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Related Concept Videos

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.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

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In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
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2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

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Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
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¹³C NMR: ¹H–¹³C Decoupling01:04

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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

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Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
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A Multimodal Wide-Field Fourier-Transform Raman Microscope
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Deriving three one dimensional NMR spectra from a single experiment through machine learning.

Alessia Vignoli1,2, Stefano Cacciatore3, Leonardo Tenori4,5

  • 1Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy.

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|November 19, 2025
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Summary
This summary is machine-generated.

This study introduces a machine learning method to predict Nuclear Magnetic Resonance (NMR) spectra, reducing time and resources for metabolomics research. The approach uses Nuclear Overhauser Effect SpectroscopY (NOESY) data to generate other NMR spectra efficiently.

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Nuclear Magnetic Resonance (NMR) spectroscopy is vital for analyzing complex biological mixtures.
  • NMR offers detailed molecular insights and preserves sample integrity, crucial for metabolomics.
  • Standard NMR techniques like NOESY, CPMG, diffusion-edited, and JRES provide complementary data but require significant time for high-throughput studies.

Purpose of the Study:

  • To develop a machine learning model for predicting NMR spectra.
  • To streamline NMR-based metabolomics analysis by reducing experimental time and resource requirements.
  • To demonstrate the feasibility of predicting CPMG, diffusion-edited, and JRES spectra from NOESY spectra using serum samples.

Main Methods:

  • Utilized a machine learning approach to predict NMR spectra.
  • Leveraged Nuclear Overhauser Effect SpectroscopY (NOESY) spectra as input data.
  • Applied the method to serum samples for metabolomic analysis.

Main Results:

  • Successfully predicted CPMG, diffusion-edited, and JRES spectra from NOESY spectra.
  • Demonstrated a streamlined and efficient method for NMR-based metabolomics.
  • Validated the approach using serum samples.

Conclusions:

  • The proposed machine learning strategy significantly enhances efficiency in NMR-based metabolomics.
  • This method reduces the need for acquiring multiple NMR spectra, saving time and resources.
  • The approach holds promise for accelerating high-throughput metabolomic analyses.