Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9771
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dc.contributor.authorKorel, Figen-
dc.contributor.authorBalaban, Murat Ömer-
dc.date.accessioned2021-01-24T18:28:28Z-
dc.date.available2021-01-24T18:28:28Z-
dc.date.issued2010-
dc.identifier.isbn978-140518070-2-
dc.identifier.urihttps://doi.org/10.1002/9781444325546.ch6-
dc.identifier.urihttps://hdl.handle.net/11147/9771-
dc.description.abstractThe increase in demand for seafood products has catalyzed the desire for higher standards regarding safety and quality issues. Since seafoods are perishable, freshness is a major quality parameter to be considered [1,2]. There is no unique freshness or spoilage indicator for seafood, therefore combinations of selected indicators need to be used to evaluate freshness [3,4]. An important and widely used method to determine freshness is sensory evaluation [5]. The Quality Index Method (QIM) uses a demerit point scoring system [6] based on the evaluation of the important sensory attributes (odour, texture, and appearance) of fish and other aquatic foods. The sensory quality is expressed by the sum of the demerit points, and a linear correlation between these points and the storage time is used to predict the freshness of the target seafood [5,7,8]. The QIM has been developed for various seafood species and products, such as Atlantic mackerel (Scomber scombrus), horse mackerel (Trachurus trachurus), European sardine (Sardina pilchardus) [9], gilthead seabream (Sparus aurata) [10], farmed Atlantic salmon (Salmo salar) [11,12], and cod (Gadus morhua) [13], etc. Even though QIM is fast and reliable in determining the freshness of seafood, it still requires experts to evaluate the quality attributes. Alternatively, appearance, odour, and taste can be measured by machine vision system (MVS), electronic nose (e-nose), and electronic tongue (e-tongue), respectively.en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofHandbook of Seafood Quality, Safety and Health Applicationsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectQuality Index Methoden_US
dc.subjectElectronic noseen_US
dc.subjectElectronic tongueen_US
dc.subjectAquatic foodsen_US
dc.titleQuality assessment of aquatic foods by machine vision, electronic nose, and electronic tongueen_US
dc.typeBook Parten_US
dc.institutionauthorKorel, Figen-
dc.departmentİzmir Institute of Technology. Food Engineeringen_US
dc.identifier.startpage68en_US
dc.identifier.endpage81en_US
dc.identifier.scopus2-s2.0-79952733493en_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.identifier.doi10.1002/9781444325546.ch6-
dc.relation.doi10.1002/9781444325546.ch6en_US
dc.coverage.doi10.1002/9781444325546.ch6en_US
dc.identifier.wosqualityN/A-
dc.identifier.scopusqualityN/A-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeBook Part-
crisitem.author.dept03.08. Department of Food Engineering-
Appears in Collections:Food Engineering / Gıda Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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