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In High-Performance Liquid Chromatography (HPLC), the elution process is critical to the separation of analytes and the quality of chromatographic results. Elution describes how compounds move through the column and separate based on their interactions with the mobile and stationary phases. This process determines the resolution, peak shape, and retention times in the chromatogram, which are essential for identifying and quantifying components in complex mixtures. Understanding the elution...
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High-performance liquid chromatography(HPLC), formerly referred to as High-pressure liquid chromatography, is a powerful technique used to separate, identify, and quantify components in complex mixtures. The term "high pressure" refers to using high pressure to push the liquid mobile phase through the tightly packed columns.
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Related Experiment Video

Updated: Nov 11, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass

Alisa O Tokareva1,2,3, Vitaliy V Chagovets1, Alexey S Kononikhin1,4

  • 1National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of the Russian Federation, Moscow, 117997, Russia.

Analytical and Bioanalytical Chemistry
|March 24, 2021
PubMed
Summary

Autoscaling is the most effective data normalization method for liquid chromatography-mass spectrometry metabolomics, reducing interbatch variation better than quantile or probabilistic quotient normalization.

Keywords:
Interbatch correctionLiquid chromatography-mass spectrometryNormalizationScaling

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Area of Science:

  • Metabolomics
  • Mass Spectrometry
  • Data Analysis

Background:

  • Data normalization is crucial for large-scale untargeted mass spectrometry metabolomics.
  • Accurate data processing is essential for reliable clinical study results.

Purpose of the Study:

  • To compare the effectiveness of different data normalization methods for liquid chromatography-mass spectrometry (LC-MS) data.
  • To identify the optimal normalization strategy for reducing batch effects in metabolomics.

Main Methods:

  • Compared autoscaling, Pareto scaling, range scaling, and level scaling with quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization.
  • Evaluated normalization methods on eight diverse clinical study datasets.
  • Assessed normalization efficiency using principal component analysis (PCA) and feature-wise statistical significance between batches and clinical groups.

Main Results:

  • Autoscaling demonstrated the most effective reduction in interbatch variation.
  • PCA-based cluster distances indicated autoscaling's superiority in separating batch effects.
  • Fewer features showed statistically significant differences between batches after autoscaling compared to other methods.

Conclusions:

  • Autoscaling is a highly effective normalization method for LC-MS metabolomics data.
  • Autoscaling can be preferable to probabilistic quotient or quantile normalization for minimizing batch effects.
  • This finding is critical for improving the reliability of large-scale untargeted metabolomics studies.