Publication
A machine learning approach for enhanced glucose prediction in biosensors
| datacite.subject.fos | Ciências Médicas::Ciências da Saúde | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Abreu, António | |
| dc.contributor.author | Oliveira, Daniela dos Santos | |
| dc.contributor.author | Vinagre, Inês | |
| dc.contributor.author | Cavouras, Dionisios | |
| dc.contributor.author | Alves, Joaquim A. | |
| dc.contributor.author | Pereira, Ana I. | |
| dc.contributor.author | Lima, Jose | |
| dc.contributor.author | Moreira, Felismina T. C. | |
| dc.date.accessioned | 2025-06-27T09:46:23Z | |
| dc.date.available | 2025-06-27T09:46:23Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The detection of glucose is crucial for diagnosing diseases such as diabetes and enables timely medical intervention. In this study, a disposable enzymatic screen-printed electrode electrochemical biosensor enhanced with machine learning (ML) for quantifying glucose in serum is presented. The platinum working surface was modified by chemical adsorption with biographene (BGr) and glucose oxidase, and the enzyme was encapsulated in polydopamine (PDP) by electropolymerisation. Electrochemical characterisation and morphological analysis (scanning and transmission electron microscopy) confirmed the modifications. Calibration curves in Cormay serum (CS) and selectivity tests with chronoamperometry were used to evaluate the biosensor’s performance. Non-linear ML regression algorithms for modelling glucose concentration and calibration parameters were tested to find the best-fit model for accurate predictions. The biosensor with BGr and enzyme encapsulation showed excellent performance with a linear range of 0.75–40 mM, a correlation of 0.988, and a detection limit of 0.078 mM. Of the algorithms tested, the decision tree accurately predicted calibration parameters and achieved a coefficient of determination above 0.9 for most metrics. Multilayer perceptron models effectively predicted glucose concentration with a coefficient of determination of 0.828, demonstrating the synergy of biosensor technology and ML for reliable glucose detection. | por |
| dc.description.sponsorship | The authors acknowledge the financial support from the IBEROS+ project (Instituto de Biofabricación en Red para el Envejecimiento Saludable, INTERREG POCTEP/0072_IBEROS_MAIS_1_E) within the cooperation region of Galicia/Spain and North of Portugal. Additionally, they would like to thank the partial support from the Portuguese Foundation for Science and Technology (FCT), through grants UIDB/04730/2020 and NotUIDP/04730/2020 | |
| dc.identifier.citation | Abreu, António; Oliveira, Daniela dos Santos; Cavouras, Dionisios; Alves, Joaquim A.; Pereira, Ana I.; Lima, Jose; Moreira, Felismina T. C. (2025). A machine learning approach for enhanced glucose prediction in biosensors. Chemosensors. ISSN 2227-9040. 13:2, p. 1-17 | |
| dc.identifier.doi | 10.3390/chemosensors13020052 | |
| dc.identifier.issn | 2227-9040 | |
| dc.identifier.uri | http://hdl.handle.net/10198/34628 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation | 04730/2020 | |
| dc.relation.ispartof | Chemosensors | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Enzymatic biosensor | |
| dc.subject | Glucose | |
| dc.subject | Electrochemistry | |
| dc.subject | Machine learning | |
| dc.title | A machine learning approach for enhanced glucose prediction in biosensors | por |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 17 | |
| oaire.citation.issue | 2 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | Chemosensors | |
| oaire.citation.volume | 13 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Pereira | |
| person.familyName | Lima | |
| person.givenName | Ana I. | |
| person.givenName | Jose | |
| person.identifier.ciencia-id | 0716-B7C2-93E4 | |
| person.identifier.orcid | 0000-0003-3803-2043 | |
| person.identifier.orcid | 0000-0002-3666-1439 | |
| person.identifier.rid | F-3168-2010 | |
| person.identifier.scopus-author-id | 15071961600 | |
| relation.isAuthorOfPublication | e9981d62-2a2b-4fef-b75e-c2a14b0e7846 | |
| relation.isAuthorOfPublication | 51292fe4-0d7a-4427-9499-6ccde02b78bc | |
| relation.isAuthorOfPublication.latestForDiscovery | e9981d62-2a2b-4fef-b75e-c2a14b0e7846 |