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Updated: May 21, 2026

Curtain Flow Column: Optimization of Efficiency and Sensitivity
Published on: June 12, 2016
Minimum required signal-to-noise ratio for optimal precision in HPLC and CE
Christoph Meyer1, Patricia Seiler, Celine Bies
1Novartis Pharma AG, Basel, Switzerland. christoph.meyer@novartis.com
Achieving 2% repeatability in analytical methods like HPLC requires a signal-to-noise ratio (S/N) of at least 50, contrary to common assumptions. Optimal precision necessitates an S/N greater than 100 for reliable assay determinations.
Area of Science:
- Analytical Chemistry
- Chromatography
- Method Development
Background:
- Current assumptions suggest a signal-to-noise ratio (S/N) of 10 is sufficient for analytical High-Performance Liquid Chromatography (HPLC).
- Repeatability is crucial for the reliability of assay determinations across various analytes and concentration levels.
Purpose of the Study:
- To determine the minimum signal-to-noise ratio (S/N) required for achieving high repeatability in analytical assays.
- To establish empirical relationships between S/N and percent relative standard deviation (%RSD) for HPLC and Capillary Electrophoresis (CE).
Main Methods:
- Analysis of over 100 assay determinations across different analytes and concentration levels.
- Collection and statistical analysis of signal-to-noise ratio (S/N) data.
- Derivation of empirical functions relating %RSD to S/N for HPLC and CE.
Main Results:
- A signal-to-noise ratio (S/N) of at least 50 is required to achieve 2% repeatability, challenging the assumption of S/N 10 sufficiency.
- Empirical functions derived: %RSD = 58/(S/N) + 0.30 for HPLC and %RSD = 73/(S/N) + 1.07 for CE.
- An S/N greater than 100 is necessary for optimal precision in analytical methods.
Conclusions:
- The study confirms that a significantly higher S/N than commonly assumed is essential for reliable analytical method performance.
- Achieving optimal precision requires prioritizing S/N > 100 before optimizing other method parameters like sample pretreatment.
- Reducing detection-related variability is a prerequisite for effectively addressing other sources of variation in analytical methods.
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