A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples
A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples
复制标题
用于分析植物样品近红外光谱的共识最小二乘支持向量回归 (LS-SVR)
DOI：
10.1016/j.talanta.2006.10.022
复制
发表时间：
2007-04-15
期刊：
影响因子：
6.1
通讯作者：
Cai, Wensheng
中科院分区：
文献类型：
--
作者：
Li, Yankun;Shao, Xueguang;Cai, Wensheng
将多个独立模型的结果组合在一起产生一个单一预测的共识模型避免了单个模型的不稳定性。基于共识建模原理，提出了一种基于共识最小二乘支持向量回归（LS-SVR）的近红外光谱校正方法。该方法首先利用离散小波变换（DWT）对植物样品的近红外光谱进行预处理，滤除光谱背景和噪声，然后利用一致LS-SVR技术建立校准模型。通过对模型参数的优化，获得了较为满意的植物样品中还原糖含量预测模型。预测结果表明，共识LS-SVR模型比传统的偏最小二乘（PLS）和LS-SVR方法具有更强的鲁棒性和可靠性。(C) 2006 Elsevier B.V.版权所有
Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods. (C) 2006 Elsevier B.V. All rights reserved.