Comprehensive profiling of primary and secondary metabolites was performed to understand metabolic differences associated with color formation in pigmented rice (Oryza sativa L.). Overall, 110 metabolites from non-pigmented, black, and red rice cultivars were identified. Black and red rice contained high levels of flavonoids associated with plant color. Black rice also contained high levels of terpenoids (carotenoids, tocopherols, phytosterols, and monoterpenes). The non-pigmented rice contained relatively low levels of secondary metabolites. Multivariate and pathway analyses were performed to data-mine the metabolite profiles. Hierarchical clustering analysis of correlation coefficients revealed metabolite clusters based on nitrogen and carbon sources. These clusters suggested a negative correlation between nitrogen and carbon. Pathway analysis revealed that black rice was rich in carbon-based secondary metabolites, with relatively low levels of primary metabolites compared with other rice cultivars. These data highlight the complex interactions between nitrogen and carbon metabolism of primary and secondary metabolites in rice. For the first time, the relationships and metabolic differences in terpenoid content (monoterpenes, triterpenes, and tetraterpenes) of non-pigmented and pigmented rice cultivars were analyzed. These findings should greatly contribute to the understanding of pigmented rice metabolome and inform breeding programs for new rice cultivars.

As international food trade increases, consumers are becoming increasingly interested in food safety and authenticity, which are linked to geographical origin. Adzuki beans (Vigna angularis) are cultivated worldwide, but there are no tools for accurately discriminating their geographical origin. Thus, our study aims to develop a method for discriminating the geographical origin of adzuki beans through targeted and non-targeted metabolite profiling with gas chromatography time-of-flight mass spectrometry combined with multivariate analysis. Orthogonal partial least squares discriminant analysis showed clear discrimination between adzuki beans cultivated in Korea and China. Non-targeted metabolite profiling showed better separation than targeted profiling. Furthermore, citric acid and malic acid were the most notable metabolites for discriminating adzuki beans cultivated in Korea and China. The geographical discrimination method combining non-targeted metabolite profiling and pareto-scaling showed excellent predictability (Q² = 0.812). Therefore, it is a suitable prediction tool for the discrimination of geographical origin and is expected to be applicable to the geographical authentication of adzuki beans.

Perilla and sesame are traditional sources of edible oils in Asian and African countries. In addition, perilla and sesame seeds are rich sources of health-promoting compounds, such as fatty acids, tocopherols, phytosterols and policosanols. Thus, developing a method to determine the geographic origin of these seeds is important for ensuring authenticity, safety and traceability and to prevent cheating. We aimed to develop a discriminatory predictive model for determining the geographic origin of perilla and sesame seeds using comprehensive metabolite profiling coupled with chemometrics. The orthogonal partial least squares-discriminant analysis models were well established with good validation values (Q² = 0.761 to 0.799). Perilla and sesame seed samples used in this study showed a clear separation between Korea and China as geographic origins in our predictive models. We found that glycolic acid could be a potential biomarker for perilla seeds and proline and glycine for sesame seeds. Our findings provide a comprehensive quality assessment of perilla and sesame seeds. We believe that our models can be used for regional authentication of perilla and sesame seeds cultivated in diverse geographic regions.

Climate change has increased variable weather patterns that affect plants. To address these issues, we developed a microbial biocontrol agent against drought stress in kimchi cabbage (Brassica rapa L. subsp. pekinensis). We selected three bacterial strains (Leifsonia sp. CS9, Bacillus toyonensis TSJ7, and Lysinibacillus capsici TT41) because they showed a survival rate of up to 50% and good growth rate when treated with 30% PEG 6000. The three strains were treated with kimchi cabbage to confirm their enhanced drought stress resistance under non-watering conditions. Among the three strains, the TT41 treated group showed a significant increase in various plant parameters compared with the negative control on the 7th day. We performed extensive profiling of primary and secondary metabolites from kimchi cabbage and the TT41 strain. Multivariate and pathway analyses revealed that only the TT41 group clustered with the well-watered group and showed almost the same metabolome on the 7th day. When treated with TT41, lactic acid was identified as an indicator metabolite that significantly improved drought stress tolerance. Furthermore, lactic acid treatment effectively induced drought stress tolerance in kimchi cabbage, similar to that achieved with the TT41 strain.