Smartphone-based autofluorescence imaging to detect bacterial species on laboratory surfaces
Affiliation
Department of Biomedical Engineering, The University of ArizonaIssue Date
2022
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Show full item recordPublisher
Royal Society of ChemistryCitation
Buchanan, B. C., Safavinia, B., Wu, L., & Yoon, J.-Y. (2022). Smartphone-based autofluorescence imaging to detect bacterial species on laboratory surfaces. Analyst.Journal
AnalystRights
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 ChemistryNote
12 month embargo; first published: 24 May 2022ISSN
0003-2654PubMed ID
35648102Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1039/d2an00358a
Scopus Count
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