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IR@PKUHSC  > 北京大学药学院  > 天然药物学系  > 期刊论文
学科主题: 药学
题名:
Recognition of three classes of skullcaps by FTIR spectroscopy combined with artificial neural networks
作者: Xu, YQ; Sun, SQ; Zhou, Q; Cai, SQ
关键词: FTIR ; pattern recognition ; ANN ; Scutellaria baicalensis ; Scutellaria bge
刊名: SPECTROSCOPY AND SPECTRAL ANALYSIS
发表日期: 2002-12-01
卷: 22, 期:6, 页:945-948
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology
类目[WOS]: Spectroscopy
研究领域[WOS]: Spectroscopy
关键词[WOS]: DRUGS
英文摘要:

In order to recognition of three classes of skullcaps (cultivated, wild Scutellaria baicalensis Georgi and Scutellaria viscidula. Bge) three kinds of models of artificial neural networks (ANN), nonlinear-linear, linear-linear and nonlinear-nonlinear model, were used combined with their infrared spectra. Skullcaps samples were collected by Fourier Transform Infrared (FTIR) spectra. 42 samples were gathered as a train set, and 34 samples as a test set, then their supervision trains were performed using three models each. When the summation of error square of train target was selected as 0. 01, the correct. rate for recognition of three classes of skullcaps using each ANN was 100% for the train set, but was different for the test set, which depended on the number of node in hidden layer, S1. It was found that with the increase of S1, the correct rate would decrease oppositely. This may be caused by the high degree of the non-linearity of the networks, so that the models of networks were not fit for the train of this kind of sample set. When using linear-linear model of ANN varied with S1 in some extent, the correct rate was generally about 85%. Recognizability obtained using nonlinear-linear model of ANN was the best. Its correct rate of recognition was > 97% when S1 = 3, and so this method can be used to recognize three of skullcaps simply, rapidly, and accurately.

语种: 中文
WOS记录号: WOS:000180384600020
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.bjmu.edu.cn/handle/400002259/56650
Appears in Collections:北京大学药学院_天然药物学系_期刊论文

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作者单位: 1.Huanggang Normal Univ, Dept Chem, Huanggang 438000, Peoples R China
2.Peking Univ, Dept Nat Med, Beijing 100083, Peoples R China
3.Tsing Hua Univ, Dept Chem, Beijing 100084, Peoples R China

Recommended Citation:
Xu, YQ,Sun, SQ,Zhou, Q,et al. Recognition of three classes of skullcaps by FTIR spectroscopy combined with artificial neural networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2002,22(6):945-948.
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