Discrimination of the Geographical Origin of Soybeans Using NMR-Based Metabolomics

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.

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

“…2a). Orthogonal partial least squares-discriminant analysis (OPLS-DA) has shown to generally outperform the unsupervised PCA method in classifying various agri-products [39][40][41][42][43] . Therefore, it was applied in the current study.…”

Advances in Plant Metabolomics and Its Applications in Stress and Single-Cell Biology

“…It requires metabolites at microgram/micromole levels, while MS can detect metabolites at femtomole or even attomole levels. The most common method used for analysis in plant metabolomics is 1D (1H) NMR, e.g., metabolomics of abiotic and biotic stresses [52][53][54], novel compound identification [55], and evolutionary research [56]. This method, 1D NMR, allows high-throughput analysis of hundreds, or even thousands, of samples, since it takes only a few minutes per sample.…”

Predicting metabolizable energy of soybean meal and rapeseed meal from chemical composition in broilers of different ages

“…Soybean meal (SBM) and rapeseed meal (RSM) are the byproducts of oil extraction and primary protein ingredients for broilers. The ME values vary depending on chemical composition of each source which relates to the varieties, growing environment, agronomy of oilseeds and oil extraction process ( Qin et al, 1998 ; Baker et al, 2011 ; Banaszkiewicz, 2011 ; Khajali and Slominski, 2012 ; Zhou et al, 2021 ). Thus, many nutritionists have aimed to establish ME prediction models from chemical composition of SBM and RSM for poultry.…”