An Adaptive and Fully Automated Baseline Correction Method for Raman Spectroscopy Based on Morphological Operations...

Area of Science:

• Spectroscopy
• Data Analysis
• Biophysics

Background:

• Baseline drift is a significant challenge in Raman spectroscopy, particularly for biological samples.
• Fluorescence from samples is the primary cause of baseline drift, impacting spectral analysis.
• Accurate baseline correction is crucial for reliable qualitative and quantitative analysis.

Purpose of the Study:

• To develop an adaptive and fully automated algorithm for baseline estimation in Raman spectra.
• To address the limitations of existing methods in handling diverse baseline shapes and amplitudes.
• To improve the accuracy, adaptivity, and computational speed of baseline correction techniques.

Main Methods:

• An iterative algorithm employing morphological opening and closing operations for baseline estimation.
• Testing the algorithm on both simulated and experimental Raman spectra.
• Comparison with morphology-based and penalized least squares-based methods.

Main Results:

• The proposed method demonstrates superior performance compared to existing algorithms.
• Achieved advantages in accuracy, adaptivity to different baseline types, and computing speed.
• Validated effectiveness on diverse spectral data.

Conclusions:

• The developed algorithm provides an effective solution for baseline drift in Raman spectroscopy.
• The method's adaptability makes it suitable for various spectroscopic and one-dimensional data.
• Offers a significant advancement for accurate spectral data analysis.