Abstract
There is usually some fake sesame oil in Chinese market that mixes sesame oil flavor into other kinds of edible oils to cheat customers, but it is hard to discriminate. A new analytical method based on directly mid-infrared spectroscopy combined with chemometrics was studied for identifying adulterated sesame oil in this study. We analyzed the spectral characteristics of sesame oil, three kinds of edible oils, and sesame oil flavor and found that there are obvious spectral differences between them. Through principal component analysis (PCA) method, the pure sesame oil and other edible oils that mixed with sesame oil flavor can be obviously classified. To further demonstrate the ability of infrared spectroscopy to discriminate fake sesame oil, we established independent modeling of class analogy (SIMCA) and partial least square discriminate analysis (PLS-DA) model and found there are effective for discrimination. We also studied the spectral changes with different adulteration rates of sesame oil flavor and found that the absorption peak at 1055–1025 cm−1 was weakened and shifted with the increase in the concentration of sesame oil flavor.
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Acknowledgments
This work was supported by the National Science & Technology Pillar Program (2013BAD19B02) and Natural Science Foundation of China (31271614). Xiande Zhao, Daming Dong, Wengang Zheng, Leizi Jiao and Yun Lang have received research grants from National Natural Science Foundation of China and Beijing Research Center of Intelligent Equipment for Agriculture.
Conflict of Interest
Xiande Zhao declares that he has no conflict of interest. Daming Dong declares that he has no conflict of interest. Wengang Zheng declares that he has no conflict of interest. Leizi Jiao declares that he has no conflict of interest. Yun Lang declares that she has no conflict of interest. This article does not contain any studies with human or animal subjects.
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Zhao, X., Dong, D., Zheng, W. et al. Discrimination of Adulterated Sesame Oil Using Mid-infrared Spectroscopy and Chemometrics. Food Anal. Methods 8, 2308–2314 (2015). https://doi.org/10.1007/s12161-015-0125-7
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DOI: https://doi.org/10.1007/s12161-015-0125-7