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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

302
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...
302
Sampling Plans01:23

Sampling Plans

180
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...
180
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Sample Preparation for Analysis: Overview01:21

Sample Preparation for Analysis: Overview

212
Sample preparation is an essential step in the analytical process. It involves preparing a sample so that it can be analyzed accurately. The goal is to extract the analyte, the substance you want to measure, from the sample while removing any components that may interfere with the analysis. Sample preparation techniques vary depending on the physical state of the sample.
Bulk or large solid samples are typically reduced in size using grinding, crushing, or milling techniques to increase the...
212
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

211
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...
211
Convenience Sampling Method00:55

Convenience Sampling Method

8.8K
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...
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Advanced Method Optimization for Sampling and Analysis Instrumentation.

Stephanie N Gamble1, Caroline O Granger1, Joseph M Mannion1

  • 1Savannah River National Laboratory, Aiken 29808, South Carolina, United States.

Analytical Chemistry
|July 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a generalized method optimization strategy using multivariate, multiobjective optimization and Karush-Kuhn-Tucker conditions. This approach enhances analytical method development, improving data quality and accelerating new technique creation.

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Area of Science:

  • Analytical Chemistry
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Traditional analytical method development is often time-consuming and relies on historical techniques.
  • Modern optimization techniques offer potential for improved efficiency and accuracy but require integration with physical constraints.

Purpose of the Study:

  • To present a generalized, robust strategy for analytical method optimization.
  • To bridge the gap between historical and modern optimization techniques for broader applicability.
  • To enable rapid development of improved analytical methods for chemometrics and machine learning.

Main Methods:

  • Utilized multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions.
  • Incorporated screening experiments and ANOVA to identify significant parameters.
  • Employed experimental designs (e.g., Box-Behnken) and Lagrangian solutions for parameter optimization.
  • Applied the strategy to gas chromatography-mass spectrometry (GC-MS) method development.

Main Results:

  • Developed a generalized framework for analytical method optimization.
  • Demonstrated the ability to bound optimization within physical instrumentation limitations.
  • Successfully applied the strategy to optimize GC-MS methods, reducing development time and improving data quality.

Conclusions:

  • The proposed optimization strategy offers a powerful tool for enhancing analytical method development across various applications.
  • This approach can lead to significant cost reductions in research and facilitate the rapid creation of new analytical techniques.
  • Improved data quality from optimized methods supports advanced data analysis, including chemometrics and machine learning.