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Related Concept Videos

Mass Spectrometers01:16

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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:
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Mass Spectrometry: Complex Analysis01:21

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

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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.
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Mass Spectrum: Interpretation01:24

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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.
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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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High-Resolution Mass Spectrometry (HRMS)01:15

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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...
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Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
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Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry.

Nicole M North1, Abigail A Enders1, Morgan L Cable2

  • 1Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.

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This summary is machine-generated.

Artificial intelligence (AI) models can identify functional groups from mass spectrometry data. Logistic regression models achieved higher accuracy than convolutional neural networks (CNNs) for this chemical analysis task.

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Artificial Intelligence in Chemistry

Background:

  • Mass spectrometry (MS) is a key technique for chemical analysis, generating fragmentation patterns useful for identifying molecular structures.
  • Artificial intelligence (AI) offers potential for automating and accelerating the analysis of complex MS data, particularly for identifying functional groups.

Purpose of the Study:

  • To develop and evaluate AI models for automated functional group identification from electron ionization mass spectra.
  • To compare the performance of convolutional neural networks (CNNs) and logistic regression models in this task.
  • To identify the most informative mass-to-charge ratio (m/z) ranges and key fragments for accurate functional group detection.

Main Methods:

  • Trained logistic regression models using array-based spectral data and CNN models (Inception V3) on 2D spectral images.
  • Utilized a dataset of 21,166 mass spectra from the NIST Webbook for model training.
  • Evaluated models on their ability to identify specific (e.g., amines, esters) and generalized functional groups (e.g., aromatics).

Main Results:

  • Logistic regression models demonstrated higher accuracy in identifying functional groups compared to CNN transfer learning models.
  • The mass range of 0-100 m/z was found to be most critical for effective functional group analysis.
  • A methodology was developed to pinpoint impactful fragments contributing to accurate functional group identification.

Conclusions:

  • AI, particularly logistic regression, shows significant promise for automating functional group analysis in mass spectrometry.
  • The findings provide a pathway for efficient screening and analysis of large-scale mass spectral datasets.
  • Understanding key spectral regions and fragments enhances the reliability of AI-driven chemical analysis.