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Soybeans are a significant agricultural product in China, with certain geographical locations often yielding higher quality, and thus more expensive, soybean crops. In this study, metabolomics and transcriptomics analyses were conducted on soybean samples from nine regions in Heilongjiang and Liaoning Provinces using untargeted liquid chromatography–mass spectrometry (LC–MS) and Illumina sequencing technologies. The primary objective was to devise an effective and unbiased method for determining the geographical origin of each soybean variety to mitigate potential fraudulent practices. Through multidimensional and unidimensional analyses, successful identification of differentially expressed metabolites (DEMs) and differentially expressed genes (DEGs) was achieved, yielding statistically significant outcomes. Integration of the metabolomics and transcriptomics datasets facilitated the construction of a correlation network model capable of distinguishing soybeans originating from different geographical locations, leading to the identification of significant biomarkers exemplifying noteworthy distinctions. To validate the feasibility of this method in practical applications, partial least squares discriminant analysis was employed to differentiate soybean samples from the nine regions. The results convincingly showcased the applicability and reliability of this approach in accurately pinpointing the geographical origin of soybeans. Distinguishing itself from prior research in soybean traceability, this study incorporates an integrated analysis of metabolomics and transcriptomics data, thereby unveiling biomarkers that offer a more precise differentiation of soybean traits across distinct regions, thereby bridging a critical research gap within the soybean traceability domain. This innovative dual-data integration analysis methodology is poised to enhance the accuracy of soybean traceability tools and lay a new foundation for future agricultural product identification research. [ABSTRACT FROM AUTHOR]

[Display omitted] • Assessing Ecological Vulnerability through the Combination of PCA and AHP. • Northwestern Liaoning has high ecological vulnerability with a declining trend. • Soil and land use types drive spatial ecological vulnerability. • Identified key factors driving short and long-term ecological vulnerability changes. Evaluating the ecological vulnerability of the northwestern region of Liaoning Province, a typical ecologically vulnerable area in China, and exploring its inherent patterns help formulate effective measures for ecological protection and restoration. Existing studies in this region have focused on the county, city, or provincial level, failing to capture the overall ecological vulnerability. Based on the "sensitivity-degradation" model framework, this study employed the Analytic Hierarchy Process, Principal Component Analysis, and geographical detector methods to evaluate the ecological vulnerability in the northwestern region of Liaoning Province from 2000 to 2022 and analyzed the possible driving factors. The results showed that, from 2000 to 2022, areas with moderate or higher vulnerability accounted for an average of 64.7% of the total area, while severely and extremely vulnerable areas accounted for 32.5%. These severely and extremely vulnerable areas are mainly distributed along both banks of the Liuhe River and in the northern part of Liaoning Province, where it adjoins the Horqin Sandy Land. From 2000 to 2022, the overall ecological vulnerability in the northwestern region of Liaoning Province showed a decreasing trend, with the proportion of severe vulnerability significantly decreasing from 35.6% to 8.6%. Vegetation and land use were the primary factors leading to long-term changes in ecological vulnerability. This research can also provide an example for the similar issues in other study areas. [ABSTRACT FROM AUTHOR]