Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.3K
Gas Chromatography–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

6.0K
Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....
6.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluation of metabolite biomarker candidates in detecting HCC in patients with liver cirrhosis.

Metabolomics : Official journal of the Metabolomic Society·2026
Same author

Glycoproteome Profiling of Human Serum for Hepatocellular Carcinoma Biomarker Discovery.

Journal of proteome research·2026
Same author

Proteomic profiling of olfactory exfoliates from people with subjective cognitive complaints reveal networks of olfactory biomarkers of cognitive performance.

Frontiers in aging neuroscience·2026
Same author

Editorial: Exploring epigenetic mechanisms in cancer.

Frontiers in oncology·2026
Same author

A Polymeric Zwitterionic Hydrophilic Probe for Mapping the Human Serum Endogenous Glycopeptidome.

Journal of separation science·2026
Same author

Phosphopeptidome Profiling of Human Plasma for Hepatocellular Carcinoma Biomarker Discovery.

Journal of proteome research·2025

Related Experiment Video

Updated: May 2, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.6K

Gaussian process regression model for normalization of LC-MS data using scan-level information.

Mohammad R Nezami Ranjbar, Yi Zhao, Mahlet G Tadesse

    Proteome Science
    |February 26, 2014
    PubMed
    Summary

    We introduce a new Gaussian Process Regression Model (GPRM) for normalizing liquid chromatography-mass spectrometry (LC-MS) data. This method reduces systematic bias in biomolecule quantitation, outperforming existing techniques in reducing variability in quality control samples.

    More Related Videos

    Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells
    08:48

    Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells

    Published on: May 23, 2025

    1.0K
    Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
    07:10

    Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

    Published on: March 13, 2020

    8.7K

    Related Experiment Videos

    Last Updated: May 2, 2026

    Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
    08:27

    Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

    Published on: July 27, 2021

    4.6K
    Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells
    08:48

    Fluorescence-Guided Matrix-assisted Laser Desorption/Ionization with Laser-Induced Postionization Mass Spectrometry of Individual Rat Neural Cells

    Published on: May 23, 2025

    1.0K
    Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
    07:10

    Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

    Published on: March 13, 2020

    8.7K

    Area of Science:

    • Biomolecular analysis
    • Analytical chemistry
    • Biostatistics

    Background:

    • Liquid chromatography-mass spectrometry (LC-MS) data is prone to bias from sample collection, extraction, and instrument variability.
    • Normalization methods are crucial for correcting these biases in quantitative LC-MS analysis.
    • Existing normalization techniques may not be universally applicable, necessitating new approaches.

    Purpose of the Study:

    • To introduce a novel normalization method, the Gaussian Process Regression Model (GPRM), for LC-MS data.
    • To evaluate the performance of GPRM against existing normalization methods.
    • To address systematic bias in label-free LC-MS quantitation of biomolecules.

    Main Methods:

    • Developed a GPRM utilizing individual scans within extracted ion chromatograms (EICs) for normalization.
    • Applied a maximum likelihood approach to optimize GPRM parameters.
    • Utilized quality control (QC) samples to estimate measurement variabilities and correct for instrument drift.
    • Compared GPRM performance with several existing normalization methods using metabolomic LC-MS data.

    Main Results:

    • The GPRM effectively utilizes information from individual scans within EICs.
    • GPRM demonstrated superior performance in decreasing ion intensity variability among QC runs compared to other methods.
    • Analysis of liver cancer patient sera revealed potential biomarkers through ANOVA on normalized data.

    Conclusions:

    • GPRM offers a robust method for normalizing LC-MS data, particularly for analysis order-dependent biases.
    • The GPRM shows significant improvement in reducing measurement variability in QC samples.
    • Careful selection of normalization methods is essential for accurate biomolecule quantitation in LC-MS studies.