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Related Concept Videos

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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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 electron 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 behind a...
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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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In mass spectroscopy, amines undergo fragmentation to give parent ions with odd molecule weights. This observed mass spectrum follows the nitrogen rule; a molecule with an odd number of nitrogen atoms produces a molecular ion with an odd molecular weight. Amines undergo fragmentation through α cleavage, producing nitrogen-containing cations—iminium ions—and alkyl radicals. Mass spectra of aromatic and cyclic aliphatic amines exhibit strong molecular ion peaks, but acyclic...
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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
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Related Experiment Video

Updated: Feb 3, 2026

Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics
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Quality assessment and interference detection in targeted mass spectrometry data using machine learning.

Shadi Toghi Eshghi1, Paul Auger1, W Rodney Mathews1

  • 1OMNI-Biomarker Development, Genentech Inc., South San Francisco, CA 94080 USA.

Clinical Proteomics
|October 17, 2018
PubMed
Summary
This summary is machine-generated.

TargetedMSQC is a new R package for automated quality control of targeted mass spectrometry data. It uses machine learning to quickly and accurately verify chromatographic peaks, improving data analysis efficiency.

Keywords:
Automated analysisBioinformaticsInterference detectionMachine learningMass spectrometryQuality controlQuantitativeTargeted proteomics

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Targeted proteomics and mass spectrometry have advanced assay sensitivity and multiplexing.
  • High-throughput experiments generate large datasets, demanding novel analytical tools for processing.
  • Manual inspection of chromatographic peaks for quality control is time-consuming and variable.

Purpose of the Study:

  • To develop an automated tool for quality control and verification of chromatographic peaks in targeted proteomics datasets.
  • To address the limitations of manual peak inspection in terms of time, variability, and efficiency.
  • To improve the speed and accuracy of interference detection in mass spectrometry data.

Main Methods:

  • Developed TargetedMSQC, an R package for quality control of targeted mass spectrometry data.
  • The tool calculates metrics for peak symmetry, co-elution, transition ratios, and retention time consistency.
  • Employs supervised machine learning for interference and poor chromatography detection based on expert-annotated peaks.

Main Results:

  • TargetedMSQC reduces manual inspection time for targeted proteomics data.
  • Improves the speed and accuracy of interference detection in chromatographic peaks.
  • Offers customizable quality assessment for different datasets, enhancing user flexibility.

Conclusions:

  • Automated, quantitative peak quality assessment provides a more objective framework for high-throughput analysis.
  • TargetedMSQC facilitates more robust and faster implementation of targeted mass spectrometry assays.
  • The R package enhances the efficiency and reliability of data interpretation in targeted proteomics.