Orthogonal signal correction of near-infrared spectra
Orthogonal signal correction of near-infrared spectra
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DOI：
10.1016/s0169-7439(98)00109-9
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发表时间：
1998-12-14
影响因子：
3.9
通讯作者：
Öhman, J
中科院分区：
文献类型：
--
作者：
Wold, S;Antti, H;Öhman, J
近红外（NIR）光谱通常进行预处理，以消除系统噪声，如基线变化和乘法散射效应。这通过将光谱微分为一阶或二阶导数、通过乘法信号校正（MSC）或通过类似的数学滤波方法来完成。然而，该预处理还可以从光谱中去除关于Y（多变量校准应用中的测量的响应变量）的信息。我们在这里展示了如何使用PLS的变体来实现尽可能接近给定Y向量或Y矩阵正交的信号校正。因此，确保信号校正去除尽可能少的关于Y的信息。在X变量的数目（K）超过观测的数目（N）的情况下，获得严格正交性。该方法被称为正交信号校正（OSC），并在这里适用于四个不同的数据集的多元校正。与传统的信号校正以及与那些没有预处理的结果进行了比较，和OSC示出给实质性的改善。模型开发中未使用的新数据的预测集用于比较。(C)1998 Elsevier Science B.V.保留所有权利。
Near-infrared (NIR) spectra are often pre-processed in order to remove systematic noise such as base-line variation and multiplicative scatter effects. This is done by differentiating the spectra to first or second derivatives, by multiplicative signal correction (MSC), or by similar mathematical filtering methods. This pre-processing may, however, also remove information from the spectra regarding Y (the measured response variable in multivariate calibration applications). We here show how a variant of PLS can be used to achieve a signal correction that is as close to orthogonal as possible to a given Y-vector or Y-matrix. Thus, one ensures that the signal correction removes as little information as possible regarding Y. In the case when the number of X-variables (K) exceeds the number of observations (N), strict orthogonality is obtained. The approach is called orthogonal signal correction (OSC) and is here applied to four different data sets of multivariate calibration. The results are compared with those of traditional signal correction as well as with those of no pre-processing, and OSC is shown to give substantial improvements. Prediction sets of new data, not used in the model development, are used for the comparisons. (C) 1998 Elsevier Science B.V. All rights reserved.