| 光谱学与光谱分析 |
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| Study on Application of Fourier Transformation Near-Infrared Spectroscopy Analysis with Support Vector Machine (SVM) |
| ZHANG Lu-da1，SU Shi-guang1，WANG Lai-sheng1，LI Jun-hui2，YANG Li-ming1 |
1. College of Science, China Agricultural University，Beijing 100094，China
2. College of Information, China Agricultural University，Beijing 100094，China |
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Abstract Support Vector Machine (SVM) is a method for the research oｎ identifying two types of problem. Ｉt is the latest branch in the statistics study theories, and the identifｉｃａｔｉｏｎ model has ａ strict mathematics foundation. In this paper, the basic principle and method of SVM are not only introduced, but also applied to chemometrics. One hundred and three rhubarb samples were ｕｓｅｄ as experimentａｌ materials. The identifｉｃａｔｉｏｎ models ｗｅre established with ｎear-infrared spectroscopy and SVM training method ｗｉｔｈ the intention of identifying whether the rhubarb samples ａｒｅ true or false. The thirty-three samples in training set were identified by the identifying models with the accurate rate ｏｆ 100%, while seventy estimate samples had an accurate rate of 96.77%. The research result provided the method of identifying the traditional Chinese medicine rhubarb quickly. So, it shows the feasibility of establishing the models with ｎear-infrared ｓpectroscopy and SVM method to identify biological samples. This paper introduced the theme of SVM training method in order to beget the attention of the research members who deal with chemometrics.
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Received: 2003-02-25
Accepted: 2003-08-06
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Corresponding Authors:
ZHANG Lu-da
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| Cite this article: |
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ZHANG Lu-da,SU Shi-guang,WANG Lai-sheng, et al. Study on Application of Fourier Transformation Near-Infrared Spectroscopy Analysis with Support Vector Machine (SVM) [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2005, 25(01): 33-35.
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| URL: |
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https://www.gpxygpfx.com/EN/Y2005/V25/I01/33 |
[1] Vapnik V N. The Nature of Statistical Learning Theory. NY: Springer-Verlag, 1995.
[2] ZHANG Xue-gong(张学工). Acta Automatica Sinica(自动化学报)，2000，26(1)：32.
[3] XU Guang-tong，YUAN Hong-fu，LU Wan-zhen(徐广通，袁洪福，陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000, 20(2): 134.
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