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Related Concept Videos

NMR Spectrometers: Overview01:20

NMR Spectrometers: Overview

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NMR spectrometers consist of a strong magnet, a radiofrequency transmitter, and a detector attached to a computer console for recording spectra of samples containing NMR-active nuclei. In first-generation NMR instruments called continuous-wave spectrometers, the resonance frequencies of the nuclei are determined by frequency-sweep or field-sweep methods. The magnetic field strength is fixed and the rf signal is swept in the former, while the radiofrequency signal is fixed and the magnetic field...
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Nuclear Magnetic Resonance (NMR): Overview01:07

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Nuclear magnetic resonance (NMR) is a phenomenon exhibited by certain nuclei that can absorb characteristic radio frequency radiation under certain conditions. NMR has been extensively applied in molecular spectroscopy and medical diagnostic imaging. In both these applications, the molecule or subject under study is placed in a magnetic field and irradiated with radio frequency energy.
NMR spectroscopy generates a spectrum where the characteristic absorption frequencies of the sample are...
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Applications Of NMR In Biology01:25

Applications Of NMR In Biology

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Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
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¹H NMR Signal Integration: Overview00:58

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The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
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NMR Spectroscopy of Aromatic Compounds01:14

NMR Spectroscopy of Aromatic Compounds

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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.
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Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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Machine Learning Enhanced Spectrum Recognition Based on Computer Vision (SRCV) for Intelligent NMR Data Extraction.

Wenqiang Jia1, Zhuo Yang1, Minjian Yang1

  • 1State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Department of Medicinal Chemistry, Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, China.

Journal of Chemical Information and Modeling
|November 10, 2020
PubMed
Summary
This summary is machine-generated.

A new AI system, spectrum recognition based on computer vision (SRCV), efficiently extracts data from carbon-13 and proton-1 NMR spectra. This tool achieves 100% accuracy in number recognition, enabling high-throughput analysis and database construction.

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Spectroscopy

Background:

  • Nuclear Magnetic Resonance (NMR) spectroscopy is crucial for chemical structure determination.
  • Manual data extraction from NMR spectra is time-consuming and prone to errors.
  • Automating data extraction is essential for high-throughput analysis and large-scale spectral database construction.

Purpose of the Study:

  • To develop an AI-powered system for automated data extraction from 13C and 1H NMR spectra.
  • To enhance the efficiency and accuracy of processing NMR spectral data.
  • To facilitate the construction of spectral databases and integration with computer-assisted structure elucidation.

Main Methods:

  • Development of a spectrum recognition based on computer vision (SRCV) system utilizing machine learning.
  • Implementation of four functional modules for extracting chemical shifts, integrals, and shift ranges from NMR images.
  • Pre-training of three machine learning models for number recognition, with the k-nearest neighbor (kNN) method optimized for 100% accuracy.

Main Results:

  • The SRCV system demonstrated high accuracy in extracting data from 13C and 1H NMR spectra.
  • Processing time per spectral image ranged from 11 to 21 seconds, indicating efficient performance.
  • The kNN model achieved a 100% recognition rate for numerical data within the spectra.

Conclusions:

  • SRCV offers a robust solution for high-throughput data extraction from NMR spectra.
  • The system has significant potential for building comprehensive spectral databases.
  • SRCV can be integrated into computer-assisted structure elucidation workflows, automating a critical step.