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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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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 Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

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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 low-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...
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MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
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Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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.
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Updated: Sep 3, 2025

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
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Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.

Shijinqiu Gao1, Hoi Yan Katharine Chau1, Kuijun Wang1

  • 1Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.

Metabolites
|July 27, 2022
PubMed
Summary

Metabolite annotation in untargeted metabolomics is challenging due to limited spectral libraries. A new convolutional neural network (CNN) model, MetFID, predicts molecular fingerprints from tandem mass spectrometry (MS/MS) data to improve metabolite identification.

Keywords:
deep learningmetabolite identificationmetabolomicsmolecular fingerprint

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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

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Area of Science:

  • Analytical Chemistry
  • Computational Biology
  • Biochemistry

Background:

  • Metabolite annotation in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is hindered by incomplete spectral libraries.
  • Publicly available tandem mass spectrometry (MS/MS) databases cover only a fraction of known metabolites, complicating identification.
  • Machine learning offers a promising approach to predict molecular properties from MS/MS data for enhanced metabolite identification.

Purpose of the Study:

  • To develop and evaluate a machine learning model for predicting molecular fingerprints from MS/MS spectra.
  • To improve the ranking of putative metabolite identifications in untargeted metabolomics.
  • To provide a user-friendly tool for metabolite annotation when spectral data is limited.

Main Methods:

  • A convolutional neural network (CNN) was trained using over 680,000 MS/MS spectra from the MoNA repository and NIST 20, covering approximately 36,000 compounds.
  • The CNN model, MetFID, predicts molecular fingerprints based on input MS/MS data.
  • Performance was evaluated using the CASMI 2016 benchmark dataset and compared against existing tools like CSI:FingerID and ChemDistiller.

Main Results:

  • The developed CNN model, MetFID, demonstrated effective prediction of molecular fingerprints from MS/MS data.
  • MetFID successfully ranked putative metabolite identifications, aiding in the annotation process.
  • MetFID outperformed two other machine learning-based tools (CSI:FingerID and ChemDistiller) on the CASMI 2016 benchmark dataset.

Conclusions:

  • Convolutional neural networks can effectively predict molecular fingerprints from MS/MS data, addressing limitations in spectral library coverage.
  • The MetFID package provides a valuable tool for improving metabolite annotation in untargeted metabolomics.
  • This approach enhances the ability to identify metabolites, particularly when direct spectral matches are unavailable.