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dc.contributor.authorÜnlütürk, Sevcan
dc.contributor.authorPelvan, Merve
dc.contributor.authorÜnlütürk, Mehmet S.
dc.date.accessioned2017-03-27T08:11:51Z
dc.date.available2017-03-27T08:11:51Z
dc.date.issued2013-11
dc.identifier.citationÜnlütürk, S., Pelvan, M., and Ünlütürk, M.S. (2013). The discrimination of raw and UHT milk samples contaminated with penicillin G and ampicillin using image processing neural network and biocrystallization methods. Journal of Food Composition and Analysis, 32(1), 12-19. doi:10.1016/j.jfca.2013.06.007en_US
dc.identifier.issn0889-1575
dc.identifier.urihttps://doi.org/10.1016/j.jfca.2013.06.007
dc.identifier.urihttp://hdl.handle.net/11147/5146
dc.description.abstractThis paper utilized a neural network for texture image analysis to differentiate between milk, either raw or ultra high temperature (UHT) with antibiotic residues (e.g., penicillin G and ampicillin) and milk without antibiotic residues. The biocrystallization method was applied to obtain biocrystallogram images for milk samples spiked with penicillin G and ampicillin at different concentration levels. The biocrystallogram images were used as an input for a designed neural network called the image processing neural network (ImgProcNN). The visual differences in these images that were based on textural properties, including the distribution of crystals on the circular grass underlay, the thin or thick structure of the crystal needles, and the angles between the branches and the side needles, were used to discriminate the antibiotic-free milk samples from samples with antibiotic residues. The visual description and definition of these images have major disadvantages. In this study, the ImgProcNN was developed to overcome the shortcomings of these visual descriptions and definitions. Overall, the neural network achieved an average recognition performance between 86% and 100%. This high level of recognition suggests that the neural network used in this paper has potential as a method for discriminating raw and UHT milk samples contaminated with different antibiotics.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.jfca.2013.06.007en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAmpicillinen_US
dc.subjectAntibiotic residuesen_US
dc.subjectBiocrystallizationen_US
dc.subjectFood analysisen_US
dc.subjectVeterinary residuesen_US
dc.subjectNeural networksen_US
dc.subjectPenicillin Gen_US
dc.titleThe discrimination of raw and UHT milk samples contaminated with penicillin G and ampicillin using image processing neural network and biocrystallization methodsen_US
dc.typearticleen_US
dc.contributor.authorIDTR44047en_US
dc.contributor.institutionauthorÜnlütürk, Sevcan
dc.contributor.institutionauthorPelvan, Merve
dc.relation.journalJournal of Food Composition and Analysisen_US
dc.contributor.departmentIzmir Institute of Technology. Food Engineeringen_US
dc.identifier.volume32en_US
dc.identifier.issue1en_US
dc.identifier.startpage12en_US
dc.identifier.endpage19en_US
dc.identifier.wosWOS:000328308300003
dc.identifier.scopusSCOPUS:2-s2.0-84887247290
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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