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| 近红外光谱定量分析的新方法:半监督最小二乘支持向量回归机 | |
| 引用本文： | Li L,Xu S,An X,Zhang LD. 近红外光谱定量分析的新方法:半监督最小二乘支持向量回归机[J]. 光谱学与光谱分析, 2011, 31(10): 2702-2705. DOI: 10.3964/j.issn.1000-0593(2011)10-2702-04 |
| 作者姓名： | Li L Xu S An X Zhang LD |
| 作者单位： | 1. 中国农业大学信息与电气工程学院,北京,100193 2. 中国科学技术信息研究所信息技术支持中心,北京,100038 3. 对外经济贸易大学国际经济与贸易学院,北京,100029 4. 中国农业大学理学院,北京,100193 |
| 基金项目： | 国家“十一五”科技支撑计划(2007BAD36B01-04); 中央高校基本科研业务费专项资金(2009-2-05); 公益性行业(农业)科研专项(200903021)资助 |
| 摘 要： | 在近红外光谱定量分析中,样品化学值测定的准确度是运用数学模型进行定量分析精确度的理论极限。但能够准确获取化学值的样品数量比较少,许多模型在建模时只考虑这部分样品数据,而不考虑大量的无化学值的样品数据。针对该问题,本文在LS-SVR的基础上,提出了可以同时利用有化学值(标签)和无化学值样品数据的半监督LS-SVR(S2LS-SVR)模型。类似于LS-SVR,该模型也只需求解一个线性方程组。最后,以烤烟样品数据集为实验材料,建立了四种样品成分(总糖、还原糖、总氮和烟碱)的定量分析模型。四种样品成分的预测值与实际值的平均误差分别为6.62%,7.56%,6.11%和8.20%,相关系数分别为0.974 1,0.973 3,0.923 0和0.948 6。经分析比较发现S2LS-SVR模型优于PLS和LS-SVR,从而验证了S2LS-SVR模型的可行性和有效性。 |
| 关 键 词： | 近红外光谱 化学计量学 半监督LS-SVR(S2LS-SVR) |
A novel approach to NIR spectral quantitative analysis: semi-supervised least-squares support vector regression machine |
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| Li Lin,Xu Shuo,An Xin,Zhang Lu-Da. A novel approach to NIR spectral quantitative analysis: semi-supervised least-squares support vector regression machine[J]. Spectroscopy and Spectral Analysis, 2011, 31(10): 2702-2705. DOI: 10.3964/j.issn.1000-0593(2011)10-2702-04 | |
| Authors: | Li Lin Xu Shuo An Xin Zhang Lu-Da |
| Affiliation: | LI Lin1,XU Shuo2*,AN Xin3,ZHANG Lu-da41.College of Information and Electrical Engineering,China Agricultural University,Beijing 100193,China2.Information Technology Supporting Center,Institute of Scientific and Technical Information of China,Beijing 100038,China3.School of International Trade and Economics,University of International Business and Economics,Beijing 100029,China4.College of Science,China |
| Abstract: | In near infrared spectral quantitative analysis,the precision of measured samples' chemical values is the theoretical limit of those of quantitative analysis with mathematical models.However,the number of samples that can obtain accurately their chemical values is few.Many models exclude the amount of samples without chemical values,and consider only these samples with chemical values when modeling sample compositions' contents.To address this problem,a semi-supervised LS-SVR(S2LS-SVR) model is proposed on ... |
| Keywords: | Near infrared spectrum Chemometrics Semi-supervised LS-SVR(S2LS-SVR) |
| 本文献已被 CNKI 万方数据 PubMed 等数据库收录！ | |