들깨 잎의 FT-IR 스펙트럼 데이터로부터 다변량 통계분석을 이용한 생산연도 판별
Determination of Production Year Using Multivariate Statistical Analysis from FTIR Spectrum Data of Perilla Leaves
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초록
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.
Abstract
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.
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