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Sampling Plans01:23

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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.
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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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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Developing nonuniform sampling strategies to improve sensitivity and resolution in 1,1-ADEQUATE experiments.

Mark S Roginkin1, Ikenna E Ndukwe2,3, D Levi Craft1

  • 1Department of Chemistry, Bucknell University, Lewisburg, PA, USA.

Magnetic Resonance in Chemistry : MRC
|January 9, 2020
PubMed
Summary

Nonuniform sampling (NUS) improves sensitivity and signal clarity in 1,1-ADEQUATE NMR spectroscopy. Optimized NUS strategies enable better structure elucidation by revealing weak signals and correlations.

Keywords:
1,1-ADEQUATE1,1-HD-ADEQUATEHDNUShomodecouplednonuniform sampling

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Area of Science:

  • Nuclear Magnetic Resonance (NMR) Spectroscopy
  • Analytical Chemistry
  • Spectroscopic Methods

Background:

  • High-resolution NMR spectroscopy is crucial for molecular structure elucidation.
  • Conventional uniform sampling can be time-consuming and limit spectral resolution.
  • Nonuniform sampling (NUS) offers a strategy to accelerate data acquisition and improve spectral quality.

Purpose of the Study:

  • To develop and optimize nonuniform sampling (NUS) strategies for acquiring high-resolution 1,1-ADEQUATE Nuclear Magnetic Resonance (NMR) spectra.
  • To compare different NUS methods, including quantile-directed and Poisson gap sampling, for efficiency and sensitivity.
  • To evaluate spectral estimation algorithms and weighting schemes for NUS data.

Main Methods:

  • Comparison of quantile-directed and Poisson gap sampling methods for nonuniform data distribution.
  • Optimization of quantile schedules for sample weighting.
  • Evaluation of maximum entropy and iterative soft thresholding spectral estimation algorithms.
  • Application of NUS to 1,1-ADEQUATE and homodecoupled (HD) variants.

Main Results:

  • NUS strategies are robust for moderate data reduction (approx. 50% sampling points).
  • Weighted quantile schedules effectively reduce sampling and suppress noise.
  • NUS enhances sensitivity by 5-20%, disambiguates weak signals, and reveals obscured correlations.
  • Longer evolution times become accessible, improving spectral quality.

Conclusions:

  • Developed NUS strategies enhance sensitivity and spectral resolution in 1,1-ADEQUATE NMR experiments.
  • Optimized NUS methods, particularly weighted quantile schedules, facilitate challenging structure elucidation.
  • The developed sample schedules are provided for broader application of ADEQUATE NMR techniques.