들깨의 FT-IR 스펙트럼 데이터로부터 다변량통계분석을 이용한 원산지 판별
Multivariate Analysis of FT-IR Spectroscopy Data from Different Countries of Perilla Seeds
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To determine whether Fourier-transform infrared (FT-IR) spectral analysis based on multivariate analysis for whole-cell extractscan 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), andhierarchical 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 (1,700-1,500cm-1), phosphodiester groups in nucleic acids and phospholipids (1,500-1,300 cm-1), and carbohydrates (1,100-950 cm-1). PCA revealed separateclusters corresponding to their country relationship. Thus, PCA could be used to distinguish between countries of origin with different metabolitecontents. And PLS-DA showed a similar country classification of Perilla seeds. Furthermore, these metabolic discrimination systems couldbe used for the rapid selection and classification of useful field crop cultivars.
Abstract
To determine whether Fourier-transform infrared (FT-IR) spectral analysis based on multivariate analysis for whole-cell extractscan 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), andhierarchical 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 (1,700-1,500cm-1), phosphodiester groups in nucleic acids and phospholipids (1,500-1,300 cm-1), and carbohydrates (1,100-950 cm-1). PCA revealed separateclusters corresponding to their country relationship. Thus, PCA could be used to distinguish between countries of origin with different metabolitecontents. And PLS-DA showed a similar country classification of Perilla seeds. Furthermore, these metabolic discrimination systems couldbe used for the rapid selection and classification of useful field crop cultivars.
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