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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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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...
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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. Among the various sampling methods used by...
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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.
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Systematic Sampling Method01:17

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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.
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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. 
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Sampling materials are classified into three main types: solid, liquid, and gas.
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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Active nonuniform sampling.

Bradley Worley1

  • 1Numerion Labs, San Francisco, CA, USA.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|March 7, 2026
PubMed
Summary
This summary is machine-generated.

Nonuniform sampling (NUS) accelerates multidimensional NMR experiments by intelligently selecting data points. This study introduces active nonuniform sampling (ANS) to optimize data acquisition for improved spectral quality and reduced experiment times.

Keywords:
Active learningCompressed sensingNonuniform samplingStatistical inferenceVariational inference

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Area of Science:

  • Nuclear Magnetic Resonance (NMR) spectroscopy
  • Analytical Chemistry
  • Physical Chemistry

Background:

  • Multidimensional NMR experiments require significant acquisition time.
  • Nonuniform sampling (NUS) is a strategy to reduce experiment duration.
  • Current NUS schedule selection often relies on empirical methods.

Purpose of the Study:

  • To investigate the principles of active nonuniform sampling (ANS).
  • To develop and evaluate a candidate ANS algorithm for NMR experiments.
  • To assess the performance and practicality of ANS in reducing experiment times.

Main Methods:

  • Development of an active nonuniform sampling (ANS) algorithm.
  • Integration of a reconstruction algorithm within the NMR acquisition loop.
  • Application and testing of the ANS algorithm in two-dimensional NMR experiments.

Main Results:

  • The developed ANS algorithm demonstrated potential for optimizing sampling schedules.
  • Active feedback during acquisition allows for dynamic adjustment of sampling points.
  • Initial assessments suggest ANS can enhance spectral quality within reduced acquisition times.

Conclusions:

  • Active nonuniform sampling (ANS) offers a promising approach to further accelerate NMR experiments.
  • ANS moves beyond static sampling schedules by incorporating real-time optimization.
  • The developed candidate ANS algorithm shows practical utility for two-dimensional NMR.