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Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass.  One common type of ionization, known as electrospray ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave...
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Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
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Mass Spectrometers01:16

Mass Spectrometers

5.8K
This lesson details the instrumentation of a mass spectrometer—a physical instrument to perform mass spectrometry on analyte molecules and record the characteristic mass spectra. This is achieved via three chief functions:
5.8K
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.6K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Related Experiment Video

Updated: Aug 6, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

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Statistics and Machine Learning in Mass Spectrometry-Based Metabolomics Analysis.

Sili Fan1, Christopher M Wilson2, Brooke L Fridley3

  • 1Graduate Group of Biostatistics, University of California, Davis, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|March 17, 2023
PubMed
Summary
This summary is machine-generated.

This review covers advanced statistical and machine learning techniques for handling missing data and batch effects in mass spectrometry metabolomics. It details methods for imputation, normalization, and downstream analysis for better biological insights.

Keywords:
ImputationIntegrative analysisMass spectrometryMetabolomicsNormalizationStatistical and machine learning

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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Related Experiment Videos

Last Updated: Aug 6, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Analytical Chemistry

Background:

  • Mass spectrometry metabolomics generates complex datasets with missing values and batch effects.
  • Accurate data processing is crucial for reliable biological interpretation.

Purpose of the Study:

  • To review cutting-edge statistical and machine learning methods for mass spectrometry metabolomics data analysis.
  • To provide practical guidance on missing value imputation, normalization, and downstream analyses.

Main Methods:

  • Exploration of various imputation techniques: zero/limit of detection, regression-based, distribution-based, and random forest prediction.
  • Overview of normalization strategies: data-driven, internal standard-based, and quality control sample-based methods for batch effect removal.
  • Summary of downstream analyses: metabolic biomarker inference, profile clustering, metabolite module building, and transcriptome integration.

Main Results:

  • Demonstration of diverse methods for addressing missing data in metabolomics.
  • Effective strategies for mitigating batch effects to improve data consistency.
  • Comprehensive approaches for extracting biological meaning from metabolomic data.

Conclusions:

  • Advanced statistical and machine learning methods are essential for robust metabolomics data analysis.
  • Proper handling of missing values and batch effects significantly enhances the reliability of biological discoveries.
  • Integrative analyses offer deeper insights into metabolic pathways and disease mechanisms.