FT-IR 스펙트럼 데이터의 다변량 통계분석 기법을 이용한 반하의 원산지 식별 및 예측기술
Prediction and Discrimination of Cultivation Origin of Pinellia Tubers Using FT-IR Spectroscopy Combined by Multivariate Analysis
To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts can be used to discriminate cultivation origin metabolically, eight Pinellia tubers (Pinellia ternata) were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data from Pinellia tubers were analyzed by principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). The dendrogram based on hierarchical clustering analysis of these PCA and PLS-DA data on Pinellia tubers showed that tuber samples were segregated into Chinese and Korean Pinellia in a cultivation origin-dependent manner. Thus we suggested that the hierarchical dendrogram based on PCA and PLS-DA of FT-IR spectral data from tubers represented the most probable chemical relationship between Pinellia tubers. The overall prediction accuracy for discrimination of cultivation origin was 100% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from Pinellia tubers can be applied as an alternative tool for discriminating of cultivation origin. Furthermore these metabolic discrimination systems could be applied for standardization for herbal medicine resources.
목차
Abstract 서론 본론 1. 재료 및 방법 2. 결과 및 고찰 결론 감사의 글 참고문헌