‘‘Ssagirang’ is a hulled barley cultivar that exhibits high resistance to lodging and cold stress. It demonstrates high yield potential and superior functional properties in barley sprouts. The heading date of ‘Ssagirang’ was April 24, and its maturity date was June 2. The plant height was 80 cm, which was 4 cm shorter than that of ‘Olbori’ (84 cm), and the spike length was 4.9 cm, which was longer than that of ‘Olbori’ (4.3 cm). The number of grains per spike was 56, higher than ‘Olbori’ (49 grains per spike), and the 1,000-grain weight was 35.2 g, heavier than that of ‘Olbori’ (34.6 g). In relation to disease resistance, ‘Ssagirang’ exhibited resistance to barley yellow mosaic virus (BaYMV), with a disease severity score of 1, and showed similar winter hardiness to ‘Olbori.’ In terms of quality characteristics, protein and β-glucan contents were 10.2% and 4.3%, respectively, similar to those of ‘Olbori’. The potential diastatic power of ‘Ssagirang’ was 107 DP, comparable to that of ‘Olbori’ (99 DP). Polyphenol content of ‘Ssagirang’ was 178 mg/100g, higher than that of ‘Olbori’ (164 mg/100g). The mean grain yield of ‘Ssagirang’ was 557 kg/10a across all regions, which was 21% higher than that of ‘Olbori’ (462 kg/10a). The barley sprout yield of ‘Ssagirang’ was 157 g_dw/m², greater than that of ‘Olbori’ (133 g_dw/m²), and its policosanol content was 448 mg/100g, which was significantly higher than that of ‘Olbori’ (245 mg/100g). On account of its strong cold tolerance, high yield, and excellent processing suitability, it is anticipated that ‘Ssagirang’ will be employed as a raw material for long malt, barley tea, and barley sprouts (Registration No. 9728).

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.