title: Determination of butter adulteration with margarine using Raman spectroscopy.
abstract: In this study, adulteration of butter with margarine was analysed using Raman spectroscopy combined with chemometric methods (principal component analysis (PCA), principal component regression (PCR), partial least squares (PLS)) and artificial neural networks (ANNs). Different butter and margarine samples were mixed at various concentrations ranging from 0% to 100% w/w. PCA analysis was applied for the classification of butters, margarines and mixtures. PCR, PLS and ANN were used for the detection of adulteration ratios of butter. Models were created using a calibration data set and developed models were evaluated using a validation data set. The coefficient of determination (R(2)) values between actual and predicted values obtained for PCR, PLS and ANN for the validation data set were 0.968, 0.987 and 0.978, respectively. In conclusion, a combination of Raman spectroscopy with chemometrics and ANN methods can be applied for testing butter adulteration.
authors.affiliation: Department of Food Engineering, Faculty of Engineering, Hacettepe University, Beytepe, 06800 Ankara, Turkey.
authors.forename: Reyhan Selin
authors.initials: RS
authors.lastname: Uysal
authors.forename: Ismail Hakki
authors.initials: IH
authors.lastname: Boyaci
authors.forename: Hüseyin Efe
authors.initials: HE
authors.lastname: Genis
authors.forename: Ugur
authors.initials: U
authors.lastname: Tamer
mesh_terms: D002079:Butter / Q000032:analysis*; D016002:Discriminant Analysis; D005506:Food Contamination / Q000032:analysis*; D008383:Margarine / Q000032:analysis*; D025341:Principal Component Analysis; D013059:Spectrum Analysis, Raman / Q000379:methods*
chemical_list: D002079:Butter; D008383:Margarine
keywords: Artificial neural networks; Butter adulteration; Chemometrics; Margarine; Raman spectroscopy