Publication:
Artificial intelligence-based identification of butter variations as a model study for detecting food adulteration

dc.contributor.coauthorErgen, Onur
dc.contributor.departmentN/A
dc.contributor.departmentN/A
dc.contributor.departmentN/A
dc.contributor.kuauthorİymen, Gökçe
dc.contributor.kuauthorTanrıver, Gizem
dc.contributor.kuauthorHayırlıoğlu, Yusuf Ziya
dc.contributor.kuprofileMaster Student
dc.contributor.kuprofileMaster Student
dc.contributor.kuprofileMaster Student
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.yokidN/A
dc.contributor.yokidN/A
dc.contributor.yokidN/A
dc.date.accessioned2024-11-09T23:22:59Z
dc.date.issued2020
dc.description.abstractThe demand for high-quality food products is increasing globally at unprecedented rates in response to growing health concerns and consumer awareness about healthy food options. Yet, the tools for determining food quality remain restricted to well-equipped laboratories, not readily accessible to consumers. Unfortunately, the current inspection mechanisms are limited and cannot keep track of all the products continuously, which exposes weakness in the system towards adulteration, falsification, and mislabeling products. Consumers rely on manufacturer labeling alone, with no convenient and user-friendly tool to confirm quality, especially for organic products. The advancement of Artificial Intelligence (AI) provides an opportunity for these tools to be developed. In this study, we demonstrate that simple sound vibrations traversing the food products can be used in conjunction with deep learning models to verify high quality products with no additives, as well as organic food products. Our neural network models, namely Parallel CNN-RNN and CRNN, achieve high accuracy on the defined classification tasks. To our knowledge, this is the first report of an AI-based tool utilizing simple sound vibrations to identify adulteration in food products.
dc.description.indexedbyWoS
dc.description.indexedbyScopus
dc.description.openaccessNO
dc.description.publisherscopeInternational
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) This research was fully supported by The Scientific and Technological Research Council of Turkey (TUBITAK).
dc.description.volume66
dc.identifier.doi10.1016/j.ifset.2020.102527
dc.identifier.eissn1878-5522
dc.identifier.issn1466-8564
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85094316825
dc.identifier.urihttp://dx.doi.org/10.1016/j.ifset.2020.102527
dc.identifier.urihttps://hdl.handle.net/20.500.14288/11165
dc.identifier.wos599527300012
dc.keywordsArtificial intelligence
dc.keywordsMachine learning
dc.keywordsFood fraud
dc.keywordsAdulteration
dc.keywordsMobile application
dc.keywordsOrganic food products
dc.keywordsFrequency response
dc.keywordsMFCCs
dc.keywordsParallel CNN-RNN
dc.keywordsCRNN
dc.keywordsConsumer perceptions
dc.keywordsOrganic food
dc.keywordsFraud
dc.keywordsMilk
dc.languageEnglish
dc.publisherElsevier Sci Ltd
dc.sourceInnovative Food Science and Emerging Technologies
dc.subjectFood science and technology
dc.titleArtificial intelligence-based identification of butter variations as a model study for detecting food adulteration
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.authorid0000-0002-6458-395X
local.contributor.authorid0000-0002-0195-5672
local.contributor.authoridN/A
local.contributor.kuauthorİymen, Gökçe
local.contributor.kuauthorTanrıver, Gizem
local.contributor.kuauthorHayırlıoğlu, Yusuf Ziya

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