Baseline Search in Raman Spectroscopy by Modified Tikhonov Regularization with Automatic Choice of Both Parameters

Area of Science:

• Spectroscopy
• Data Analysis
• Computational Chemistry

Background:

• Baseline estimation is crucial in vibrational spectroscopy for accurate analysis.
• Current methods like Tikhonov regularization with asymmetric weighting require manual parameter tuning.
• Existing parameter selection lacks objective criteria, often relying on visual inspection.

Purpose of the Study:

• To develop objective criteria for optimizing regularization parameters in spectroscopic baseline estimation.
• To provide a method for automatic and accurate parameter selection, moving beyond visual assessment.
• To enhance the efficiency and reliability of Tikhonov regularization for spectral data.

Main Methods:

• Development of a specific functional to guide parameter optimization.
• Application of derivative-and-peak-screened Asymmetric Least Squares (ALS) algorithm.
• Implementation using Python code for practical application.

Main Results:

• The proposed functional enables objective optimization of regularization parameters (λ and p).
• This approach eliminates the need for subjective visual parameter selection.
• The method is demonstrated to be efficient and applicable to real spectral data.

Conclusions:

• Objective criteria for regularization parameter selection in baseline estimation are now feasible.
• The developed functional improves the accuracy and reproducibility of spectroscopic data analysis.
• The provided Python code facilitates the implementation of this advanced baseline correction technique.