The separation of predictive and nonpredictive (or orthogonal) information in linear regression problems is considered to be an important issue in chemometrics. Approaches including net analyte preprocessing methods and various orthogonal signal correction (OSC) methods have been studied in a considerable number of publications. In the present paper, we focus on the simplest single response versions of some of the early OSC approaches including Fearns OSC, the orthogonal projections to latent structures, the target projection (TP), and the projections to latent structures (PLS) postprocessing by similarity transformation. These methods are claimed to yield improved model building and interpretation alternatives compared with ordinary PLS, by filtering “off” the response‐orthogonal parts of the samples in a dataset. We point out at some fundamental misconceptions that were made in the justification of the PLS‐related OSC algorithms and explain the key properties of the resulting modelling.

中文翻译：

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正交信号校正的 O-PLS 方法——是校正还是混淆？

线性回归问题中预测和非预测（或正交）信息的分离被认为是化学计量学中的一个重要问题。大量出版物研究了包括净分析物预处理方法和各种正交信号校正 (OSC) 方法在内的方法。在本文中，我们专注于一些早期 OSC 方法的最简单的单一响应版本，包括 Fearns OSC、潜在结构的正交投影、目标投影 (TP) 以及通过相似性后处理的潜在结构 (PLS) 投影转型。据称，与普通 PLS 相比，这些方法通过过滤“关闭”数据集中样本的响应正交部分，可以产生改进的模型构建和解释替代方案。