VaMPIS - Validation of Measurement Procedures that Include Sampling

Significance of the Topic

Validation of analytical methods traditionally focuses on laboratory steps, often overlooking primary sampling. Including sampling in validation addresses the full measurement chain, ensuring reliable results and informed decisions in regulatory and industrial contexts.

Objectives and Overview of the Guide

The VaMPIS guidance defines a structured approach to validate measurement procedures that include sampling. It applies to both unified validation of sampling and analysis or sequential validation when the analytical method is already validated.

Methodology and Instrumentation

VaMPIS uses measurement uncertainty as the integrative performance characteristic. Key steps include:

• Defining measurand and sampling target
• Mapping the measurement procedure into sampling and analytical phases
• Designing validation experiments
• Conducting sampling and analysis on duplicate samples
• Applying analytical quality control
• Estimating uncertainty, including sampling uncertainty, via the duplicate method and ANOVA
• Comparing actual uncertainty against a target value
• Iterating procedure optimization if fitness for purpose is not achieved

Instrumentation Used

The guide highlights both ex situ and in situ approaches. Instruments and tools include:

• Portable X‐ray fluorescence (pXRF) for direct soil measurement
• Standard wet chemistry or instrumental analysis for laboratory samples
• Statistical software for ANOVA and uncertainty estimation

Main Results and Discussion

VaMPIS positions measurement uncertainty as the decisive criterion for validation. Worked examples demonstrate its application to nitrate in composite lettuce samples (ex situ) and lead in soil by pXRF (in situ). The iterative flowchart of eleven steps guides practitioners to achieve a target uncertainty that includes sampling contributions.

Benefits and Practical Applications of the Method

By integrating sampling into validation, laboratories and field teams can:

• Ensure compliance with regulatory limits
• Improve confidence in measurement results
• Optimize procedures according to cost and uncertainty contributions

Future Trends and Opportunities for Use

Emerging directions include real-time uncertainty monitoring, digital sampling protocols, advanced portable instrumentation, and dedicated software tools for uncertainty budgeting. Enhanced collaboration between sampling organizations and analytical laboratories will further streamline validation workflows.

Conclusion

The VaMPIS framework offers a comprehensive, uncertainty-driven strategy to validate full measurement procedures. Its iterative design fosters continuous improvement, supporting robust decision making across research, quality assurance, and regulatory applications.

References

• Ramsey M. H., Ellison S. L. R., Rostron P. (eds.) Measurement Uncertainty Arising from Sampling, Eurachem (2nd ed. 2019)
• Cantwell H. (ed.) The Fitness for Purpose of Analytical Methods, Eurachem (3rd ed. 2025)
• Ramsey M. H., Rostron P. D., Raposo F. C. (eds.) Validation of Measurement Procedures that Include Sampling, Eurachem (2024)