| 光谱学与光谱分析 |
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| Applied Study on Support Vector Machine (SVM) Regression Method in Quantitative Analysis with Near-Infrared Spectroscopy |
| ZHANG Lu-da1，JIN Ze-chen2，SHEN Xiao-nan1，ZHAO Long-lian2，LI Jun-hui2，YAN Yan-lu2 |
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 This paper introduced the application of support vector machines(SVM) regression method based on statistics study theory to the quantitative analysis with near-infrared (NIR) spectroscopy. Sixty-six wheat samples were used as experimental materials, and thirty-three of them were used as calibration samples. The protein contents and NIR spectra of the calibration samples were used to build SVM regression models by four different kernel functions. The protein content of the predicting samples are estimated by four different SVM regression models. All of the correlation coefficients between the estimated values by different SVM regression models and the standard chemical values of protein content by Kjeldahl’s method are more than 0.97. The average absolute error is less than 0.32. To investigate the predicting effect, it is compared with PLS regression models. The result suggested that the SVM regression, which was built to estimate the protein content of wheat samples, can also be used in the quantitative analysis of real samples by NIR.
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Received: 2004-03-31
Accepted: 2004-07-28
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Corresponding Authors:
ZHANG Lu-da
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| Cite this article: |
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ZHANG Lu-da,JIN Ze-chen,SHEN Xiao-nan, et al. Applied Study on Support Vector Machine (SVM) Regression Method in Quantitative Analysis with Near-Infrared Spectroscopy [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2005, 25(09): 1400-1403.
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| URL: |
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https://www.gpxygpfx.com/EN/Y2005/V25/I09/1400 |
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