A model recalibration method based on additive Partial Least Squares (PLS) regression is generalized for multi-adjustment scenarios of independent variance sources (referred to as additive PLS - aPLS). aPLS allows for effortless model readjustment under changing measurement conditions and the combination of independent variance sources with the initial model by means of additive modelling. We demonstrate these distinguishing features on two NIR spectroscopic case-studies. In case study 1 aPLS was used as a readjustment method for an emerging offset. The achieved RMS error of prediction (1.91 a.u.) was of similar level as before the offset occurred (2.11 a.u.). In case-study 2 a calibration combining different variance sources was conducted. The achieved performance was of sufficient level with an absolute error being better than 0.8% of the mean concentration, therefore being able to compensate negative effects of two independent variance sources. The presented results show the applicability of the aPLS approach. The main advantages of the method are that the original model stays unadjusted and that the modelling is conducted on concrete changes in the spectra thus supporting efficient (in most cases straightforward) modelling. Additionally, the method is put into context of existing machine learning algorithms.

中文翻译：

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用于对实际案例研究中展示的独立方差源进行有效建模的加性偏最小二乘法

一种基于加性偏最小二乘（PLS）回归的模型重新校准方法被推广用于独立方差源的多调整场景（称为加性PLS-aPLS）。aPLS 允许在不断变化的测量条件下轻松地重新调整模型，并通过加性建模将独立方差源与初始模型相结合。我们在两个 NIR 光谱案例研究中展示了这些区别特征。在案例研究 1 中，aPLS 被用作新兴偏移的重新调整方法。实现的预测 RMS 误差 (1.91 au) 与偏移发生之前 (2.11 au) 的水平相似。在案例研究 2 中，进行了结合不同方差源的校准。达到的性能达到了足够的水平，绝对误差优于 0。平均浓度的 8%，因此能够补偿两个独立方差源的负面影响。所呈现的结果显示了 aPLS 方法的适用性。该方法的主要优点是原始模型保持未调整状态，并且建模是根据光谱的具体变化进行的，从而支持高效（在大多数情况下是直接的）建模。此外，该方法被置于现有机器学习算法的上下文中。该方法的主要优点是原始模型保持未调整状态，并且建模是根据光谱的具体变化进行的，从而支持高效（在大多数情况下是直接的）建模。此外，该方法被置于现有机器学习算法的上下文中。该方法的主要优点是原始模型保持未调整状态，并且建模是根据光谱的具体变化进行的，从而支持高效（在大多数情况下是直接的）建模。此外，该方法被置于现有机器学习算法的上下文中。