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An electronic nose as a non-destructive analytical tool to identify the geographical origin of portuguese olive oils from two adjacent regions

2022Journal article Open access
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dc.contributor.authorRodrigues, Nuno
dc.contributor.authorFerreiro, Nuno Manuel
dc.contributor.authorVeloso, Ana C.A.
dc.contributor.authorPereira, J.A.
dc.contributor.authorPeres, António M.
dc.date.accessioned2023-02-07T15:56:52Z
dc.date.available2023-02-07T15:56:52Z
dc.date.issued2022
dc.description.abstractThe geographical traceability of extra virgin olive oils (EVOO) is of paramount importance for oil chain actors and consumers. Oils produced in two adjacent Portuguese regions, Côa (36 oils) and Douro (31 oils), were evaluated and fulfilled the European legal thresholds for EVOO categorization. Compared to the Douro region, oils from Côa had higher total phenol contents (505 versus 279 mg GAE/kg) and greater oxidative stabilities (17.5 versus 10.6 h). The majority of Côa oils were fruity-green, bitter, and pungent oils. Conversely, Douro oils exhibited a more intense fruity-ripe and sweet sensation. Accordingly, different volatiles were detected, belonging to eight chemical families, from which aldehydes were the most abundant. Additionally, all oils were evaluated using a lab-made electronic nose, with metal oxide semiconductor sensors. The electrical fingerprints, together with principal component analysis, enabled the unsupervised recognition of the oils’ geographical origin, and their successful supervised linear discrimination (sensitivity of 98.5% and specificity of 98.4%; internal validation). The E-nose also quantified the contents of the two main volatile chemical classes (alcohols and aldehydes) and of the total volatiles content, for the studied olive oils split by geographical origin, using multivariate linear regression models (0.981 < R2 < 0.998 and 0.40 < RMSE < 2.79 mg/kg oil; internal validation). The E-nose-MOS was shown to be a fast, green, non-invasive and cost-effective tool for authenticating the geographical origin of the studied olive oils and to estimate the contents of the most abundant chemical classes of volatiles.pt_PT
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support by national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020), to CEB (UIDB/04469/2020) and to the Associate Laboratory SusTEC (LA/P/0007/2020). The authors are also grateful to the “Project OLIVECOA—Centenarian olive trees of Côa Valley region: rediscovering the past to valorize the future” (ref. COA/BRB/0035/2019), financed by FCT (Portugal). Nuno Rodrigues thanks the National funding by FCT- Foundation for Science and Technology, P.I., through the institutional scientific employment program-contract.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationRodrigues, Nuno; Ferreiro, Nuno; Veloso, Ana C.A.; Pereira, J.A.; Peres, António M. (2022). An electronic nose as a non-destructive analytical tool to identify the geographical origin of portuguese olive oils from two adjacent regions. Sensors. ISSN 1424-3210. 22:24, p. 1-14pt_PT
dc.identifier.doi10.3390/s22249651pt_PT
dc.identifier.eissn1424-8220
dc.identifier.issn1424-3210
dc.identifier.urihttp://hdl.handle.net/10198/26780
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationLA/P/0007/2020pt_PT
dc.relationMountain Research Center
dc.relationMountain Research Center
dc.relationCentre of Biological Engineering of the University of Minho
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEVOO qualitypt_PT
dc.subjectSensory analysispt_PT
dc.subjectOxidative stabilitypt_PT
dc.subjectMetal oxide semiconductor sensorspt_PT
dc.subjectMultivariate qualitative-quantitative analysispt_PT
dc.subjectResistance electrical signalspt_PT
dc.subjectFeature extraction parameterspt_PT
dc.titleAn electronic nose as a non-destructive analytical tool to identify the geographical origin of portuguese olive oils from two adjacent regionspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleMountain Research Center
oaire.awardTitleMountain Research Center
oaire.awardTitleCentre of Biological Engineering of the University of Minho
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00690%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04469%2F2020/PT
oaire.citation.issue24pt_PT
oaire.citation.startPage9651pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume22pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameRodrigues
person.familyNamePereira
person.familyNamePeres
person.givenNameNuno
person.givenNameJosé Alberto
person.givenNameAntónio M.
person.identifier107333
person.identifier.ciencia-idF41D-B424-5F78
person.identifier.ciencia-id611F-80B2-A7C1
person.identifier.ciencia-idCF16-5443-F420
person.identifier.orcid0000-0002-9305-0976
person.identifier.orcid0000-0002-2260-0600
person.identifier.orcid0000-0001-6595-9165
person.identifier.ridL-6798-2014
person.identifier.ridI-8470-2012
person.identifier.scopus-author-id55258560600
person.identifier.scopus-author-id57204366348
person.identifier.scopus-author-id7102331969
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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