Leveraging probabilistic peak detection to estimate baseline drift in complex chromatographic samples.

Area of Science:

• Analytical Chemistry
• Chromatography
• Data Analysis

Background:

• Baseline drift is a common issue in chromatographic data analysis.
• Existing methods like asymmetrically weighted regression struggle with highly saturated peaks and low signal-to-noise ratios (s/n<40).

Purpose of the Study:

• To introduce a novel baseline estimation method using probabilistic peak detection.
• To evaluate the performance of this new method against established techniques under various chromatographic conditions.

Main Methods:

• A probabilistic peak detection algorithm computes posterior probabilities for each data point.
• These probabilities generate weights for a non-iterative baseline estimation.
• Comparison with Asymmetric Least Squares (ALS) and Mixture Model (MM) methods.

Main Results:

• The proposed Peak Weighted (PW) method shows significant improvement for extremely saturated chromatograms.
• ALS and MM methods are faster and more accurate for low-noise, well-resolved peaks.
• Performance varies based on peak saturation and signal-to-noise ratio.

Conclusions:

• The PW method offers a robust solution for challenging saturated chromatograms.
• ALS and MM remain superior for less complex chromatographic data.
• Method selection should be guided by specific chromatographic conditions for optimal baseline correction.