Analysis of Fatty Acids in Kernel, Flour, and Oil Samples of Maize by NIR Spectroscopy Using Conventional Regression Methods

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dc.authoridKahrıman, Fatih/0000-0001-6944-0512
dc.contributor.authorEgesel, Cem Omer
dc.contributor.authorKahrıman, Fatih
dc.contributor.authorEkinci, Neslihan
dc.contributor.authorKavdir, Ismail
dc.contributor.authorBuyukcan, M. Burak
dc.date.accessioned2025-01-27T20:47:21Z
dc.date.available2025-01-27T20:47:21Z
dc.date.issued2016
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractHigh cost and painstaking procedures associated with fatty acid analyses of maize kernel necessitate the use of alternative methods. NIR spectroscopy offers advantages in this respect for a variety of areas such as plant breeding, food and feed industries, and biofuel production, in which different forms of maize kernel (e.g., intact kernel, flour, or oil) are used as material. We investigated the possibility of estimating maize oil quality traits by using different samples (intact kernel, flour, and oil) and conventional regression methods (multiple linear regression [MLR] and partial least squares regression [PLSR]) applied to their NIR spectra. MLR and PLSR calibration models were developed for oleic acid, linoleic acid, oleic/linoleic acid ratios, total monounsaturated fatty acid, total polyunsaturated fatty acid (PUFA), and total saturated fatty acid by analyzing 120 maize samples. Robustness in terms of prediction accuracy of the models developed here was tested with a reserved set of samples (n = 30). The results suggested that fatty acids could be possibly estimated by calibrations developed from flour and oil samples with a high degree of accuracy, whereas intact samples did not offer satisfactory results. PLSR and MLR methods gave better results in flour and oil samples, respectively. PUFA was the trait that was most successfully estimated from both flour (for the PLSR model, standard error of the estimate [SEP] of 1.78%, relative performance to deviation [RPD] of 3.09, R-2 = 0.93) and oil (for the MLR model, SEP of 0.85%, RPD of 6.52, R-2 = 0.98) samples. We concluded that sample type and chemometric method should be handled as important factors in calibration development, and the effects of these factors may vary depending on the trait being analyzed.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [112O429]
dc.description.sponsorshipThe authors acknowledge the financial support of the Scientific and Technological Research Council of Turkey (TUBITAK, Project 112O429) for this study.
dc.identifier.doi10.1094/CCHEM-12-15-0247-R
dc.identifier.endpage492
dc.identifier.issn0009-0352
dc.identifier.issn1943-3638
dc.identifier.issue5
dc.identifier.scopus2-s2.0-84989324680
dc.identifier.scopusqualityQ2
dc.identifier.startpage487
dc.identifier.urihttps://doi.org/10.1094/CCHEM-12-15-0247-R
dc.identifier.urihttps://hdl.handle.net/20.500.12428/24859
dc.identifier.volume93
dc.identifier.wosWOS:000383963800009
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofCereal Chemistry
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectNear-Infrared Spectroscopy
dc.subjectReflectance-Spectroscopy
dc.subjectCalibration
dc.subjectCorn
dc.subjectProtein
dc.subjectPrediction
dc.subjectStarch
dc.titleAnalysis of Fatty Acids in Kernel, Flour, and Oil Samples of Maize by NIR Spectroscopy Using Conventional Regression Methods
dc.typeArticle

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