Discrimination of the Geographical Origin of Soybeans Using NMR-Based Metabolomics
- 주제(키워드) metabolic profiling , Glycine max , NMR , geographical location , prediction
- 주제(기타) Food Science & Technology
- 설명문(일반) [Zhou, Yaoyao; Kim, Seok-Young; Lee, Jae-Soung; Choi, Hyung-Kyoon] Chung Ang Univ, Coll Pharm, Seoul 06974, South Korea; [Shin, Byeung-Kon] Natl Agr Prod Qual Management Serv, Gimcheon 39660, South Korea; [Seo, Jeong-Ah] Soongsil Univ, Sch Syst Biomed Sci, Seoul 06978, South Korea; [Kim, Young-Suk] Ewha Womans Univ, Dept Food Sci & Engn, Seoul 03760, South Korea; [Lee, Do-Yup] Seoul Natl Univ, Ctr Food & Bioconvergence, Dept Agr Biotechnol, Res Inst Agr & Life Sci,CALS, Seoul 08826, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 MDPI
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000181427
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/foods10020435
초록/요약
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.
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