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dc.contributor.authorAyvaz, Hüseyin
dc.contributor.authorMortas, Mustafa
dc.contributor.authorDoğan, Muhammed Ali
dc.contributor.authorAtan, Mustafa
dc.contributor.authorYıldız Tiryaki, Gülgün
dc.contributor.authorKaragül Yüceer, Yonca
dc.date.accessioned2022-12-05T11:34:17Z
dc.date.available2022-12-05T11:34:17Z
dc.date.issued2021en_US
dc.identifier.citationAyvaz, H., Mortaş, M., Doğan, M. A., Atan, M., Tiryaki, G. Y. Yüceer, Y. K. (2021). Near- and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species. Journal of Food Science and Technology, 58(10), 3981–3992. https://doi.org/10.1007/s13197-020-04861-0
dc.identifier.issn0022-1155 / 0975-8402
dc.identifier.urihttps://doi.org/10.1007/s13197-020-04861-0
dc.identifier.urihttps://hdl.handle.net/20.500.12428/3750
dc.description.abstractThis study aimed to evaluate the performance of both near-infrared (NIR) diffuse reflectance and mid-infrared-attenuated total reflectance (MIR-ATR) in determining some quality parameters of a commercial white cheese made of unknown ratios of various milk species. For this purpose, 81 commercial Ezine cheese samples, a special ripened cheese produced in Turkey, containing unknown ratios of bovine, caprine, and ovine milk, were used. Reference analyses, including textural properties, protein content, nitrogen fractions, ripening index coefficients, fat, salt, dry matter-moisture, and ash contents as well as pH and titratable acidity levels, were conducted in the samples following the traditional gold standards. For NIR applications, the spectra of both intact cubes and hand-crushed cheese samples were collected, whereas the spectra of only hand-crushed cheese samples were collected for MIR-ATR. PLSR (Partial Least Squares Regression) calibration models were developed for each parameter (n = 61) and then validated using both cross-validation (leave-one-out approach) and an external validation set (n = 20). Overall, PLSR models developed for total protein, fat, salt, dry matter, moisture, and ash content, as well as pH and titratable acidity, yielded satisfactory performance statistics in the complementary use of NIR and MIR spectroscopy. However, PLSR models of the other parameters, including textural properties, nitrogen fractions, and the ripening index, could only separate high and low values and were not able to make accurate quantitative predictions. NIR spectroscopy was found to be more accurate than that of MIR-ATR spectroscopy for almost all the parameters except for pH and titratable acidity, for which MIR-ATR spectroscopy was superior.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCheeseen_US
dc.subjectChemometricsen_US
dc.subjectDairyen_US
dc.subjectEzineen_US
dc.subjectNearinfrareden_US
dc.subjectMid-infrareden_US
dc.subjectQualityen_US
dc.titleNear- and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk speciesen_US
dc.typearticleen_US
dc.authorid0000-0001-9705-6921en_US
dc.authorid0000-0002-5524-7567en_US
dc.authorid-en_US
dc.authorid0000-0003-1920-8082en_US
dc.authorid0000-0002-9028-2923en_US
dc.relation.ispartofJournal of Food Science and Technology (JFST)en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Gıda Mühendisliği Bölümüen_US
dc.identifier.volume58en_US
dc.identifier.issue10en_US
dc.identifier.startpage3981en_US
dc.identifier.endpage3992en_US
dc.institutionauthorAyvaz, Hüseyin
dc.institutionauthorDoğan, Muhammed Ali
dc.institutionauthorAtan, Mustafa
dc.institutionauthorYıldız Tiryaki, Gülgün
dc.institutionauthorKaragül Yüceer, Yonca
dc.identifier.doi10.1007/s13197-020-04861-0en_US
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/116O737
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorwosidGBZ-7261-2022en_US
dc.authorwosidEVD-5701-2022en_US
dc.authorwosidENU-6053-2022en_US
dc.authorwosidEAF-2829-2022en_US
dc.authorwosidABI-7510-2020en_US
dc.identifier.wosqualityQ2en_US
dc.identifier.wosWOS:000579681900002en_US
dc.identifier.scopus2-s2.0-85092897946en_US
dc.identifier.pmidPMID: 34471322en_US


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