| 主成分分析-支持向量回归建模方法及应用研究 |
| |
| 引用本文： | 侯振雨,蔡文生,邵学广.主成分分析-支持向量回归建模方法及应用研究[J].分析化学,2006,34(5):617-620. |
| |
| 作者姓名： | 侯振雨 蔡文生 邵学广 |
| |
| 作者单位： | 中国科技大学化学系,合肥,230026;河南科技学院化工系,新乡,453003;中国科技大学化学系,合肥,230026;南开大学化学系,天津,300071;南开大学化学系,天津,300071 |
| |
| 基金项目： | 国家自然科学基金(No.20325517)，教育部高等学校优秀青年教师教学科研奖励计划资助 |
| |
| 摘 要： | 将主成分分析(PCA)用于近红外光谱的特征提取,并与支持向量回归(SVR)相结合,实现了主成分分析-支持向量回归(PCA-SVR)用于近红外光谱定量分析的建模方法。与单纯的SVR方法相比,不仅提高了运算速度,而且提高了模型的预测准确度。将PCA-SVR方法用于烟草样品中总糖和总挥发碱含量的测定,所得结果的预测均方根误差分别为1.323和0.0477;回收率分别为91.8%~112.6%和88.9%~120.2%。
|
| 关 键 词： | 主成分分析 支持向量回归 近红外光谱 |
| 收稿时间： | 06 13 2005 12:00AM |
| 修稿时间： | 2005-06-132005-11-06 |
Principal Component Analysis-Support Vector Regression and Its Application In Near Infrared Spectral Analysis |
| |
| Hou Zhenyu,Cai Wensheng,Shao Xueguang.Principal Component Analysis-Support Vector Regression and Its Application In Near Infrared Spectral Analysis[J].Chinese Journal of Analytical Chemistry,2006,34(5):617-620. |
| |
| Authors: | Hou Zhenyu Cai Wensheng Shao Xueguang |
| |
| Institution: | 1.Department of Chemistry, University of Science and Technology of China, Hefei 230026;2. Department of Chemical Engineering, Henan lnstittae of Science and Technology, Xinxiang 453003;3.Department of Chemistry, Nankai University, Tianjin 300071 |
| |
| Abstract: | A new method for quantitative prediction of total sugar (TS) and total volatile alkali (TVA) content in tobacco samples from near infrared (NIR) spectrometry was proposed. The method is a combination of principal component analysis (PCA) and support vector regression (SVR), which extracts features from the NIR spectra using PCA at first and then builds a nonlinear model using SVR. Therefore, the new method is fast in computation and accurate in prediction. 110 NIR spectra was used for investigation of method, taking 60 as calibration set, 20 as test set and 30 as prediction set. Results show that the RMSEPs (root mean square error of prediction) are 1.323 and 0.0477, and the recoveries are 91.8%-112.6% and 88.9%-120.2% for TS and TVA, respectively. |
| |
| Keywords: | Principal component analysis support vector regression near infrared spectrometry |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
