Near- and Mid-Infrared Spectroscopy Combined with Machine Learning Algorithms to Determine Minerals and Antioxidant Activity in Commercial Cheese

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dc.authoridAyvaz, Hüseyin / 0000-0001-9705-6921
dc.contributor.authorMenevşeoğlu, Ahmed
dc.contributor.authorGüneş, Nurhan
dc.contributor.authorAyvaz, Hüseyin
dc.contributor.authorÖztürk Sarıkaya, Sevim Beyza
dc.contributor.authorZehiroğlu, Cuma
dc.date.accessioned2025-01-27T19:26:38Z
dc.date.available2025-01-27T19:26:38Z
dc.date.issued2023
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractErzincan Tulum Cheese (ETC) holds a significant place among the most popular cheeses in Türkiye. It has been awarded Protected Geographical Indication status, which restricts the allowable milk species, its production area, and specific sheep breed used in its production. Mineral content and antioxidant activity of ETC were aimed to be predicted using conventional FT-NIR and a portable FT-MIR spectrometer combined with partial least square regression (PLSR) and machine learning algorithms based on conditional entropy. Seventy ETC samples were analyzed for their mineral (Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, and P) content using ICP-MS. The samples' antioxidant activity was measured using the DPPH•+ scavenging activity method. PLSR combined with FT-NIR spectral data correlated with antioxidant activity (r=0.89) and minerals (as low as r=0.83) except for Cr and Fe. FT-MIR data provided a good correlation for minerals (as low as r=0.82) except for Cr and Mn and a moderate correlation with antioxidant activity (r=0.64). Information theory was applied to select wavenumbers used in machine learning algorithms, and better results were obtained compared to PLSR. Overall, FT-NIR and FT-MIR spectroscopy provided rapid (~ 1 min), non-destructive, sensitive, and reliable output for mineral and antioxidant activity predictions in commercial cheese samples.
dc.identifier.doi10.24925/turjaf.v11i12.2435-2445.6526
dc.identifier.endpage2445
dc.identifier.issn2148-127X
dc.identifier.issue12
dc.identifier.startpage2435
dc.identifier.trdizinid1257227
dc.identifier.urihttps://doi.org/10.24925/turjaf.v11i12.2435-2445.6526
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1257227
dc.identifier.urihttps://hdl.handle.net/20.500.12428/15570
dc.identifier.volume11
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTurkish Science and Technology Publishing (TURSTEP)
dc.relation.ispartofTürk Tarım - Gıda Bilim ve Teknoloji Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TRD_20250125
dc.subjectErzincan Tulum Cheese
dc.subjectFT-MIR
dc.subjectMinerals
dc.subjectFT-NIR
dc.subjectMachine learning
dc.titleNear- and Mid-Infrared Spectroscopy Combined with Machine Learning Algorithms to Determine Minerals and Antioxidant Activity in Commercial Cheese
dc.typeArticle

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