Determining the quality level of ready to-eat stuffed mussels with Arduino-based electronic nose

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dc.authoridYavuzer, Emre/0000-0002-9192-713X
dc.contributor.authorYavuzer, Emre
dc.contributor.authorKose, Memduh
dc.contributor.authorUslu, Hasan
dc.date.accessioned2024-11-07T13:31:20Z
dc.date.available2024-11-07T13:31:20Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this study, the performance of a pre-designed and low-cost Arduino electronic nose for determining the quality of stuffed mussels was analyzed. In addition, 1000 images were taken on each storage day in order to determine the quality levels of stuffed mussel groups with open and closed shells by machine learning. Freshness limit values of stuffed mussels were determined as 200 for MQ3 and MQ135 sensors and 100 for MQ9 on the 3rd storage day when the total viable count (TVC) value exceeded 3 log CFU/g. In the study, faster neural networks with lower prediction times, such as SqueezeNet and GoogLeNet, were compared with ResNet-50, ResNet-101 and DenseNet-201 neural networks, which have larger prediction times but better accuracy. Study data showed that residual network (ResNet) 50 and Teachable Machine (TM) had high success in determining the quality levels of stuffed mussels.
dc.description.sponsorshipNigde Omer Halisdemir University
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.1007/s11694-024-02593-9
dc.identifier.endpage5637
dc.identifier.issn2193-4126
dc.identifier.issn2193-4134
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85193573670
dc.identifier.scopusqualityQ2
dc.identifier.startpage5629
dc.identifier.urihttps://doi.org/10.1007/s11694-024-02593-9
dc.identifier.urihttps://hdl.handle.net/11480/14759
dc.identifier.volume18
dc.identifier.wosWOS:001228360800002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Food Measurement and Characterization
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectElectronic nose
dc.subjectPrediction accuracy
dc.subjectMQ sensor
dc.subjectMussel quality
dc.titleDetermining the quality level of ready to-eat stuffed mussels with Arduino-based electronic nose
dc.typeArticle

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