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Mass Spectrometry of Amines01:15

Mass Spectrometry of Amines

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In mass spectroscopy, amines undergo fragmentation to give parent ions with odd molecule weights. This observed mass spectrum follows the nitrogen rule; a molecule with an odd number of nitrogen atoms produces a molecular ion with an odd molecular weight. Amines undergo fragmentation through α cleavage, producing nitrogen-containing cations—iminium ions—and alkyl radicals. Mass spectra of aromatic and cyclic aliphatic amines exhibit strong molecular ion peaks, but acyclic...
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NMR Spectroscopy Of Amines01:19

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In proton NMR spectroscopy, primary amines and secondary amines showcase their N–H protons as a broad signal in the chemical shift range between δ 0.5 and 5 ppm. The exact position in this range depends on several factors, including sample concentration, hydrogen bonding, and the type of solvent used. Since amine protons undergo fast proton exchange in solution, the protons are labile and therefore do not participate in any splitting with adjacent protons. Thus, the observed peak is...
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Mass Spectrometry: Amine Fragmentation00:55

Mass Spectrometry: Amine Fragmentation

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Amines can be identified using mass spectroscopy based on their characteristic fragmentation patterns. The molecular ions of amines undergo fragmentation via ⍺-cleavage. The ⍺-cleavage of the carbon-carbon bonds in amines generates an alkyl radical and resonance-stabilized nitrogen-containing cation.
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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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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In aldehydes, the hydrogen atom connected to the carbonyl carbon helps distinguish aldehydes from other carbonyl compounds using ¹H NMR spectroscopy. The closeness of aldehydic hydrogen to the electrophilic carbonyl carbon highly deshields the hydrogen atom causing its signal to appear around 10 ppm in the ¹H NMR spectra. α hydrogens split the aldehydic proton signal, which helps identify the number of α hydrogens in the molecule. For instance, one α hydrogen creates a...
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From Mass to Class: Classification of Amphetamines MS/MS Spectra via Graph Neural Networks.

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A novel graph neural network (GNN) model effectively identifies amphetamine derivatives, a type of novel psychoactive substance (NPS), using mass spectrometry fragmentation patterns. This approach aids in detecting new drugs when reference libraries are incomplete.

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Area of Science:

  • Forensic Chemistry
  • Computational Chemistry
  • Analytical Chemistry

Background:

  • Identifying novel psychoactive substances (NPS) is challenging due to their absence in mass spectral libraries.
  • Liquid chromatography high-resolution tandem mass spectrometry (LC-HR-MS/MS) is a key technique for NPS detection.
  • Amphetamine derivatives represent a significant subclass of emerging NPS.

Purpose of the Study:

  • To develop a graph neural network (GNN) model for detecting amphetamine derivatives.
  • To utilize fragmentation patterns from LC-HR-MS/MS data for classification.
  • To overcome limitations of traditional spectral library matching for novel compounds.

Main Methods:

  • Generated a dataset by transforming mass/charge (m/z) values into fragment chemical compositions.
  • Constructed fully connected graphs from spectral data.
  • Developed and optimized a GNN classification model, adjusting for class imbalance with a weighted loss function.
  • Included collision energy as an informative parameter.

Main Results:

  • The GNN model achieved a recall of 0.86 and a precision of 0.11 on a test subset.
  • The model demonstrated successful detection of a spiked amphetamine derivative in human plasma.
  • Achieved a low false positive rate (1%) in complex biological matrices.

Conclusions:

  • The developed GNN model shows promise for identifying amphetamine derivatives, even without spectral library matches.
  • This computational approach enhances the detection capabilities for NPS in forensic and clinical settings.
  • The model's performance in biological samples highlights its practical applicability.