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Related Concept Videos

2D NMR: Homonuclear Correlation Spectroscopy (COSY)01:06

2D NMR: Homonuclear Correlation Spectroscopy (COSY)

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Homonuclear correlation spectroscopy, or COSY, is a 2-dimensional NMR technique that provides information about coupled protons. Typically, the geminal and vicinal coupling are observed. For example, consider the COSY spectrum of ethyl acetate, where its 1D proton NMR spectrum is plotted along the vertical and horizontal axes with their corresponding chemical shift scale. Three spots on the diagonal corresponding to the three peaks in the 1D proton spectrum are called diagonal peaks. The COSY...
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2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

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Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
647
2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

173
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.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
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2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

159
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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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....
626
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Updated: Jun 9, 2025

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
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Wheat Flour Discrimination Using Two-Dimensional Correlation Spectroscopy and Deep Learning.

Tianrui Zhang1, Yifan Wang1, Jiansong Sun1

  • 1College of Artificial Intelligence, Nankai University, Tianjin, China.

Applied Spectroscopy
|October 30, 2024
PubMed
Summary

This study introduces a novel deep learning approach combined with two-dimensional correlation spectroscopy (2D-COS) for highly accurate wheat flour identification. The method achieved 100% recognition accuracy, outperforming traditional techniques.

Keywords:
2D-COSNIR spectraNear-infrared spectradeep learningidentificationtwo-dimensional correlation spectroscopy

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Area of Science:

  • Spectroscopy
  • Chemometrics
  • Machine Learning

Background:

  • Deep learning is rapidly advancing spectroscopic analysis.
  • Accurate identification of wheat flour types is crucial for quality control.

Purpose of the Study:

  • To develop a more efficient and accurate method for wheat flour identification using spectroscopy.
  • To combine two-dimensional correlation spectroscopy (2D-COS) with deep learning.

Main Methods:

  • Collected 316 near-infrared (NIR) spectral samples of four wheat flour types.
  • Applied three 2D-COS techniques to generate 948 2D-COS images from 1D spectra.
  • Developed an 18-layer residual network with a convolutional attention mechanism for 2D-COS image analysis.

Main Results:

  • Achieved 100% recognition accuracy for wheat flour identification using synchronous 2D-COS data.
  • Visualized distinctive 2D-COS features within the deep learning architecture using t-distributed stochastic neighbor embedding.
  • Demonstrated superior performance compared to random forest, gradient boosting decision tree, and artificial neural network methods.

Conclusions:

  • The combination of 2D-COS and deep learning offers a powerful and efficient solution for wheat flour categorization.
  • This approach significantly enhances the capabilities of spectroscopic analysis in quality control applications.
  • Highlights the potential of deep learning in advancing spectroscopic techniques.