Spectroscopic measurements can show distorted spectra 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 concentration such that prediction can be enhanced. Performance will be access 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，吸收与散射贡献的混合可能导致光谱形状发生畸变。这些畸变（即基线）通常表现为非恒定偏移或低频振荡，进而对分析和定量结果产生不利影响。基线校正是一个统称性术语，指通过预处理方法获取基线光谱（即非期望畸变），再通过差分方法去除畸变。然而，当前最先进的基线校正方法即使获得了分析物浓度数据，或该浓度对观测到的光谱变异性具有显著贡献，也未能加以利用。本研究对一类前沿方法（惩罚式基线校正）进行探究，并对其进行改进，使其能够整合先验分析物浓度信息，从而增强预测能力。我们将通过两组近红外光谱数据集，对经典惩罚式基线校正方法（不含分析物信息）与改进型惩罚式基线校正方法（利用分析物信息）的性能进行评估。