Improved Accuracy and Reliability in Untargeted Analysis with LC-ESI-QTOF/MS¹ by Ensemble Averaging

Area of Science:

• Analytical Chemistry
• Mass Spectrometry
• Chromatography

Background:

• Untargeted liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) enables comprehensive chemical analysis.
• Data complexity and variability in LC-HRMS can lead to significant errors and reduced reliability.
• Improving signal-to-noise (S/N) ratios and feature detection is crucial for accurate analysis.

Purpose of the Study:

• To investigate the application of ensemble averaging to mitigate errors in LC-HRMS data.
• To assess the impact of ensemble averaging on S/N ratios, feature detection, and data quality.
• To evaluate the benefits of ensemble averaging for quantitative analysis.

Main Methods:

• Averaging of 256 LC-qTOF/MS data sets from Morning Glory seeds to create merged data sets.
• Systematic variation of the number of pooled data sets in merged files.
• Examination of feature numbers, S/N ratios, mass accuracy, precision, relative intensities, and migration times.

Main Results:

• Ensemble averaging increased S/N ratios by up to a factor of 10.
• Relative standard deviation of accurate masses and retention times decreased by a factor of 10.
• The number of detected features per data set increased from 1192 ± 129 to 4408.
• Quantitative analysis showed a decrease in standard deviation by up to a factor of 10, with absolute error below 3% for isotopic patterns.
• A robust clustering approach identified 204 clusters of related features (adducts, isotopes, fragments).

Conclusions:

• Ensemble averaging is an effective strategy to improve S/N ratios, feature detection, and data quality in LC-HRMS.
• The method significantly enhances the accuracy and precision of quantitative analysis.
• Ensemble averaging facilitates reliable molecular formula confirmation and robust feature clustering.