Wavelength selection is a critical step in multivariate calibration. Variable selection methods are used to find the most relevant variables, leading to improved prediction accuracy, while simplifying both the built models and their interpretation. In addition, different spectrophotometer designs and measurement principles result in non-destructive technologies applied in many fields, such as agriculture, food chemistry and pharmaceutics. However, an on-chip or portable device does not allow acquiring data from a large number of wavelengths. Therefore, the most informative combination of a limited number of variables should be selected. The Replacement Orthogonal Wavelengths Selection (ROWS) method is described here as a new method. This algorithm aims at selecting as few wavelengths as possible, while keeping or improving the prediction performance of the model, compared to when no variable selection is applied. The ROWS is applied to several near infrared spectroscopic data sets leading to improved analytical figures of merits upon wavelength selection in comparison to a built PLS model using entire spectral range. The performance of the ROWS-MLR method was compared to the FCAM-PLS method. The resulting models are not significantly different from those of FCAM-PLS; however, it involves a significantly smaller amount of variables. © 2018

中文翻译：

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替代正交波长选择作为光谱学中多元校准的新方法

波长选择是多元校准的关键步骤。变量选择方法用于找到最相关的变量，从而提高预测准确性，同时简化构建的模型及其解释。此外，不同的分光光度计设计和测量原理导致无损技术应用于许多领域，如农业、食品化学和制药。然而，片上或便携式设备不允许从大量波长获取数据。因此，应选择数量有限的变量中信息量最大的组合。替换正交波长选择 (ROWS) 方法在此被描述为一种新方法。该算法旨在选择尽可能少的波长，在保持或提高模型的预测性能的同时，与未应用变量选择时相比。与使用整个光谱范围的构建 PLS 模型相比，ROWS 应用于几个近红外光谱数据集，从而改善了波长选择时的分析品质因数。ROWS-MLR 方法的性能与 FCAM-PLS 方法进行了比较。所得模型与 FCAM-PLS 的模型没有显着差异；然而，它涉及的变量数量要少得多。© 2018 ROWS-MLR 方法的性能与 FCAM-PLS 方法进行了比较。所得模型与 FCAM-PLS 的模型没有显着差异；然而，它涉及的变量数量要少得多。© 2018 ROWS-MLR 方法的性能与 FCAM-PLS 方法进行了比较。所得模型与 FCAM-PLS 的模型没有显着差异；然而，它涉及的变量数量要少得多。© 2018