skip to main content
Idioma:

AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water

Yi, Jiyoon ; Wisuthiphaet, Nicharee ; Raja, Pranav ; Nitin, Nitin ; Earles, J. Mason

Water research (Oxford), 2023-08, Vol.242, p.120258-120258, Article 120258 [Periódico revisado por pares]

England: Elsevier Ltd

Texto completo disponível

Citações Citado por
  • Título:
    AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water
  • Autor: Yi, Jiyoon ; Wisuthiphaet, Nicharee ; Raja, Pranav ; Nitin, Nitin ; Earles, J. Mason
  • Assuntos: Biosensing ; Data augmentation ; Deep learning ; Pathogen detection ; Water safety
  • É parte de: Water research (Oxford), 2023-08, Vol.242, p.120258-120258, Article 120258
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: •AI-biosensing framework rapidly detects pathogens in water samples (< 5.5 h).•Deep learning identifies microscopic patterns of target cells generated by phages.•Model trained on lab-cultured bacteria achieved high accuracy on real-world samples.•Training with microbial background noise improves model generalizability.•AI-biosensing framework has potential in microbial water quality monitoring. Rapid pathogen detection in food and agricultural water is essential for ensuring food safety and public health. However, complex and noisy environmental background matrices delay the identification of pathogens and require highly trained personnel. Here, we present an AI-biosensing framework for accelerated and automated pathogen detection in various water samples, from liquid food to agricultural water. A deep learning model was used to identify and quantify target bacteria based on their microscopic patterns generated by specific interactions with bacteriophages. The model was trained on augmented datasets to maximize data efficiency, using input images of selected bacterial species, and then fine-tuned on a mixed culture. Model inference was performed on real-world water samples containing environmental noises unseen during model training. Overall, our AI model trained solely on lab-cultured bacteria achieved rapid (< 5.5 h) prediction with 80–100% accuracy on the real-world water samples, demonstrating its ability to generalize to unseen data. Our study highlights the potential applications in microbial water quality monitoring during food and agricultural processes. [Display omitted]
  • Editor: England: Elsevier Ltd
  • Idioma: Inglês
Links
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.