Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers

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dc.authorid0000-0003-0450-8058en_US
dc.authorid0000-0002-5500-0441en_US
dc.authorid0000-0003-4905-5021en_US
dc.contributor.authorYaman, Hülya
dc.contributor.authorAykas, Didem Peren
dc.contributor.authorFlores, Rafael Jimenez
dc.contributor.authorSaona, Luis E. Rodriguez
dc.date.accessioned2023-06-21T13:21:22Z
dc.date.available2023-06-21T13:21:22Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Yeniçağa Yaşar Çelik Meslek Yüksekokulu, Gıda Kalite Kontrolü ve Analizi Bölümüen_US
dc.descriptionThis research did not receive any specific grant from funding agencies in the public, commercial, or not-forprofit sectors. The author Hulya Yaman thanks The Scientific and Technological Research Council of Turkey (TUBITAK) for supporting her research at The Ohio State University. The authors have not stated any conflicts of interest.en_US
dc.description.abstractMonitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (RPre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.en_US
dc.identifier.citationYaman, H., Aykas, D. P., Jiménez-Flores, R., & Rodriguez-Saona, L. E. (2022). Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers. Journal of Dairy Science, 105(1), 40-55.en_US
dc.identifier.doi10.3168/jds.2021-20313
dc.identifier.endpage55en_US
dc.identifier.issn0022-0302
dc.identifier.issn1525-3198
dc.identifier.issue1en_US
dc.identifier.pmid34696910en_US
dc.identifier.scopus2-s2.0-85117825528en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage40en_US
dc.identifier.urihttp://dx.doi.org/10.3168/jds.2021-20313
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11167
dc.identifier.volume105en_US
dc.identifier.wosWOS:000734351700005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorYaman, Hülya
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofJournal of Dairy Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMid-Infrared Spectroscopyen_US
dc.subjectNear-Infrared Spectroscopyen_US
dc.subjectRaman Spectroscopyen_US
dc.subjectTurkish White Cheeseen_US
dc.subjectRipeningen_US
dc.subjectFatty-Acid-Compositionen_US
dc.titleMonitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometersen_US
dc.typeArticleen_US

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