Combining non-destructive devices and multivariate analysis as a tool to quantify the fatty acid profiles of linseed genotypes

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dc.authorscopusid57209328308
dc.authorscopusid57415989500
dc.authorscopusid57202255975
dc.authorscopusid6507115235
dc.authorscopusid6603760664
dc.contributor.authorArslan, Aysel
dc.contributor.authorAygun, Yusuf Ziya
dc.contributor.authorTurkmen, Musa
dc.contributor.authorCeliktas, Nafiz
dc.contributor.authorMert, Mehmet
dc.date.accessioned2024-09-19T15:45:29Z
dc.date.available2024-09-19T15:45:29Z
dc.date.issued2025
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractLinseed (Linum usitatissimum L.) and linseed oil, with a fatty acid profile rich in both macro and micro elements, are recognized as functional foods due to their valuable positive effects on health. Fatty acids composition (FAC) is a key indicator in assessing the quality of linseeds. The FAC of linseed is typically determined using chromatographic methods, yielding highly accurate results. However, chromatographic methods entail drawbacks such as requiring pre-chemical processes, generating chemical waste, and being both expensive and time-consuming, similar to chemical analyses. This study focused on the feasibility of colorimeter and FT-NIRS data to determine the FAC (%), protein (%) and neutral detergent fiber (NDF %) in linseed samples. By employing the PLSR analysis based on FT-NIRS, it was determined that the ratios of stearic (R2val = 0.74, RMSEP = 0.09 %), oleic (R2val = 0.75, RMSEP = 0.26 %), linoleic (R2val = 0.85, RMSEP = 0.58 %), linolenic (R2val = 0.71, RMSEP = 1.07 %), 8,11,14 eicosatrienoic (R2val = 0.77, RMSEP = 0.02 %), margaric (R2val = 0.71, RMSEP = 0.01 %), myristic (R2val = 0.75, RMSEP = 0.02 %), and behenic (R2val = 0.74, RMSEP = 1.12 %) in linseed could be successfully predicted. Furthermore, results demonstrated that the protein (R2val = 0.87, RMSEP = 0.9 %) and NDF (R2val = 0.90, RMSEP = 0.6 %) content in linseeds can be successfully predicted. PLSR has demonstrated that FT-NIRS has relatively higher predictive capability compared to color models. © 2024 Elsevier B.V.en_US
dc.identifier.doi10.1016/j.talanta.2024.126798
dc.identifier.issn0039-9140
dc.identifier.scopus2-s2.0-85203003190en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.talanta.2024.126798
dc.identifier.urihttps://hdl.handle.net/20.500.12483/14720
dc.identifier.volume281en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofTalantaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChemometricsen_US
dc.subjectFatty aciden_US
dc.subjectFourier transformen_US
dc.subjectFunctional foodsen_US
dc.subjectGas chromatographyen_US
dc.subjectLinseeden_US
dc.titleCombining non-destructive devices and multivariate analysis as a tool to quantify the fatty acid profiles of linseed genotypesen_US
dc.typeArticleen_US

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