Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least...

Area of Science:

• Spectroscopy
• Analytical Chemistry
• Signal Processing

Background:

• Raman spectra analysis requires baseline correction due to fluorescence interference.
• Existing asymmetric least squares (ALS) methods can cause smoothing distortion by focusing only on signal regions.
• Oversmoothing in baseline fitting negatively impacts accuracy and iteration depth.

Purpose of the Study:

• To introduce a novel spectral estimation based asymmetric least squares (SEALS) method for improved Raman spectra baseline correction.
• To address the limitations of current ALS methods, specifically smoothing distortion and oversmoothing.
• To enhance the accuracy and efficiency of baseline correction in spectroscopic analysis.

Main Methods:

• Developed the spectral estimation based asymmetric least squares (SEALS) method.
• Incorporated spectral estimation using inverse Fourier and autoregressive models to estimate energy distribution.
• Optimized asymmetric weighting of data points to balance noise and signal energy.

Main Results:

• SEALS demonstrated superior baseline fitting performance on simulated spectra compared to advanced methods, especially under strong fluorescence.
• The method showed high resistance to noise interference.
• Applied to real Raman spectra, SEALS effectively restored weak peaks and removed fluorescence.
• Achieved a computation time of approximately 0.05 seconds for real-time applications.

Conclusions:

• SEALS offers a significant improvement over existing methods for Raman spectra baseline correction.
• The method accurately handles noise and fluorescence, preserving weak spectral signals.
• SEALS is suitable for real-time spectroscopic applications due to its speed and accuracy.