Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support...

Area of Science:

• Metabolomics
• Analytical Chemistry
• Chemometrics

Background:

• Liquid chromatography-mass spectrometry (LC-MS) is crucial for metabolomics.
• Intra-batch effects in LC-MS reduce data accuracy and reproducibility.
• Post-acquisition correction methods are needed to address these effects.

Purpose of the Study:

• To develop and evaluate a novel chemometric method for correcting intra-batch effects in LC-MS data.
• To assess the performance of the proposed method against existing techniques.

Main Methods:

• Implementation of quality control-support vector regression (QC-SVRC) using a radial basis function kernel.
• Utilizing repeated analysis of a single sample to assess correction accuracy.
• Comparison with a robust cubic smoothing splines (QC-RSC) method.

Main Results:

• QC-SVRC effectively corrects intra-batch effects in LC-MS data.
• The method demonstrated slightly superior performance compared to QC-RSC.
• QC-SVRC allows straightforward parameter fitting using the ε-insensitive loss parameter.

Conclusions:

• QC-SVRC is a viable and effective technique for correcting intra-batch variations in LC-MS metabolomics.
• This method enhances the reliability and interpretability of metabolomics data.
• The approach offers a practical solution for improving LC-MS data quality.