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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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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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An accelerated background subtraction algorithm for processing high-resolution MS data and its application to

Venkateswaran Shekar1, Abhi Shah1, Mohammad Shadid1

  • 1Department of Drug Metabolism & Pharmacokinetics, Takeda Pharmaceuticals International Co., 35 Landsdowne Street, Cambridge, MA, USA.

Bioanalysis
|July 28, 2016
PubMed
Summary
This summary is machine-generated.

A new algorithm, A-BgS, effectively removes background noise in mass spectrometry for accurate metabolite identification. This speeds up the analysis of biological samples, aiding in drug development.

Keywords:
A-BgSbackground subtractionmatrix interferencemetabolite identification

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Pharmacology

Background:

  • Metabolite identification is hindered by matrix-related interferences in mass spectrometry.
  • Lack of radiolabeled compounds complicates accurate metabolite analysis.

Purpose of the Study:

  • To develop a novel algorithm for effective background subtraction in high-resolution mass spectral data.
  • To improve the accuracy and efficiency of metabolite identification in biological samples.

Main Methods:

  • Development of a background subtraction algorithm (A-BgS).
  • Utilized graphics processing unit (GPU) and multicore central processing unit (CPU) for accelerated data processing.
  • Applied A-BgS to high-resolution mass spectral data of biological matrices.

Main Results:

  • A-BgS selectively removes matrix-related components, enhancing spectral clarity.
  • Processing speed increased significantly (several 1000-fold) compared to single CPU.
  • Successfully demonstrated background peak removal in delavirdine and metoclopramide metabolite identification.

Conclusions:

  • A-BgS algorithm offers fast, user-friendly, and reliable removal of matrix-related ions.
  • The algorithm is beneficial for detecting and identifying both in vivo and in vitro metabolites.
  • Facilitates more accurate and efficient metabolite profiling in complex biological samples.