Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5869
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dc.contributor.authorKadiroğlu, Pınar-
dc.contributor.authorKorel, Figen-
dc.date.accessioned2017-07-06T08:21:11Z-
dc.date.available2017-07-06T08:21:11Z-
dc.date.issued2015-09-
dc.identifier.citationKadiroğlu, P., and Korel, F. (2015). Chemometric studies on zNose™ and machine vision technologies for discrimination of commercial extra virgin olive oils. JAOCS, Journal of the American Oil Chemists' Society, 92(9), 1235-1242. doi:10.1007/s11746-015-2697-1en_US
dc.identifier.issn0003-021X-
dc.identifier.urihttps://doi.org/10.1007/s11746-015-2697-1-
dc.identifier.urihttp://hdl.handle.net/11147/5869-
dc.description.abstractThe aim of this study was to classify Turkish commercial extra virgin olive oil (EVOO) samples according to geographical origins by using surface acoustic wave sensing electronic nose (zNose™) and machine vision system (MVS) analyses in combination with chemometric approaches. EVOO samples obtained from north and south Aegean region were used in the study. The data analyses were performed with principal component analysis class models, partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). Based on the zNose™ analysis, it was found that EVOO aroma profiles could be discriminated successfully according to geographical origin of the samples with the aid of the PLS-DA method. Color analysis was conducted as an additional sensory quality parameter that is preferred by the consumers. The results of HCA and PLS-DA methods demonstrated that color measurement alone was not an effective discriminative factor for classification of EVOO. However, PLS-DA and HCA methods provided clear differentiation among the EVOO samples in terms of electronic nose and color measurements. This study is significant from the point of evaluating the potential of zNose™ in combination with MVS as a rapid method for the classification of geographically different EVOO produced in industry.en_US
dc.language.isoenen_US
dc.publisherJohn Wiley and Sons Inc.en_US
dc.relation.ispartofJAOCS, Journal of the American Oil Chemists' Societyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChemometricsen_US
dc.subjectElectronic noseen_US
dc.subjectExtra virgin olive oilen_US
dc.subjectMachine vision systemen_US
dc.subjectSensory analysisen_US
dc.titleChemometric studies on zNose™ and machine vision technologies for discrimination of commercial extra virgin olive oilsen_US
dc.typeArticleen_US
dc.authoridTR110179en_US
dc.departmentIzmir Institute of Technology. Food Engineeringen_US
dc.identifier.volume92en_US
dc.identifier.issue9en_US
dc.identifier.startpage1235en_US
dc.identifier.endpage1242en_US
dc.identifier.wosWOS:000360934700001
dc.identifier.scopusSCOPUS:2-s2.0-84941315064
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s11746-015-2697-1-
dc.relation.doi10.1007/s11746-015-2697-1en_US
dc.coverage.doi10.1007/s11746-015-2697-1en_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.deptDepartment of Food Engineering-
Appears in Collections:Food Engineering / Gıda Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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