期刊
ANALYTICA CHIMICA ACTA
卷 647, 期 2, 页码 149-158出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2009.06.021
关键词
Multi-wavelength high-performance liquid chromatography fingerprinting; Cassia occidentalis L. seed; Classification; Chemometrics
资金
- National Natural Science Foundation of China [NSFC20562009]
- Chemo/Biosensing and Chemometrics of Hunan Universit [SKLCBC2005-22]
- Jiangxi Provincial Natural Science Foundation [OXNSF062004]
- State Key Laboratory of Food Science and Technology [SKLF-MB-200807, SKLF-TS200819]
- Changjiang Scholars and Innovative Research Team in Universities [IRT0540]
Multi-wavelength fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography (HPLC) at two wavelengths with the use of diode array detection. The two data sets of chromatograms were combined by the data fusion-based method. This data set of fingerprints was compared separately with the two data sets collected at each of the two wavelengths. It was demonstrated with the use of principal component analysis (PCA). that multi-wavelength fingerprints provided a much improved representation of the differences in the samples. Thereafter, the multi-wavelength fingerprint data set was submitted for classification to a suite of chemometrics methods viz. fuzzy clustering (FC), SIMCA and the rank ordering MCDM PROMETHEE and GAIA. Each method highlighted different properties of the data matrix according to the fingerprints from different types of Cassia seeds. In general, the PROMETHEE and GAIA MCDM methods provided the most comprehensive information for matching and discrimination of the fingerprints, and appeared to be best suited for quality assurance purposes for these and similar types of sample. (C) 2009 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据