Even well validated LC–UV methods can produce misleading potency results. Why? Because real world conditions rarely match validation perfectly.

Small differences in extraction, chromatographic separation, or reference standard assignment can introduce hidden bias – impacting accuracy, consistency, and confidence in your data.

Where does bias creep in?

Bias doesn’t come from one place – it builds across your workflow:

• Incomplete extraction reduces API recovery and underestimates potency.
• Poor chromatographic selectivity allows co eluting species to distort results.
• Inaccurate reference standards scale every result in the wrong direction.

Even minor issues like filtration losses or detector variability can compound the problem.

Calibration is the critical control point

In LC–UV assays, everything ties back to calibration.

Your reference standard defines the baseline – and any inaccuracy is carried through every result.

Getting this right isn’t optional. It’s essential for defensible, high quality data.

What you’ll learn

This practical, case study led application note shows you how to:

• Identify hidden sources of bias across your workflow.
• Understand the real impact of extraction, matrix effects, and selectivity.
• Strengthen calibration using well characterised reference standards.
• Improve method robustness for more reliable, repeatable results.

Turn insight into better data

Backed by real analytical scenarios, this application note goes beyond theory – showing how small decisions impact real world performance, and how to fix them.

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