Rapid detection of sea bass quality level with machine learning and electronic nose

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dc.authoridYavuzer, Emre/0000-0002-9192-713X
dc.contributor.authorYavuzer, Emre
dc.date.accessioned2024-11-07T13:24:25Z
dc.date.available2024-11-07T13:24:25Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn the present study, the odour changes of sea bass was measured and recorded with Arduino-based six gas (MQ) sensors during 7 days of storage. In addition, the changes in biogenic amines (BAs) and total viable count (TVC) occurring in fish meat during storage were determined daily and compared with electronic nose data. For image processing, 500 fish data were taught to Teachable Machine (TM) daily, a web-based machine learning platform. In the study, it was determined that if the level of putrescine (PUT) exceeds 1 mg kg(-1) and spermidine (SPMD) exceeds the level of 2 mg kg(-1), the MQ4, MQ5, MQ8 and MQ9 sensors exceed the value of 500. In the study, it was determined that combined machine learning and electronic nose could be used as a rapid quality determination tool in a perishable food such as fish.
dc.identifier.doi10.1111/ijfs.16365
dc.identifier.endpage2359
dc.identifier.issn0950-5423
dc.identifier.issn1365-2621
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85149226883
dc.identifier.scopusqualityQ1
dc.identifier.startpage2355
dc.identifier.urihttps://doi.org/10.1111/ijfs.16365
dc.identifier.urihttps://hdl.handle.net/11480/14102
dc.identifier.volume58
dc.identifier.wosWOS:000943732300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal of Food Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjecte Nose
dc.subjectmachine learning
dc.subjectsea bass
dc.subjectteachable machine
dc.titleRapid detection of sea bass quality level with machine learning and electronic nose
dc.typeArticle

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