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Assessing the Shelf-Life of Olive Oil Under Different Storage Conditions: A Review of Predictive Models

2025Artigo científico Desconhecido
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datacite.subject.fosCiências Agrárias::Agricultura, Silvicultura e Pescas
datacite.subject.fosCiências Agrárias::Biotecnologia Agrária e Alimentar
datacite.subject.fosCiências Naturais::Ciências da Terra e do Ambiente
datacite.subject.sdg12:Produção e Consumo Sustentáveis
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorFerreiro, Nuno Manuel
dc.contributor.authorVeloso, Ana C.A.
dc.contributor.authorPereira, José Alberto
dc.contributor.authorRodrigues, Nuno
dc.contributor.authorPeres, António M.
dc.date.accessioned2026-02-10T16:28:29Z
dc.date.available2026-02-10T16:28:29Z
dc.date.issued2025
dc.description.abstractOlive oil holds a significant position in the global vegetable oil market, often reaching high prices compared to other vegetable oils. However, like other oils, it is vulnerable to oxidation, which can degrade its quality during storage, making it essential to determine its shelf-life. So, kinetic or empirical models have been developed to estimate how long olive oil can maintain the legal quality standards necessary for its commercial classification or to be marketed with nutritional or health claim. This study reviews recent advancements in modelling approaches to predict the shelf-life of olive oil under different storage conditions, namely storage duration (from 2 months to 2 years), temperature (20–50 ºC), and light exposure (light versus dark storage). Most models estimate the timeframe in which olive oil remains compliant with regulatory requirements for specific commercial grades, namely extra virgin olive oil, with fewer models addressing health-related claims. Developed models include pseudo zero-, pseudo first-, and pseudo second-order kinetic models and empirical models, derived from experimental data on the oil’s chemical stability over time. While empirical models can be highly accurate, they often require extensive chemical data, including for compounds for which no legal thresholds exist, and complex statistical techniques, limiting their use by non-specialists. In contrast, kinetic models offer simpler and user-friendly mathematical equations. Nonetheless, olive oil’s shelf-life predictions remain influenced by factors such as initial oil composition, packaging materials, and storage conditions, underscoring the ongoing need to refine the predictive models.eng
dc.description.sponsorshipOpen access funding provided by FCT|FCCN (b-on). The 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, https://doi.org/10.54499/UIDB/00690/2020; and UIDP/00690/2020, https://doi.org/https://doi.org/10.54499/UIDP/00690/2020) unit as well as to the Associate Laboratory SusTEC (LA/P/0007/2020). The authors are also grateful to the project “Agenda VIIAFOOD—Platform for Valorization, Industrialization and Commercial Innovation in Agri-Food” (no. C644929456- 00000040), financed by the Recovery and Resilience Plan. Nuno Ferreiro acknowledges the Ph.D. research grant (2022.10072.BD) provided by FCT. Nuno Rodrigues also acknowledges the national funding by FCT, through the institutional scientific employment program-contract.
dc.identifier.citationFerreiro, Nuno; Veloso, Ana C.A.; Pereira, José Alberto; Rodrigues, Nuno; Peres, António M. (2025). Assessing the Shelf-Life of Olive Oil Under Different Storage Conditions: A Review of Predictive Models. Food Engineering Reviews. ISSN 1866-7910. 17:3, p. 1-19
dc.identifier.doi10.1007/s12393-025-09409-6
dc.identifier.eissn1866-7929
dc.identifier.issn1866-7910
dc.identifier.urihttp://hdl.handle.net/10198/35707
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationMountain Research Center
dc.relationMountain Research Center
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.ispartofFood Engineering Reviews
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectKinetic-based models
dc.subjectEmpirical-based models
dc.subjectStorage conditions
dc.subjectCommercial category
dc.subjectNutritional claims
dc.subjectHealth claims
dc.titleAssessing the Shelf-Life of Olive Oil Under Different Storage Conditions: A Review of Predictive Modelseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/00690/2020
oaire.awardNumberUIDP/00690/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleMountain Research Center
oaire.awardTitleMountain Research Center
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
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/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage19
oaire.citation.issue3
oaire.citation.startPage1
oaire.citation.titleFood Engineering Reviews
oaire.citation.volume17
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamePereira
person.familyNameRodrigues
person.familyNamePeres
person.givenNameJosé Alberto
person.givenNameNuno
person.givenNameAntónio M.
person.identifier107333
person.identifier.ciencia-id611F-80B2-A7C1
person.identifier.ciencia-idF41D-B424-5F78
person.identifier.ciencia-idCF16-5443-F420
person.identifier.orcid0000-0002-2260-0600
person.identifier.orcid0000-0002-9305-0976
person.identifier.orcid0000-0001-6595-9165
person.identifier.ridL-6798-2014
person.identifier.ridI-8470-2012
person.identifier.scopus-author-id57204366348
person.identifier.scopus-author-id55258560600
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
relation.isAuthorOfPublication7932162e-a2da-4913-b00d-17babbe51857
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