS

In this work, an intelligent multichannel fluorescent immunosensor was developed for the simultaneous identification and detection of four Salmonella serotypes by using multicolor iridium-doped silica nanoparticles, combined with smartphone-based fluorescence imaging and convolutional neural network (CNN)-assisted analysis. In addition to conventional fluorescence spectral analysis, a portable fluorescence imaging system was successfully constructed using a 96-well plate, a smartphone, and a custom-built black box. The captured fluorescence images were further processed by a ResNet-based CNN model, enabling rapid classification of the four Salmonella serotypes. The proposed immunosensor achieved detection limits below 100 CFU/mL for all four targets and showed lower relative standard deviations in real sample analysis.

SIGNIFICANCE AND NOVELTY: These results demonstrate that the proposed portable intelligent immunosensor provides a promising strategy for multiplexed pathogen detection.