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Related Concept Videos

Quality Assurance01:19

Quality Assurance

901
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
901
Data Validation01:15

Data Validation

511
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
511
Quality Control01:05

Quality Control

1.1K
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
1.1K

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Related Experiment Video

Updated: Dec 30, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Assessing accuracy, precision and selectivity using quality controls for non-targeted analysis.

Brian Ng1, Natalia Quinete2, Piero R Gardinali3

  • 1Department of Chemistry and Biochemistry, Florida International University, Miami, FL, United States of America.

The Science of the Total Environment
|January 20, 2020
PubMed
Summary
This summary is machine-generated.

New quality control (QC) guidelines improve data reliability in non-target screening. This study establishes benchmarks for analytical performance, enhancing reproducibility in complex chemical analyses.

Keywords:
Environmental workflowLC-HRMSNon-target analysisOrbitrapOrganic pollutantsQuality control

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Area of Science:

  • Analytical Chemistry
  • Environmental Science

Background:

  • Reproducibility benchmarks are poorly defined for non-target analysis.
  • Standard analytical performance parameters (accuracy, precision, selectivity) are established for target analysis but not for non-target screening.

Purpose of the Study:

  • To propose quality control (QC) guidelines for reliable non-target screening data.
  • To assess workflow reproducibility using a defined set of standards and LC-HRMS.

Main Methods:

  • Online solid phase extraction (SPE) coupled with liquid chromatography-high resolution mass spectrometry (LC-HRMS).
  • Utilized Compound Discoverer software with environmental templates and multiple databases for data processing.
  • Evaluated method specificity, precision, accuracy, and reproducibility (peak area, retention time, identification rates).

Main Results:

  • Satisfactory accuracy with ≥70% identification rate for QC compounds.
  • Peak area precision (RSD) ranged from 30-50%; data normalization did not improve variability.
  • Retention time precision demonstrated high repeatability and reproducibility (RSD ≤ 5%).
  • A retention time vs. log KOW model reduced false positives by 49.1%.

Conclusions:

  • Proposed QC guidelines enhance reliability in non-target screening.
  • The developed method provides satisfactory accuracy and excellent retention time reproducibility.
  • The RT vs. log KOW model is effective in reducing false positives in non-target screening analysis.