Two-dimensional semi-parametric alignment of chromatograms.

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Bioinformatics

Background:

• Chromatogram alignment is crucial for comparing complex samples in analytical chemistry.
• Existing 2D alignment methods face challenges with overlapping or missing peaks.

Purpose of the Study:

• To introduce a novel two-dimensional alignment algorithm extending the semi-parametric approach.
• To address limitations of current algorithms in handling peak variations.

Main Methods:

• Developed a 2D warp function to model and correct shifts between sample and reference chromatograms.
• Minimized squared intensity differences for alignment.
• Algorithm accommodates overlapping and missing peaks.

Main Results:

• Successfully aligned GC×GC diesel oil data and LC-MS mouse breast cancer data.
• Demonstrated robust performance in handling complex chromatographic profiles.
• The warp function approach efficiently managed overlapping peaks and allowed for missing peaks under specific conditions.

Conclusions:

• The proposed 2D alignment algorithm offers a robust and flexible solution for complex chromatographic data.
• It provides advantages over existing methods by effectively managing peak variations.
• The algorithm shows promise for applications in various fields, including environmental analysis and biomedical research.