Recent advances in sensor arrays aided by machine learning for pathogen identification

The development of rapid and accurate pathogen detection methods is of paramount importance for slowing the evolution of antibiotic resistance in bacteria. However, the high similarity between different pathogens, especially between antibiotic-sensitive and antibiotic-resistant strains of the same species, presents great challenges for the precise discrimination of pathogens. In recent years, chemical nose strategies, i.e. sensor arrays, have achieved certain success in pathogen discrimination. Currently, chemical nose strategies for identifying pathogens are mainly designed from two perspectives: the disparity in extrinsic properties (biomolecules, charge, and hydrophobicity of the bacterial surface) and intrinsic properties (processes and products mediated by bacterial enzymes) among different pathogens. Biosensing probes capable of responding to these properties are introduced for pathogen detection. The output signals are then processed and analyzed by machine learning algorithms to visualize the multidimensional detection results and achieve pathogen discrimination. This paper introduces the latest developments in sensor arrays for pathogen identification based on the extrinsic and intrinsic nature of bacteria, highlights the recognition mechanism of probes for bacteria, and outlines the current challenges and prospects of sensor arrays for pathogen discrimination.