Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification
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Peptide Manuscript BB Final ...
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Final Accepted Manuscript
Author
Kim, SangsikLee, Min Hee
Wiwasuku, Theanchai
Day, Alexander S
Youngme, Sujittra
Hwang, Dong Soo
Yoon, Jeong-Yeol
Affiliation
Department of Biosystems Engineering, The University of ArizonaDepartment of Biomedical Engineering, The University of Arizona
Issue Date
2021-05-14Keywords
Bacteria identificationbiofilm
Biointerface
Paper microfluidic chip
pathogen
Support vector machine (SVM)
Metadata
Show full item recordPublisher
Elsevier LtdCitation
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 & bioelectronicsRights
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 2021EISSN
1873-4235PubMed ID
34030093Version
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1016/j.bios.2021.113335