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.