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。基线校正是一个统称，指通过预处理方法获取基线光谱（即非期望畸变），再通过差分去除畸变的过程。然而，当前最先进的基线校正方法即便在分析物浓度可用、或其对观测光谱变异性具有显著贡献时，也未能利用该信息。本研究考察了一类前沿方法（惩罚基线校正），并对其进行改进，使其能够整合先验的分析物浓度信息以提升预测能力。通过两组近红外数据集，分别对经典惩罚基线校正方法（无分析物信息）和改进型惩罚基线校正方法（利用分析物信息）的性能进行评估。