Automatic baseline recognition for the correction of large sets of spectra using continuous wavelet transform and...

Area of Science:

• Spectroscopy
• Data Analysis
• Signal Processing

Background:

• Accurate spectral analysis requires robust baseline correction.
• High-throughput instruments generate large datasets demanding efficient processing.
• Existing methods may require significant user intervention.

Purpose of the Study:

• To develop an automated algorithm for peak and baseline recognition in spectra.
• To create a fast and simple baseline correction method for large spectral datasets.
• To minimize user intervention in spectral data processing.

Main Methods:

• Algorithm based on continuous wavelet transform.
• Automatic parameter determination using Shannon entropy and noise statistics.
• Combination with iterative polynomial fitting for baseline estimation.

Main Results:

• Successfully recognized baseline points in simulated spectra across various noise levels and baseline amplitudes.
• Demonstrated high accuracy, with minor limitations on extremely weak or noisy signals.
• Processed a 40,000-pixel Raman image in approximately 2.5 hours.

Conclusions:

• The developed algorithm offers an efficient and automated solution for spectral baseline correction.
• Suitable for processing large volumes of spectral data from high-throughput applications.
• Paves the way for faster and more accessible spectral data analysis.