School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
Anal Chem. 2022 Mar 29;94(12):5113-5121. doi: 10.1021/acs.analchem.1c05443. Epub 2022 Mar 18.
Baseline correction is a critical step for eliminating the interference of baseline drift in spectroscopic analysis. The recently proposed sparse Bayesian learning (SBL)-based method can significantly improve the baseline correction performance. However, it has at least two disadvantages: (i) it works poorly for large-scale datasets and (ii) it completely ignores the burst-sparsity structure of the sparse representation of the pure spectrum. In this paper, we present a new fast burst-sparsity learning method for baseline correction to overcome these shortcomings. The first novelty of the proposed method is to jointly adopt a down-sampling strategy and construct a multiple measurements block-sparse recovery problem with the down-sampling sequences. The down-sampling strategy can significantly reduce the dimension of the spectrum; while jointly exploiting the block sparsity among the down-sampling sequences avoids losing the information contained in the original spectrum. The second novelty of the proposed method is introducing the pattern-coupled prior into the SBL framework to characterize the inherent burst-sparsity in the sparse representation of spectrum. As illustrated in the paper, burst-sparsity commonly occurs in peak zones with more denser nonzero coefficients. Properly utilizing such burst-sparsity can further enhance the baseline correction performance. Results on both simulated and real datasets (such as FT-IR, Raman spectrum, and chromatography) verify the substantial improvement, in terms of estimation accuracy and computational complexity.
基线校正对于消除光谱分析中基线漂移的干扰是至关重要的。最近提出的基于稀疏贝叶斯学习（SBL）的方法可以显著提高基线校正的性能。然而，它至少有两个缺点：（i）它在大规模数据集上的效果不佳，（ii）它完全忽略了纯光谱稀疏表示的突发稀疏结构。在本文中，我们提出了一种新的快速突发稀疏学习方法用于基线校正，以克服这些缺点。所提出方法的第一个新颖之处在于联合采用降采样策略，并利用降采样序列构建多个测量块稀疏恢复问题。降采样策略可以显著降低光谱的维度；同时，联合利用降采样序列之间的块稀疏性可以避免丢失原始光谱中包含的信息。所提出方法的第二个新颖之处在于将模式耦合先验引入到 SBL 框架中，以描述光谱稀疏表示中的固有突发稀疏性。如本文所述，突发稀疏性通常出现在具有更密集非零系数的峰区。正确利用这种突发稀疏性可以进一步提高基线校正的性能。在模拟和真实数据集（如 FT-IR、拉曼光谱和色谱）上的结果验证了在估计准确性和计算复杂度方面的显著改进。
