Multivariate analysis to discriminate the origin of sesame seeds by multi-element analysis inductively coupled plasma-mass spectrometry
2017
Choi, Y.H., Seoul Metropolitan Government Research Institute of Public Health and Environment, Seoul, Republic of Korea | Hong, C.Y., Seoul Metropolitan Government Research Institute of Public Health and Environment, Seoul, Republic of Korea | Kim, M., Seoul Metropolitan Government Research Institute of Public Health and Environment, Seoul, Republic of Korea | Jung, S.O., Seoul Metropolitan Government Research Institute of Public Health and Environment, Seoul, Republic of Korea | Park, J., Seoul Metropolitan Government Research Institute of Public Health and Environment, Seoul, Republic of Korea | Oh, Y.H., Seoul Metropolitan Government Research Institute of Public Health and Environment, Seoul, Republic of Korea | Kwon, J.H., Kyungpook National University, Daegu, Republic of Korea
In this study, inductively coupled plasma-mass spectrometry (ICP-MS) was used to determine the concentration of 15 elements (Mg, Al, K, Ca, Cr, Mn, Co, Ni, Cu, Zn, Rb, Sr, Cd, Ba, and Pb) of sesame seeds. Multivariate analysis was then performed to discriminate the origin of sesame seeds. Korean (48), Chinese (44), and Indian (21) samples were used to develop the calibration model. Another 10 samples were used to validate this model. All elements were significantly different (p less than 0.05) among the samples from three countries, and all elements were subjected to both principal component analysis (PCA) and discriminant analysis. The concentrations of multi-element showed a trend of clustering according to the origin of samples based on PCA. They showed a discrimination rate of 92.0% in the discriminant analysis. The results demonstrated that a combination of ICP-MS multi-element determination and multivariate analysis could be used to discriminate the sesame seed origin.
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