Comparison of two exploratory data analysis methods for classification of Phyllanthus chemical fingerprint: unsupervised vs. supervised pattern recognition technologies

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• 作者：Jianru Guo (1)
  QianQian Chen (1)
  Caiyun Wang (1)
  Hongcong Qiu (2)
  Buming Liu (2)
  Zhi-Hong Jiang (1)
  Wei Zhang (1)
  
  1. State Key Laboratory of Quality Research in Chinese Medicines ; Macau Institute for Applied Research in Medicine and Health ; Macau University of Science and Technology ; Taipa ; Macau ; China
  2. Guangxi Key Laboratory of Traditional Chinese Medicine Quality Standards ; Guangxi Institute of Traditional Medical and Pharmaceutical Sciences ; Nanning ; 530022 ; China
  
• 关键词：Phyllanthus ; Unsupervised ; Supervised ; Pattern recognition ; High ; performance liquid chromatography time ; of ; flight mass spectrometry
• 刊名：Analytical and Bioanalytical Chemistry
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：407
• 期：5
• 页码：1389-1401
• 全文大小：2,109 KB
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• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Analytical Chemistry
  Food Science
  Inorganic Chemistry
  Physical Chemistry
  Monitoring, Environmental Analysis and Environmental Ecotoxicology
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1618-2650

文摘

In this study, unsupervised and supervised classification methods were compared for comprehensive analysis of the fingerprints of 26 Phyllanthus samples from different geographical regions and species. A total of 63 compounds were identified and tentatively assigned structures for the establishment of fingerprints using high-performance liquid chromatography time-of-flight mass spectrometry (HPLC/TOFMS). Unsupervised and supervised pattern recognition technologies including principal component analysis (PCA), nearest neighbors algorithm (NN), partial least squares discriminant analysis (PLS-DA), and artificial neural network (ANN) were employed. Results showed that Phyllanthus could be correctly classified according to their geographical locations and species through ANN and PLS-DA. Important variables for clusters discrimination were also identified by PCA. Although unsupervised and supervised pattern recognitions have their own disadvantage and application scope, they are effective and reliable for studying fingerprints of traditional Chinese medicines (TCM). These two technologies are complementary and can be superimposed. Our study is the first holistic comparison of supervised and unsupervised pattern recognition technologies in the TCM chemical fingerprinting. They showed advantages in sample classification and data mining, respectively.