[Locally Dynamically Moving Average Algorithm for the Fully Automated Baseline Correction of Raman Spectrum].

Area of Science:

• Spectroscopy
• Analytical Chemistry
• Signal Processing

Context:

• Baseline correction is crucial for accurate spectral analysis.
• Current methods are often manual or semi-automated, introducing subjectivity and inconvenience.
• Automated solutions are needed to improve efficiency and reproducibility.

Purpose:

• To introduce a novel, fully automated baseline correction algorithm for Raman spectra.
• To detail the principles and implementation of the locally-dynamically moving average (LDMA) algorithm.
• To address the limitations of existing manual and semi-automated baseline correction techniques.

Summary:

• The locally-dynamically moving average (LDMA) algorithm utilizes a modified moving averaging approach to automatically identify and correct spectral baselines.
• It dynamically adjusts smoothing parameters within subintervals, effectively stripping Raman peaks.
• This approach prevents overcorrection and under-correction, improving spectral data quality.

Impact:

• The LDMA algorithm demonstrates significant effectiveness on both synthetic and real Raman spectra with various baseline types.
• It offers a robust and user-friendly solution for automated spectral preprocessing.
• Enhances the reliability and accuracy of downstream spectral analysis in research and industry.