Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry peak sorting algorithm.

Area of Science:

• Analytical Chemistry
• Metabolomics
• Mass Spectrometry

Background:

• Accurate metabolite identification is crucial for comparative analyses in complex biological samples.
• Thousands of peaks in GC x GC/TOF-MS data present challenges for reliable peak sorting and metabolite recognition.

Purpose of the Study:

• To develop and validate a novel algorithm for automated peak sorting in GC x GC/TOF-MS data.
• To improve the accuracy and efficiency of identifying the same metabolite across different samples.

Main Methods:

• Utilized first- and second-dimension retention times and mass spectra from ChromaTOF software.
• Developed an algorithm to search peak tables for metabolite-specific peaks using defined criteria.
• Incorporated options to exclude non-target peaks, such as contaminants.

Main Results:

• The algorithm successfully sorts peaks by leveraging chromatographic and spectral data.
• Tested with standard metabolite mixtures and spiked human serum, demonstrating high accuracy.
• Manual validation confirmed the reliability of the peak sorting method.

Conclusions:

• The novel peak sorting algorithm significantly enhances the identification of metabolites in GC x GC/TOF-MS analyses.
• This method offers a robust solution for handling complex datasets and improving the accuracy of comparative metabolomics studies.