Functionalized carbon quantum dots fluorescent sensor array assisted by a machine learning algorithm for rapid foodborne pathogens identification

Minghui Xiao

doi:10.1016/j.microc.2024.110701

Functionalized carbon quantum dots fluorescent sensor array assisted by a machine learning algorithm for rapid foodborne pathogens identification

2024-06-01

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DOI：10.1016/j.microc.2024.110701

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Food safety is a global concern, and conventional methods for detecting foodborne bacteria, such as polymerase chain reaction and enzyme-linked immunosorbent assay, often involve time-consuming processes, specialized equipment, or specific recognition of particular bacterial strains. There is an urgent need for more efficient and convenient detection methods for foodborne pathogens. This study addresses this need by introducing an easily constructed fluorescent sensor array for the identification of various foodborne bacteria. The sensor array comprises carbon quantum dots (CQDs) functionalized with ampicillin, polymyxin, and gentamicin, each exhibiting different affinities for binding with specific bacteria. Leveraging machine learning algorithms, the proposed sensor array enables rapid, accurate, and highly sensitive identification of foodborne pathogens. This approach offers a convenient solution for the development of rapid, accurate, and hypersensitive detection methods for multiple bioactive samples. In summary, the fluorescent sensor array in this study holds promise for advancing the field of foodborne bacteria detection.

Keywords:

Carbon quantum dots

Fluorescent sensor array

Pathogen identification

Machine learning

Functionalized carbon quantum dots fluorescent sensor array assisted by a machine learning algorithm for rapid foodborne pathogens identification

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