Quantitative LC–MS plays a critical role across pharmaceutical, clinical, food and environmental analysis.

How robust is your LC–MS calibration strategy?

Quantitative LC–MS and the importance of calibration strategy

Quantitative LC–MS plays a critical role across pharmaceutical, clinical, food and environmental analysis. However, the reliability of quantitative results ultimately depends on calibration strategies that are often selected out of habit rather than fully evaluated.

From matrix effects to analyte variability, calibration choices can introduce significant sources of uncertainty if not carefully considered. Despite this, the practical impact of different calibration approaches is rarely discussed in detail in everyday laboratory workflows.

Common calibration challenges in quantitative LC–MS analysis

Selecting an appropriate calibration strategy is particularly challenging when dealing with complex sample matrices, low-level analytes, or variable ionisation effects. Small methodological decisions can have a disproportionate effect on quantitative accuracy, precision, and confidence in reported data.

Understanding the strengths and limitations of different approaches is therefore essential for analysts seeking robust and reproducible results.

What this LC–MS calibration white paper covers

This practical white paper examines widely used calibration strategies and their influence on quantitative LC–MS performance, including:

  • External calibration and standard addition – benefits, limitations and use-case considerations
  • Surrogate matrices and surrogate analytes – when they help, and where they can introduce bias
  • Isotope-labelled standards – how they influence quantitative accuracy and data reliability

Download the Free white paper

Real-world case studies from recent LC–MS literature

Supported by case studies drawn from recent scientific literature, the paper demonstrates how calibration choices affect quantitative LC–MS performance in real analytical scenarios. By highlighting practical outcomes rather than theoretical models, the white paper helps analysts better understand where errors can arise and how calibration strategies can be optimised to improve data quality.

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