Orthogonal projections to latent structures (O-PLS)
Orthogonal projections to latent structures (O-PLS)
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DOI：
10.1002/cem.695
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发表时间：
2002-03-01
影响因子：
2.4
通讯作者：
Wold, S
中科院分区：
文献类型：
--
作者：
Trygg, J;Wold, S
描述了一种通用的多变量数据预处理方法，称为潜在结构的正交投影(O-PLS)。O-PLS从X(描述符变量)中去除与Y(属性变量，例如产量、成本或毒性)无关的变量。用数学术语来说，这相当于消除了X中与Y垂直的系统变化。在早期的一篇论文中，Wold等人。(化学计量学Intell，Lab.系统1998；44：175-185)描述了正交信号校正(OSC)。本文描述了一种目标相同、手段不同的方法。所提出的O-偏最小二乘方法分析了每个偏最小二乘分量中解释的变化。去除了X中不相关的系统变化，使得对所得到的偏最小二乘模型的解释更容易，并且附加的好处是，不相关的变化本身可以被进一步分析。以木片为例，对其近红外反射光谱进行了分析。O-偏最小二乘法的应用降低了模型的复杂性，保留了预测能力，有效地去除了X中的不相关变化，尤其是提高了对近红外光谱中相关和不相关变化的解释能力。版权所有(C)2002 John Wiley Sons，Ltd.
A generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described. O-PLS removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity). In mathematical terms this is equivalent to removing systematic variation in X that is orthogonal to Y. In an earlier paper, Wold et al. (Chemometrics Intell, Lab. Syst. 1998; 44:175-185) described orthogonal signal correction (OSC). In this paper a method with the same objective but with different means is described. The proposed O-PLS method analyzes the variation explained in each PLS component. The non-correlated systematic variation in X is removed, making interpretation of the resulting PLS model easier and with the additional benefit that the non-correlated variation itself can be analyzed further. As an example, near-infrared (NIR) reflectance spectra of wood chips were analyzed. Applying O-PLS resulted in reduced model complexity with preserved prediction ability, effective removal of noncorrelated variation in X and, not least, improved interpretational ability of both correlated and noncorrelated variation in the NIR spectra. Copyright (C) 2002 John Wiley Sons, Ltd.