Thermal decomposition of medicinal plant raw materials based on principal component analysis

被引:9
|
作者
Wesolowski, M [1 ]
Konieczynski, P [1 ]
Ulewicz-Magulska, B [1 ]
机构
[1] Med Univ Gdansk, Dept Analyt Chem, PL-80416 Gdansk, Poland
来源
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY | 2001年 / 66卷 / 02期
关键词
barks; DTA-TG-DTG; flowers; fruits; herbs; inflorescences; leaves; principal component analysis (PCA); raw plant materials; rhizomes; roots;
D O I
10.1023/A:1013137606170
中图分类号
O414.1 [热力学];
学科分类号
摘要
Studies on the thermal decomposition of commercial raw plant materials used in medicine were performed. 144 independent samples of plant materials - herbs, leaves, flowers, inflorescences, fruits, roots, rhizomes and barks, collected by Medicinal Plant Works 'Herbapol', were analyzed. Thermal decomposition was performed using OD-103 Derivatograph. As a result of analysis, it was established, that thermal decomposition of majority of samples proceeds through three stages. The analysis of fruits revealed, that their thermal decomposition proceeds in four stages. In order to obtain a more clear classification of the analyzed plant materials principal component analysis (PCA) was applied. Interpretation of the PCA results allows to state, that samples of raw materials from the same plant species in majority of cases are characterized by similar course of thermal decomposition due to similar chemical composition. In this way the differences in general chemical composition of medicinal plants raw materials can be determined.
引用
收藏
页码:593 / 601
页数:9
相关论文
共 50 条
  • [41] Shadow Detection Based on Combinations of Hessenberg Decomposition and Principal Component Analysis in Surveillance Applications
    Moghimi, Mohammad Kazem
    Pourghassem, Hossein
    IETE JOURNAL OF RESEARCH, 2015, 61 (03) : 269 - 284
  • [42] Structural damage identification of intelligent composite materials based on principal component analysis
    Wan L.
    Gong L.
    Jia M.
    Fangzhi Xuebao/Journal of Textile Research, 2019, 40 (05): : 53 - 58
  • [43] Empirical mode decomposition de-noising method based on principal component analysis
    Wang, W.-B. (wwb0178@yahoo.com.cn), 2013, Chinese Institute of Electronics (41):
  • [44] Fault detection of rolling bearing based on principal component analysis and empirical mode decomposition
    Yuan, Yu
    Chen, Chen
    AIMS MATHEMATICS, 2020, 5 (06): : 5916 - 5938
  • [45] Tensor robust principal component analysis based on Bayesian Tucker decomposition for thermographic inspection
    Hu, Yue
    Cui, Fangsen
    Zhao, Yifan
    Li, Fucai
    Cao, Shuai
    Xuan, Fu-zhen
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 204
  • [46] Fault detection and diagnosis using empirical mode decomposition based principal component analysis
    Du, Yuncheng
    Du, Dongping
    COMPUTERS & CHEMICAL ENGINEERING, 2018, 115 : 1 - 21
  • [47] A Digital Image Watermarking Scheme Based on Singular Value Decomposition and Principal Component Analysis
    Hu Rong
    Qian Bin
    Zhang Yunsheng
    PROCEEDINGS OF THE 27TH CHINESE CONTROL CONFERENCE, VOL 5, 2008, : 753 - 757
  • [48] Research on empirical mode decomposition based on spectrum entropy methods and principal component analysis
    Li, Xue-Yao
    Zou, Xiao-Jie
    Zhang, Ru-Bo
    Qian, Zhen
    Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, 2009, 30 (07): : 797 - 803
  • [49] Blind Modulation Format Identification Based on Principal Component Analysis and Singular Value Decomposition
    Jiang, Jinkun
    Zhang, Qi
    Xin, Xiangjun
    Gao, Ran
    Wang, Xishuo
    Tian, Feng
    Tian, Qinghua
    Liu, Bingchun
    Wang, Yongjun
    ELECTRONICS, 2022, 11 (04)
  • [50] Decomposition and analysis of process variability using constrained principal component analysis
    Cho, Choongyeun
    Kim, Daeik D.
    Kim, Jonghae
    Plouchart, Jean-Olivier
    Lim, Daihyun
    Cho, Sangyeun
    Trzcinski, Robert
    IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, 2008, 21 (01) : 55 - 62