Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance...

Area of Science:

• Analytical Chemistry
• Chromatography
• Data Science

Background:

• Comparing analytical data, particularly chromatographic separations, is crucial for identifying similarities and differences.
• Existing methods may lack efficiency in handling multiple datasets simultaneously or in cumulative optimization processes.

Purpose of the Study:

• To develop and evaluate a novel peak-tracking algorithm for comparing multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography.
• To investigate two distinct application strategies: simultaneous data processing and cumulative method optimization.

Main Methods:

• Development of a peak-tracking algorithm applicable to both 1D and 2D chromatographic data.
• Testing the algorithm on diverse samples including monoclonal antibody digest, wine volatiles, polymer headspace, and mayonnaise using comprehensive 2D liquid and gas chromatography.
• Evaluating two strategies: simultaneous processing of all chromatograms and cumulative processing for method optimization.

Main Results:

• The algorithm successfully tracked peaks across up to 29 chromatograms in simultaneous processing, with potential for upscaling.
• The cumulative processing strategy proved more efficient for automated method development, quickly identifying poorly resolved peaks.
• Accuracy for trace analytes was noted as a limitation due to detection challenges.

Conclusions:

• The developed peak-tracking algorithm is a valuable tool for comparative data analysis in chromatography.
• The cumulative processing strategy significantly benefits automated method development by enabling rapid identification of chromatographic improvements.
• Further refinement may be needed to enhance performance for detecting and tracking trace-level compounds.