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Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based
William T Foreman1, Teresa L Williams2, Edward T Furlong1
1U.S. Geological Survey, Strategic Laboratory Science Branch, PO Box 25585, Denver, CO, USA.
Talanta
|March 28, 2021
Summary
Blank-based detection limit (DL) procedures, particularly for water samples, often provide more accurate results than spike-based methods. Different procedures yield varying DLs, best viewed as a range rather than a single value.
Area of Science:
- Environmental Chemistry
- Analytical Chemistry
- Water Quality Analysis
Background:
- Accurate estimation of detection limits (DLs) is crucial for environmental monitoring.
- Existing methods like the U.S. Environmental Protection Agency's (EPA) Method Detection Limit (MDL) have limitations.
- Comparing different DL estimation procedures is essential for reliable water sample analysis.
Purpose of the Study:
- To compare spike- and blank-based procedures for estimating detection limits (DLs) in water samples.
- To evaluate these procedures against EPA's MDL revisions 1.11 and 2.0.
- To assess the performance of ASTM Within-laboratory Critical Level (DQCALC) and EPA's Lowest Concentration Minimum Reporting Level.
Main Methods:
- Application of spike- and blank-based procedures for DL estimation.
- Comparison with EPA Method Detection Limit (MDL) procedures (revisions 1.11 and 2.0).
- Utilized DQCALC and EPA's Lowest Concentration Minimum Reporting Level for multi-concentration spike-based comparisons.
Main Results:
- Blank-based DLs (MDLb99, MDLbY) using numerous blank samples often achieve the desired ≤1% false positive risk.
- Spike-based procedures (DQCALC, MDL rev. 2.0) are necessary for organic methods lacking sufficient blank data.
- DLs are procedure-dependent and dynamic, best represented as a range, informed by both blank and spike methods.
Conclusions:
- Blank-based DL estimation is often superior for water samples, especially with large numbers of blanks.
- Spike-based methods are vital for specific analytical challenges, like organic analyses with limited blank data.
- DLs should be considered a dynamic range, not a static value, to accurately reflect analytical capabilities and minimize reporting risks.
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