Yaoyao Zhou1, Seok-Young Kim1, Jae-Soung Lee1, Byeung-Kon Shin2, Jeong-Ah Seo3, Young-Suk Kim4, Do-Yup Lee5, Hyung-Kyoon Choi1. 1. College of Pharmacy, Chung-Ang University, Seoul 06974, Korea. 2. National Agricultural Products Quality Management Service, Gimcheon 39660, Korea. 3. School of Systems Biomedical Science, Soongsil University, Seoul 06978, Korea. 4. Department of Food Science and Engineering, Ewha Womans University, Seoul 03760, Korea. 5. Center for Food and Bioconvergence, Department of Agricultural Biotechnology, Research Institute for Agricultural and Life Sciences, CALS, Seoul National University, Seoul 08826, Korea.
Abstract
With the increase in soybean trade between countries, the intentional mislabeling of the origin of soybeans has become a serious problem worldwide. In this study, metabolic profiling of soybeans from the Republic of Korea and China was performed by nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to predict the geographical origin of soybeans. The optimal orthogonal partial least squares-discriminant analysis (OPLS-DA) model was obtained using total area normalization and unit variance (UV) scaling, without applying the variable influences on projection (VIP) cut-off value, resulting in 96.9% sensitivity, 94.4% specificity, and 95.6% accuracy in the leave-one-out cross validation (LOO-CV) test for discriminating between Korean and Chinese soybeans. Soybeans from the northeastern, middle, and southern regions of China were successfully differentiated by standardized area normalization and UV scaling with a VIP cut-off value of 1.0, resulting in 100% sensitivity, 91.7%-100% specificity, and 94.4%-100% accuracy in a LOO-CV test. The methods employed in this study can be used to obtain essential information for the authentication of soybean samples from diverse geographical locations in future studies.
With the increase in soybean trade between countries, the intentional mislabeling of the origin of n>an class="Species">soybeans has become a serious problem worldwide. In this study, metabolic profiling of soybeans from the Republic of Korea and China was performed by nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to predict the geographical origin of soybeans. The optimal orthogonal partial least squares-discriminant analysis (OPLS-DA) model was obtained using total area normalization and unit variance (UV) scaling, without applying the variable influences on projection (VIP) cut-off value, resulting in 96.9% sensitivity, 94.4% specificity, and 95.6% accuracy in the leave-one-out cross validation (LOO-CV) test for discriminating between Korean and Chinese soybeans. Soybeans from the northeastern, middle, and southern regions of China were successfully differentiated by standardized area normalization and UV scaling with a VIP cut-off value of 1.0, resulting in 100% sensitivity, 91.7%-100% specificity, and 94.4%-100% accuracy in a LOO-CV test. The methods employed in this study can be used to obtain essential information for the authentication of soybean samples from diverse geographical locations in future studies.