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Related Concept Videos

Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Convenience Sampling Method00:55

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...

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An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

Applications of non-uniform sampling and processing.

Sven G Hyberts1, Haribabu Arthanari, Gerhard Wagner

  • 1Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.

Topics in Current Chemistry
|July 29, 2011
PubMed
Summary
This summary is machine-generated.

Non-uniform sampling (NUS) accelerates multidimensional NMR experiments by reducing measurement times. This study evaluates sampling schedules and reconstruction methods for optimal spectral quality, especially for high-dynamic range spectra.

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Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
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Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

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Last Updated: May 30, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

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Published on: April 13, 2019

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
05:07

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Biophysical Chemistry

Background:

  • Modern high-field Nuclear Magnetic Resonance (NMR) instruments offer high resolution.
  • Standard linear sampling in multidimensional NMR is impractical due to long measurement times.
  • Non-uniform sampling (NUS) methods have been developed over the past 20 years to address this.

Purpose of the Study:

  • To evaluate various sampling schedules and reconstruction algorithms for multidimensional NMR.
  • To focus on selecting optimal methods for high-dynamic range spectra, such as 3D and 4D NOESYs.
  • To demonstrate the utility of NUS for experiments with less severe dynamic range issues.

Main Methods:

  • Utilizing the forward maximum entropy (FM) reconstruction method.
  • Evaluating several alternative non-uniform sampling schedules.
  • Comparing spectral quality based on sampling density and reconstruction algorithms.

Main Results:

  • Multidimensional NMR spectra without significant dynamic range issues (e.g., triple resonance experiments) are faithfully reconstructed using NUS.
  • NUS enables the efficient recording of these experiments, leveraging modern NMR instrument capabilities.
  • For high-dynamic range spectra (e.g., 3D/4D NOESYs), the choice of sampling schedule and reconstruction method is critical for detecting weak signals.

Conclusions:

  • Non-uniform sampling is a viable strategy for modern multidimensional NMR, especially for experiments with limited dynamic range.
  • Optimal sampling schedules and reconstruction methods are essential for successful data processing of high-dynamic range NMR spectra.
  • This work provides a framework for selecting appropriate NUS strategies to maximize spectral information recovery.