Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk.

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  • Author(s): Wang Y;Wang Y; Feng Y; Feng Y; Xiao Z; Xiao Z; Luo Y; Luo Y
  • Source:
    Food chemistry [Food Chem] 2025 Jan 15; Vol. 463 (Pt 2), pp. 141115. Date of Electronic Publication: 2024 Sep 06.
  • Publication Type:
    Journal Article; Evaluation Study
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 7702639 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7072 (Electronic) Linking ISSN: 03088146 NLM ISO Abbreviation: Food Chem Subsets: MEDLINE
    • Publication Information:
      Publication: Barking : Elsevier Applied Science Publishers
      Original Publication: Barking, Eng., Applied Science Publishers.
    • Subject Terms:
      Milk*/microbiology ; Milk*/chemistry ; Machine Learning* ; Bacteria*/isolation & purification ; Bacteria*/genetics ; Bacteria*/classification ; DNA, Single-Stranded*/chemistry ; Food Contamination*/analysis ; Food Microbiology*; Animals ; DNA, Bacterial/genetics ; Cattle ; Biosensing Techniques/instrumentation ; Neural Networks, Computer
    • Abstract:
      Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In this study, a non-specific optical sensor array was proposed for the identification of multiple pathogenic bacteria in contaminated milk samples. Fluorescence-labeled single-stranded DNA was efficiently quenched by two-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial species, enabling the sensor array to identify eight bacteria (pathogenic and spoilage) within a few hours. Four traditional machine learning models and two artificial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubation to 120 min increased the accuracy of the multiplayer perceptron to 98.4 %. This sensor array is a novel, low-cost, and high-accuracy approach for the identification of multiple bacteria, providing an alternative to plate counting and ELISA methods.
      Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
      (Copyright © 2024 Elsevier Ltd. All rights reserved.)
    • Contributed Indexing:
      Keywords: Machine learning; Milk; Multiplexed bacteria detection; Sensor array
    • Accession Number:
      0 (DNA, Single-Stranded)
      0 (DNA, Bacterial)
    • Publication Date:
      Date Created: 20240912 Date Completed: 20241105 Latest Revision: 20241105
    • Publication Date:
      20241105
    • Accession Number:
      10.1016/j.foodchem.2024.141115
    • Accession Number:
      39265300