A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean

Towards Verifying the Imported Soybeans of China Using Stable Isotope and Elemental Analysis Coupled with Chemometrics

Verifying the geographical origin of soybeans (Glycine max [Linn.] Merr.) is a major challenge as there is little available information regarding non-parametric statistical origin approaches for Chinese domestic and imported soybeans. Commercially procured soybean samples from China (n = 33) and soybeans imported from Brazil (n = 90), the United States of America (n = 6), and Argentina (n = 27) were collected to characterize different producing origins using stable isotopes (δ2H, δ18O, δ15N, δ13C, and δ34S), non-metallic element content (% N, % C, and % S), and 23 mineral elements. Chemometric techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and BP-artificial neural network (BP-ANN) were applied to classify each origin profile. The feasibility of stable isotopes and elemental analysis combined with chemometrics as a discrimination tool to determine the geographical origin of soybeans was evaluated, and origin traceability models were developed. A PCA model indicated that origin discriminant separation was possible between the four soybean origins. Soybean mineral element content was found to be more indicative of origin than stable isotopes or non-metallic element contents. A comparison of two chemometric discriminant models, LDA and BP-ANN, showed both achieved an overall accuracy of 100% for testing and training sets when using a combined isotope and elemental approach. Our findings elucidate the importance of a combined approach in developing a reliable origin labeling method for domestic and imported soybeans in China.

Soy Consumption and the Risk of Type 2 Diabetes and Cardiovascular Diseases: A Systematic Review and Meta-Analysis

Soy is rich in plant protein, isoflavones, and polyunsaturated fatty acids. To clarify the associations between soy intake and type 2 diabetes (T2D) and cardiovascular diseases (CVDs) events, we performed a meta-analysis and review. A total of 1963 studies met the inclusion criteria, and 29 articles with 16,521 T2D and 54,213 CVDs events were identified by the eligibility criteria. During a follow-up of 2.5–24 years, the risk of T2D, CVDs, coronary heart disease, and stroke in participants with the highest soy consumption decreased by 17% (total relative risk (TRR) = 0.83, 95% CI: 0.74–0.93), 13% (TRR = 0.87, 95% CI: 0.81–0.94), 21% (TRR = 0.79, 95% CI: 0.71–0.88), and 12% (TRR = 0.88, 95% CI: 0.79–0.99), respectively, compared to the lowest sot consumption. A daily intake of 26.7 g of tofu reduced CVDs risk by 18% (TRR = 0.82, 95% CI: 0.74–0.92) and 11.1 g of natto lowered the risk of CVDs by 17% (TRR = 0.83, 95% CI: 0.78–0.89), especially stroke. This meta-analysis demonstrated that soy consumption was negatively associated with the risks of T2D and CVDs and a specific quantity of soy products was the most beneficial for the prevention of T2D and CVDs. This study has been registered on PROSPERO (registration number: CRD42022360504).