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

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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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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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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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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Related Experiment Video

Updated: Jan 7, 2026

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
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A Masi-Entropy Image Thresholding Based on Long-Range Correlation.

Perfilino Eugênio Ferreira Júnior1, Vinícius Moreira Mello1, Enzo P Silva Ribeiro1

  • 1Department of Mathematics, Federal University of Bahia, Salvador 40170-110, BA, Brazil.

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|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved image thresholding technique using Masi entropy, outperforming existing methods. The enhanced algorithm optimizes parameters with simulated annealing for superior segmentation accuracy in various image types.

Keywords:
entropyimage thresholdinginfrared imageslocal long-range correlation

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Area of Science:

  • Image Processing and Computer Vision
  • Information Theory
  • Computational Physics

Background:

  • Entropy-based image thresholding is a key segmentation technique.
  • Tsallis and Masi entropies capture long-range interactions, while Shannon entropy suits short-range correlations.
  • Existing methods have limitations in capturing complex image features.

Purpose of the Study:

  • To enhance image thresholding by integrating Masi entropy into existing frameworks.
  • To develop an optimized thresholding algorithm using simulated annealing.
  • To evaluate the proposed method against various entropy-based and machine learning techniques.

Main Methods:

  • A novel thresholding technique replacing Tsallis with Masi entropy.
  • Integration of a simulated annealing algorithm for entropic parameter optimization.
  • Comparative analysis with Masi, Tsallis, Shannon, Sine, and Hill entropy methods, including kernel support vector machines.

Main Results:

  • The proposed Masi entropy-based method demonstrates superior segmentation accuracy.
  • Optimized parameter selection via simulated annealing enhances performance.
  • The method shows effectiveness across infrared, NDT, and RGB (BSDS500) image datasets.

Conclusions:

  • The Masi entropy-based thresholding with simulated annealing offers a significant improvement over traditional methods.
  • The approach provides robust and accurate image segmentation for diverse applications.
  • Further exploration in relation to deep learning approaches is warranted.