Fast Burst-Sparsity Learning-Based Baseline Correction (FBSL-BC) Algorithm for Signals of Analytical Instruments

Area of Science:

• Spectroscopic analysis
• Computational chemistry
• Data science

Background:

• Baseline drift is a significant interference in spectroscopic analysis.
• Sparse Bayesian Learning (SBL) improves baseline correction but struggles with large datasets and ignores spectral burst-sparsity.
• Existing methods fail to fully utilize the inherent structure of spectral data.

Purpose of the Study:

• To develop a fast burst-sparsity learning method for enhanced spectroscopic baseline correction.
• To address the limitations of existing SBL methods in handling large datasets and spectral structure.
• To improve the accuracy and computational efficiency of baseline correction.

Main Methods:

• A novel fast burst-sparsity learning algorithm is proposed.
• Combines a down-sampling strategy with block-sparse recovery across down-sampled sequences.
• Introduces a pattern-coupled prior into the SBL framework to exploit spectral burst-sparsity.

Main Results:

• The proposed method significantly reduces spectral data dimensions via down-sampling while preserving information.
• Jointly exploiting block sparsity across sequences enhances information retention.
• The pattern-coupled prior effectively characterizes and utilizes burst-sparsity for improved correction.
• Demonstrated substantial improvements in estimation accuracy and computational complexity on simulated and real spectroscopic data (FT-IR, Raman, chromatography).

Conclusions:

• The fast burst-sparsity learning method offers a superior alternative for spectroscopic baseline correction.
• This approach effectively handles large-scale datasets and leverages spectral data structure.
• The method achieves higher accuracy and computational efficiency compared to existing techniques.