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dc.contributor.authorKim, Sangsik
dc.contributor.authorLee, Min Hee
dc.contributor.authorWiwasuku, Theanchai
dc.contributor.authorDay, Alexander S
dc.contributor.authorYoungme, Sujittra
dc.contributor.authorHwang, Dong Soo
dc.contributor.authorYoon, Jeong-Yeol
dc.date.accessioned2021-06-11T02:01:33Z
dc.date.available2021-06-11T02:01:33Z
dc.date.issued2021-05-14
dc.identifier.citationKim, 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.en_US
dc.identifier.pmid34030093
dc.identifier.doi10.1016/j.bios.2021.113335
dc.identifier.urihttp://hdl.handle.net/10150/659870
dc.description.abstractBacteria 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.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.rightsCopyright © 2021 Elsevier B.V. All rights reserved.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectBacteria identificationen_US
dc.subjectbiofilmen_US
dc.subjectBiointerfaceen_US
dc.subjectPaper microfluidic chipen_US
dc.subjectpathogenen_US
dc.subjectSupport vector machine (SVM)en_US
dc.titleHuman sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classificationen_US
dc.typeArticleen_US
dc.identifier.eissn1873-4235
dc.contributor.departmentDepartment of Biosystems Engineering, The University of Arizonaen_US
dc.contributor.departmentDepartment of Biomedical Engineering, The University of Arizonaen_US
dc.identifier.journalBiosensors & bioelectronicsen_US
dc.description.note24 month embargo; available online 14 May 2021en_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleBiosensors & bioelectronics
dc.source.volume188
dc.source.beginpage113335
dc.source.endpage
dc.source.countryEngland


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