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Rapid Identification of Dendrobium Plants Based on Near Infrared Diffuse Reflection Spectroscopy

文献类型： 外文期刊

作者： Ding Chang-chun 1 ; Fang Xiang-jing 4 ; Zhao Yan-li 5 ; Li Gui-xiang 4 ; Li Tao 6 ; Wang Yuan-zhong 5 ; Xia Nian-he 1 ;

作者机构： 1.Chinese Acad Sci, South China Bot Garden, Guangzhou 510650, Guangdong, Peoples R China

2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China

3.Wenshan Univ, Dept Biochem, Wenshan 663000, Peoples R China

4.Yunnan Acad Forestry, Kunming 650204, Peoples R China

5.Yunnan Acad Agr Sci, Inst Med Plants, Kunming 650200, Peoples R China

6.Yuxi Normal Univ, Resources & Environm Coll, Yuxi 653100, Peoples R China

关键词： Dendrobium;Near infrared diffuse reflection spectroscopy;Principal component analysis;Partial least squares discriminant analysis

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2014 年 34 卷 1 期

页码：

收录情况： SCI

摘要： Near infrared diffuse reflection spectra of 15 species' 171 samples of Dendrobium combined with chemometrics statistical analysis were used to build prediction model, in order to discriminate different species of Dendrobium quickly and nondestructively. Hotel ling T-2 was applied to stability analysis of spectrum of 5 random drawing samples, and the results showed that the samples spectrum possessed good stability. Orthogonal test L-24 (2 X 4 X 3 X 8) was designed to optimize optical path type, spectral band, derivative and smooth. The result of orthogonal test was analyzed by principal component analysis, which revealed that when 6 500 similar to 4 000 cm(-1) spectral band was applied, and with multiplicative scatter correction, second derivative, Norris smooth, and the number of principal components 7, the spectrum distinguishing accuracy was 100%. With the optimized condition of orthogonal test as the input value of partial least squares discriminant analysis and random drawing 123 samples as calibration set to establish the prediction model, and the rest 48 samples as prediction set were use to assess the property of the prediction model, the results indicated that the accumulating contribution rate of the first 3 principal components of the model was 99. 36%, the identification of the standard deviation was +/- 0. 1, and the correct recognition rate of the model was 97. 92%. The results were satisfied. The method provided a new way for the rapid identification of different species of Dendrobium, and also supplied a reference for the authentication of medicinal plants.

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