Spectrometric and analytical techniques in general collect multivariate signals from chemical or biological materials by means of a specific measurement instrumentation, usually in order to characterize or classify them through the estimation of one of several compounds of interest. However, measurement conditions might induce various additive (baseline) or multiplicative effects on the collected signals, which may jeopardize the accuracy and generalizability of estimation models. A common way of dealing with such issues is signal normalization and in particular, when the baseline is constant, the standard normal variate (SNV) transform. Despite its efficiency, SNV has important drawbacks, in terms of physical interpretation and robustness of estimation models, because all the variables are equally considered, independently on what their actual relationship with the response(s) of interest is. In the present study, a novel algorithm is proposed, named variable sorting for normalization (VSN). This algorithm automatically produces, for a given set of multivariate signals, a weighting function favoring signal variables that are only impacted by additive and multiplicative effects, and not by the response(s) of interest. When introduced in SNV preprocessing, this weighting function significantly improves signal shape and model interpretation. Moreover, VSN can be successfully used not only for constant but also with more complex baselines, such as polynomial ones. Together with the description of the theory behind VSN, its application on various synthetic multivariate data, as well as on real SWIR spectral data, is presented and discussed.

中文翻译：

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VSN：标准化的变量排序

光谱和分析技术通常通过特定的测量仪器从化学或生物材料中收集多变量信号，通常是为了通过估计几种感兴趣的化合物之一来表征或分类它们。然而，测量条件可能会对收集的信号产生各种加法（基线）或乘法效应，这可能会危及估计模型的准确性和通用性。处理此类问题的常用方法是信号归一化，特别是当基线不变时，标准正态变量 (SNV) 变换。尽管效率很高，但 SNV 在物理解释和估计模型的稳健性方面有重要的缺点，因为所有变量都被同等考虑，独立于它们与感兴趣的响应的实际关系是什么。在本研究中，提出了一种新算法，称为归一化变量排序（VSN）。对于给定的一组多元信号，该算法会自动生成一个加权函数，该加权函数有利于仅受加性和乘性效应影响的信号变量，而不是受感兴趣的响应影响。在 SNV 预处理中引入时，此加权函数显着改善了信号形状和模型解释。此外，VSN 不仅可以成功用于常量，还可以用于更复杂的基线，例如多项式基线。连同对 VSN 背后理论的描述，介绍和讨论了它在各种合成多元数据以及实际 SWIR 光谱数据上的应用。