Spectroscopic measurements can show distorted spectral shapes arising from a mixture of absorbing and scattering contributions. These distortions (or baselines) often manifest themselves as non-constant offsets or low-frequency oscillations. As a result, these baselines can adversely affect analytical and quantitative results. Baseline correction is an umbrella term where one applies pre-processing methods to obtain baseline spectra (the unwanted distortions) and then remove the distortions by differencing. However, current state-of-the art baseline correction methods do not utilize analyte concentrations even if they are available, or even if they contribute significantly to the observed spectral variability. We examine a class of state-of-the-art methods (penalized baseline correction) and modify them such that they can accommodate a priori analyte concentrations such that prediction can be enhanced. Performance will be assessed on two near infra-red data sets across both classical penalized baseline correction methods (without analyte information) and modified penalized baseline correction methods (leveraging analyte information).

翻译：光谱测量中，吸收和散射贡献的混合可能导致光谱形状出现畸变。这些畸变（即基线）通常表现为非恒定偏移或低频振荡，从而对分析和定量结果产生不利影响。基线校正是一种预处理方法的统称，通过获取基线光谱（即不需要的畸变）并利用差分法消除畸变。然而，当前最先进的基线校正方法即使存在分析物浓度或该浓度对观测到的光谱变异性有显著贡献时，也未能充分利用这些信息。本文研究了一类最先进的方法（惩罚基线校正）并对其进行改进，使其能够纳入先验分析物浓度以增强预测能力。通过在两个近红外数据集上，对经典惩罚基线校正方法（无分析物信息）与改进的惩罚基线校正方法（利用分析物信息）进行性能评估。