title: [Analysis and Discrimination of the Medicinal Plants Swertia Davidi Franch Based on Infrared Spectroscopy].
abstract: Fourier-transform infrared spectroscopy combined with partial least squares discriminate analysis (PLS-DA) and hierarchical cluster analysis (HCA) were used to rapidly discriminate the Swertia davidi Franch which collected from different origins. The original infrared spectra data of different parts of all the 70 samples which collected from four different regions were preprocessed by automatic calibration, automatic smoothing, the first derivative and the,second derivative. Then the processed data were imported into OMNIC 8.2 and the absorption peaks were compared; PLS-DA was performed by SIMCA-P⁺ 10.0 and the effect of discrimination of different origins was compared by 3D score plot of the first three principal components; the infrared spectral data were imported into SPSS 19. 0 for HCA to compare classification results of different parts by the dendrogram. The results showed that: (1) There were differences among the spectra of the roots of different origins in the spectral peaks in 1,739, 1,647, 1,614, 1,503, 1,271, 1,243, 1,072 cm⁻¹. The spectra of the stems of different origins showed differentiation in the wavelength in 1 503, 1 270, 1 246 cm⁻¹; (2) The characteristic peaks of different parts of the same origin were different; (3) PLS-DA indicated that the data which were processed by automatic correction, automatic smoothing and second derivative have showed the best classification. In addition, the discrimination of roots which collected from different origins could be the best; (4) Tree diagram of HCA showed that the accuracy rate of cluster in roots, stems and leaves were 83%, 56%, and 70%, respectively. In conclusion: FTIR combined with PLS-DA and HCA can rapidly and accurately differentiate S. davidi that collected from different origins, the origin discrimination effect of different parts was clearly different that the classification of roots is the best, the second derivative could enhance the specificity of the samples, the classification in 3D score plot could be visualized and obvious.
authors.forename: Zhun
authors.initials: Z
authors.lastname: Di
authors.forename: Yan-li
authors.initials: YL
authors.lastname: Zhao
authors.forename: Zhi-tian
authors.initials: ZT
authors.lastname: Zuo
authors.forename: Hua
authors.initials: H
authors.lastname: Long
authors.forename: Xue
authors.initials: X
authors.lastname: Zhang
authors.forename: Yuan-zhong
authors.initials: YZ
authors.lastname: Wang
authors.forename: Li
authors.initials: L
authors.lastname: Li
mesh_terms: D016018:Least-Squares Analysis; D010946:Plants, Medicinal / Q000737:chemistry / Q000145:classification*; D017550:Spectroscopy, Fourier Transform Infrared; D029967:Swertia / Q000737:chemistry / Q000145:classification*