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    Smartphone-based autofluorescence imaging to detect bacterial species on laboratory surfaces

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    Author
    Buchanan, B.C.
    Safavinia, B.
    Wu, L.
    Yoon, J.-Y.
    Affiliation
    Department of Biomedical Engineering, The University of Arizona
    Issue Date
    2022
    
    Metadata
    Show full item record
    Publisher
    Royal Society of Chemistry
    Citation
    Buchanan, B. C., Safavinia, B., Wu, L., & Yoon, J.-Y. (2022). Smartphone-based autofluorescence imaging to detect bacterial species on laboratory surfaces. Analyst.
    Journal
    Analyst
    Rights
    Copyright © 2022 The Author(s). This journal is copyright the Royal Society of Chemistry.
    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
    The potential of bacterial contamination is commonly seen in biological and clinical laboratory surfaces, creating a need to detect the presence of bacteria on a surface. Various bacterial species have been found to naturally exist on surfaces, including Escherichia coli, Salmonella Typhimurium, and Staphylococcus aureus that were investigated in this study. Bacterial presence was identified from laboratory surfaces using a smartphone and low-cost components without culturing or staining. Autofluorescence from bacteria was quantified using a 405 nm LED as an excitation light source. A low-cost acrylic film could isolate the autofluorescence emission. ImageJ was used to process and analyze the images and quantify the emitted autofluorescence signal. This imaging platform successfully detected the presence of all three bacterial species from the heavily used laboratory surfaces. A trend of decreasing fluorescence signal was observed with decreasing bacterial concentration, and the limit of detection was 104 CFU cm−2. It could also distinguish from tap water, protein (bovine serum albumin), and NaCl solutions. This preliminary work emphasizes the ability to detect autofluorescence signals of bacteria and non-microbial surface contaminants using a cost-effective and straightforward imaging platform. © 2022 The Royal Society of Chemistry
    Note
    12 month embargo; first published: 24 May 2022
    ISSN
    0003-2654
    PubMed ID
    35648102
    DOI
    10.1039/d2an00358a
    Version
    Final published version
    ae974a485f413a2113503eed53cd6c53
    10.1039/d2an00358a
    Scopus Count
    Collections
    UA Faculty Publications

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