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Qualitative analysis of desi ghee,edible oils,and spreads using Raman spectroscopy
Authors:Hina Ali  H. Nawaz  M. Saleem  F. Nurjis  M. Ahmed
Abstract:Keeping in view the importance of dietary fats in modulating disease risk, a study was planned to compare edible oils, spreads, and desi ghee based on fatty acid composition through Raman spectroscopy. The double bonds in unsaturated oils tend to react more with oxygen causing oxidative stress in living cells; therefore, the excessive use of processed vegetable oils may pose risk for human health. In the spectral analysis, Raman peaks at 1063 and 1127 cm−1 represent out‐of‐phase and in‐phase aliphatic C C stretch for saturated fatty acids. The peak at 1300 cm−1, labeled for alkane, decreases with increase in the double bond contents (unsaturation). Further, the Raman peak at 1655 cm−1 showed a monotonic increase as a function of unsaturation. The double bond contents in the Raman spectra from 1650–1657 cm−1 represent unsaturated fatty acids that changes during the synthesis of spreads and banaspati ghee. Desi ghee, extracted from cow and buffalo milk, showed distinctive Raman peaks at 1650 and 1655 cm−1, which originates because of isomers of conjugated linoleic acid. These Raman shifts differentiated desi ghee from other artificially produced banaspati ghee, spreads, and oils. Conjugated linoleic acid has proved to be anti‐carcinogenic, anti‐inflammatory, and anti‐allergic properties; therefore, the limited use of desi ghee may reduce the risk of cardiac diseases. Principal component analysis has been applied on the Raman spectra that clearly differentiated desi ghee, mono‐unsaturated extra virgin olive oil, and extra virgin olive oil spread from other oils, oil mixtures, spreads, and ghee. In addition, principal component analysis has been blindly applied successfully on 13 unknown samples to classify them with reference to the known ghee sample. Copyright © 2016 John Wiley & Sons, Ltd.
Keywords:Raman micro‐spectroscopy  desi ghee  edible oils  fatty acids  principal components analysis (PCA)
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