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    Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification

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    Name:
    Peptide Manuscript BB Final ...
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    Author
    Kim, Sangsik
    Lee, Min Hee
    Wiwasuku, Theanchai
    Day, Alexander S
    Youngme, Sujittra
    Hwang, Dong Soo
    Yoon, Jeong-Yeol
    Affiliation
    Department of Biosystems Engineering, The University of Arizona
    Department of Biomedical Engineering, The University of Arizona
    Issue Date
    2021-05-14
    Keywords
    Bacteria identification
    biofilm
    Biointerface
    Paper microfluidic chip
    pathogen
    Support vector machine (SVM)
    
    Metadata
    Show full item record
    Publisher
    Elsevier Ltd
    Citation
    Kim, S., Lee, M. H., Wiwasuku, T., Day, A. S., Youngme, S., Hwang, D. S., & Yoon, J. Y. (2021). Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification. Biosensors and Bioelectronics, 113335.
    Journal
    Biosensors & bioelectronics
    Rights
    Copyright © 2021 Elsevier B.V. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the surface tension and viscosity of the liquid flowing through the paper pores, altering the flow velocity at different extents. A smartphone camera captures this flow velocity without being affected by ambient and environmental conditions, towards a low-cost, rapid, and field-ready assay. A collection of such flow velocity data generates a unique fingerprinting profile for each bacterial species. Support vector machine is utilized to classify the species. At optimized conditions, the training model can predict the species at 93.3% accuracy out of five bacteria: Escherichia coli, Staphylococcus aureus, Salmonella Typhimurium, Enterococcus faecium, and Pseudomonas aeruginosa. Flow rates are monitored for less than 6 s and the sample-to-answer assay time is less than 10 min. The demonstrated method can open a new way of analyzing complex biological and environmental samples in a biomimetic manner with machine learning classification. © 2021 Elsevier B.V.
    Note
    24 month embargo; available online 14 May 2021
    EISSN
    1873-4235
    PubMed ID
    34030093
    DOI
    10.1016/j.bios.2021.113335
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.bios.2021.113335
    Scopus Count
    Collections
    UA Faculty Publications

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