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Related Concept Videos

Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

3.8K
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...
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Mass Spectrometry: Aromatic Compound Fragmentation01:23

Mass Spectrometry: Aromatic Compound Fragmentation

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Upon ionization, aromatic compounds generate a molecular ion that is observed as a prominent peak in their mass spectra. For example, the molecular ion peak for benzene appears at a mass-to-charge ratio of 78, while toluene is observed at a mass-to-charge ratio of 92. The molecular ion benzene is highly stable and does not readily undergo further fragmentation due to the significant amount of energy required to disrupt the aromatic stability of the benzene ring. In contrast, the molecular ion...
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NMR Spectroscopy of Aromatic Compounds01:14

NMR Spectroscopy of Aromatic Compounds

6.6K
Aromatic compounds can be identified or analyzed using proton NMR and carbon‐13 NMR. Typically, aromatic hydrogens or hydrogens directly bonded to the aromatic rings are strongly deshielded by the aromatic ring current. Therefore, they absorb in the range of 6.5–8.0 ppm in proton NMR spectra. For instance, aromatic hydrogens directly bonded to the benzene ring absorb at 7.3 ppm. However, aromatic hydrogens of larger rings absorb farther upfield or downfield than the ideal range.
6.6K
Mass Spectrum01:23

Mass Spectrum

5.3K
A mass spectrum is the graphical representation of the relative abundance of the charged fragments in an analyte plotted against their mass-to-charge ratio (m/z). The plot's x-axis represents the ratio of the mass of the charged fragment to the number of charges it carries. The y axis of the plot represents the relative abundance of each charged species. The relative abundance is calculated from the signal intensity of each charged species recorded at the detector. The most intense signal (the...
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NMR and Mass Spectroscopy of Carboxylic Acids01:30

NMR and Mass Spectroscopy of Carboxylic Acids

5.5K
In ¹H NMR spectroscopy, acidic protons (–COOH) of carboxylic acids are highly deshielded and absorb far downfield, at around 9–12 ppm. The chemical shift value depends on the concentration and solvent used.
While α protons of carboxylic acids absorb at 2–2.5 ppm, β protons absorb further upfield.
Carboxylic acids are easily identified by dissolving them in deuterium oxide, which results in a rapid exchange of the acidic protons with deuterium. This leads to the...
5.5K
Spectroscopy of Carboxylic Acid Derivatives01:26

Spectroscopy of Carboxylic Acid Derivatives

3.2K
Infrared spectroscopy is primarily used to determine the types of bonds and functional groups. In carboxylic acid derivatives, a typical carbonyl bond absorption is observed around 1650–1850 cm−1. For esters, the absorption is recorded at around 1740 cm−1, while acid halides show the absorption at about 1800 cm−1. Another acid derivative, the acid anhydrides, exhibit two carbonyl absorption around 1760 cm−1 and 1820 cm−1, arising from the symmetrical and...
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Odor Impression Prediction from Mass Spectra.

Yuji Nozaki1, Takamichi Nakamoto1,2

  • 1Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Kanagawa, Japan.

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Researchers developed a new artificial neural network model to predict odor impression from chemical mass spectra. This deep learning approach shows improved accuracy in forecasting how humans perceive chemical odors.

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Area of Science:

  • Computational chemistry
  • Chemosensation
  • Artificial intelligence

Background:

  • The sense of smell involves complex interactions between chemical structures and human perception.
  • Predicting odor impression from physicochemical properties remains challenging due to the intricate relationship.
  • Current methods lack a general approach for odor prediction based solely on chemical properties.

Purpose of the Study:

  • To develop a novel predictive model for odor impression using artificial neural networks.
  • To utilize mass spectra data for predicting sensory perception of chemicals.
  • To evaluate the performance of the proposed deep learning model.

Main Methods:

  • Designed a deep artificial neural network model.
  • Employed autoencoders to extract feature vectors from mass spectra.
  • Built a mapping function from mass spectra features to sensory data features.

Main Results:

  • Achieved a prediction accuracy of R≅0.76 for odor impression.
  • Demonstrated notable accuracy compared to conventional methods (R≅0.61).
  • Validated the model's performance through computational analyses.

Conclusions:

  • The novel deep learning model effectively predicts odor impression from mass spectra.
  • This approach offers a significant improvement over existing methods for odor prediction.
  • The study advances the understanding of structure-odor relationships through computational modeling.