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Related Concept Videos

High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For example, the mass of helium...
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

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 axis.
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

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.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

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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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Genetic algorithm-based feature selection in high-resolution NMR spectra.

Hyun-Woo Cho1, Seoung Bum Kim, Myong K Jeong

  • 1Department of Industrial and Information Engineering, The University of Tennessee, Knoxville, TN 37996, USA.

Expert Systems with Applications
|April 8, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a genetic algorithm (GA) for feature selection in nuclear magnetic resonance (NMR) spectroscopy. This method enhances the identification of key metabolites for distinguishing biological samples under various conditions.

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Last Updated: Jun 3, 2026

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Bioinformatics

Background:

  • High-resolution nuclear magnetic resonance (NMR) spectroscopy detects metabolic alterations in biological systems due to disease or external factors.
  • Statistical pattern recognition methods are crucial for analyzing complex NMR spectra and identifying underlying metabolic patterns.
  • Existing methods for NMR spectral analysis can be complex and may not always pinpoint the most discriminative features.

Purpose of the Study:

  • To develop a genetic algorithm (GA)-based feature selection method for identifying significant metabolite features in high-resolution NMR spectra.
  • To improve the discrimination of biological samples based on their metabolic profiles.
  • To enhance the interpretability and efficiency of NMR spectral data analysis.

Main Methods:

  • Application of a genetic algorithm (GA) for feature selection to identify key metabolite biomarkers.
  • Utilization of an orthogonal signal filter as a data preprocessor to remove irrelevant variations.
  • Analysis of human plasma NMR spectra using k-nearest neighbors (k-NN) and partial least squares discriminant analysis (PLS-DA).

Main Results:

  • The GA-based feature selection effectively identified major metabolite features crucial for sample discrimination.
  • The orthogonal signal filter successfully removed unwanted data variations, improving spectral clarity.
  • Combined GA feature selection and orthogonal signal filtering demonstrated superior performance in classifying samples based on NMR spectra.

Conclusions:

  • GA-based feature selection combined with orthogonal signal filtering is a powerful approach for analyzing high-resolution NMR spectra.
  • This integrated method enhances the ability to detect and recognize metabolic changes for disease or condition differentiation.
  • The findings highlight the potential of this approach for biomarker discovery and metabolic profiling in biological research.