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Related Concept Videos

Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For example, the mass of helium...
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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...
Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation01:01

Mass Spectrometry: Carboxylic Acid, Ester, and Amide Fragmentation

The fragmentation patterns observed for compounds such as carboxylic acids, esters, and amides in the mass spectra include ⍺-cleavage and McLafferty rearrangement. Fragmentation by ⍺-cleavage preferentially occurs at the carbon-carbon bond at the ⍺-position next to the carboxylic group to generate a neutral radical and a cation. Long chain compounds with hydrogen at their γ-carbon undergo McLafferty rearrangement to give a radical cation and a neutral alkene.
For example, the fragmentation of...
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

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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Quantitative and Qualitative Method for Sphingomyelin by LC-MS Using Two Stable Isotopically Labeled Sphingomyelin Species
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Monte Carlo Wavelet Analysis for Objective Peak Detection in SRM LC-MS/MS Analysis.

Randall K Julian1,2, Brian A Rappold3,4, Fan Yi5

  • 1Indigo BioAutomation, Inc., Carmel, Indiana 46032, United States.

Analytical Chemistry
|June 12, 2026
PubMed
Summary

A new wavelet-based Monte Carlo method objectively identifies low-level analytes in complex mass spectrometry data. This approach distinguishes true peaks from noise, improving accuracy in detecting substances like ketamine in clinical samples.

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Last Updated: Jun 13, 2026

Quantitative and Qualitative Method for Sphingomyelin by LC-MS Using Two Stable Isotopically Labeled Sphingomyelin Species
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Biology

Background:

  • Accurate detection of low-level analytes in complex chromatographic-mass spectrometric data is challenging.
  • Conventional signal-to-noise ratios fail due to structured chemical noise and coeluting interferences.
  • A robust statistical criterion is needed to differentiate true peaks from background noise.

Purpose of the Study:

  • To introduce a wavelet-based Monte Carlo technique for statistically validating Selected Reaction Monitoring Liquid Chromatography-Tandem Mass Spectrometry (SRM LC-MS/MS) peaks.
  • To develop an objective and reproducible method for peak integration decisions in complex datasets.
  • To address limitations of traditional signal-to-noise criteria in the presence of structured chemical noise.

Main Methods:

  • Developed a wavelet-based Monte Carlo approach to characterize chemical noise.
  • Constructed a generative noise-only null model using Monte Carlo resampling.
  • Controlled for family-wise error rate (FWER) to assign statistically significant p values.
  • Validated the method using SRM dilution series in plasma and a clinical pain panel.

Main Results:

  • Peaks with adjusted p values < 0.05 correctly identified true positives above the limit of detection.
  • The method accurately classified matrix blanks and biological negatives below the limit of detection.
  • Successfully detected ketamine in confirmed positive samples below the lowest calibration standard.
  • Demonstrated application on a lipid mediator dataset, replacing subjective noise selection with formal hypothesis testing.

Conclusions:

  • The wavelet-based Monte Carlo method provides a statistically rigorous and objective criterion for peak detection in LC-MS/MS.
  • This approach enhances the reliability of analyte quantification, particularly at low concentrations.
  • The method offers improved performance over conventional signal-to-noise ratios in complex matrices.