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SVM回归法在近红外光谱定量分析中的应用研究
引用本文:张录达,金泽宸,沈晓南,赵龙莲,李军会,YAN Yan-lu,严衍禄.SVM回归法在近红外光谱定量分析中的应用研究[J].光谱学与光谱分析,2005,25(9):1400-1403.
作者姓名:张录达  金泽宸  沈晓南  赵龙莲  李军会  YAN Yan-lu  严衍禄
作者单位:1. 中国农业大学理学院,北京,100094
2. 中国农业大学信息学院,北京,100094
基金项目:国家高技术研究发展计划(863计划)项目(2002AA248051)(2002AA243011),“十五”国家科技攻关项目(2004BA210A03),国家重大基础研究前期研究专项(2002CCA00800)和农业科技成果转化资金项目(02EFN216900720)资助
摘    要:研究了基于统计学习理论的支持向量机(SVM)回归法在近红外光谱定量分析中的应用。以66个小麦样品为实验材料,由33个小麦样品作为校正样品,采用4种不同核函数方法对小麦样品蛋白质含量与小麦样品近红外光谱进行SVM回归建模。以所建4种不同SVM回归模型对33个小麦预测样品的蛋白质含量进行了预测;不同回归模型的预测结果与凯氏定氮法确定的蛋白质含量的标准化学值间的相关系数均在0.97以上,平均绝对误差小于0.32。为了考察SVM回归校正模型的预测效果,同所建PLS回归模型的预测结果进行了比较,表明所建预测小麦样品蛋白质含量的SVM回归模型亦可通过近红外光谱进行实际样品的定量分析,且有较好的分析效果。

关 键 词:支持向量机回归  近红外光谱  定量分析
文章编号:1000-0593(2005)09-1400-04
收稿时间:2004-03-31
修稿时间:2004-07-28

Applied Study on Support Vector Machine (SVM) Regression Method in Quantitative Analysis with Near-Infrared Spectroscopy
ZHANG Lu-da , JIN Ze-chen, SHEN Xiao-nan , ZHAO Long-lian, LI Jun-hui, YAN Yan-lu.Applied Study on Support Vector Machine (SVM) Regression Method in Quantitative Analysis with Near-Infrared Spectroscopy[J].Spectroscopy and Spectral Analysis,2005,25(9):1400-1403.
Authors:ZHANG Lu-da  JIN Ze-chen  SHEN Xiao-nan  ZHAO Long-lian  LI Jun-hui  YAN Yan-lu
Institution:1. College of Science, China Agricultural University, Beijing 100094, China;2. College of Information. China Agricultural University, Beijing 100094, China
Abstract:This paper introduced the application of support vector machines(SVM) regression method based on statistics studytheory 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.
Keywords:Support vector machine regression  Near-infrared spectroscopy  Quantitative analysis
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