DC FieldValueLanguage
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorSpiliopoulos, Ioakeim-
dc.date.accessioned2024-07-05T08:14:16Z-
dc.date.available2024-07-05T08:14:16Z-
dc.date.issued2024-01-01-
dc.identifierscopus-85194149723-
dc.identifier.issn2224-3496-
dc.identifier.issn1790-5079-
dc.identifier.other85194149723-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2677-
dc.description.abstractKalamata is a smart city located in southeastern Greece in the Mediterranean basin and it is the capital of the Messenia regional unit. It is known for the famous Protected Designation of Origin (PDO) Kalamata olive oil produced mainly from the Koroneiki olive variety. The PDO Kalamata olive oil, established by Council regulation (EC) No 510/2006, owes its quality and special characteristics to the geographical environment, olive tree variety, and human factor. The PDO Kalamata olive oil is produced exclusively in the regional unit of Messenia, being the main profit of local farmers. However, soil chemical composition, microclimates, and agronomic factors are changed within the Messenia spatial area leading to differentiation of PDO Kalamata olive oil characteristic. In this paper, we use statistical machine learning algorithms to determine the geographical origin of Kalamata olive oil at PDO level based on synchronous excitation−emission fluorescence spectroscopy of olive oils. Evaluations of the statistical models are promising for differentiating the origin of PDO Kalamata olive oil with high values of prediction accuracy thus enabling companies that process and bottle kalamata olive oil to choose olive oil from a specific region of Messenia that fulfills certain characteristics. Concretely, the current research effort focuses on a specific olive oil variety within a limited geographic region. Intuitively, future research should also focus on validation of the proposed methodology to other olive oil varieties and production areas.en_US
dc.language.isoenen_US
dc.relation.ispartofWSEAS Transactions on Environment and Developmenten_US
dc.subjectData fusionen_US
dc.subjectData visualizationen_US
dc.subjectFluorescence spectroscopyen_US
dc.subjectModel evaluationen_US
dc.subjectMulticlass classificationen_US
dc.subjectPDO Kalamata olive oilen_US
dc.subjectStatistical machine learningen_US
dc.subjectSynchronous emission-excitationen_US
dc.titleClassifying PDO Kalamata Olive Oil from Geographic Origins of the Messenia Region based on Statistical Machine Learningen_US
dc.typeArticleen_US
dc.identifier.doi10.37394/232015.2024.20.15en_US
dc.identifier.scopus2-s2.0-85194149723-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume20en_US
dc.identifier.spage137en_US
dc.identifier.epage147en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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