Mung bean (Vigna radiata) is a rich natural source of vitexin and isovitexin—flavonoids known for their potential anti-inflammatory properties. The aim of this study was to evaluate, through the use of RAW 264.7 macrophages, the anti-inflammatory effects of extracts from mung bean seeds containing vitexin and isovitexin. High-performance liquid chromatography (HPLC) was employed to quantify the levels of these compounds in various mung bean cultivars: “Jinhwang,” “Samhwang,” “Seonhwa,” and “Jangan,” as well as “Dahyeon,” and “Sanpo,” two of the most widely cultivated varieties in Korea. Cytotoxicity assays revealed no significant toxicity at concentrations of 25, 50, and 100 µg/mL, allowing further analysis at these levels. In nitric oxide (NO) inhibition assays, “Samhwang” (10.61 µM) and “Seonhwa” (9.7 µM) demonstrated the highest NO-suppressing activity at 50 µg/mL. Tumor necrosis factor-alpha (TNF-α) levels were significantly reduced by “Seonhwa” (83.6 pg/mL) and “Jangan” (72.3 pg/mL), with “Jangan” showing the strongest inhibitory effect. Interleukin-6 (IL-6) analysis revealed notable suppression in “Samhwang,” “Seonhwa,” and “Jangan” at 50 µg/mL, with “Samhwang” exhibiting the most potent effect (78.6 pg/mL). These findings suggest that the “Samhwang” cultivar, in particular, possesses significant anti-inflammatory potential and may serve as a valuable candidate for the development of natural anti-inflammatory agents.

In this study, we established a system to analyze and accurately distinguish changes in the metabolite content of mung bean sprouts at various growth stages. Specific regions of the FT-IR spectrum (1700–1500 cm^-1, 1500–300 cm^-1, and 1100–950 cm^-1) reflected qualitative and quantitative changes in key metabolites, such as amino acids, proteins, nucleic acids, lipids, and sugars. These regions play crucial roles in assessing metabolic changes at different growth stages. The results of the PCA revealed that metabolite clusters were distinctly separated according to growth stage, with notable differences observed between days 7, 10, 14, and 17. This indicated significant differences in metabolite changes across growth stages. PLS-DA analysis also confirmed similar results, clearly distinguishing metabolite changes according to growth stage, thus providing valuable information for identifying growth stages. This technique can be an important tool in the selection and breeding of mung bean varieties and can contribute to the development of functional materials. Furthermore, it is expected to contribute significantly to enhancing mung bean productivity and research on functional substances.

This study used perilla seeds produced in 2019, 2020, and 2021 to determine the year of production using multivariate statistical analysis of Fourier-transform infrared (FTIR) spectral data of perilla leaves. Spectral analysis based on multivariate statistical analysis of whole-cell extracts was used to distinguish the perilla leaves at the metabolic level. FT-IR spectral data of the leaves were analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The FTIR spectrum identified spectral differences between the frequency regions of 1,700 to 1,500, 1,500 to 1,300, and 1,100 to 950 cm^-1. This spectral region reflects quantitative and qualitative changes in amides I, II in amino acids and proteins (1,700–1,500 cm^-1), phosphodiester groups from nucleic acids and phospholipids (1,500–1,300 cm^-1), and carbohydrate compounds (1,100–950 cm^-1). PCA revealed separate clusters corresponding to production traceability relationships. Therefore, PCA can be used to distinguish between production in 2019, 2020, and 2021 based on different metabolite contents. PLS-DA showed a similar production traceability classification for the perilla seeds. In addition, this metabolic identification system can be used to rapidly select and classify useful perilla seed varieties.

To determine whether Fourier-transform infrared (FT-IR) spectral analysis based on multivariate analysis for whole-cell extracts can be used to discriminate different countries of Perilla seeds at the metabolic level, leaves of Perilla seeds were subjected to FT-IR spectroscopy. FT-IR spectral data of leaves were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA). FT-IR spectra confirmed typical spectral differences between frequency regions of 1,700-1,500, 1,500-1,300, and 1,100-950 cm^-1. These spectral regions reflect the quantitative and qualitative variations of amide I, II in amino acids and proteins (1,700-1,500 cm^-1), phosphodiester groups in nucleic acids and phospholipids (1,500-1,300 cm^-1), and carbohydrates (1,100-950 cm^-1). PCA revealed separate clusters corresponding to their country relationship. Thus, PCA could be used to distinguish between countries of origin with different metabolite contents. And PLS-DA showed a similar country classification of Perilla seeds. Furthermore, these metabolic discrimination systems could be used for the rapid selection and classification of useful field crop cultivars.

FT-IR spectroscopy, combined with multivariate analysis, was used to determine whether 67 different wild and rootstock peach accessions could be discriminated from each other. Genomic DNA was isolated from leaves, and the purified genomic DNA was analyzed by FT-IR spectroscopy in the spectral region from 1800 to 800 cm^-1. FT-IR spectra showed that typical spectral differences existed in the frequency regions of N-H stretching (amide I), C=O stretching vibrations (amide II), and PO₂⁻ ionized asymmetric and symmetric stretching. Principal component analysis (PCA) was able to discriminate three groups. The partial least squares discriminant analysis (PLS-DA) yielded more clear discrimination among the three groups of peach accessions. The FT-IR spectral differences might be directly related to subtle changes in the base functional group and backbone structures of genomic DNA. This technique could provide a research foundation for FT-IR spectral-based rapid diagnosis, selection, and discrimination of peach accessions for rootstock.