FT-IR 스펙트럼 데이터로부터 다변량통계분석기법을 이용한 커피의 대사체 수준 품종 분류

FT-IR spectral analysis based on multivariate analysis can be used to discriminate between coffee (C. arabica) plants leaf. Whole cell extracts can be used to leaves eight coffee plants and the metabolic level was subjected to Fourier transform infrared (FT-IR) spectroscopy. FT-IR spectral data from leaves were analyzed by PCA (principal component analysis), PLS-DA (partial least square discriminant analysis) and HCA (hierarchical clustering analysis). FT-IR spectrum confirmed differences between the frequency regions of 1,700-1,500, 1,500-1,300 and 1,100-950 cm -1 , respectively. These spectral regions reflect the quantitative and qualitative variations of amide I, II from 1,700-1,500cm -1 (amino acids and proteins), phosphodiester groups from 1,500-1,300cm -1 (nucleic acid and phospholipid) and 1,100-950cm -1 (carbohydrate compounds). PCA revealed separate clusters that corresponded to similar species relationship. And PLS-DA showed similar species classification of coffee (C. arabica). Thus, PCA and PLS-DA could be used to the the distinction between coffee species with different metabolite contents. This study, these metabolic discrimination systems could be used for the rapid selection and classification of useful coffee cultivars.