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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Effect of multiple error sources on the calibration uncertainty.

Denis Badocco1, Irma Lavagnini1, Andrea Mondin1

  • 1Department of Chemical Sciences, University of Padua, Via Marzolo 1, 35131 Padua, Italy.

Food Chemistry
|February 10, 2015
PubMed
Summary

Estimating calibration uncertainty in drinking water analysis using ICP-MS requires accounting for both instrumental and operational errors. A two-component variance regression provides a more accurate uncertainty estimation than traditional methods.

Keywords:
CalibrationInductively coupled plasmaMass spectrometryUncertaintyVariance components

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Area of Science:

  • Analytical Chemistry
  • Environmental Science

Background:

  • Accurate determination of trace metals in drinking water is crucial for public health.
  • Inductively Coupled Plasma Mass Spectrometry (ICP-MS) is a common technique for metal analysis.
  • Calibration uncertainty is a key factor in the reliability of analytical results.

Purpose of the Study:

  • To estimate calibration uncertainty in trace metal determination by ICP-MS in drinking water.
  • To investigate the impact of instrumental and operational errors on calibration uncertainty.
  • To compare a two-component variance regression model with a one-variance model for uncertainty estimation.

Main Methods:

  • Utilized a two-component variance regression model to account for instrumental (random) and operational (systematic) errors.
  • Employed an F-test to determine the presence or absence of these error contributions.
  • Applied the methodology to multi-element calibration and real drinking water samples.

Main Results:

  • The two-component variance regression model yielded a greater uncertainty estimation compared to the one-variance regression.
  • The proposed approach for calibration uncertainty estimation proved necessary for most analyzed elements.
  • Identified significant contributions from both instrumental and operational errors in trace metal analysis.

Conclusions:

  • A comprehensive approach considering both random and systematic errors is essential for accurate calibration uncertainty estimation in ICP-MS analysis of drinking water.
  • The two-component variance regression model offers a more robust estimation of uncertainty.
  • This method enhances the reliability of trace metal quantification in environmental samples.