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  1. Iradukunda C, Aida WMW, Ouafi AT, Barkouch Y, Boussaid A
    J. Dairy Res., 2018 Feb;85(1):114-120.
    PMID: 29468995 DOI: 10.1017/S0022029917000796
    Matched MeSH terms: Butter/analysis*
  2. Ewe JA, Loo SY
    Food Chem, 2016 Jun 15;201:29-36.
    PMID: 26868544 DOI: 10.1016/j.foodchem.2016.01.049
    The primary objective of this study was to evaluate the physicochemical and rheological properties of butter produced by Lactobacillus helveticus fermented cream. The incorporation of putative probiotic - the L. helveticus, to ferment cream prior to butter production was anticipated to alter the nutritional composition of butter. Changes in crude macronutrients and the resultant modification relating to textural properties of butter induced upon metabolic activities of L. helveticus in cream were focused in this research. Fermented butter (LH-butter) was produced by churning the cream that was fermented by lactobacilli at 37 °C for 24 h. Physicochemical analysis, proximate analysis and rheology properties of LH-butter were compared with butter produced using unfermented cream (control). LH-butter showed a significantly (P<0.05) higher fat content and acid value; lower moisture and ash; and was softer than the control. Cream fermentation modified nutritional and textural properties of butter in which LH-butter contained higher health beneficial unsaturated fatty acids than the control and thus rendered the product softer. Its enrichment with probiotics could thus further enhance its functional property.
    Matched MeSH terms: Butter/analysis*
  3. Fadzlillah NA, Rohman A, Ismail A, Mustafa S, Khatib A
    J Oleo Sci, 2013;62(8):555-62.
    PMID: 23985484
    In dairy product sector, butter is one of the potential sources of fat soluble vitamins, namely vitamin A, D, E, K; consequently, butter is taken into account as high valuable price from other dairy products. This fact has attracted unscrupulous market players to blind butter with other animal fats to gain economic profit. Animal fats like mutton fat (MF) are potential to be mixed with butter due to the similarity in terms of fatty acid composition. This study focused on the application of FTIR-ATR spectroscopy in conjunction with chemometrics for classification and quantification of MF as adulterant in butter. The FTIR spectral region of 3910-710 cm⁻¹ was used for classification between butter and butter blended with MF at various concentrations with the aid of discriminant analysis (DA). DA is able to classify butter and adulterated butter without any mistakenly grouped. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the frequency regions of 3910-710 cm⁻¹. The equation obtained for the relationship between actual value of MF and FTIR predicted values of MF in PLS calibration model was y = 0.998x + 1.033, with the values of coefficient of determination (R²) and root mean square error of calibration are 0.998 and 0.046% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with MF. Using 9 principal components, root mean square error of prediction (RMSEP) is 1.68% (v/v). The results showed that FTIR spectroscopy can be used for the classification and quantification of MF in butter formulation for verification purposes.
    Matched MeSH terms: Butter/analysis*
  4. Fadzillah NA, Man Yb, Rohman A, Rosman AS, Ismail A, Mustafa S, et al.
    J Oleo Sci, 2015;64(7):697-703.
    PMID: 25994556 DOI: 10.5650/jos.ess14255
    The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.
    Matched MeSH terms: Butter/analysis*
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