A weighted multiscale regression for multivariate calibration of near infrared spectra
A weighted multiscale regression for multivariate calibration of near infrared spectra
复制标题
近红外光谱多变量校准的加权多尺度回归
DOI：
10.1039/b810623a
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发表时间：
2009-01-01
期刊：
影响因子：
4.2
通讯作者：
Shao, Xueguang
中科院分区：
文献类型：
--
作者：
Liu, Zhichao;Cai, Wensheng;Shao, Xueguang
提出了一种用于建立近红外光谱多元校正组合模型的加权多尺度回归方法。该方法首先利用小波变换将频谱分解成不同的尺度块(或频率分量)，然后用分解后的分量建立偏最小二乘模型，最后通过加权平均建立组合模型。每个模型的权重由蒙特卡罗交叉验证(MCCV)获得的预测残差平方和(PRESS)值确定。该策略的基本原理是，可以将有用的信息嵌入到小波变换获得的所有分量中，尽管高频分量和低频分量分别主要表示噪声和背景。为了验证该方法的有效性和普适性，将该方法应用于两组不同的烟叶近红外光谱。与常用的偏最小二乘法相比，该方法是一种高效的复杂近红外光谱多变量校正方法。
A weighted multiscale regression for building a combined model in multivariate calibration of near infrared spectra is proposed. In the approach, the spectra are decomposed into different scale blocks (or frequency components) by wavelet transform (WT) at first, then partial least squares (PLS) models are built with the decomposed components, and at last a combined model is built by a weighted averaging. The weight of each model is determined by the prediction residual error sum of squares (PRESS) value obtained with Monte Carlo cross validation (MCCV). The underlying philosophy of the strategy is that useful information may be embedded in all the components obtained by WT, although the higher and lower frequency components mainly represent noise and background, respectively. To validate the effectiveness and universality of the proposed method, it was applied to two different sets of near-infrared (NIR) spectra of tobacco lamina. Compared with the results obtained with commonly used PLS methods, the proposed method is proved to be a high-performance tool for multivariate calibration of complex NIR spectra.