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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

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.

Contaminants and Errors

Contaminants and Errors

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.
Another key consideration is determining the appropriate number of samples required to...

Estimation of the Physical Quantities

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...

Quantitative Analysis

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

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Related Experiment Video

Updated: Sep 8, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

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Uncertainty estimation strategies for quantitative non-targeted analysis.

Louis C Groff^1,2, Jarod N Grossman^3,4, Anneli Kruve⁵

¹US Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, NC, 27711, USA. groff.louis@epa.gov.

Analytical and Bioanalytical Chemistry

|June 14, 2022

Summary

This summary is machine-generated.

New statistical methods improve quantitative non-targeted analysis (qNTA) for chemical risk assessment. These methods provide confidence limits for chemical concentrations, advancing the use of high-resolution mass spectrometry (HRMS) data.

Keywords:

ENTACT Exposure HRMS NTA Quantitative Uncertainty

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Area of Science:

Environmental Chemistry
Analytical Chemistry
Computational Chemistry

Background:

Non-targeted analysis (NTA) is crucial for chemical discovery but lacks robust quantitation methods.
Estimating chemical concentrations with confidence limits is a significant challenge in NTA.
Existing methods struggle to provide reliable quantitative data for risk characterization.

Purpose of the Study:

To develop and evaluate novel statistical methods for quantitative NTA (qNTA).
To enable confident estimation of chemical concentrations and their confidence limits using HRMS data.
To assess the performance of these methods in the context of chemical risk characterization.

Main Methods:

Utilized EPA's ENTACT high-resolution mass spectrometry (HRMS) data.
Implemented two qNTA methods: bounded response factor and ionization efficiency estimation.
Employed non-parametric bootstrap and ionization efficiency predictions to estimate response factors (RFs).

Main Results:

Traditional calibration curves yielded upper confidence limits within ~tenfold of true concentrations.
The ionization efficiency method resulted in ~60-120-fold errors.
The bounded response factor method showed ~130-150-fold errors.
Both qNTA methods provided confidence limit estimation strategies.

Conclusions:

Successfully implemented confidence limit estimation strategies to support qNTA.
This work is a critical step towards using NTA data for risk-based assessments.
Further refinement of qNTA methods is needed for improved accuracy in chemical quantitation.