Abstract Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l 1 -norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l 0 -norm problem instead of the l 1 -norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction.

中文翻译：

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用于基线校正的稀疏贝叶斯学习方法

摘要 分析化学中的光谱技术在实际应用中经常受到基线的影响。如果没有基线校正，定性/定量分析结果的准确性可能会大大降低。稀疏表示最近已应用于基线校正，可以提供最先进的性能。然而，当使用 l 1 -范数近似实现时，它可能会降低性能。为了显着提高基线校正的性能，本文提出了一种用于联合纯光谱拟合和基线校正的稀疏贝叶斯学习 (SBL) 框架。由于 SBL 框架提供了高度的灵活性来解决最小 l 0 -范数问题而不是 l 1 -范数近似，所以有可能产生更高的基线校正精度。而且，所提出的方法具有固有的学习能力，因此不需要额外的正则化项。请注意，如果字典矩阵中使用的网格点不够密集，稀疏表示的性能会下降。因此，我们进一步将网格点视为可调参数，然后采用网格细化技术来处理离网间隙。模拟数据集和真实数据集的结果表明，所提出的基于 SBL 的方法在基线校正方面的性能优于现有方案。我们进一步将网格点视为可调参数，然后采用网格细化技术来处理离网间隙。模拟数据集和真实数据集的结果表明，所提出的基于 SBL 的方法在基线校正方面的性能优于现有方案。我们进一步将网格点视为可调参数，然后采用网格细化技术来处理离网间隙。模拟数据集和真实数据集的结果表明，所提出的基于 SBL 的方法在基线校正方面的性能优于现有方案。