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dc.contributor.authorAyvaz, Hüseyin
dc.contributor.authorKorkmaz, Fatma
dc.contributor.authorPolat, Havva
dc.contributor.authorAyvaz, Zayde
dc.contributor.authorTuncel, Necati Barış
dc.date.accessioned2024-12-25T06:41:33Z
dc.date.available2024-12-25T06:41:33Z
dc.date.issued2021en_US
dc.identifier.citationAyvaz, H., Korkmaz, F., Polat, H., Ayvaz, Z., & Barış Tuncel, N. (2021). Detection of einkorn flour adulteration in flour and bread samples using Computer-Based Image Analysis and Near-Infrared Spectroscopy. Food Control, 127, 108162. https://doi.org/10.1016/j.foodcont.2021.108162en_US
dc.identifier.issn0956-7135 / 1873-7129
dc.identifier.urihttps://doi.org/10.1016/j.foodcont.2021.108162
dc.identifier.urihttps://hdl.handle.net/20.500.12428/6790
dc.description.abstractEinkorn (Triticum monocum L.) is an old variety of wheat and is considered an ancient grain. Currently, limited amounts of einkorn are produced within a few regions of Europe and the US, and therefore it is sold at higher prices than common wheat. Either for unfair economic gain (adulteration) or to compensate its weaker gluten structure, einkorn flour tends to be adulterated with bread wheat flour, which is frequently encountered in commercial einkorn flour or einkorn baked products. In this study, einkorn and bread wheat flours were initially analyzed for their common quality properties following the traditional analytical methods. Then, two rapid methods, Computer-Based Image (CBI) Analysis and Near-Infrared Spectroscopy (NIRS), were evaluated to rapidly estimate the % level of bread wheat flour in both einkorn-wheat flour mixtures and the bread made of those mixtures. For this purpose, binary mixtures of einkorn flour and the adulterating bread wheat flour were prepared for calibration (46 samples) and external validation (18 samples) sets, with wheat flour content in the mixtures ranging between 5 and 95% (w/w). Then, for each binary mixture, a loaf of conventional bread was produced. Flour mixtures and loaves of bread samples produced were analyzed by both CBI and NIRS. Our results suggested that CBI could only yield high correlation levels between the wheat flour content and some color properties in bread samples (>0.96), while no sufficient correlations were observed in flour mixtures. Regarding the NIRS, highly accurate models were developed for both flour mixtures (correlation coefficients > 0.99, standard errors < 1.39% and RPD level of 19.3) and bread samples (correlation coefficients > 0.94, standard errors < 2.64% and RPD level of 10.1). Our results indicate that both NIRS and CBI may be implemented in the rapid and easy screening of wheat flour adulteration in einkorn bread, while only NIRS is suggested to be used for the same purpose in flour mixtures.en_US
dc.language.isoengen_US
dc.publisherElsevier Ltden_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdulterationen_US
dc.subjectChemometricsen_US
dc.subjectEinkornen_US
dc.subjectFlouren_US
dc.subjectImage analysisen_US
dc.subjectNear-infrareden_US
dc.titleDetection of einkorn flour adulteration in flour and bread samples using Computer-Based Image Analysis and Near-Infrared Spectroscopyen_US
dc.typearticleen_US
dc.authorid0000-0001-9705-6921en_US
dc.authorid0000-0002-6834-4833en_US
dc.authorid0000-0002-7872-3810en_US
dc.authorid0000-0002-8102-0577en_US
dc.authorid0000-0001-9885-5063en_US
dc.relation.ispartofFood Controlen_US
dc.departmentFakülteler, Çanakkale Uygulamalı Bilimler Fakültesi, Gıda Teknolojisi Bölümüen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Gıda Mühendisliği Bölümüen_US
dc.identifier.volume127en_US
dc.institutionauthorAyvaz, Hüseyin
dc.institutionauthorKorkmaz, Fatma
dc.institutionauthorPolat, Havva
dc.institutionauthorAyvaz, Zayde
dc.institutionauthorTuncel, Necati Barış
dc.identifier.doi10.1016/j.foodcont.2021.108162en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorwosidGBZ-7261-2022en_US
dc.authorwosidGZL-6673-2022en_US
dc.authorwosidHDM-5441-2022en_US
dc.authorwosidE-4827-2012en_US
dc.authorwosidAAG-4333-2019en_US
dc.authorscopusid54792612800en_US
dc.authorscopusid57225350939en_US
dc.authorscopusid7003889205en_US
dc.authorscopusid57191202269en_US
dc.authorscopusid56253984800en_US
dc.identifier.wosqualityQ1en_US
dc.identifier.wosWOS:000649606900011en_US
dc.identifier.scopus2-s2.0-85104438775en_US


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