Discrimination of the geographical origin of soybeans using nmr-based metabolomics

Yaoyao Zhou, Seok Young Kim, Jae Soung Lee, Byeung Kon Shin, Jeong Ah Seo, Young Suk Kim, Do Yup Lee, Hyung Kyoon Choi

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Article number435
Pages (from-to)1-16
Number of pages16
JournalFoods
Volume10
Issue number2
DOIs
StatePublished - Feb 2021

Keywords

  • Geographical location
  • Glycine max
  • Metabolic profiling
  • NMR
  • Prediction

Fingerprint

Dive into the research topics of 'Discrimination of the geographical origin of soybeans using nmr-based metabolomics'. Together they form a unique fingerprint.

Cite this