Artificial intelligence-driven quantification of antibiotic-resistant Bacteria in food by color-encoded multiplex hydrogel digital LAMP.

Journal: Food chemistry
PMID: 39667227

Abstract

Antibiotic-resistant bacteria pose considerable risks to global health, particularly through transmission in the food chain. Herein, we developed the artificial intelligence-driven quantification of antibiotic-resistant bacteria in food using a color-encoded multiplex hydrogel digital loop-mediated isothermal amplification (LAMP) system. The quenching of unincorporated amplification signal reporters (QUASR) was first introduced in multiplex digital LAMP. During amplification, primers labeled with different fluorophores were integrated into amplicons, generating color-specific fluorescent spots. While excess primers were quenched by complementary quenching probes. After amplification, fluorescent spots in red, green, and blue emerged in hydrogels, which were automatically identified and quantified using a deep learning model. Methicillin-resistant Staphylococcus aureus and carbapenem-resistant Escherichia coli in real fruit and vegetable samples were also successfully detected. This artificial intelligence-driven color-encoded multiplex hydrogel LAMP offers promising potential for the digital quantification of antibiotic-resistant bacteria in the food industry.

Authors

  • Tao Yang
    The First Clinical Medical College, The Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
  • Xinyang Zhang
    Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Yuhua Yan
    Institute of Food Science, Wenzhou Academy of Agricultural Science, Wenzhou 325006, China.
  • Yuanjie Liu
    Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China.
  • Xingyu Lin
    College of Biosystems Engineering and Food Science, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.

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