Publication:
Smartphone-based colorimetric detection system for portable health tracking

dc.contributor.coauthorBalbach, Samira
dc.contributor.coauthorJiang, Nan
dc.contributor.coauthorMoreddu, Rosalia
dc.contributor.coauthorDong, Xingchen
dc.contributor.coauthorKurz, Wolfgang
dc.contributor.coauthorWang, Congyan
dc.contributor.coauthorDong, Jie
dc.contributor.coauthorYin, Yixia
dc.contributor.coauthorButt, Haider
dc.contributor.coauthorBrischwein, Martin
dc.contributor.coauthorHayden, Oliver
dc.contributor.coauthorJakobi, Martin
dc.contributor.coauthorKoch, Alexander W.
dc.contributor.coauthorYetisen, Ali K.
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.kuauthorTaşoğlu, Savaş
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2024-11-09T12:00:03Z
dc.date.issued2021
dc.description.abstractColorimetric tests for at-home health monitoring became popular 50 years ago with the advent of the urinalysis test strips, due to their reduced costs, practicality, and ease of operation. However, developing digital systems that can interface these sensors in an efficient manner remains a challenge. Efforts have been put towards the development of portable optical readout systems, such as smartphones. However, their use in daily settings is still limited by their error-prone nature associated to optical noise from the ambient lighting, and their low sensitivity. Here, a smartphone application (Colourine) to readout colorimetric signals was developed on Android OS and tested on commercial urinalysis test strips for pH, proteins, and glucose detection. The novelty of this approach includes two features: a pre-calibration step where the user is asked to take a photo of the commercial reference chart, and a CIE-RGB-to-HSV color space transformation of the acquired data. These two elements allow the background noise given by environmental lighting to be minimized. The sensors were characterized in the ambient light range 100-400 lx, yielding a reliable output. Readouts were taken from urine strips in buffer solutions of pH (5.0-9.0 units), proteins (0-500 mg dL(-1)) and glucose (0-1000 mg dL(-1)), yielding a limit of detection (LOD) of 0.13 units (pH), 7.5 mg dL(-1) (proteins) and 22 mg dL(-1) (glucose), resulting in an average LOD decrease by about 2.8 fold compared to the visual method.
dc.description.fulltextYES
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue38
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC) New Investigator Award
dc.description.sponsorshipCentral Universities Fundamental Research Funds
dc.description.sponsorshipKU-KAIST Joint Research Center
dc.description.sponsorshipSandooq Al Watan LLC (SWARD)
dc.description.versionPublisher version
dc.description.volume13
dc.identifier.doi10.1039/d1ay01209f
dc.identifier.eissn1759-9679
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR03206
dc.identifier.issn1759-9660
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85117497818
dc.identifier.urihttps://hdl.handle.net/20.500.14288/936
dc.identifier.wos693674400001
dc.keywordsPaper
dc.keywordsPoint of care testing
dc.keywordsSmartphone
dc.language.isoeng
dc.publisherRoyal Society of Chemistry (RSC)
dc.relation.grantnoEP/T013567/1
dc.relation.grantnoYJ202152
dc.relation.grantno8474000220-KKJRC-2019-Health1
dc.relation.grantno8434000391-EX2020-044
dc.relation.ispartofAnalytical Methods
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/9968
dc.subjectChemistry
dc.subjectFood science and technology
dc.subjectSpectroscopy
dc.titleSmartphone-based colorimetric detection system for portable health tracking
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorTaşoğlu, Savaş
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Mechanical Engineering
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relation.isOrgUnitOfPublication.latestForDiscoveryba2836f3-206d-4724-918c-f598f0086a36
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