Research on the Similarity Algorithm of Chromatographic Fingerprint Based on Information Entropy

DOI

10.2991/emeit.2012.42How to use a DOI?

Keywords

information entropy, chromatographic fingerprint, similarity

Abstract

In order to reduce the error of interangle cosine method or correlation method in the chromatographic fingerprints analysis, an improved similarity algorithm that assigns different weight according to information entropy was proposed in this paper. In this way, 23 samples of Exocarpium Citrus Grandis collected from different places were obviously classified as two different species, which solved the problem that it is difficult to make an accurate identification either by the traditional similarity algorithm or its’ optimization by coefficient of variation. Further more, better results were obtained by compared with discrimination model base on principal component analysis and artificial neural network. So, the method based on information entropy was more suitable for quick, effective discrimination of species and origin of traditional Chinese medicine.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hang Wei
AU  - Li Lin
AU  - Pingping Chen
AU  - Dingying Tan
AU  - Qinqun Chen
PY  - 2012/09
DA  - 2012/09
TI  - Research on the Similarity Algorithm of Chromatographic Fingerprint Based on Information Entropy
BT  - Proceedings of the 2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT 2012)
PB  - Atlantis Press
SP  - 214
EP  - 218
SN  - 1951-6851
UR  - https://doi.org/10.2991/emeit.2012.42
DO  - 10.2991/emeit.2012.42
ID  - Wei2012/09
ER  -